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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.

The fumonisins are mycotoxins produced mainly by Fusarium verticillioides and F. proliferatum in maize, the predominant cereal staple for subsistence farming communities in southern Africa. In order to assess exposure to these mycotoxins in the Bizana (now known as Mbizana) and Centane magisterial areas of the former Transkei region of the Eastern Cape Province of South Africa, the actual maize consumption by different age groups in these communities was measured. In the groups 1-9 years (n = 215) and 10-17 (n = 240) years, mean consumption (+/-standard error) was 246 +/- 10.8 and 368 +/- 10.3 g per person day(-1), respectively, with no significant difference (p > 0.05) between the magisterial areas. For adults (18-65 years) mean maize consumption in Bizana (n = 229) and Centane (n = 178) were significantly different (p < 0.05) at 379 +/- 10.5 and 456 +/- 11.9 g per person day(-1), respectively. An exposure assessment was performed by combining the maize consumption distribution with previously determined levels of total fumonisin (fumonisins B(1) and B(2) combined) contamination in home-grown maize in these two areas. Assuming an individual adult body weight of 60 kg, fumonisin exposure in Bizana, an area of relatively low oesophageal cancer incidence, was 3.43 +/- 0.15 microg kg(-1) body weight day(-1), which was significantly lower (p < 0.05) than that in Centane (8.67 +/- 0.18 microg kg(-1) body weight day(-1)), an area of high oesophageal cancer incidence. Mean fumonisin exposures in all age groups in both Bizana and Centane were above the provisional maximum tolerable daily intake (PMTDI) of 2 microg kg(-1) body weight day(-1) set by the Joint FAO/WHO Expert Committee on Food Additives.

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.