How to contour the different heart subregions for future deep-learning modeling of the heart: A practical pictorial proposal for radiation oncologists.

There are currently no accurate rules for manually delineating the subregions of the heart (cavities, vessels, aortic/mitral valves, Planning organ at Risk Volumes for coronary arteries) with the perspective of deep-learning based modeling. Our objective was to present a practical pictorial view for radiation oncologists, based on the RTOG atlas and anatomical complementary considerations for the cases where the RTOG guidelines are missing.

High-dose chemotherapy followed by whole lung irradiation in pulmonary relapse Ewing's sarcomas: a retrospective multicenter study.

OBJECTIVE: Regarding the efficiency of Bu-Mel-based high-dose-chemotherapy (Bu-Mel-HDCT) and whole lung irradiation (WLI), the objective was to evaluate the efficiency and safety of this salvage sequence in Ewing sarcoma (ES) lung relapses. METHODS: All eligible pediatric ES patients (1991-2020) identified in SFCE departments were retrospectively reviewed. Seven patients were (1) diagnosed with a pulmonary relapse, isolated or not, (2) naïve from both HCDT and WLI (3) treated by the salvage sequence of conventional chemotherapy, Bu-Mel-HDCT and WLI. The main endpoint was OS evaluation. WLI toxicities were scored using CTC-V5. RESULTS: With a 13 years median follow-up (FU), 5/7 patients are alive and in complete remission. 10y-EFS is 71.4%. Three patients experienced transitory radio-induced pneumopathy (RIP). A patient developed RIP (gr.3) and finally progressive lung fibrosis leading to death. CONCLUSION: This study reports seven ES patients treated for lung metastatic relapses, using an aggressive strategy, with favorable survival long-term results which should be balanced with the risk of lung toxicity. ADVANCES IN KNOWLEDGE: The approach of surgery, Bu-Mel HDCT followed by WLI can be discussed in selected ES patients with lung relapse, naive from HDCT or WLI, providing an optimal chemosensitivity. A special vigilance is necessary regarding the incidence rate of lung toxicity which can be mitigated by limiting the radiotherapy dose, and observing optimal timing of radiotherapy after HDCT.

Pretreatment 18F-FDG PET/CT Radiomics Predict Local Recurrence in Patients Treated with Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer: A Multicentric Study.

The aim of this retrospective multicentric study was to develop and evaluate a prognostic 18F-FDG PET/CT radiomic signature in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy (SBRT). Methods: Patients from 3 different centers (n = 27, 29, and 8) were pooled to constitute the training set, whereas the patients from a fourth center (n = 23) were used as the testing set. The primary endpoint was local control. The primary tumor was semiautomatically delineated in the PET images using the fuzzy locally adaptive Bayesian algorithm, and manually in the low-dose CT images. In total, 184 Image Biomarkers Standardization Initiative-compliant radiomic features were extracted. Seven clinical and treatment parameters were included. We used ComBat to harmonize radiomic features extracted from the 4 institutions relying on different PET/CT scanners. In the training set, variables found significant in the univariate analysis were fed into a multivariate regression model, and models were built by combining independent prognostic factors. Results: Median follow-up was 21.1 mo (range, 1.7-63.4 mo) and 25.5 mo (range, 7.7-57.8 mo) in training and testing sets, respectively. In univariate analysis, none of the clinical variables, 2 PET features, and 2 CT features were significantly predictive of local control. The best predictive models in the training set were obtained by combining one feature from PET (Information Correlation 2) and one feature from CT (flatness), reaching a sensitivity of 100% and a specificity of 96%. Another model combining 2 PET features (Information Correlation 2 and strength) reached sensitivity of 100% and specificity of 88%, both with an undefined hazard ratio (P < 0.001). The latter model obtained an accuracy of 0.91 (sensitivity, 100%; specificity, 81%), with a hazard ratio undefined (P = 0.023) in the testing set; however, other models relying on CT radiomic features only or the combination of PET and CT features failed to validate in the testing set. Conclusion: We showed that 2 radiomic features derived from 18F-FDG PET were independently associated with local control in patients with non-small cell lung cancer undergoing SBRT and could be combined in an accurate predictive model. This model could provide local relapse-related information and could be helpful in clinical decision making.

Update in pediatric nasopharyngeal undifferentiated carcinoma.

Many of the principles established in adults with undifferentiated nasopharyngeal carcinoma (NPC) apply to children, adolescents and young adults. However, NPC in young patients should be distinguished from the adult form by several points. This review focuses mainly on differences between adult and pediatric NPC. The role of biology and genetics in pediatric NPC is discussed. Systemic treatment modalities including type of chemotherapy induction, timing of treatment, role of immunotherapy as adjuvant treatment, or in relapsing/ metastatic diseases are reported. Radiation modalities (doses, techniques…) in children are also reviewed. Long-term effects including secondary cancers are finally be discussed in this young NPC population.

Statistical Shape Model to Generate a Planning Library for Cervical Adaptive Radiotherapy.

External beam radiotherapy is extensively used to treat cervical carcinomas. A single planning CT scan enables the calculation of the dose distribution. The treatment is delivered over five weeks. Large per-treatment anatomical variations may hamper the dose delivery, with the potential of an organ-at-risk (OAR) overdose and a tumor underdose. To anticipate these deformations, a recent approach proposed three planning CTs with variable bladder volumes, which had the limitation of not covering all per-treatment anatomical variations. An original patient-specific population-based library has been proposed. It consisted of generating two representative anatomies, in addition to the standard planning CT anatomy. First, the cervix and bladder meshes of a population of 20 patients (314 images) were registered to an anatomical template, using a deformable mesh registration. An iterative point-matching algorithm was developed based on local shape context (histogram of polar or cylindrical coordinates and geodesic distance to the base) and on a topology constraint filter. Second, a standard principal component analysis (PCA) model of the cervix and bladder was generated to extract the dominant deformation modes. Finally, specific deformations were obtained using posterior PCA models, with a constraint representing the top of the uterus deformation. For a new patient, the cervix-uterus and bladder were registered to the template, and the patient's modeled planning library was built according to the model deformations. This method was applied following a leave-one-patient-out cross-validation. The performances of the modeled library were compared to those of the three-CT-based library, showing an improvement in both target coverage and OAR sparing.