Extensive clinical testing of Deep Learning Segmentation models for thorax and breast cancer radiotherapy planning.

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.

Dose painting for re-irradiation of head and neck cancer.

BACKGROUND: For patients with recurrent or second primary disease, re-irradiation can be challenging due to overlap with previously irradiated volumes. Dose painting may be attractive for these patients, as the focus is on delivering maximal dose to areas of high tumor activity. Here, we compare dose painting by contours (DPBC) treatment plans based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) with conventional plans. MATERIAL AND METHODS: We included 10 patients with recurrent or second primary head and neck cancer (HNC) eligible for re-irradiation. Our conventional re-irradiation regimen is hyperfractionated radiotherapy 1.5 Gy twice daily over 4 weeks, giving a total dose of 60 Gy. For DPBC, we defined two prescription volumes, PV33 and PV66, corresponding to 33 and 66% of the highest FDG uptake in the tumor. The clinical target volume (CTV) prescription dose was 60 Gy, PV33; 65-67 Gy and PV66; 70-73 Gy. The DPBC plan is to be given the first 20 fractions and the conventional plan the last 20 fractions. Dose to organs at risk (OARs) were compared for DPBC and conventional treatment. By summation of the initial curative plan and the re-irradiation plan, we also evaluated differences in dose to the 2 ccm hot spot (D2cc). RESULTS: We achieved DPBC plans with adequate target coverage for all 10 patients. There were no significant differences in OAR doses between the standard plans and the DPBC plans (p=.7). Summation of the initial curative plan and the re-irradiation plan showed that the median D2cc increased from 130 Gy (range 113-132 Gy; conventional) to 140 Gy (range 115-145 Gy; DPBC). CONCLUSIONS: Our proposed DPBC could be straightforwardly implemented and all plans met the objectives. Re-irradiation of HNC with DPBC may increase tumor control without more side effects compared to conventional radiotherapy.