This work was funded by the National Cancer Institute and the Wellcome Trust, with additional support from Varian Medical Systems. Our current system uses Eclipse for treatment planning functions. We would also like to thank Ann Sutton of Editing Services, Research Medical Library, UT MD Anderson Cancer Center. In addition to the institutional funding for RPA development, our research teams receive funding from the Cancer Prevention and Research Institute of Texas (CPRIT) and the Fund for Innovation in Cancer Informatics, The University of Texas MD Anderson Cancer Center.

All patient data used for evaluating the RPA were used retrospectively, with approval from The University of Texas MD Anderson Institutional Review Board. The RPA comprises a series of dashboards located to the left of the main menu of the RPA webpage (Figure 2). Figure 2 shows the main dashboard. All dashboards have a similar look but focus on different tasks and personnel. The following protocol describes the key processes for the automatic creation of a treatment plan.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65504/65504fig02.jpg" /> 
Figure 2: Screenshot of the RPA main dashboard. <a href="https://www.jove.com/files/ftp_upload/65504/65504fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
1. Complete Service Request
<ol>
	<li>To create and accept a new Service Request for automatic planning:
	<ol>
		<li>Go to the Service Request dashboard by clicking on the Service Request channel.</li>
		<li>Click on New Form to generate a new Service Request form.</li>
		<li>In Section 1: Demographics, complete the questions. Select the treatment technique from the Treatment pulldown. Click on the individual <img alt="Equation 1" src="/files/ftp_upload/65504/65504eq01.jpg" style="margin: auto;" /> in the treatment technique pulldown to access further details of the treatment technique.</li>
		<li>In Section 2: Treatment-General Questions, complete the questions. These questions are the same for all patients; their purpose is to actively engage the user in determining whether the RPA Plan is appropriate for the current patient.</li>
		<li>Section 3: Treatment-Specific Questions, complete the questions for the selected treatment approach, including details of the treatment targets and prescription.</li>
		<li>Click on submit. Once the Service Request PDF has been automatically generated, select the patient in the patient list (on the Service Request dashboard). Review the Service Request PDF (Figure 3), scrolling if necessary, and click on Accept to approve the Service Request. 
		NOTE: This PDF must be accepted by a radiation oncologist before the RPA will initiate automated contouring and planning. The status of the RPA Plan can be determined on the Service Request page, as shown in Table 2. An example Service Request that was created for a soft tissue-based 4-field plan for cervical cancer is shown in Figure 3.</li>
	</ol>
	</li>
</ol>
&#160;&#160;&#160;
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Status</td>
			<td colspan="4">Summary</td>
		</tr>
		<tr>
			<td>Pending Review</td>
			<td colspan="4">The Service Request for this patient has already been created and is waiting for the radiation oncologist to Accept it.</td>
		</tr>
		<tr>
			<td>Accepted</td>
			<td colspan="4">The Service Request for this patient has been Accepted. The status of this patient in the Service Request dashboard will not change until a CT scan for this patient is Accepted.</td>
		</tr>
		<tr>
			<td>Rejected by User</td>
			<td colspan="4">The User rejected the Service Request.</td>
		</tr>
		<tr>
			<td>Submitted</td>
			<td colspan="4">This case has been submitted to the RPA&#8211;more details on this patient&#8217;s status can be found on the main dashboard.</td>
		</tr>
		<tr>
			<td>RPA System Error</td>
			<td colspan="4">Processing by the RPA was initiated, but the RPA encountered an error and was unable to complete its task.</td>
		</tr>
	</tbody>
</table>
Table 2: Patient status categories for the Service Request Dashboard.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65504/65504fig03.jpg" /> 
Figure 3: An example Service Request created for a soft tissue-based 4-field plan for cervical cancer. <a href="https://www.jove.com/files/ftp_upload/65504/65504fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Upload CT scan and accept for automated planning
Figure 4 shows a screenshot of the CT review workspace. To upload and review a CT scan:
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65504/65504fig04.jpg" /> 
Figure 4: A screenshot of the CT review workspace. <a href="https://www.jove.com/files/ftp_upload/65504/65504fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Go to the CT Scans dashboard by clicking on the CT Scans channel.</li>
	<li>Click the Upload CT button. Select the folder where the CT scan (DICOM format) is stored using the folder explorer that automatically opens. Follow the prompts to confirm the selection.</li>
	<li>Review the uploaded CT scan by selecting the patient in the patient list to open the CT viewer for this patient (Figure 4), and then use the following controls:
	<ol>
		<li>Load the entire 3D CT image set by clicking on Load CT. 
		NOTE: Other navigation tools are not active until this is complete.</li>
		<li>Scroll between slices in all views, and use any of the following tools:
		<ol>
			<li>Click on &#60;, &#62; buttons below each CT view to move to the next slice.</li>
			<li>Click on &#60;&#60;, &#62;&#62; buttons below each CT view to move five slices in the selected direction.</li>
			<li>Mouse scroll wheel: Move the cursor over any CT view and then use the mouse scroll wheel to scroll between slices.</li>
			<li>Click the Intersect button to overlay a crosshair on each CT view (axial, coronal, and sagittal). Click on any of the three views to move the crosshair to this point-the other views will follow accordingly. Click the Intersect button to turn this tool on/off.</li>
			<li>Click the Zoom/Pan button. Further review the image using the mouse wheel to zoom the view where the cursor is positioned or right-click and hold down on a CT view and then move the mouse to pan.</li>
			<li>Click on Marked Iso to move the CT views to the marked isocenter (based on automated detection of three radiopaque markers). 
			NOTE: This button is inactive if the 3D CT image set has not been loaded (click Load 3D to resolve this) or if the RPA was unable to detect a marked isocenter (as identified with a three-point setup).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Click on the Ref Point button to add a reference point.
	<ol>
		<li>Move the three views to the desired Reference Point location using the intersect tool.</li>
		<li>Click on the Ref Point button to add a Reference Point.</li>
		<li>If a Reference Point has already been selected, clicking on Ref Point will move the three views to this point. 
		NOTE: This button adds a Reference Point to an image if a Reference Point does not exist. It can also move the CT image views to the Reference Point if one already exists. A new Reference Point can only be selected for CT scans that have not been accepted. Below are the steps to add a Reference Point:</li>
		<li>To select a new Reference Point, first clear the current Reference Point by clicking on Clear Ref Point and then add a new one. The RPA only Accepts one Reference Point.</li>
	</ol>
	</li>
	<li>Accept the CT scan. After reviewing the patient&#39;s CT scan as already described above, perform the following steps:
	<ol>
		<li>Answer the questions below the CT images to minimize risk and reduce the occurrence of errors in the subsequent RPA calculations.</li>
		<li>Select Accept and follow the prompts. 
		&#8203;NOTE: This task can be performed by anybody identified as a Clinical User of the RPA. The status categories of current patients can be viewed on the CT Scans dashboard and are shown in Table 3.</li>
	</ol>
	</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Status</td>
			<td colspan="4">Summary</td>
		</tr>
		<tr>
			<td>Pending Review</td>
			<td colspan="4">The CT scan has undergone initial processing and is waiting for the user to review and Accept the scan.</td>
		</tr>
		<tr>
			<td>Accepted</td>
			<td colspan="4">The CT scan for this patient has been Accepted. Please note that the status of this patient in the CT Scan dashboard will not change until a Service Request is Accepted.</td>
		</tr>
		<tr>
			<td>Rejected by User</td>
			<td colspan="4">The CT scan was rejected by the user.</td>
		</tr>
		<tr>
			<td>Submitted</td>
			<td colspan="4">This case has been submitted to the RPA&#8211;more details on this patient&#8217;s status can be found on the main dashboard.</td>
		</tr>
		<tr>
			<td>RPA System Error</td>
			<td colspan="4">Processing by the RPA was initiated, but the RPA encountered an error and was unable to complete its tasks.</td>
		</tr>
	</tbody>
</table>
Table 3: Patient status categories for the CT Scans dashboard.
3. Monitor planning progress
The status categories of current patients can be viewed on the main dashboard (Table 4). To perform a preliminary review of any completed RPA plan and download it for use:
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Status</td>
			<td colspan="4">Summary</td>
		</tr>
		<tr>
			<td>No Accepted CT Scan</td>
			<td colspan="4">This patient has no Accepted CT scan (but an Accepted Service Request is available).</td>
		</tr>
		<tr>
			<td>No Accepted Service Request</td>
			<td colspan="4">This patient has no Accepted Service Request (but an Accepted CT scan is available).</td>
		</tr>
		<tr>
			<td>Queued</td>
			<td colspan="4">The data for this patient have been sent to the RPA system and are queued for processing.</td>
		</tr>
		<tr>
			<td>Processing</td>
			<td colspan="4">Initial processing of this patient&#8217;s data is underway.</td>
		</tr>
		<tr>
			<td>Processing&#8211;contouring</td>
			<td colspan="4">The RPA Contours are being generated.</td>
		</tr>
		<tr>
			<td>Processing&#8211;contouring report</td>
			<td colspan="4">The RPA Contours Report is being generated.</td>
		</tr>
		<tr>
			<td>Complete&#8211;RPA contours</td>
			<td colspan="4">Contours generated by the RPA are complete and ready for the user to download and edit (2-step workflows only).&#160;</td>
		</tr>
		<tr>
			<td>Pending contour review</td>
			<td colspan="4">Planning contours (i.e. contours after the user has made edits/additions) have been uploaded back to the RPA and the Plan Contours Report has been generated. The user needs to Accept this report (from the contours dashboard).</td>
		</tr>
		<tr>
			<td>Queued&#8211;plan contours</td>
			<td colspan="4">The planning contours (i.e. contours after the user has made edits/additions) are queued for processing prior to the RPA Planning process.</td>
		</tr>
		<tr>
			<td>Queued&#8211;preplan</td>
			<td colspan="4">This patient&#8217;s plan is queued for the planning process.</td>
		</tr>
		<tr>
			<td>Processing&#8211;preplan</td>
			<td colspan="4">Preplanning processing is underway.</td>
		</tr>
		<tr>
			<td>Queued&#8211;optimization</td>
			<td colspan="4">This patient&#8217;s plan is queued for the plan optimization process.</td>
		</tr>
		<tr>
			<td>Processing&#8211;optimization</td>
			<td colspan="4">Plan optimization is underway.</td>
		</tr>
		<tr>
			<td>Queued&#8211;QA</td>
			<td colspan="4">This patient&#8217;s plan is queued for the automated quality assurance (QA) process.</td>
		</tr>
		<tr>
			<td>Processing&#8211;QA</td>
			<td colspan="4">The plan QA is underway.</td>
		</tr>
		<tr>
			<td>Processing&#8211;plan report</td>
			<td colspan="4">The Final Plan report is being processed.</td>
		</tr>
		<tr>
			<td>Complete&#8211;RPA Plan</td>
			<td colspan="4">The RPA Plan is complete and ready for download.</td>
		</tr>
		<tr>
			<td>Fail&#8211;RPA fail</td>
			<td colspan="4">An RPA process has failed.</td>
		</tr>
	</tbody>
</table>
Table 4: Patient status categories for the main dashboard.
<ol>
	<li>Review a completed RPA plan by selecting the patient and then selecting Review at the top of the main dashboard. Review the RPA Plan Report (PDF) for that patient that automatically opens in a new tab. 
	NOTE: The RPA Plan Report PDF can also be accessed from the download window.</li>
	<li>Download a completed RPA Plan by clicking on the download icon. Wait for a window to open and the DICOM files, along with the RPA Plan Report (PDF), will be downloaded for import into the treatment planning system.</li>
</ol>
4. Importing the RPA Plan into the User&#39;s treatment planning system and reviewing for clinical use
NOTE: Once the RPA plan is downloaded (DICOM files), the following steps should be completed in the user&#39;s treatment planning system:
<ol>
	<li>Import the patient&#39;s CT scan into the local TPS. This is the original scan that was uploaded to the RPA.</li>
	<li>Import the RPA Plan and RPA contours into the local TPS.</li>
	<li>Recalculate the dose using the local commissioned dose calculation algorithm and the used fixed MU option.</li>
	<li>Compare the imported contours and calculated dose with those in the RPA Report (to check for correct import).</li>
	<li>Review the plan for appropriateness and edit as necessary. 
	&#8203;NOTE: This step may include editing field shapes and renormalizing the fields. It is very important that the clinical team review the final plan in their treatment planning system and make any edits before clinical use.</li>
</ol>
5. Uploading the Final Plan to the RPA website and reviewing the automatic comparison of the Final Plan and the RPA Plan
Patients for whom an RPA Plan has been generated will appear in the plan comparison dashboard. The plan comparison dashboard provides the status categories shown in Table 5 for current patients. To upload the final User Plan and review an automatic comparison of the User Plan and the RPA Plan:
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Status</td>
			<td colspan="4">Summary</td>
		</tr>
		<tr>
			<td>Pending Plan Upload</td>
			<td colspan="4">This status displays when a case has been generated.</td>
		</tr>
		<tr>
			<td>Processing</td>
			<td colspan="4">The plan comparison is processing.</td>
		</tr>
		<tr>
			<td>Pending Plan Upload&#8211;RETRY</td>
			<td colspan="4">Plan comparison is not possible. The user should review the uploaded files and retry. Possible reasons for this status include uploading the incorrect files.</td>
		</tr>
		<tr>
			<td>Comparison Ready&#8211;Pass</td>
			<td colspan="4">The Plan Comparison Report is ready for review. All plan comparisons passed the criteria. Some comparisons may be flagged&#8211;the user should review the report.</td>
		</tr>
		<tr>
			<td>Comparison Ready&#8211;Fail</td>
			<td colspan="4">The Plan Comparison Report is ready for review. Some comparisons failed the set criteria&#8211;the user should carefully review the report and determine the cause.</td>
		</tr>
	</tbody>
</table>
Table 5: Patient status categories for the plan comparison dashboard.
<ol>
	<li>Select the patient and then click on Upload Plan.</li>
	<li>Select the DICOM structure file, plan, and dose files for upload.</li>
	<li>Review the Plan Comparison Report by first selecting the patient. Then, review the Plan Comparison report (Figure 5) which opens at the bottom of the screen, (an example is shown in Figure 5).</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65504/65504fig05.jpg" /> 
Figure 5: An example of the automatic plan comparison report. <a href="https://www.jove.com/files/ftp_upload/65504/65504fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Enhancing Radiotherapy with Web-Based Radiation Planning Assistant

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J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+treatment+planning+framework+for+spinal+radiation+therapy+and+vertebral-level+second+check.">An automated treatment planning framework for spinal radiation therapy and vertebral-level second check.</a> International Journal of Radiation Oncology, Biology, Physics. 114 (3), 516-528 (2022).</li><li>Olanrewaju, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+acceptability+of+automated+radiation+treatment+planning+for+head+and+neck+cancer+using+the+Radiation+Planning+Assistant.">Clinical acceptability of automated radiation treatment planning for head and neck cancer using the Radiation Planning Assistant.</a> Practical Radiation Oncology. 11 (3), 177-184 (2021).</li><li>Rhee, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+contouring+QA+method+using+a+deep+learning-based+autocontouring+system.">Automatic contouring QA method using a deep learning-based autocontouring system.</a> Journal of Applied Clinical Medical Physics. 23 (8), e13647 (2022).</li><li>Rhee, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+detection+of+contouring+errors+using+convolutional+neural+networks.">Automatic detection of contouring errors using convolutional neural networks.</a> Medical Physics. 46 (11), 5086-5097 (2019).</li><li>Kisling, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+risk+assessment+of+automated+treatment+planning+and+recommendations+for+clinical+deployment.">A risk assessment of automated treatment planning and recommendations for clinical deployment.</a> Medical Physics. 46 (6), 2567-2574 (2019).</li><li>Nealon, K. A., Court, L. E., Douglas, R. J., Zhang, L., Han, E. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+validation+of+a+checklist+for+use+with+automatically+generated+radiotherapy+plans.">Development and validation of a checklist for use with automatically generated radiotherapy plans.</a> Journal of Applied Clinical Medical Physics. 23 (9), e13694 (2022).</li></ol>

LEC has stock options in Leo Cancer Care. HB is now employed at Varian Medical Systems. AJ has a consulting position with Genentech. JB has a consulting position with Icon Cancer Care; WS has a consulting position with IBA Dosimetry; JY has received travel funding from SunNuclear Corp; HS has received honoraria from Cipla.

This protocol outlines the steps in creating automated treatment plans using the RPA. The key steps are (1) CT upload and approval, (2) Service Request completion and approval, (3) plan download and importation into the user&#39;s TPS and recalculation of the dose and plan editing, and (4) upload of the final edited plan for comparison with the RPA Plan. The order of the CT approval and Service Request approval are interchangeable. Some plans, specifically volume-modulated arc therapy plans for head and neck and cervical cancer, are created in a two-step process in which additional user interactions and the contours and plans are generated separately. Overall, however, the processes are similar, and we expect that these advanced treatment planning approaches can be changed to a one-step process in the future. The overall clinical acceptability that can be expected from these tools, and from tools that are under development for future versions can be found in our published work10,12,14,15,16,17,18,19,20,21,22.
These tools have several limitations, as described in our earlier work, which investigated risk when deploying the Radiation Planning Assistant in radiotherapy clinics9,23. Although the user interface has been designed to minimize the risk of inappropriate data entry, such as CT images that do not have sufficient field-of-view or errors in manual data entry, there is still potential for error. Specifically, human error, automation bias (over-reliance on the results), and software error are of concern9. Careful review and, if necessary, editing of the automatically generated contours and plans, is essential for the safe use of the Radiation Planning Assistant. In general, these reviews should follow the same process as is followed for the review of clinical plans by physicists and radiation oncologists, although this may be supported by the use of checklists developed specifically to supplement manual review of automatically created treatment plans24.
There are situations where the RPA will not be able to generate a plan and will report an error to the user. In almost all cases, this will be caused by the RPA encountering unexpected data that it cannot interpret, such as insufficient field-of-view or patient positioning (e.g., if a CT image was taken using a supine protocol, but with the patient in a prone position). The user may be able to identify the problem based on where the error is reported. In most cases, these situations can only be remedied by manual contouring or planning. The RPA Team may also be able to review log files to identify the problem.
The RPA has been designed and developed specifically to bring high-quality autocontouring and autoplanning tools to clinics with limited resources, especially those in low- and middle-income countries. We are currently working through the regulatory, legal, and administrative processes that will lead to the RPA being used clinically. Once that happens, we expect to carefully monitor the use and make changes to the workflow or user interfaces in response to any unanticipated risks or other user feedback. The goal is to provide tools that support radiotherapy services, such that local clinical teams can scale their efforts, improving access to high-quality, consistent radiotherapy plans. We hope that this will lead to improved patient outcomes, as well as reduced wait times. Although the current portfolio is limited to cancers of the head and neck, breast, and cervix, as well as whole brain irradiation for brain metastases, we are working on additional treatments that will be incorporated in future versions17,18,19.

The global number of cancer cases is expected to grow to approximately 24.6 million by 2030, with the largest burden in low- and middle-income countries (LMICs)1. Radiotherapy is a cost-effective, curative, and palliative treatment for cancer, providing benefits to approximately 50% of patients with cancer and to 60-70% in low-income countries where patients are more likely to present at a late stage2,3. However, access to radiotherapy worldwide is limited4; for example, no countries in Africa have the radiotherapy capacity to meet their estimated need5. Several studies have estimated these impending shortages and what would be required to meet upcoming needs6,7.
The Lancet Oncology Commission made the compelling case that investment in radiotherapy capacity improvements would not only save lives but would also bring positive economic benefits3. They also specifically pointed out that automation of radiotherapy contouring and treatment planning may help clinical teams scale their efforts by significantly reducing the time spent on these tasks by oncologists and physicists, respectively, making the goals more attainable.
Our research group has been working in collaboration with clinical teams at MD Anderson and at hospitals across the world to develop web-based automated tools. This suite of tools (called the RPA) provides Artificial Intelligence-based contouring (outlining tumors and nearby organs on CT scans) and radiotherapy treatment planning (which defines exactly how the radiation is delivered). This web-based platform offers the advantage of reduced time and resources needed to prepare high-quality plans for each patient.
Our experience with an early version of an AI-based tool at MD Anderson has shown that automated contouring can save up to 2 h per patient-a significant streamlining of the workflow. This means that current clinical staff will be able to scale their effort, treating more patients with higher-quality radiotherapy. By offering these tools via a fully automated, web-based service (Radiation Planning Assistant [RPA], RPA.mdanderson.org), we can minimize the cost to the patients and providers and maximize the reach of this tool.
We have been developing the RPA for 6 years, and several significant changes have been made since we first published on the RPA workflows8. These include developing the RPA into a web-based tool, thus reducing the costs associated with installation and maintenance, and improving the system&#39;s robustness. Other improvements include changes in the user interfaces to improve usability and reduce the risk of error9 and expanding treatment options (specifically, radiotherapy planning for post-mastectomy breast10 and metastases to the brain11). Thus, the protocol described here is substantially more advanced than the initial version previously published.
The RPA uses a one-step process to create contours and plans in situations in which editing of the contours is not generally necessary to create the treatment plan. This includes four-field box treatment planning for cervical cancer (based on bony landmarks or automatically generated soft tissue contours)12,13,14,15, tangential or supraclavicular fields for post-mastectomy breast cancer11, and opposed laterals for whole-brain treatments16. In the near future, we expect to add craniospinal treatments for pediatric cancers17, three-field treatments for rectal cancer18, and treatment planning for various palliative cases (vertebral bodies, hips, and ribs)19, as well as lung and bladder cancers. Currently, more advanced treatments, specifically volume-modulated arc therapy (VMAT), require a two-step process in which automatically generated contours are edited prior to treatment planning13,20. However, the quality of deep learning-based autocontouring is such that we expect to change these planning approaches to a one-step process in the future. This protocol focuses on one-step planning.
Figure 1 shows the overall workflow for creating a radiotherapy treatment plan using the RPA, with more details on the different tasks shown in Table 1. In summary, the RPA requires a completed Service Request (which includes information such as the dose prescription and treatment approach) and a patient&#39;s individual CT scan. The Service Request must be accepted by a radiation oncologist. The CT scan must be accepted by a Clinical User to ensure that the RPA calculations are performed on the correct CT scan. Once the RPA has generated a plan, it should be downloaded from the RPA website and imported into the user&#39;s treatment planning system, where the dose should be recalculated. This is necessary because the RPA calculates plans on standard beams (available for several linac models), which may not exactly match the beam characteristics of the local linac. This approach was taken to reduce cost, although customization may be needed if the local beams are significantly different from our standard beams. The users (treatment planner and radiation oncologist) may make edits to the plan. The plan then enters the user&#39;s typical clinical workflow, including local quality assurance checks. Finally, the user should upload their final (recalculated and edited) plan to the RPA website, where an automated comparison between the Final Plan and the RPA Plan is performed. This is a useful check of data integrity in the overall workflow.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65504/65504fig01.jpg" /> 
Figure 1: Workflow of the automated treatment planning process. <a href="https://www.jove.com/files/ftp_upload/65504/65504fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Task # in Figure 1</td>
			<td colspan="3">Task description</td>
			<td colspan="2">Location</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">Review the status of any past patients</td>
			<td colspan="2">Main dashboard</td>
		</tr>
		<tr>
			<td>1</td>
			<td colspan="3">Complete a Service Request on the RPA website</td>
			<td colspan="2">Service Request dashboard</td>
		</tr>
		<tr>
			<td>2</td>
			<td colspan="3">Upload a CT scan to the RPA website</td>
			<td colspan="2">CT scan dashboard</td>
		</tr>
		<tr>
			<td rowspan="2">3</td>
			<td colspan="3">Check on the status of the patient</td>
			<td colspan="2">Main dashboard</td>
		</tr>
		<tr>
			<td colspan="3">Review and download the RPA Plan</td>
			<td colspan="2">Main dashboard</td>
		</tr>
		<tr>
			<td>4</td>
			<td colspan="3">Import the plan into the user&#8217;s TPS, recalculate the dose, and make edits as necessary</td>
			<td colspan="2">Local TPS</td>
		</tr>
		<tr>
			<td rowspan="2">5, 6</td>
			<td colspan="3">Upload the Final Plan to the RPA website</td>
			<td colspan="2">Plan comparison dashboard</td>
		</tr>
		<tr>
			<td colspan="3">Review the automatic comparison of the Final Plan and the RPA Plan</td>
			<td colspan="2">Plan comparison dashboard</td>
		</tr>
		<tr>
			<td>-</td>
			<td colspan="3">The plan enters the user&#8217;s routine clinical workflow, including regular quality assurance</td>
			<td colspan="2">User&#8217;s own software</td>
		</tr>
	</tbody>
</table>
Table 1: Overview of the tasks involved in creating an RPA Plan using the 1-step workflow. Local TPS: User&#39;s Treatment Planning System.
This manuscript describes this one-step workflow for the RPA and presents some example results of the treatment planning process output. Currently, the following planning approaches use this one-step workflow: i) Four-field box treatment plans for cervical cancer patients (bony landmark-based apertures); ii) four-field box treatment plans for cervical cancer patients (soft tissue-based field apertures); iii) tangential and supraclavicular treatment plans for chest wall patients; iv) whole-brain treatment plans.

<table><tbody><tr><td>Radiation Planning Assistant</td><td>MD Anderson Cancer Center</td><td>na</td><td>webpage</td></tr></tbody></table>

radiation planning assistant - a web-based tool to support high-quality radiotherapy in clinics with limited resources

Access to radiotherapy worldwide is limited. The Radiation Planning Assistant (RPA) is a fully automated, web-based tool that is being developed to offer fully automated radiotherapy treatment planning tools to clinics with limited resources. The goal is to help clinical teams scale their efforts, thus reaching more patients with cancer. The user connects to the RPA via a webpage, completes a Service Request (prescription and information about the radiotherapy targets), and uploads the patient&#39;s CT image set. The RPA offers two approaches to automated planning. In one-step planning, the system uses the Service Request and CT scan to automatically generate the necessary contours and treatment plan. In two-step planning, the user reviews and edits the automatically generated contours before the RPA continues to generate a volume-modulated arc therapy plan. The final plan is downloaded from the RPA website and imported into the user&#39;s local treatment planning system, where the dose is recalculated for the locally commissioned linac; if necessary, the plan is edited prior to approval for clinical use.

The purpose of the main dashboard (Figure 1) is to provide a quick overview of the status of current patients in the RPA, allow a quick review of completed plans, download completed plans for review and editing in the user&#39;s treatment planning system, and provide advanced patient navigation and sorting tools. For patients to appear here, they must have at least one of the following: (1) An Accepted CT scan or (2) an Accepted Service Form. The status categories of current patients can be viewed on the main dashboard (Table 4).
An example lateral field from a whole-brain radiotherapy plan is shown in Figure 6. An example lateral field from a bony landmark-based 4-field box plan for cervical cancer is shown in Figure 7. In both cases, the final plan must be downloaded and then imported into the user&#39;s treatment planning system where the results must be reviewed, edited, and recalculated. The RPA also creates a final Plan Report (PDF) that includes the Service Request (see the example in Figure 3), CT approval report, and other details of the treatment plan.
The purpose of the Service Request&#160;dashboard (Table 2) is to provide a quick status overview of the Service Request for current patients in the RPA, create a new Service Request, accept a completed Service Request, and edit a Service Request. Patients who have a submitted or Accepted Service Request are displayed on this dashboard, which is accessible to all users of the RPA. However, only users registered in the RPA system as radiation oncologists can Accept a Service Request.
The purpose of the CT dashboard (Table 3) is to provide a quick status overview of CT scans for current patients in the RPA, upload new CT scans, review and Accept CT scans, and add Reference Points to CT scans. Reference points are added to guide the RPA in some specific situations, such as when the user wants to use a non-standard superior border for simple 4-field box plans for cervical cancer. Patients for whom a CT scan has been uploaded are shown here. Any user can view the CT dashboard, but only users registered as Clinical Users can Accept the CT scans.
Once the user has confirmed their final plan, they can export it from their TPS and upload it to the RPA. The purpose of this process is to provide a way to check that the data were properly communicated between different devices.
Patients for whom an RPA Plan has been generated will appear in the plan comparison&#160;dashboard. The plan comparison&#160;dashboard provides the status categories shown in Table 5 for current patients.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65504/65504fig06.jpg" /> 
Figure 6: Typical lateral field for whole-brain radiotherapy. This view shows the projections of the structure contours as well as the positions of the main collimators (yellow) and the multi-leaf collimators (blue). <a href="https://www.jove.com/files/ftp_upload/65504/65504fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65504/65504fig07.jpg" /> 
Figure 7: An example lateral field from a bony landmark-based 4-field box plan for cervical cancer. This view shows the positions of the main collimators (yellow) and the multi-leaf collimators(blue). <a href="https://www.jove.com/files/ftp_upload/65504/65504fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant at JoVE.com

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

This protocol describes a series of automated tools designed for high-quality radiotherapy autocontouring and autoplanning that are being packaged into a web-based service to maximize robustness and scalability while minimizing operational costs.

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Access to radiotherapy worldwide is limited. The Radiation Planning Assistant (RPA) is a fully automated, web-based tool that is being developed to offer ...

radiation-planning-assistant-web-based-tool-to-support-high-quality

Research

JoVE Journal

Medicine

1.3K Views.  The University of Texas MD Anderson Cancer Center. We hebben de Radiation Planning Assistant ontwikkeld als een webgebaseerde tool die bedoeld is om lage- en middeninkomenslanden te helpen hun inspanningen op te schalen om hoogwaardige contouren en behandelplanning voor patiënten te bieden. De toegang tot radiotherapie is op de meeste plaatsen in de wereld zeer beperkt, en dat is te wijten aan meerdere factoren, waaronder een tekort aan personeel en gekwalificeerde radiotherapeut-oncologen, therapie-radiografen en medisch fysici. En wat we h...

Video: Auteur in de schijnwerpers: de toegang tot bestralingstherapie verbeteren met de assistent voor stralingsplanning

PET and MRI Guided Irradiation of a Glioblastoma Rat Model Using a Micro-irradiator

For decades, small animal radiation research was mostly performed using fairly crude experimental setups applying simple single-beam techniques without the ability to target a specific or well-delineated tumor volume. The delivery of radiation was achieved using fixed radiation sources or linear accelerators producing megavoltage (MV) X-rays. These devices are unable to achieve sub-millimeter precision required for small animals. Furthermore, the high doses delivered to healthy surrounding tissue hamper response assessment. To increase the translation between small animal studies and humans, our goal was to mimic the treatment of human glioblastoma in a rat model. To enable a more accurate irradiation in a preclinical setting, recently, precision image-guided small animal radiation research platforms were developed. Similar to human planning systems, treatment planning on these micro-irradiators is based on computed tomography (CT). However, low soft-tissue contrast on CT makes it very challenging to localize targets in certain tissues, such as the brain. Therefore, incorporating magnetic resonance imaging (MRI), which has excellent soft-tissue contrast compared to CT, would enable a more precise delineation of the target for irradiation. In the last decade also biological imaging techniques, such as positron emission tomography (PET) gained interest for radiation therapy treatment guidance. PET enables the visualization of e.g., glucose consumption, amino-acid transport, or hypoxia, present in the tumor. Targeting those highly proliferative or radio-resistant parts of the tumor with a higher dose could give a survival benefit. This hypothesis led to the introduction of the biological tumor volume (BTV), besides the conventional gross target volume (GTV), clinical target volume (CTV), and planned target volume (PTV).
At the preclinical imaging lab of Ghent University, a micro-irradiator, a small animal PET, and a 7 T small animal MRI are available. The goal was to incorporate MRI-guided irradiation and PET-guided sub-volume boosting in a glioblastoma rat model.

In the past, small animal irradiation was usually performed without the ability to target a well-delineated tumor volume. The goal was to mimic the treatment of human glioblastoma in rats. Using a small animal irradiation platform, we performed MRI-guided 3D conformal irradiation with PET-based sub-volume boosting in a preclinical setting.

For decades, small animal radiation research was mostly performed using fairly crude experimental setups applying simple single-beam techniques without ...

Cancer Research

Stem Cell Transplantation Strategies for the Restoration of Cognitive Dysfunction Caused by Cranial Radiotherapy

Radiotherapy often provides the only clinical recourse for those afflicted with primary or metastatic brain tumors. While beneficial, cranial irradiation can induce a progressive and debilitating decline in cognition that may, in part, be caused by the depletion of neural stem cells. Given the increased survival of patients diagnosed with brain cancer, quality of life in terms of cognitive health has become an increasing concern, especially in the absence of any satisfactory long-term treatments.
To address this serious health concern we have used stem cell replacement as a strategy to combat radiation-induced cognitive decline. Our model utilizes athymic nude rats subjected to cranial irradiation. The ionizing radiation is delivered as either whole brain or as a highly focused beam to the hippocampus via linear accelerator (LINAC) based stereotaxic radiosurgery. Two days following irradiation, human neural stem cells (hNSCs) were stereotaxically transplanted into the hippocampus. Rats were then assessed for changes in cognition, grafted cell survival and for the expression of differentiation-specific markers 1 and 4-months after irradiation. Our cognitive testing paradigms have demonstrated that animals engrafted with hNSCs exhibit significant improvements in cognitive function. Unbiased stereology reveals significant survival (10-40%) of the engrafted cells at 1 and 4-months after transplantation, dependent on the amount and type of cells grafted. Engrafted cells migrate extensively, differentiate along glial and neuronal lineages, and express a range of immature and mature phenotypic markers.
Our data demonstrate direct cognitive benefits derived from engrafted human stem cells, suggesting that this procedure may one day afford a promising strategy for the long-term functional restoration of cognition in individuals subjected to cranial radiotherapy. To promote the dissemination of the critical procedures necessary to replicate and extend our studies, we have provided written and visual documentation of several key steps in our experimental plan, with an emphasis on stereotaxic radiosurgey and transplantation.

Brain tumor patients routinely undergo cranial radiotherapy, and while beneficial, this treatment often results in debilitating cognitive dysfunction. This serious unresolved problem has at present, no clinical recourse, and has driven our efforts to devise stem cell based therapies for the recovery of radiation-induced cognitive decrements.

Radiotherapy often provides the only clinical recourse for those afflicted with primary or metastatic brain tumors. While beneficial, cranial irradiation ...

Quantifying Cognitive Decrements Caused by Cranial Radiotherapy

With the exception of survival, cognitive impairment stemming from the clinical management of cancer is a major factor dictating therapeutic outcome. For many patients afflicted with CNS and non-CNS malignancies, radiotherapy and chemotherapy offer the best options for disease control. These treatments however come at a cost, and nearly all cancer survivors (~11 million in the US alone as of 2006) incur some risk for developing cognitive dysfunction, with the most severe cases found in patients subjected to cranial radiotherapy (~200,000/yr) for the control of primary and metastatic brain tumors1. Particularly problematic are pediatric cases, whose long-term survival plagued with marked cognitive decrements results in significant socioeconomic burdens2. To date, there are still no satisfactory solutions to this significant clinical problem.
We have addressed this serious health concern using transplanted stem cells to combat radiation-induced cognitive decline in athymic rats subjected to cranial irradiation3. Details of the stereotaxic irradiation and the in vitro culturing and transplantation of human neural stem cells (hNSCs) can be found in our companion paper (Acharya et al., JoVE reference). Following irradiation and transplantation surgery, rats are then assessed for changes in cognition, grafted cell survival and expression of differentiation-specific markers 1 and 4-months after irradiation. To critically evaluate the success or failure of any potential intervention designed to ameliorate radiation-induced cognitive sequelae, a rigorous series of quantitative cognitive tasks must be performed. To accomplish this, we subject our animals to a suite of cognitive testing paradigms including novel place recognition, water maze, elevated plus maze and fear conditioning, in order to quantify hippocampal and non-hippocampal learning and memory. We have demonstrated the utility of these tests for quantifying specific types of cognitive decrements in irradiated animals, and used them to show that animals engrafted with hNSCs exhibit significant improvements in cognitive function3.
The cognitive benefits derived from engrafted human stem cells suggest that similar strategies may one day provide much needed clinical recourse to cancer survivors suffering from impaired cognition. Accordingly, we have provided written and visual documentation of the critical steps used in our cognitive testing paradigms to facilitate the translation of our promising results into the clinic.

Cognitive impairment resulting from the radiotherapeutic management of brain tumors represents a clinically intractable condition that adversely impacts quality of life. The capability to critically evaluate potential interventions for ameliorating radiation-induced cognitive decrements ultimately depends on the capability to undertake rigorous quantitative assessments of cognition.

With the exception of survival, cognitive impairment stemming from the clinical management of cancer is a major factor dictating therapeutic outcome. For ...

Combination Radiotherapy in an Orthotopic Mouse Brain Tumor Model

Glioblastoma multiforme (GBM) are the most common and aggressive adult primary brain tumors1. In recent years there has been substantial progress in the understanding of the mechanics of tumor invasion, and direct intracerebral inoculation of tumor provides the opportunity of observing the invasive process in a physiologically appropriate environment2. As far as human brain tumors are concerned, the orthotopic models currently available are established either by stereotaxic injection of cell suspensions or implantation of a solid piece of tumor through a complicated craniotomy procedure3. In our technique we harvest cells from tissue culture to create a cell suspension used to implant directly into the brain. The duration of the surgery is approximately 30 minutes, and as the mouse needs to be in a constant surgical plane, an injectable anesthetic is used. The mouse is placed in a stereotaxic jig made by Stoetling (figure 1). After the surgical area is cleaned and prepared, an incision is made; and the bregma is located to determine the location of the craniotomy. The location of the craniotomy is 2 mm to the right and 1 mm rostral to the bregma. The depth is 3 mm from the surface of the skull, and cells are injected at a rate of 2 &mu;l every 2 minutes. The skin is sutured with 5-0 PDS, and the mouse is allowed to wake up on a heating pad. From our experience, depending on the cell line, treatment can take place from 7-10 days after surgery. Drug delivery is dependent on the drug composition. For radiation treatment the mice are anesthetized, and put into a custom made jig. Lead covers the mouse's body and exposes only the brain of the mouse. The study of tumorigenesis and the evaluation of new therapies for GBM require accurate and reproducible brain tumor animal models. Thus we use this orthotopic brain model to study the interaction of the microenvironment of the brain and the tumor, to test the effectiveness of different therapeutic agents with and without radiation.

The purpose of this article is to describe the use of an orthotopic glioblastoma model for chemoradiation studies. This article will go though cell processing, implanting, and radiotherapy of the mouse using an intracranial model.

Glioblastoma multiforme (GBM) are the most common and aggressive adult primary brain tumors1. In recent years there has been substantial progress in the ...

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

A rat glioblastoma model to mimic chemo-radiation treatment of human glioblastoma in the clinic was previously established. Similar to the clinical treatment, computed tomography (CT) and magnetic resonance imaging (MRI) were combined during the treatment-planning process. Positron emission tomography (PET) imaging was subsequently added to implement sub-volume boosting using a micro-irradiation system. However, combining three imaging modalities (CT, MRI, and PET) using a micro-irradiation system proved to be labor-intensive because multimodal imaging, treatment planning, and dose delivery have to be completed sequentially in the preclinical setting. This also results in a workflow that is more prone to human error. Therefore, a user-friendly algorithm to further optimize preclinical multimodal imaging-based radiation treatment planning was implemented. This software tool was used to evaluate the accuracy and efficiency of dose painting radiation therapy with micro-irradiation by using an in silico study design. The new methodology for dose painting radiation therapy is superior to the previously described method in terms of accuracy, time efficiency, and intra- and inter-user variability. It is also an important step towards the implementation of inverse treatment planning on micro-irradiators, where forward planning is still commonly used, in contrast to clinical systems.

Here we present a protocol to perform preclinical positron emission tomography-based radiotherapy in a rat glioblastoma model using algorithms developed in-house to optimize the accuracy and efficiency.

A rat glioblastoma model to mimic chemo-radiation treatment of human glioblastoma in the clinic was previously established. Similar to the clinical ...

Clinical Examination Protocol to Detect Atypical and Classical Scrapie in Sheep

The diagnosis of scrapie, a transmissible spongiform encephalopathy (TSEs) of sheep and goats, is currently based on the detection of disease-associated prion protein by post mortem tests. Unless a random sample of the sheep or goat population is actively monitored for scrapie, identification of scrapie cases relies on the reporting of clinical suspects, which is dependent on the individual&#39;s familiarization with the disease and ability to recognize clinical signs associated with scrapie. Scrapie may not be considered in the differential diagnosis of neurological diseases in small ruminants, particularly in countries with low scrapie prevalence, or not recognized&#160;if it presents as nonpruritic form like atypical scrapie. To aid in the identification of clinical suspects, a short examination protocol is presented to assess the display of specific clinical signs associated with pruritic and nonpruritic forms of TSEs in sheep, which could also be applied to goats. This includes assessment of behavior, vision (by testing of the menace response), pruritus (by testing the response to scratching), and movement (with and without blindfolding). This may lead to a more detailed neurologic examination&#160;of reporting animals as scrapie suspects. It&#160;could also be used in experimental TSE studies of sheep or goats to evaluate disease progression or to identify clinical end-point.

A short clinical examination protocol is presented to aid in the detection of classical and atypical scrapie in sheep and goats.

The diagnosis of scrapie, a transmissible spongiform encephalopathy (TSEs) of sheep and goats, is currently based on the detection of disease-associated ...

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

The prognosis of patients with metastatic cancers has improved in the past decades due to effective chemotherapy and oligometastatic surgery. For inoperable patients, local ablation therapies, such as stereotactic body radiotherapy (SBRT), can provide effective local tumor control with minimal toxicity. Because of its high precision and accuracy, SBRT delivers a higher radiation dose per fraction, is more effective, and targets smaller irradiation volumes than does conventional radiotherapy. In addition, steep dose gradients from target lesions to surrounding normal tissues are achieved using SBRT; thus, SBRT provides more effective tumor control and exhibits fewer side effects than conventional radiotherapy. The use of SBRT is prevalent for treating intracranial lesions (known as stereotactic radiosurgery); however, it is now also used for treating spinal and adrenal metastases. Because of advancements in image-guided assistance and respiratory motion management, several studies have investigated the use of SBRT for treating lung or liver tumors, which move as a patient breathes. The results of these studies have suggested that SBRT favorably controls tumors in the case of moving lesions.
Four-dimensional computed tomography (4D-CT) with an abdominal compressor (AC) is clinically convenient for effective respiratory motion management. Because this method is noninvasive and allows free breathing, its use reduces complications. Furthermore, patients consider this method convenient. Moreover, it is considered more efficient than other methods of respiratory motion management by physicians and therapists. The use of 4D-CT with an AC for treating pulmonary lesions has also been widely investigated, and the technique is gaining acceptance for treating hepatic lesions. However, the protocols for using 4D-CT with an AC for treating hepatic lesions are different from those used for treating pulmonary lesions. In this article, we describe a new protocol for SBRT with 4D-CT and an AC for treating liver metastases.

Stereotactic body radiotherapy (SBRT) requires rigorous accuracy and precision for delivering high radiation doses per fraction to small treatment volumes to improve tumor control and simultaneously reduce toxicity. Herein, we present a noninvasive and clinically convenient respiratory motion management protocol for SBRT for liver metastases.

The prognosis of patients with metastatic cancers has improved in the past decades due to effective chemotherapy and oligometastatic surgery. For ...

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

The Radiation Planning Assistant (RPA) is a system developed for the fully automated creation of radiotherapy treatment plans, including volume-modulated arc therapy (VMAT) plans for patients with head/neck cancer and 4-field box plans for patients with cervical cancer. It is a combination of specially developed in-house software that uses an application programming interface to communicate with a commercial radiotherapy treatment planning system. It also interfaces with a commercial secondary dose verification software. The necessary inputs to the system are a Treatment Plan Order, approved by the radiation oncologist, and a simulation computed tomography (CT) image, approved by the radiographer. The RPA then generates a complete radiotherapy treatment plan. For the cervical cancer treatment plans, no additional user intervention is necessary until the plan is complete. For head/neck treatment plans, after the normal tissue and some of the target structures are automatically delineated on the CT image, the radiation oncologist must review the contours, making edits if necessary. They also delineate the gross tumor volume. The RPA then completes the treatment planning process, creating a VMAT plan. Finally, the completed plan must be reviewed by qualified clinical staff.

Radiation therapy is a highly complex cancer treatment that requires multiple specialists to create a treatment plan and provide quality assurance (QA) prior to delivery to a patient. This protocol describes the use of a fully automated system, the Radiation Planning Assistant (RPA), to create high-quality radiation treatment plans.

The Radiation Planning Assistant (RPA) is a system developed for the fully automated creation of radiotherapy treatment plans, including volume-modulated ...

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Radiation therapy is a frequently used modality for the treatment of solid cancers. Although the mechanisms of cell kill are similar for all forms of radiation, the in vivo properties of photon and proton beams differ greatly and maybe exploited to optimize clinical outcomes. In particular, proton particles lose energy in a predictable manner as they pass through the body. This property is used clinically to control the depth at which the proton beam is terminated, and to limit radiation dose beyond the target region. This strategy can allow for substantial reductions in radiation dose to normal tissues located just beyond a tumor target. However, the degradation of proton energy in the body remains highly sensitive to tissue density. As a consequence, any changes in tissue density during the course of treatment may significantly alter proton dosimetry. Such changes may occur through alterations in body weight, respiration, or bowel filling/gas, and may result in unfavorable dose deposition. In this manuscript, we provide a detailed method for the delivery of proton therapy using both passive scatter and pencil beam scanning techniques for prostate cancer. Although the described procedure directly pertains to prostate cancer patients, the method may be adapted and applied for the treatment of virtually all solid tumors. Our aim is to equip readers with a better understanding of proton therapy delivery and outcomes in order to facilitate the appropriate integration of this modality during cancer therapy.

The fundamentals of radiation planning and delivery for proton therapy using prostate cancer as a model are presented. The application of these principles to other selected disease sites highlights how proton radiotherapy may enhance clinical outcomes for cancer patients.

Radiation therapy is a frequently used modality for the treatment of solid cancers. Although the mechanisms of cell kill are similar for all forms of ...

Irradiator Commissioning and Dosimetry for Assessment of LQ &alpha; and &beta; Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

Radiation dosimetry is critical in the accurate delivery and reproducibility of radiation schemes in preclinical models for high translational relevance. Prior to performing any in vitro or in vivo experiments, the specific dose output for the irradiator and individual experimental designs must be assessed. Using an ionization chamber, electrometer, and solid water setup, the dose output of wide fields at isocenter can be determined. Using a similar setup with radiochromic films in the place of the ionization chamber, dose rates for smaller fields at different depths can also be determined. In vitro clonogenic survival assays of cancer cells in response to radiation treatment are inexpensive experiments that provide a measure of inherent radio-sensitivity of cell lines by fitting these data with the traditional linear-quadratic model. Model parameters estimated from these assays, combined with the principles of biologic effective doses, allows one to develop varying fractionation schedules for radiation treatment that provide equivalent effective doses in tumor-bearing animal experiments. This is an important factor to consider and correct for in comparing in vivo radiation therapy schedules to eliminate potential confounding of results due to variance in the delivered effective doses. Taken together, this article provides a general method for dose output verification preclinical animal and cabinet irradiators, in vitro assessment of radio-sensitivity, and verification of radiation delivery in small living organisms.

Irradiator Commissioning and Dosimetry for Assessment of LQ &#945; and &#946; Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

Radiation dosimetry provides a technique for enhancing the accuracy of preclinical experiments and ensuring that the radiation doses delivered are closely related to clinical parameters. This protocol describes steps to be taken at each phase during preclinical radiation experiments to ensure proper experimental design.

Radiation dosimetry is critical in the accurate delivery and reproducibility of radiation schemes in preclinical models for high translational relevance. ...

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

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