Dosimetric Study of Total Marrow and Lymphoid Irradiation on a Ring Gantry-Based Medical Linac with a Two-Layer Multi-Leaf Collimator

PURPOSE/OBJECTIVE(S)

In this study, we aimed to evaluate dosimetric quality of total marrow and lymphoid irradiation (TMLI) plans for a ring gantry-based medical Linac with a two-layer multi-leaf collimator.

MATERIALS/METHODS

We retrospectively retrieved treatment planning CT images, structure sets, and plan dose for four adult patients, two male and two female, who previously received TMLI treatments on helical tomotherapy (HT) at our institution. TMLI plans were optimized for a ring gantry-based medical Linac with a two-layer multi-leaf collimator (Halcyon, Varian Medical Systems, Inc., Palo Alto, CA). A prescription dose of 12 Gy in 8 fractions was prescribed to the skeletal bones from the skull to mid-thigh, spleen, spinal canal, and lymphoid volume. Five or six isocenters were placed with equal spacing along the patient's longitudinal direction in each TMLI plan with two 6-MV flattening filter-free volumetric modulated arc therapy (VMAT) fields at each isocenter. Isocenter separation ranged from 15 cm to 16.5 cm. Each VMAT field has a field size of 28 cm to 28 cm with the collimator at 90° and a full gantry rotation. The nominal dose rate was 800 MU/minute, and the maximum gantry rotation speed was 24°/sec. Institutional dosimetric constraints were used for optimization including a mean lung dose limit of less than 8 Gy. All the plans were normalized so that 85% the primary planning target volume received the prescription dose.

RESULTS

The average mean doses to the target volumes ranged from 12.2 to 12.6 Gy in the Halcyon TMLI plans, while they ranged from 12.1 to 12.5 Gy in the HT TMLI plans. Relative to the prescription dose, the average mean dose for normal organs ranged from 21.3% to 56.6% in the Halcyon TMLI plans, while it ranged from 10.1% to 68.4% in the clinical HT plans. The difference in the average mean dose to normal organs was less than 0.5 Gy except two organs between the Halcyon and HT TMLI plans. The average median dose for normal organs ranged from 18.2% to 48.8% relative to the prescription dose in the Halcyon TMLI plans. The mean lung dose (MLD) in the Halcyon TMLI plans met the institutional limit with an average dose of 6.75±0.42 Gy (range: 6.44 - 7.36 Gy), while the average MLD was 6.54±0.77 Gy (range: 6.24 - 7.22 Gy) in the HT plans (p-value = 0.71 in the paired t-test). The average total monitor unit in the Halcyon TMLI plans was 4,425±906 MU (range: 3,470 - 5,575 MU) with an average beam-on time of 5.1±1.3 minutes (range: 4.1 - 7.0 minutes), which excludes isocenter setup time, while the average beam-on time was 22.2±3.2 minutes (range: 19.6 - 26.1 minutes) with the HT plans.

CONCLUSION

Halcyon TMLI plans met our institutional dosimetric constraints with adequate normal organ sparing and target dose coverage. The beam-on time with the Halcyon plans was significantly shorter than that with the HT plans, which could lead to shorter treatment time and increased patient comfort. This study showed the feasibility of TMLI treatments on the Halcyon machine. The same method could be used for total body irradiation on Halcyon.

Evaluation of a Deep Image to Image Network (DI2IN) Auto-Segmentation Algorithm across a Network of Cancer Centers

PURPOSE/OBJECTIVE(S)

Due to manual OAR contouring challenges, various automatic contouring solutions have been introduced. Historically, common clinical auto-segmentation algorithms used were atlas based, which required maintaining a library of self-made contours. Searching the collection was computationally intensive and could take several minutes to complete. Deep learning approaches have shown significant benefits compared to atlas-based methods in improving segmentation accuracy and efficiency in auto-segmentation algorithms. This work represents the first multi-institutional study to describe and evaluate an AI algorithm for auto-segmentation of organs at risk (OARs) based on a deep image-to-image network (DI2IN).

MATERIALS/METHODS

The AI-Rad Companion Organs RT (AIRC) algorithm (Siemens Healthineers, Erlangen, Germany) uses a two-step approach for segmentation. In the first step, the target organ region in the optimal input image is extracted using a trained Deep Reinforcement Learning network (DRL), which is then used as input to create the contours in the second step based on DI2IN. The study was initially designed as a prospective single-center evaluation. The automated contours generated by AIRC were evaluated by three experienced board-certified radiation oncologists using a 4-point scale where 4 is clinically usable and 1 requires re-contouring. After seeing favorable results in a single-center pilot study, we decided to expand the study to 6 additional institutions, encompassing 8 additional evaluators for a total of 11 physician evaluators across 7 institutions.

RESULTS

One hundred fifty-six patients and 1366 contours were prospectively evaluated. The 5 most commonly contoured organs were the lung (136 contours, average rating = 4.0), spinal cord (106 contours, average rating = 3.1), eye globe (80 contours, average rating = 3.9), lens (77 contours, average rating = 3.9), and optic nerve (75 contours, average rating = 4.0). The average rating per evaluator per contour was 3.6. On average 124 contours were evaluated by each evaluator. 65% of the contours were rated as 4 and 31% were rated as 3. Only 4% of contours were rated as 1 or 2. 33 organs were evaluated in the study, with 19 structures having a 3.5 or above average rating (ribs, abdominopelvic cavity, skeleton, larynx, lung, aorta, brachial plexus, lens, eye globe, glottis, heart, parotid glands, bladder, kidneys, supraglottic larynx, submandibular glands, esophagus, optic nerve, oral cavity) and the remaining organs having a rating of 3.0 or greater (female breast, proximal femur, seminal vesicles, rectum, sternum, brainstem, prostate, brain, lips, mandible, liver, optic chiasm, spinal cord, spleen). No organ had an average rating below 3.

CONCLUSION

AIRC performed well with greater than 95% of contours accepted by treating physicians with no or minor edits. It supported a fully automated workflow with the potential for time savings and increased standardization with the use of AI-powered algorithms for high quality OAR contouring.

Using Scorecards to Tune Ethos Directive Templates: An ARTIA Cervix Dosimetric Planning Study

PURPOSE/OBJECTIVE(S)

The Adaptive Radiation Therapy Individualized Approach (ARTIA) Cervix clinical trial uses predefined clinical directive templates (CDT) combined with RapidPlan DVH estimations (DVHe) to guide plan optimization in the Ethos treatment planning system. The dosimetric scorecard tool (DST) quantifies improvements in plan quality. This is the first study to utilize the DST to tune an Ethos CDT to improve resulting plan quality.

MATERIALS/METHODS

Iterative replanning was used to modify the draft CDT (CDT-1) in Ethos to generate a new CDT (CDT-2) that maximized the clinical consensus scorecard's total score compared toCDT-1. CDT-2 was established, and resulting plans were compared with and without a DVHe. Additional fixed field IMRT beam geometries were compared between CDT-1 and CDT-2, both with DVHe. After obtaining favorable results when comparing CDT-1 verses CDT-2 for two test cases, 10 additional cases were retrospectively identified and tested.

RESULTS

For the initial test cases, CDT-2 decreased OAR doses without compromising PTV coverage (No DVHe). While both plans met the protocol target guidelines and OAR constraints, the scorecard was able to quantify the improvement with CDT-2 on a test case with a score of 166.1 (78.7%) vs CDT-1, 163.87 (77.6%). When CDT-2 was combined with the DVHe, it still marginally outperformed CDT-1: 168.73 (76%) versus 166.13 (74.8%). Plan quality was further improved by increasing the total number of fields to 19. Combining CDT-2 and DVHe with a 19-field geometry resulted in the greatest benefit at 184.6 (83.2%). This scored higher than the ARTIA-Cervix defined delivery technique of CDT-1 and DVHe, with a 9-field geometry 166.1 (74.8%). The study was expanded to a separate analysis on 10 new cases. The 19-field approach was superior for all 10 cases and CDT-2 achieved a higher score in 7/10 cases. When comparing 9 versus 19 fields, the total optimization and calculation time increased by an average of 1.9 minutes while the beam delivery time increased by an average of 2.8 minutes (+/- 0.1). The average MU/field was 174.3 (total 1568.3) and 129.9 (total 2468) for 9 and 19 fields, respectively. Two test plans were re-optimized and calculated with Ethos 1.1 maintenance release (MR) 1 with both 9 and 19 fields. For case 1, MR 1 resulted in an 8.4% and 6.9% decrease in MU and scored -0.6% and +0.5% for 9 and 19 fields, respectively. For case 2, MR 1 resulted in a 0.8% and 3.1% decrease in MU and scored -3.4% and -0.3% for 9 and 19 fields, respectively.

CONCLUSION

The scorecard allows for easy evaluation of the dosimetric impact of other planning parameters (beam arrangements and use of DVHe) to identify the best approach. Using a scorecard to finely-tune a CDT is expected to improve planning efficiency, decrease intra-institutional plan quality and variability, improve the average calculated plan quality and benefit CBCT-guided ART.

Pelvic Nodal Auto-Segmentation Using a Deep Image to Image Network (DI2IN) Auto-Segmentation Algorithm: Comparing Male vs. Female Pelvis

PURPOSE/OBJECTIVE(S)

Deep Learning approaches have shown significant benefits compared to atlas-based methods in improving segmentation accuracy and efficiency in auto-segmentation algorithms. The AI-Rad Companion Organs RT pelvic nodal auto-segmentation feature was trained and developed for use in the male pelvis. There is no real-world data on its usability in male pelvis and whether it can be used for female pelvic nodal anatomy. This work represents the first multi-institutional study to describe and evaluate an AI algorithm for auto-segmentation of the pelvic nodal region in female patients based on a deep image-to-image network (DI2IN).

MATERIALS/METHODS

The AIRC algorithm uses a two-step approach for segmentation. In the first step, the target organ region in the optimal input image is extracted using a trained Deep Reinforcement Learning network (DRL), which is then used as input to create the contours in the second step based on DI2IN. We retrospectively evaluated AIRC pelvic nodal auto-segmentation in both male and female patients treated at our network of institutions. The automated pelvic nodal contours generated by AIRC were evaluated by one board-certified radiation oncologist, specializing in prostate and gynecologic malignancies. A 4-point scale was used, where 4 is clinically usable and 1 requires re-contouring. Pelvic nodal regions included the right and left side of the common iliac, external iliac, internal iliac, obturator and midline presacral nodes. A chi-squared test was then used to compare the scores of male and female pelvic nodal cases.

RESULTS

Fifty-two female and 51 male patients were included in the study, representing a total of 468 and 447 pelvic nodal regions, respectively. 96% (450 pelvic nodal contours) and 99% (443 pelvic nodal contours) required no or minor edits for female and male patients, respectively (p = 0.004). The right internal iliac was the only nodal group with a statistically significant difference between female (92% requiring no or minor edits) and male (100% requiring no or minimal edits) patients, p = 0.04. The percentage of patients requiring no, or minor edits was 87% (45 patients) and 92% (47 patients) for female and male patients, respectively (p = 0.36).

CONCLUSION

AIRC pelvic nodal auto-segmentation performed very well in both male and female pelvic nodal regions, with the male pelvic nodal regions performing better especially in the right internal iliac nodal group. It is usable in female pelvic nodal regions, with 96% of contours requiring no or minor edits. As auto-segmentation becomes more widespread, it may be important to have equal representation from all genders in training and validation of auto-segmentation algorithms.

Pilot Study of Peer Review in Low Middle-Income Country (LMIC) through Cloud-Based Platform

PURPOSE/OBJECTIVE(S)

Peer review is an essential step in clinical quality assurance and can impact patient safety and treatment outcomes. Most published data on peer review is from developed nations, with little data on peer review from low middle income countries (LMIC). Major challenges to peer review can include a lack of time, expertise and commitment from team members. With increasing access to advanced technology in LMIC, peer review is becoming more important to maintain quality and standard of care. We evaluated cloud-based e- peer review (Varian) in our network of hospitals in India with an aim to see feasibility and impact on care.

MATERIALS/METHODS

Four of 15 centers across India were selected for this pilot study. All team members were trained on the platform prior to implementation. New cases for the week treated with definitive intent were selected by the dosimetrist. The link to the cases were sent through email to reviewing physicians. Various aspects which were reviewed for each case were.1) Work up & staging (Documents were scanned and loaded).2) Treatment intent & prescription.3) Target contours.4) Normal Organ at risk contours.5) Dose- Volume -Histogram (DVH) with clinical goals attached. Cases were marked as "Not Appropriate", "Appropriate", "Appropriate with minor finding", "Represent with major revisions" as per volume and plan review.

RESULTS

Over a period of 2 months, a total of 80 cases underwent e-Peer Review at our network of hospitals prior to the start of treatment. Median turnover time (like from link sent to time to completion of review) was 48 (6-360) hours. Mean time taken by physician for review was 9 minutes (range 3 to15). 31.2% of cases were accepted without any changes, 51.9 % had a minor change and 16.9 % cases had major changes. Most frequent reason of major changes was contouring corrections 16.9%. 31% of major changes underwent recontouring and replanning before initiation of treatment.

CONCLUSION

Peer review was feasible in our network through this e-peer review system, with average turnover time and mean time taken for review of 48 hours & 9 min respectively. Peer review led to significant changes which could impact patient care delivery and outcome. The ability to review cases asynchronously via this cloud-based e-peer review system, helped to ease the burden of scheduling between treating and reviewing physician. We plan to implement this across the remaining centers in our network.