CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma

Construction and validation of a nomogram prediction model for the occurrence of complications in patients following robotic radical surgery for gastric cancer

BACKGROUND

In the last two decades, robotic-assisted gastrectomy has become a widely adopted surgical option for gastric cancer (GC) treatment. Despite its popularity, postoperative complications can significantly deteriorate patient quality of life and prognosis. Therefore, identifying risk factors for these complications is crucial for early detection and intervention.

OBJECTIVE

This research is designed to construct and validate a predictive model for assessing the risk of postoperative complications in patients undergoing robotic-assisted radical gastrectomy.

METHODS

A retrospective analysis was conducted on 500 GC patients from Gansu Provincial People's Hospital between December 2016 and October 2023. These patients formed the training cohort. An additional 136 patients from the 940th Hospital of Joint Logistic Support Force, the Chinese People's Liberation Army as the external validation cohort. Patients were categorized into groups with and without complications. Data collected included demographic details, laboratory results, CT quantitative body composition analysis, and clinical information. Variable selection was conducted through Lasso regression, succeeded by multivariable logistic regression to pinpoint independent risk factors. These elements facilitated the construction of a nomogram for prediction. The model's performance underwent internal validation via bootstrap techniques and external validation through a validation cohort. The efficacy of the model was quantified by the area under the receiver operating characteristic (ROC) curve (AUC), evaluated for calibration using calibration curves and the Hosmer-Lemeshow test, and assessed for clinical utility through decision curve analysis (DCA).

RESULTS

Of the 500 patients in the training cohort, 65 experienced complications, a rate of 13%. The validation cohort had a similar complication rate of 13.24% (18 out of 136 patients). Independent risk factors identified included tumor diameter (OR = 1.99, 95% CI = 1.07-3.73), TNM stage III (OR = 2.12, 95% CI = 1.03-4.36), ASA class I (OR = 0.26, 95% CI = 0.13-0.53), ASA class III (OR = 4.75, 95% CI = 2.12-10.62), and visceral fat area (VFA) (OR = 2.52, 95% CI = 1.10-5.79). The nomogram demonstrated good discrimination (AUC = 0.81, 95% CI: 0.76-0.87) in internal validation and (AUC = 0.79, 95% CI: 0.67-0.90) in external validation. Both validations confirmed the model's accurate calibration and significant clinical utility, with net benefits observed at probability thresholds ranging from 2 to 79% and 2-71%.

CONCLUSION

The developed nomogram, based on five independent risk factors-tumor diameter, TNM stage III, ASA class I, ASA class III, and VFA-effectively predicts the risk of complications in patients undergoing robotic-assisted radical gastrectomy, offering a valuable tool for clinical decision-making.

Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach

PURPOSE

Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans. We developed a deep learning (DL) model for fully automatic BC quantification on routine staging CTs and determined its prognostic role in a clinical cohort of patients with GEAC.

MATERIALS AND METHODS

We developed and tested a DL model to quantify BC measures defined as subcutaneous and visceral adipose tissue (VAT) and skeletal muscle on routine CT and investigated their prognostic value in a cohort of patients with GEAC using baseline, 3-6-month, and 6-12-month postoperative CTs. Primary outcome was all-cause mortality, and secondary outcome was disease-free survival (DFS). Cox regression assessed the association between (1) BC at baseline and mortality and (2) the decrease in BC between baseline and follow-up scans and mortality/DFS.

RESULTS

Model performance was high with Dice coefficients ≥0.94 ± 0.06. Among 299 patients with GEAC (age 63.0 ± 10.7 years; 19.4% female), 140 deaths (47%) occurred over a median follow-up of 31.3 months. At baseline, no BC measure was associated with DFS. Only a substantial decrease in VAT >70% after a 6- to 12-month follow-up was associated with mortality (hazard ratio [HR], 1.99 [95% CI, 1.18 to 3.34]; P = .009) and DFS (HR, 1.73 [95% CI, 1.01 to 2.95]; P = .045) independent of age, sex, BMI, Union for International Cancer Control stage, histologic grading, resection status, neoadjuvant therapy, and time between surgery and follow-up CT.

CONCLUSION

DL enables opportunistic estimation of BC from routine staging CT to quantify prognostic information. In patients with GEAC, only a substantial decrease of VAT 6-12 months postsurgery was an independent predictor for DFS beyond traditional risk factors, which may help to identify individuals at high risk who go otherwise unnoticed.