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Purpose To determine the most accurate similarity metric when using an independent system to verify automatically generated contours. Methods A reference autocontouring system (primary system to create clinical contours) and a verification autocontouring system (secondary system to test the primary contours) were used to generate a pair of 6 female pelvic structures (UteroCervix [uterus + cervix], CTVn [nodal clinical target volume (CTV)], PAN [para‐aortic lymph nodes], bladder, rectum, and kidneys) on 49 CT scans from our institution and 38 from other institutions. Additionally, clinically acceptable and unacceptable contours were manually generated using the 49 internal CT scans. Eleven similarity metrics (volumetric Dice similarity coefficient (DSC), Hausdorff distance, 95% Hausdorff distance, mean surface distance, and surface DSC with tolerances from 1 to 10 mm) were calculated between the reference and the verification autocontours, and between the manually generated and the verification autocontours. A support vector machine (SVM) was used to determine the threshold that separates clinically acceptable and unacceptable contours for each structure. The 11 metrics were investigated individually and in certain combinations. Linear, radial basis function, sigmoid, and polynomial kernels were tested using the combinations of metrics as inputs for the SVM. Results The highest contouring error detection accuracies were 0.91 for the UteroCervix, 0.90 for the CTVn, 0.89 for the PAN, 0.92 for the bladder, 0.95 for the rectum, and 0.97 for the kidneys and were achieved using surface DSCs with a thickness of 1, 2, or 3 mm. The linear kernel was the most accurate and consistent when a combination of metrics was used as an input for the SVM. However, the best model accuracy from the combinations of metrics was not better than the best model accuracy from a surface DSC as an input. Conclusions We distinguished clinically acceptable contours from clinically unacceptable contours with an accuracy higher than 0.9 for the targets and critical structures in patients with cervical cancer; the most accurate similarity metric was surface DSC with a thickness of 1, 2, or 3 mm.

Purpose To fully automate CT‐based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques. Methods We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4‐field‐box (4‐field‐box), 3D conformal radiotherapy (3D‐CRT), and volumetric modulated arc therapy (VMAT). These auto‐planning algorithms were combined with a previously developed auto‐contouring system. To improve the quality of the 4‐field‐box and 3D‐CRT plans, we used an in‐house, field‐in‐field (FIF) automation program. Thirty‐five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5‐point Likert scale. Results Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4‐field‐box, 3D‐CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference. Conclusion Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource‐constrained radiotherapy departments in low‐ and middle‐income countries.

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Purpose To determine the clinical outcomes and toxicities of proton beam therapy (PBT) versus 3D-conformal photon radiation therapy (XRT) in patients with testicular seminoma. Materials and Methods This observational study evaluated consecutive patients with testicular seminoma who were treated with inguinal orchiectomy and radiation therapy at a single, tertiary, high-volume center in 2008-19. Acute toxicity was scored with the Common Terminology Criteria for Adverse Events V 4.0. Organs at risk were contoured retrospectively by 2 investigators. Recurrences and secondary malignancies were based on routine follow-up imaging, either computed tomography or magnetic resonance imaging. Results Fifty-five patients were treated with radiation therapy, 11 in the PBT-arm and 44 in the XRT-arm, with a median follow-up interval of 61 months (interquartile range [IQR]: 32-79 months). Acute treatment-related diarrhea, grade 1 to 2, was more common among XRT-treated patients (0% vs 29.5%, P = .039), and dermatitis, grade 1, was more likely among PBT-treated patients (27.3% vs 2.3%, P = .004). Dosimetrically, PBT-treated patients, relative to XRT-treated patients, had lower dose to organs at risk including the kidney, bladder, femoral head, spinal cord, bowel, pancreas, and stomach. The 5-year overall survival rate was 100% and disease-free survival rate was 96.4% for all patients. Two patients, all in the XRT-arm, had disease recurrence: 1 in the pelvis and 1 in the lung. Three patients, all in the XRT-arm, were diagnosed with a secondary malignancy: 1 in-field pancreaticoblastoma, 1 in-field colon adenocarcinoma, and a stage IV T-cell lymphoma. Conclusion Proton beam therapy for testicular seminoma resulted in excellent clinical outcomes and was associated with lower rates of acute diarrhea but higher rates of acute dermatitis. Proton beam therapy resulted in no in-field secondary malignancies and a more favorable dosimetric profile for organs at risk relative to XRT. Reduced dose to organs at risk, such as the kidneys, may result in long-term improvement in function.