Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy.

Depending on the clinical situation, different combinations of lymph node (LN) levels define the elective LN target volume in head-and-neck cancer (HNC) radiotherapy. The accurate auto-contouring of individual LN levels could reduce the burden and variability of manual segmentation and be used regardless of the primary tumor location. We evaluated three deep learning approaches for the segmenting individual LN levels I−V, which were manually contoured on CT scans from 70 HNC patients.

A national study to assess outcomes of definitive chemoradiation regimens in proximal esophageal cancer.

Proximal esophageal cancer (EC) is commonly treated with definitive chemoradiation (CRT). The radiation dose and type of chemotherapy backbone are still under debate. The objective of this study was to compare the treatment outcomes of contemporary CRT regimens.