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• Clinical Article •
Prognostic value of an MRI-based radiomics machine learning model for patients with locally advanced cervical cancer undergoing concurrent chemoradiotherapy
SUN Liting  SHANG Jianan  ZHANG Qinghua 

DOI:10.12015/issn.1674-8034.2026.01.013.


[Abstract] Objective To construct a machine learning model for predicting the prognosis of concurrent chemoradiotherapy (CCRT) in patients with locally advanced cervical cancer (LACC) based on magnetic resonance imaging (MRI) radiomics features, and to evaluate its predictive performance.Materials and Methods A retrospective analysis was performed on 424 LACC patients admitted to the Affiliated Cancer Hospital of Xinjiang Medical University from February 2019 to February 2020. Patients were randomly assigned to a modeling group (n = 339) and an internal validation group (n = 85) at a 4∶1 ratio. Additionally, 120 LACC patients admitted to the Second Affiliated Hospital of Xinjiang Medical University during the same period were enrolled as the external validation group. Clinical data and MRI images (including transverse dynamic contrast-enhanced T1WI, transverse fast spin echo T2WI, and transverse diffusion-weighted imaging sequences) were collected. The region of interest (ROI) was delineated in the lesion area, and radiomics features were extracted using PyRadiomics. Dimensionality reduction and selection of radiomics features were conducted via the least absolute shrinkage and selection operator (LASSO) algorithm. A radiomics model was constructed based on the selected features, and radiomics scores (Rads) were calculated. Cox univariate and multivariate analyses were performed using patient clinical data and Rads to establish a prognostic prediction model for CCRT in LACC patients, followed by validation of the model's predictive performance.Results Thirteen MRI radiomics features were selected by the LASSO algorithm. Univariate and multivariate Cox analyses demonstrated that external beam radiotherapy dose [hazard ratio (HR) = 1.275, 95% CI: 1.186 to 1.371] and 2 Gy fractionated radiation equivalent dose (HR = 1.092, 95% CI: 1.050 to 1.137) were independent risk factors for mortality following CCRT in LACC patients, whereas hemoglobin (HR = 0.962, 95% CI: 0.940 to 0.985) and Rads (HR = 0.949, 95% CI: 0.933 to 0.965) were protective factors (all P < 0.05). Model validation showed that the area under the curve (AUC) values for internal and external validation were 0.978 (95% CI: 0.965 to 1.000) and 0.971 (95% CI: 0.958 to 0.996), respectively. The Hosmer-Lemeshow test yielded chi-square values of 8.580 (P = 0.379) and 8.691 (P = 0.370) for internal and external validation, respectively.Conclusions This study established a nomogram prediction model based on MRI radiomics, which exhibits excellent predictive performance and clinical utility for the prognosis of CCRT in LACC patients. It may serve as a reference for the formulation and adjustment of CCRT treatment plans for LACC patients.
[Keywords] uterine cervical neoplasms；chemoradiotherapy；magnetic resonance imaging；machine learning；nomograms；prognosis

SUN Liting1   SHANG Jianan1   ZHANG Qinghua2*  

1 Department of Gynecology, Cancer Hospital Affiliated to Xinjiang Medical University, Urumqi 830054, China

2 Department of Gynecology, the Second Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China

Corresponding author: ZHANG Q H, E-mail: 15739075856@163.com

Conflicts of interest   None.

Received  2025-06-25
Accepted  2025-12-06
DOI: 10.12015/issn.1674-8034.2026.01.013
DOI:10.12015/issn.1674-8034.2026.01.013.

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