Automation of Radiation Treatment Planning for Rectal Cancer

PURPOSE: To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward-planning algorithms. METHODS: We designed an algorithm to automate the clinical workflow for 3DCRT planning with field aperture creations and field-in-field (FIF) planning. DL models (DeepLabV3+ architecture) were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary (posterior-anterior [PA] and opposed laterals) and boost fields. Network inputs were digitally reconstructed radiographs, gross tumor volume (GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale (\u3e3 is acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, calculates dose, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with varying wedge angles and definitions of hotspots, and the resulting plans were scored by a physician. The end-to-end workflow was tested and scored by a physician on another 39 patients. RESULTS: The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for PA, laterals, and boost fields, respectively. Overall, 100%, 95%, and 87.5% of the PA, laterals, and boost apertures were scored as clinically acceptable, respectively. At least one auto-plan was clinically acceptable for all patients. Wedged and non-wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The hotspot dose percentage was reduced from 121% (σ = 14%) to 109% (σ = 5%) of prescription dose for all plans. The integrated end-to-end workflow of automatically generated apertures and optimized FIF planning gave clinically acceptable plans for 38/39 (97%) of patients. CONCLUSION: We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution

Identifying the Optimal Deep Learning Architecture and Parameters for Automatic Beam Aperture Definition in 3D Radiotherapy

PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS: Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS: Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance \u3c 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION: DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance

Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.

PURPOSE: Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world. METHODS: The RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale. RESULTS: For cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%). CONCLUSION: The RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics

Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant

PURPOSE: Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN: In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS: RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION: The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment

A risk assessment of automated treatment planning and recommendations for clinical deployment

CITATION: Kisling, K. et al. 2019. A risk assessment of automated treatment planning and recommendations for clinical deployment. Medical Physics, 46(6): 2567-2574. doi:10.1002/mp.13552The original publication is available at https://aapm.onlinelibrary.wiley.com/journal/24734209Purpose: To assess the risk of failure of a recently developed automated treatment planning tool, the radiation planning assistant (RPA), and to determine the reduction in these risks with implementation of a quality assurance (QA) program specifically designed for the RPA. Methods: We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a four-field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic's standard QA processes and consisted of three components: (a) automatic, independent verification of the results of automated planning; (b) automatic comparison of treatment parameters to expected values; and (c) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program. Results: In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top-ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician. Conclusions: Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified three key aspects of safe deployment of automated planning: (a) user training on potential failure modes; (b) comprehensive manual plan review by physicians and physicists; and (c) automated QA of the treatment plan.https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.13552Publisher’s versio

A retrospective analysis of the effect of planning tumour volume on survival in cervical carcinoma

CITATION: Fourie, I. & Simonds, H. M. 2018. A retrospective analysis of the effect of planning tumour volume on survival in cervical carcinoma. Southern African Journal of Gynaecological Oncology, 10(2):25-29, doi:10.1080/20742835.2018.1531469.The original publication is available at https://www.tandfonline.comIntroduction: Locally advanced stages of invasive cervical cancer (ICC) are associated with poor outcomes; factors influencing survival include increased tumour volume. In resource-constrained settings access to diagnostic imaging with CT and MRI is limited. Alternative methods of establishing tumour volume can be defined with use of the planning target volume (PTV) delineated prior to radiotherapy. The aim of this study is to determine whether increased PTV size impacted on overall survival in a cohort of cervical cancer patients with Stage IIB/IIIB disease who completed radical radiotherapy. Materials and methods: A retrospective analysis was undertaken of patients with histologically confirmed Stage IIB/IIIB ICC treated with radical radiotherapy. Exclusion criteria included patients who did not complete prescribed radiotherapy and brachytherapy. Demographic and treatment details were collected. Planning target volumes were retrieved. Kaplan–Meier analysis was used to calculate the overall survival rate. A multivariate Cox proportional hazard model was derived to assess associations with all-cause mortality. Results: A total of 71 patients met the inclusion/exclusion criteria. The median PTV was 653 cc. On univariate analysis factors significantly associated with a lower overall survival included HIV positivity and the presence of hydronephrosis. Increased PTV size paradoxically showed a trend to improved overall survival. On multivariant analysis HIV status, advanced stage, hydronephrosis and a smaller PTV were significantly related to higher all-cause mortality. Conclusion: It is concluded that, when using planning target volumes, the hypothesis that larger volumes impact on overall survival was disproved. A larger cohort and more accurate methods of determining tumour volume, including PET/CT, will be considered in future prospective studies.https://www.tandfonline.com/doi/full/10.1080/20742835.2018.1531469Publisher's versio

Hypofractionation and prostate cancer : a good option for Africa?

CITATION: Incrocci, L. et al. 2017. Hypofractionation and prostate cancer : a good option for Africa? South African Journal of Oncology, 1:a28, doi:10.4102/sajo.v1i0.28.The original publication is available at https://sajo.org.zaCancer is an emerging public health problem in Africa. According to the World Health Organization, the numbers will be doubled by 2030 because of the ageing and the growth of the population. Prostate cancer is the most common cancer among men in most African countries. Radiotherapy machines are extremely limited in Africa and therefore prostate cancer in Africa is mostly managed by urologists. However, for a large proportion of prostate cancer patients, external-beam radiotherapy (EBRT) will be the treatment of choice in Africa because of limitations of surgical expertise in many countries. The disparity between the α/β ratio for late complications and the low α/β ratio for prostate cancer widens the therapeutic window when treating prostate cancer with hypofractionation. Because of the reduced number of treatment days, hypofractionation offers economic and logistic advantages, reducing the burden of the very limited radiotherapy resources in most African countries. It also increases patient convenience. A misleading assumption is that high-level radiotherapy is not feasible in low-income countries. The gold standard option for hypofractionation includes daily image-guided radiotherapy with 3–4 implanted gold fiducials. Acceptable methods for image guidance include ultrasound and cone-beam computed tomography (CT). CT-based treatment planning with magnetic resonance imaging fusion allows for accurate volume delineation. Volumetric modulated arc therapy or inversely planned intensity modulated radiotherapy is the ideal for treatment delivery. The most vital component is safe delivery, which necessitates accurate quality assurance measures and on-board imaging. We will review the evidence and potential utilisation of hypofractionated EBRT in Africa.https://sajo.org.za/index.php/sajo/article/view/28Publisher's versio

Impact of COVID-19 on cancer care delivery Africa: A cross-sectional survey of oncology providers in Africa

PURPOSE The COVID-19 pandemic has disrupted cancer care globally. There are limited data of its impact in Africa. This study aims to characterize COVID-19 response strategies and impact of COVID-19 on cancer care and explore misconceptions in Africa. METHODS We conducted a web-based cross-sectional survey of oncology providers in Africa between June and August 2020. Descriptive statistics and comparative analysis by income groups were performed. RESULTS One hundred twenty-two participants initiated the survey, of which 79 respondents from 18 African countries contributed data. Ninety-four percent (66 of 70) reported country mitigation and suppression strategies, similar across income groups. Unique strategies included courier service and drones for delivery of cancer medications (9 of 70 and 6 of 70, respectively). Most cancer centers remained open, but . 75% providers reported a decrease in patient volume. Not previously reported is the fear of infectivity leading to staff shortages and decrease in patient volumes. Approximately one third reported modifications of all cancer treatment modalities, resulting in treatment delays. A majority of participants reported ≤ 25 confirmed cases (44 of 68, 64%) and ≤ 5 deaths because of COVID-19 (26 of 45, 58%) among patients with cancer. Common misconceptions were that Africans were less susceptible to the virus (53 of 70, 75.7%) and decreased transmission of the virus in the African heat (44 of 70, 62.9%). CONCLUSION Few COVID-19 cases and deaths were reported among patients with cancer. However, disruptions and delays in cancer care because of the pandemic were noted. The pandemic has inspired tailored innovative solutions in clinical care delivery for patients with cancer, which may serve as a blueprint for expanding care and preparing for future pandemics. Ongoing public education should address COVID-19 misconceptions. The results may not be generalizable to the entire African continent because of the small sample size

Fully automatic treatment planning for external-beam radiation therapy of locally advanced cervical cancer : a tool for low-resource clinics

CITATION: Kisling, K., et al. 2019. Fully automatic treatment planning for external-beam radiation therapy of locally advanced cervical cancer : a tool for low-resource clinics. Journal of Global Oncology, 5:1-7, doi:10.1200/JGO.18.00107.The original publication is available at https://ascopubs.org/PURPOSE: The purpose of this study was to validate a fully automatic treatment planning system for conventional radiotherapy of cervical cancer. This system was developed to mitigate staff shortages in low-resource clinics. METHODS: In collaboration with hospitals in South Africa and the United States, we have developed the Radiation Planning Assistant (RPA), which includes algorithms for automating every step of planning: delineating the body contour, detecting the marked isocenter, designing the treatment-beam apertures, and optimizing the beam weights to minimize dose heterogeneity. First, we validated the RPA retrospectively on 150 planning computed tomography (CT) scans. We then tested it remotely on 14 planning CT scans at two South African hospitals. Finally, automatically planned treatment beams were clinically deployed at our institution. RESULTS: The automatically and manually delineated body contours agreed well (median mean surface dis- tance, 0.6 mm; range, 0.4 to 1.9 mm). The automatically and manually detected marked isocenters agreed well (mean difference, 1.1 mm; range, 0.1 to 2.9 mm). In validating the automatically designed beam apertures, two physicians, one from our institution and one from a South African partner institution, rated 91% and 88% of plans acceptable for treatment, respectively. The use of automatically optimized beam weights reduced the maximum dose significantly (median, −1.9%; P , .001). Of the 14 plans from South Africa, 100% were rated clinically acceptable. Automatically planned treatment beams have been used for 24 patients with cervical cancer by physicians at our institution, with edits as needed, and its use is ongoing. CONCLUSION: We found that fully automatic treatment planning is effective for cervical cancer radiotherapy and may provide a reliable option for low-resource clinics. Prospective studies are ongoing in the United States and are planned with partner clinics.https://ascopubs.org/doi/abs/10.1200/JGO.18.00107Publisher's versio