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Background: The performance of deep learning segmentation (DLS) models for automatic organ extraction from CT images in the thorax and breast regions was investigated. Furthermore, the readiness and feasibility of integrating DLS into clinical practice were addressed by measuring the potential time savings and dosimetric impact. Material and Methods: Thirty patients referred to radiotherapy for breast cancer were prospectively included. A total of 23 clinically relevant left- and right-sided organs were contoured manually on CT images according to ESTRO guidelines. Next, auto-segmentation was executed, and the geometric agreement between the auto-segmented and manually contoured organs was qualitatively assessed applying a scale in the range [0-not acceptable, 3-no corrections]. A quantitative validation was carried out by calculating Dice coefficients (DSC) and the 95% percentile of Hausdorff distances (HD95). The dosimetric impact of optimizing the treatment plans on the uncorrected DLS contours, was investigated from a dose coverage analysis using DVH values of the manually delineated contours as references. Results: The qualitative analysis showed that 93% of the DLS generated OAR contours did not need corrections, except for the heart where 67% of the contours needed corrections. The majority of DLS generated CTVs needed corrections, whereas a minority were deemed not acceptable. Still, using the DLS-model for CTV and heart delineation is on average 14 minutes faster. An average DSC=0.91 and H95=9.8 mm were found for the left and right breasts, respectively. Likewise, and average DSC in the range [0.66, 0.76]mm and HD95 in the range [7.04, 12.05]mm were found for the lymph nodes. Conclusion: The validation showed that the DLS generated OAR contours can be used clinically. Corrections were required to most of the DLS generated CTVs, and therefore warrants more attention before possibly implementing the DLS models clinically. [ABSTRACT FROM AUTHOR] more...

Exercise could reduce the side-effects of adjuvant breast cancer treatment; however, socio-demographic, health, and intervention conditions may affect patients' adherence to interventions. This study aimed to examine adherence to a 12-month outdoor post-surgery exercise program among newly diagnosed breast cancer patients during adjuvant treatment, and to identify socio-demographic and health-related predictors. In total, 47 women with invasive breast cancer stage I–II or ductal/lobular carcinoma grade 3 were included pre-surgery and randomized two weeks post-surgery to exercise (2 × 60 min/week). Patient characteristics (body-mass index (BMI), socioeconomic status, comorbidity, physical activity, and maximal oxygen uptake (VO2max)) were recorded pre-surgery. Correlations between adherence and patient characteristics and statistics for between-group differences were performed. The mean age was 54.2 years, mean BMI 27.8 kg/m2, and 54.2% received chemotherapy. Completers had a mean adherence of 81%, independent of season. Withdrawals (23%) occurred after a mean of 6.5 weeks (0–24 weeks), they were suggestively older, had lower socioeconomic status and pre-surgery VO2max, and higher BMI. Household income was significantly lower among withdrawals. There were insignificant correlations between adherence and health conditions. High adherence is achievable in a Nordic outdoor physical exercise program in breast cancer patients during adjuvant treatment, including chemotherapy. Additional studies are needed to clarify follow-up needs in some groups. [ABSTRACT FROM AUTHOR] more...