Search

IntroductionOrgan-at-risk segmentation for head and neck cancer radiation therapy is a complex and time-consuming process (requiring up to 42 individual structure, and may delay start of treatment or even limit access to function-preserving care. Feasibility of using a deep learning (DL) based autosegmentation model to reduce contouring time without compromising contour accuracy is assessed through a blinded randomized trial of radiation oncologists (ROs) using retrospective, de-identified patient data.MethodsTwo head and neck expert ROs used dedicated time to create gold standard (GS) contours on computed tomography (CT) images. 445 CTs were used to train a custom 3D U-Net DL model covering 42 organs-at-risk, with an additional 20 CTs were held out for the randomized trial. For each held-out patient dataset, one of the eight participant ROs was randomly allocated to review and revise the contours produced by the DL model, while another reviewed contours produced by a medical dosimetry assistant (MDA), both blinded to their origin. Time required for MDAs and ROs to contour was recorded, and the unrevised DL contours, as well as the RO-revised contours by the MDAs and DL model were compared to the GS for that patient.ResultsMean time for initial MDA contouring was 2.3 hours (range 1.6-3.8 hours) and RO-revision took 1.1 hours (range, 0.4-4.4 hours), compared to 0.7 hours (range 0.1-2.0 hours) for the RO-revisions to DL contours. Total time reduced by 76% (95%-Confidence Interval: 65%-88%) and RO-revision time reduced by 35% (95%-CI,-39%-91%). All geometric and dosimetric metrics computed, agreement with GS was equivalent or significantly greater (p

Primary bone lymphoma (PBL) is a rare extranodal presentation within lymphomas and primary bone malignancies. Pathologic fracture (PF) is a common complication of metastatic bone disease but is, rarely, the presentation of a primary bone tumor. We report a case of an 83-year-old man with a history of untreated prostate cancer, presenting with atraumatic fracture of his left femur after months of intermittent pains and weight loss. Radiographic workup revealed a lytic lesion suspicious for PF secondary to metastatic prostate cancer; however, initial core biopsy results were inconclusive for malignancy. A complete blood count with differential and complete metabolic panel was within normal limits. During surgical fixation and nailing of the femur, a reaming biopsy was performed as a repeat measure and revealed diffuse large B-cell lymphoma. Staging with positron emission tomography and computed tomography found no evidence of lymphatic or visceral involvement and chemotherapy was promptly initiated. This case highlights the diagnostic workup challenges for PF secondary to PBL, especially in the setting of concurrent malignancy. Because of the non-specific presentation of a lytic lesion on imaging associated with atraumatic fracture, we highlight PBL as an important diagnostic consideration.

In this era of patient-centered, outcomes-driven and adaptive radiotherapy, deep learning is now being successfully applied to tackle imaging-related workflow bottlenecks such as autosegmentation and dose planning. These applications typically require supervised learning approaches enabled by relatively large, curated radiotherapy datasets which are highly reflective of the contemporary standard of care. However, little has been previously published describing technical infrastructure, recommendations, methods or standards for radiotherapy dataset curation in a holistic fashion. Our radiation oncology department has recently embarked on a large-scale project in partnership with an external partner to develop deep-learning-based tools to assist with our radiotherapy workflow, beginning with autosegmentation of organs-at-risk. This project will require thousands of carefully curated radiotherapy datasets comprising all body sites we routinely treat with radiotherapy. Given such a large project scope, we have approached the need for dataset curation rigorously, with an aim towards building infrastructure that is compatible with efficiency, automation and scalability. Focusing on our first use-case pertaining to head and neck cancer, we describe our developed infrastructure and novel methods applied to radiotherapy dataset curation, inclusive of personnel and workflow organization, dataset selection, expert organ-at-risk segmentation, quality assurance, patient de-identification, data archival and transfer. Over the course of approximately 13 months, our expert multidisciplinary team generated 490 curated head and neck radiotherapy datasets. This task required approximately 6000 human-expert hours in total (not including planning and infrastructure development time). This infrastructure continues to evolve and will support ongoing and future project efforts.

Purpose: To successfully plan and treat a patient with diffuse angiosarcoma involving the face and scalp with intensity-modulated proton therapy (IMPT) before surgical resection. Materials and Methods: A patient presented to the radiation oncology department for preoperative treatment of an angiosarcoma diffusely involving the face and scalp. A 4-field IMPT technique was used to create a homogeneous dose distribution to the entire target volume while sparing underlying critical structures from toxicity and low-dose spread. A custom Monte Carlo optimizer was necessary to achieve treatment goals. Biological dose was evaluated with a linear energy transfer–based biological enhancement model. Robustness criteria were evaluated per department standard. The patient was successfully planned and treated according to clinical goals. Results: The patient successfully completed the course of IMPT and was able to undergo surgical resection. Pathology indicated no presence of angiosarcoma. Conclusion: IMPT using a custom Monte Carlo optimizer is a suitable radiation therapy treatment option for patients with diffuse angiosarcoma of the scalp and face.

Purpose: Synchronous bilateral breast cancer (SBBC) poses distinct challenges for radiation therapy planning. We report our proton therapy experience in treating patients with SBBC. We also provide a dosimetric comparison of intensity modulated proton therapy (IMPT) versus photon therapy. Methods and Materials: Patients with SBBC who received IMPT at our institution were retrospectively analyzed. The clinical target volume (CTV) included the breast or chest wall and comprehensive regional lymph nodes, including axilla, supraclavicular fossa, and the internal mammary chain. Intensity modulated proton therapy and volumetric modulated arc therapy (VMAT) plans were generated with the goal that 90% of the CTV would recieve at least 90% of the prescription dose (D90>=90%). Comparisons between modalities were made using the Wilcoxon signed rank test. Physician-reported acute toxic effects and photography were collected at baseline, end of treatment, and each follow-up visit. Results: Between 2015 and 2018, 11 patients with SBBC were treated with IMPT. The prescription was 50 Gy in 25 fractions. The median CTV D90 was 99.9% for IMPT and 97.6% for VMAT (P = .001). The mean heart dose was 0.7 Gy versus 7.2 Gy (P = .001), the total lung mean dose was 7.8 Gy versus 17.3 Gy (P = .001), and the total lung volume recieving 20 Gy was 13.0% versus 27.4% (P = .001). The most common acute toxic effects were dermatitis (mostly grade 1-2 with 1 case of grade 3) and grade 1 to 2 fatigue. The most common toxic effects at the last-follow up (median, 32 months) were grade 1 skin hyperpigmentation, superficial fibrosis, and extremity lymphedema. No nondermatologic or nonfatigue adverse events of grade >1 were recorded. Conclusions: Bilateral breast and/or chest wall and comprehensive nodal IMPT is technically feasible and associated with low rates of severe acute toxic effects. Treatment with IMPT offered improved target coverage and normal-tissue sparing compared with photon therapy. Long-term follow-up is ongoing to assess efficacy and toxic effects.

Background: Emerging research supports 3D printing can provide customizable, low-cost, and replicable items for application in occupational therapy, but more research is necessary to inform occupational therapists on why and how 3D printing would be applicable and feasible in practice. Method: This study is a cost-effective analysis aimed to identify practical considerations of a selection of 3D printed items in comparison to commercially available items. Ten items of adaptive equipment were downloaded from open-sourced 3D printing design websites and printed. The estimated cost of material was calculated and each print time was recorded. Items with comparable design and function were selected from a thorough internet search for analysis and comparison to the 3D printed items. Results: The results demonstrate that each 3D printed item had a positive benefit in terms of material cost and print time compared to the cost and shipping time of each comparable item. Conclusion: The 3D printed items were the more cost-effective for all items, but most significantly for niche designs with fewer available commercial alternatives. 3D printing successfully replicated commonly used adaptive equipment for a comparable cost, while allowing for customization and the ability to provide the item in-house to clients.

Purpose: Electrocardiogram-gated computed tomography with coronary angiography can be used for cardiac substructure sparing (CSS) optimization, which identifies and improves avoidance of cardiac substructures when treating with intensity modulated radiotherapy (IMRT). We investigated whether intensity modulated proton therapy (IMPT) would further reduce dose to cardiac substructures for patients with mediastinal lymphoma. Patients and Methods: Twenty-one patients with mediastinal lymphoma were enrolled and underwent electrocardiogram-gated computed tomography angiography during or shortly after simulation for radiotherapy planning. Thirteen patients with delineated cardiac substructures underwent comparative planning with both IMPT and IMRT. Plans were normalized for equivalent (95%) target volume coverage for treatment comparison. Results: Thirteen patients met criteria for this study. The median size of the mediastinal lymphadenopathy was 7.9 cm at the greatest diameter. Compared with IMRT-CSS, IMPT-CSS significantly reduced mean dose to all cardiac substructures, including 3 coronary arteries and 4 cardiac valves. Use of IMPT significantly reduced average whole-heart dose from 9.6 to 4.9 Gy (P < .0001), and average mean lung dose was 9.7 vs 5.8 Gy (P < .0001). Prospectively defined clinically meaningful improvement was observed in at least 1 coronary artery in 9 patients (69%), at least 1 cardiac valve in 10 patients (77%), and whole heart in all 13 patients. Conclusions: For patients with mediastinal lymphoma, IMPT-CSS treatment planning significantly reduced radiation dose to cardiac substructures. The significant improvements outlined in this study for proton therapy suggest possible clinical improvement in alignment with previous analyses of CSS optimization.

Purpose: To understand how verification computed tomography-quality assurance (CTQA) scans influenced clinical decision-making to replan patients with head and neck cancer and identify predictors for replanning to guide intensity-modulated proton therapy (IMPT) clinical practice. Patients and Methods: We performed a quality-improvement study by prospectively collecting data on 160 consecutive patients with head and neck cancer treated using spot-scanning IMPT who underwent weekly verification CT-QA scans. Kaplan-Meier estimates were used to determine the cumulative probability of a replan by week. Predictors for replanning were determined with univariate (UVA) and multivariate (MVA) Cox model hazard ratios (HRs). Logistic regression was used to determine odds ratios (ORs). P < .05 was considered statistically significant. Results: Of the 160 patients, 79 (49.4%) had verification CT-QA scans, which prompted a replan. The cumulative probability of a replan by week 1 was 13.7% (95% confidence interval [CI], 8.82-18.9), week 2, 25.0% (95% CI, 18.0-31.4), week 3, 33.1% (95% CI, 25.4-40.0), week 4, 45.6% (95% CI, 37.3-52.8), and week 5 and 6, 49.4% (95% CI, 41.0- 56.6). Predictors for replanning were sinonasal disease site (UVA: HR, 1.82, P = .04; MVA: HR, 3.64, P = .03), advanced stage disease (UVA: HR, 4.68, P < .01; MVA: HR, 3.10, P < .05), dose . 60 Gy equivalent (GyE; relative biologic effectiveness, 1.1) (UVA: HR, 1.99, P < .01; MVA: HR, 2.20, P < .01), primary disease (UVA: HR, 2.00 versus recurrent, P = .01; MVA: HR, 2.46, P = .01), concurrent chemotherapy (UVA: HR, 2.05, P < .01; MVA: not statistically significant [NS]), definitive intent treatment (UVA: HR, 1.70 versus adjuvant, P < .02; MVA: NS), bilateral neck treatment (UVA: HR, 2.07, P = .03; MVA: NS), and greater number of beams (5 beam UVA: HR, 5.55 versus 1 or 2 beams, P < .02; MVA: NS). Maximal weight change from baseline was associated with higher odds of a replan (_3 kg: OR, 1.97, P = .04; _ 5 kg: OR, 2.13, P = .02). Conclusions: Weekly verification CT-QA scans frequently influenced clinical decisionmaking to replan. Additional studies that evaluate the practice of monitoring IMPTtreated patients with weekly CT-QA scans and whether that improves clinical outcomes are warranted.