Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques.

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.

Automatic contouring QA method using a deep learning-based autocontouring system.

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.

Cervix Cancer in Sub-Saharan Africa: An Assessment of Cervical Cancer Management.

PURPOSE: Underdeveloped nations carry the burden of most cervical cancer, yet access to adequate treatment can be challenging. This report assesses the current management of cervical cancer in sub-Saharan Africa to better understand the needs of underdeveloped nations in managing cervical cancer. METHODS: A pre- and postsurvey was sent to all centers participating in the Cervical Cancer Research Network's 4th annual symposium. The pre- and postsurvey evaluated human papillomavirus and HIV screening, resources available for workup and/or treatment, treatment logistics, outcomes, and enrollment on clinical trials. Descriptive analyses were performed on survey responses. RESULTS: Twenty-nine centers from 12 sub-Saharan countries saw approximately 300 new cases of cervical cancer yearly. Of the countries surveyed, 55% of countries had a human papillomavirus vaccination program and 30% (range, 0%-65%) of women in each region were estimated to have participated in a cervical cancer screening program. In the workup of patients, 43% of centers had the ability to obtain a positron emission tomography and computed tomography scan and 79% had magnetic resonance imaging capabilities. When performing surgery, 88% of those centers had a surgeon with an expertise in performing oncological surgeries. Radiation therapy was available at 96% of the centers surveyed, and chemotherapy was available in 86% of centers. Clinical trials were open at 4% of centers. CONCLUSION: There have been significant advancements being made in screening, workup, and management of patients with cervical cancer in sub-Saharan Africa; yet, improvement is still needed. Enrollment in clinical trials remains a struggle. Participants would like to enroll patients on clinical trials with Cervical Cancer Research Network's continuous support.

Automatic contouring system for cervical cancer using convolutional neural networks.

PURPOSE: To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients. METHODS: An auto-contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web-based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN-based auto-contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen-dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician-drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals. RESULTS: The average DSC, mean surface distance, and Hausdorff distance of our CNN-based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review. CONCLUSIONS: Our CNN-based auto-contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability.

Sexuality post gynaecological cancer treatment: a qualitative study with South African women.

OBJECTIVES: This study investigated women's experiences of their sexuality post gynaecological cancer treatment. Using a holistic sexuality framework, the study explored how women felt their sexual functioning, sexual relationships and sexual identity had been affected by treatment. DESIGN: The study was qualitative in nature and made use of an interpretive descriptive design. Data were analysed using thematic analysis. SETTING: Data collection took place at a follow-up clinic within the gynaeoncology unit at a public-sector tertiary hospital in Cape Town, South Africa. PARTICIPANTS: Purposive sampling was used to recruit participants, and the final sample consisted of 34 women aged 29-70 ([Formula: see text]=52). All women had received a gynaecological cancer diagnosis and had been treated with either surgery, chemotherapy, radiation or a combination of these. On average, the participants were between 12 and 30 months post treatment. RESULTS: Women expressed how their sexual functioning post treatment was both nuanced and complex, how heteronormative gender expectations influenced their intimate relationships, and how they experienced a re-embodiment of their sexual subjectivity post treatment. Participants felt that more sexual functioning information from healthcare providers, as well as peer support groups, would assist them in navigating the sexuality changes they experienced. CONCLUSIONS: The findings of this study broaden conceptualisations of sexuality post treatment by detailing the ways that it is complex, nuanced, relational and ever shifting. More research is needed about how to incorporate holistic psychosexual support post treatment into the public healthcare system in South Africa.

Fully Automatic Treatment Planning for External-Beam Radiation Therapy of Locally Advanced Cervical Cancer: A Tool for Low-Resource Clinics.

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 distance, 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.