ObjectivesNo study has reported clinical results of external-beam radiotherapy specifically for T3b prostate cancer. The possibility of escalating the dose to the involved seminal vesicles (ISV) while respecting the dose constraints in the organs at risk is thus so far not clearly demonstrated. The objective of the study was to analyze the dose distribution and the clinical outcome in a large series of patients who received IMRT for T3b prostate cancer.Materials and methodsThis retrospective analysis included all patients who received IMRT and androgen deprivation therapy for T3b prostate cancer, between 2008 and 2017, in six French institutions, with available MRI images and dosimetric data.ResultsA total of 276 T3b patients were included. The median follow-up was 26 months. The median (range) prescribed doses (Gy) to the prostate and to the ISV were 77 (70–80) and 76 (46–80), respectively. The dose constraint recommendations were exceeded in less than 12% of patients for the rectum and the bladder. The 5-year risks of biochemical and clinical recurrences and cancer-specific death were 24.8%, 21.7%, and 10.3%, respectively. The 5-year risks of local, pelvic lymph node, and metastatic recurrences were 6.4%, 11.3%, and 15%, respectively. The number of involved lymph nodes (≤ 2 or ≥ 3) on MRI was the only significant prognostic factor in clinical recurrence (HR 9.86) and death (HR 2.78). Grade ≥ 2 acute and 5-year late toxicity rates were 13.2% and 12% for digestive toxicity, and 34% and 31.5% for urinary toxicity, respectively. The dose to the pelvic lymph node and the age were predictive of late digestive toxicity.ConclusionIMRT for T3b prostate cancer allows delivery of a curative dose in the ISV, with a moderate digestive toxicity but a higher urinary toxicity. Lymph node involvement increases the risk of recurrence and death.

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Background: The protocol study will focus on the seroprevalence of IgG antibodies to SARS-CoV-2 achieved by vaccination and/or natural protection as well as the history, symptoms, and risk factors for SARS-CoV-2 in four primary health-care workers (PHCWs) and their household contacts in metropolitan France. Methods: Here, we propose a protocol for a nationwide survey to determine the seroprevalence of IgG antibodies to SARS-CoV-2 achieved by vaccination and/or natural protection in four PHCW populations (general practitioners, pediatricians, pharmacists and assistants, and dentists and assistants) and their household contacts. Participants will be included from June to July 2021 (Phase 1) among PHCW populations located throughout metropolitan France. They will be asked to provide a range of demographic and behavioral information since the first SARS-CoV-2 wave and a self-sampled dried blood spot. Phase 1 will involve also a questionnaire and serological study of PHCWs’ household contacts. Seroprevalence will be estimated using two ELISAs designed to detect specific IgG antibodies to SARS-CoV-2 in humoral fluid, and these results will be confirmed using a virus neutralization test. This study will be repeated from November to December 2021 (Phase 2) to evaluate the evolution of immune status achieved by vaccination and/or natural protection of PHCWs and to describe the history of exposure to SARS-CoV-2.

Abstract-Dose calculation from MRI is a topical issue. New treatment systems combining a linear accelerator with a MRI have been recently being developed. MRI has good soft tissue contrast without ionizing radiation exposure. However, unlike CT, MRI does not provide electron density information necessary for dose calculation. We propose in this paper a machine learning method to simulate a CT from a target MRI and co-registered CT-MRI training set. Ten prostate MR and CT images have been considered. Firstly, a reference image was randomly selected in the training set. A common space has been built thanks to affine registrations between the training set and the reference image. Multiscale image descriptors such as spatial information, gradients and texture features were extracted from MRI patches at different levels of a Gaussian pyramid and used as voxel-wise characteristics in the learning scheme. A Conditional Inference Random Forest (CIRF) modelled the relation between MRI descriptors and CT patches. For validation, test images were spatially normalized and the same descriptors were computed to generate a new pCT. Leave-one out experiments were performed. We obtained a MAE = 45.79 (pCT vs CT). Dose volume histograms inside PTV and organs at risk are in close agreement. The D98% was 0.45 % (inside PTV) and the 3D gamma pass rate (1mm, 1%) was 99,2%. Our method has better results than direct bulk assignment. And the results suggest that the method may be used for dose calculations in an MR based planning system.