MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy

• Published in: Cancer imaging 2024-10, Vol.24 (1), p.144-12, Article 144
• Main Authors: Yan, Qi, Wu, Menghan, Zhang, Jing, Yang, Jiayang, Lv, Guannan, Qu, Baojun, Zhang, Yanping, Yan, Xia, Song, Jianbo
• Format: Article
• Language: English
• Subjects: Adult; Aged; Biological markers; Biomarkers, Tumor - analysis; Cancer; Cancer patients; Care and treatment; Cervical cancer; Chemoradiotherapy - methods; Concurrent chemoradiotherapy; Development and progression; Female; Humans; Inflammation; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical research; Medicine, Experimental; Middle Aged; MRI radiomics; Nomograms; Nutritional-inflammatory biomarkers; Patient outcomes; Prognosis; Progression-Free Survival; Radiomics; Retrospective Studies; Uterine Cervical Neoplasms - diagnostic imaging; Uterine Cervical Neoplasms - mortality; Uterine Cervical Neoplasms - pathology; Uterine Cervical Neoplasms - therapy
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-024-00789-2

This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment. We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA). Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792-0.874) in the training set and 0.789 (95% CI: 0.679-0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set. The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.