Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy.

Purpose/objective(s): Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively impacting treatment outcomes. The aim of this study is to evaluate the performance and efficiency of segmentation of CTVs in planning CT images of breast cancer patients using a 3D convolutional neural network (CNN) compared to the manual process.

Benefits of Elective Para-Aortic Radiotherapy for pN1 Prostate Cancer Using Arc Therapy (Intensity-Modulated or Volumetric Modulated Arc Therapy): Protocol for a Nonrandomized Phase II Trial.

Background: In patients with prostate cancer (PCa) with histopathologically proven pelvic lymph node (LN) metastasis (pN1) after extended pelvic lymph node dissection (ePLND), multimodality treatment consisting of treatment of the primary tumor and whole pelvic radiotherapy (WPRT) combined with androgen deprivation therapy (ADT) offers promising results, leading to better cause-specific survival rates compared with ADT alone. However, in case more than one pelvic LN is invaded by the tumor, approximately 40% of the patients relapse biochemically and clinically. Clinical relapse is present in the para-aortic LNs (M1a disease) in up to 77% of the relapsing cases.

Omitting radiation therapy in women with triple-negative breast cancer leads to worse breast cancer-specific survival.

Purpose: To examine locoregional recurrence (LRR) and breast cancer-specific survival (BCSS) after breast-conserving therapy (BCT) or mastectomy (ME) with or without radiation therapy (RT) in triple-negative breast cancer (TNBC).

Material & Methods: We identified non-metastatic TNBC cases from a single institution database. BCT, ME with RT (ME + RT) and ME only were compared with respect to LRR and BCSS.