Andrew M. Wood

Objectives: To assess whether the effect of sarcopenia on neoadjuvant chemotherapy (NAC) toxicity is modified by borderline renal function (estimated glomerular filtration rate [eGFR] 40–65 mL/min) and whether sarcopenia and borderline renal function are independently associated with NAC toxicity risk. Patients and Methods: All patients with muscle‐invasive bladder cancer (MIBC) who underwent radical cystectomy (RC) between 2010 and 2022, with available cross‐sectional imaging prior to NAC initiation, were included. Skeletal mass was measured from axial computed tomography images obtained at the level of the L3 vertebral body, using Aquarius Intuition software. Sarcopenia was assigned based on consensus definitions of skeletal mass index. NAC toxicity was graded according to Common Terminology Criteria for Adverse Events version 5.0. Binary logistic regression was used to identify the predictors of NAC‐associated renal toxicity. Results: A total of 216 patients were included. Most patients had sarcopenia (83%) and received gemcitabine/cisplatin NAC (76%). In an unadjusted model, sarcopenia was associated with a significant risk of renal‐associated NAC toxicity (odds ratio [OR] 4.88, 95% confidence interval [CI] 1.65–14.44; P = 0.004). In an effect modification model evaluating the interaction between sarcopenia and renal function, the OR for renal toxicity with sarcopenia among patients with eGFR 40–65 mL/min was 8.46 (95% CI 1.06–67.72) vs 3.11 (95% CI 0.81–11.88) among patients with normal renal function ( P = 0.43). Conclusion: Among MIBC patients who received NAC, sarcopenia was associated with higher odds of NAC‐associated renal toxicity and may increase risk of renal toxicity among patients with borderline renal function.

Objective: To investigate whether preoperative body morphometry analysis can identify patients at risk of parastomal hernia (PH), which is a common complication after radical cystectomy (RC). Patients and Methods: All patients who underwent RC between 2010 and 2020 with available cross‐sectional imaging preoperatively and at 1 and 2 years postoperatively were included. Skeletal muscle mass and total fat mass (FM) were determined from preoperative axial computed tomography images obtained at the level of the L3 vertebral body using Aquarius Intuition software. Sarcopenia and obesity were assigned based on consensus definitions of skeletal muscle index (SMI) and FM index (FMI). PH were graded using both the Moreno‐Matias and European Hernia Society criteria. Binary logistic regression and recursive partitioning were used to identify patients at risk of PH. The Kaplan–Meier method with log‐rank and Cox proportional hazards models included clinical and image‐based parameters to identify predictors of PH‐free survival. Results: A total of 367 patients were included in the final analysis, with 159 (43%) developing a PH. When utilising binary logistic regression, high FMI (odds ratio [OR] 1.63, P < 0.001) and low SMI (OR 0.96, P = 0.039) were primary drivers of risk of PH. A simplified model that only relied upon FMI, SMI, and preoperative albumin improved the classification of patients at risk of PH. On Kaplan–Meier analysis, patients who were obese or obese and sarcopenic had significantly worse PH‐free survival ( P < 0.001). Conclusion: Body morphometry analysis identified FMI and SMI to be the most consistent predictors of PH after RC.

Objective: To automate the generation of three validated nephrometry scoring systems on preoperative computerised tomography (CT) scans by developing artificial intelligence (AI)‐based image processing methods. Subsequently, we aimed to evaluate the ability of these scores to predict meaningful pathological and perioperative outcomes. Patients and Methods: A total of 300 patients with preoperative CT with early arterial contrast phase were identified from a cohort of 544 consecutive patients undergoing surgical extirpation for suspected renal cancer. A deep neural network approach was used to automatically segment kidneys and tumours, and then geometric algorithms were used to measure the components of the concordance index (C‐Index), Preoperative Aspects and Dimensions Used for an Anatomical classification of renal tumours (PADUA), and tumour contact surface area (CSA) nephrometry scores. Human scores were independently calculated by medical personnel blinded to the AI scores. AI and human score agreement was assessed using linear regression and predictive abilities for meaningful outcomes were assessed using logistic regression and receiver operating characteristic curve analyses. Results: The median (interquartile range) age was 60 (51–68) years, and 40% were female. The median tumour size was 4.2 cm and 91.3% had malignant tumours. In all, 27% of the tumours were high stage, 37% high grade, and 63% of the patients underwent partial nephrectomy. There was significant agreement between human and AI scores on linear regression analyses ( R ranged from 0.574 to 0.828, all P < 0.001). The AI‐generated scores were equivalent or superior to human‐generated scores for all examined outcomes including high‐grade histology, high‐stage tumour, indolent tumour, pathological tumour necrosis, and radical nephrectomy (vs partial nephrectomy) surgical approach. Conclusions: Fully automated AI‐generated C‐Index, PADUA, and tumour CSA nephrometry scores are similar to human‐generated scores and predict a wide variety of meaningful outcomes. Once validated, our results suggest that AI‐generated nephrometry scores could be delivered automatically from a preoperative CT scan to a clinician and patient at the point of care to aid in decision making.