Artificial Intelligence-Based Assessment of Preoperative Body Composition Is Associated With Early Complications...

We aimed to use a validated artificial intelligence (AI) algorithm to extract muscle and adipose areas from CT images before radical cystectomy (RCx) and then correlate these measures with 90-day post-RCx complications.

A tertiary referral center's cystectomy registry was queried for patients who underwent RCx between 2009 and 2017 for bladder cancer. Eight hundred forty-three RCx patients with CT imaging within 90 days of preceding surgery were included, to allow for extraction of body composition parameters by AI. We assessed complications within 90 days of surgery including wound, infectious, and major complications; readmission; and death. Multivariable logistic regressions associated pre-RCx measures with post-RCx complications.

Increasing subcutaneous adipose tissue was associated with more wound complications, while patients with increasing visceral adipose tissue had greater odds of infectious-related complications. After adjusting for patient characteristics, every 10 cm² increases in fat mass index were associated with more infectious (odds ratio [OR], 1.04; P = .002) and wound (OR, 1.06; P < .001) complications. On multivariable analysis, a higher preoperative skeletal muscle index was associated with lower odds of major complications (OR, 0.75 for every 10 cm²; P = .008), while higher intramuscular adipose was associated with higher odds of major complications (OR, 1.93; P = .008).

Automated AI body composition measurements preoperatively are associated with post-RCx complications. These measurements, in addition to patient (Eastern Cooperative Oncology Group performance status and smoking status) and surgical (robotic approach and continent diversion) characteristics, can then be used to individualize patient counseling and facilitate triage of nutritional and rehabilitation efforts.