BackgroundThe association of sarcopenia and visceral obesity to treatment outcome is not clear for locally advanced rectal cancer. This study evaluates the influence of skeletal muscle and visceral fat on short‐term and long‐term outcomes in locally advanced rectal cancer patients treated with neoadjuvant chemoradiation therapy followed by curative resection.MethodsA total of 188 patients with locally advanced cancer were included between January 2009 and December 2013. Neoadjuvant chemoradiotherapy was followed by curative resection. Sarcopenia and visceral obesity were identified in initial staging CT by measuring the muscle and visceral fat area at the third lumbar vertebra level.ResultsAmong the 188 included patients, 74 (39.4%) patients were sarcopenic and 97 (51.6%) patients were viscerally obese. Sarcopenia and high levels of preoperative carcinoembryonic antigen were significant prognostic factors for overall survival (P = 0.013, 0.014, respectively) in the Cox regression multivariate analysis. Visceral obesity was not associated with overall survival; however, it did tend to shorten disease‐free survival (P = 0.079).ConclusionsSarcopenia is negatively associated with overall survival in locally advanced rectal cancer patients who underwent neoadjuvant chemoradiation therapy and curative resection. Visceral obesity tended to shorten disease‐free survival. Future studies should be directed to optimize patient conditions according to body composition status.

Technologies for the longitudinal monitoring of a person's health are poorly integrated with clinical workflows, and have rarely produced actionable biometric data for healthcare providers. Here, we describe easily deployable hardware and software for the long-term analysis of a user's excreta through data collection and models of human health. The 'smart' toilet, which is selfcontained and operates autonomously by leveraging pressure and motion sensors, analyses the user's urine using a standard-of-care colorimetric assay that traces red-green-blue values from images of urinalysis strips, calculates the flow rate and volume of urine using computer vision as a uroflowmeter, and classifies stool according to the Bristol stool form scale using deep learning, with performance that is comparable to the performance of trained medical personnel. Each user of the toilet is identified through their fingerprint and the distinctive features of their anoderm, and the data are securely stored and analysed in an encrypted cloud server. The toilet may find uses in the screening, diagnosis and longitudinal monitoring of specific patient populations.

We developed a risk stratification system that should facilitate the identification of patients with a high or low risk of lymph node metastasis. This may aid the precise selection of patients who can undergo endoscopic resection.