Published online Jul 15, 2025. doi: 10.4251/wjgo.v17.i7.105403
Revised: April 20, 2025
Accepted: June 11, 2025
Published online: July 15, 2025
Processing time: 174 Days and 22.7 Hours
Locally advanced rectal cancer (LARC) carries a substantial risk of recurrence, prompting the use of neoadjuvant chemoradiotherapy (nCRT) to improve tumor resectability and long-term outcomes. However, individual treatment responses vary considerably, highlighting the need for robust predictive tools to guide clinical decision-making.
To develop a nomogram model integrating clinical characteristics and biomarkers to predict the likelihood of poor response to nCRT in LARC.
A retrospective analysis was performed on 178 patients with stage II-III LARC treated from January 2021 to December 2023. All patients underwent standar
A total of 178 patients were enrolled, with 36 (20.2%) achieving a good response and 142 (79.8%) exhibiting a poor response to nCRT. Baseline factors, including age and comorbidities, showed no significant differences. However, poor responders more frequently had lymph node metastasis, advanced tumor node metastasis/T stage, larger tumor diameter, and elevated CRP, IL-6, and CEA levels. Logistic regression confirmed CRP, IL-6, and CEA as independent predictors of poor response. The nomogram demonstrated high accuracy (area under the curve = 0.928), good calibration (Hosmer-Lemeshow P = 0.928), and a sensitivity of 88.1% with 82.6% specificity. Internal validation via bootstrap resampling (n = 1000) yielded an adjusted C-index of 0.716, and DCA confirmed substantial clinical utility.
A nomogram incorporating serum CRP, IL-6, and CEA accurately predicts poor nCRT response in patients with LARC. This model provides a valuable framework for individualized treatment planning, potentially improving clinical outcomes.
Core Tip: Despite advancements in the use of neoadjuvant chemoradiotherapy (nCRT) to improve resectability and outcomes in locally advanced rectal cancer, predicting treatment response remains challenging. We identified key biomarkers: C-reactive protein, interleukin-6, and carcinoembryonic antigen, as independent predictors of poor response to nCRT. Using these factors, we constructed a nomogram model that demonstrated high predictive accuracy (area under the curve = 0.928), good calibration, and strong clinical utility as confirmed by decision curve analysis.