Purpose The increasing availability of clinical imaging tests (especially CT and MRI) that
directly quantify adipose tissue has led to a rapid increase in studies examining the …

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically
requiring human intelligence. Currently, AI is recognized in the broader technology radar …

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for
academic and industry researchers and graduate students taking courses on machine …

Background Body mass index (BMI) and sarcopenia status are well-established prognostic
factors in patients with lung cancer. However, the relationship between the amount of …

Body composition imaging relies on assessment of tissues composition and distribution.
Quantitative data provided by body composition imaging analysis have been linked to …

Purpose The purpose of this article is to provide an up-to-date summary of sarcopenia and
its clinical implications for patients with head and neck cancer (HNC). Methods We …

Purpose To develop a weakly supervised deep learning (WSDL) method that could utilize
incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell …

Background The association between obesity and lung cancer (LC) remains poorly
understood. However, other indices of obesity on the basis of body shape instead of body …

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and
presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk …

Background Sarcopenia or visceral adipose tissue has been reported to be related to
pancreatic cancer prognosis. However, clinical relevance of the comprehensive analysis of …