Dataset Information

MRI Radiomic Features: A Potential Biomarker for Progression-Free Survival Prediction of Patients With Locally Advanced Cervical Cancer Undergoing Surgery.

ABSTRACT:

Objectives

To investigate the prognostic role of radiomic features based on pretreatment MRI in predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC).

Methods

All 181 women with histologically confirmed LACC were randomly divided into the training cohort (n = 126) and the validation cohort (n = 55). For each patient, we extracted radiomic features from whole tumors on sagittal T2WI and axial DWI. The least absolute shrinkage and selection operator (LASSO) algorithm combined with the Cox survival analysis was applied to select features and construct a radiomic score (Rad-score) model. The cutoff value of the Rad-score was used to divide the patients into high- and low-risk groups by the X-tile. Kaplan-Meier analysis and log-rank test were used to assess the prognostic value of the Rad-score. In addition, we totally developed three models, the clinical model, the Rad-score, and the combined nomogram.

Results

The Rad-score demonstrated good performance in stratifying patients into high- and low-risk groups of progression in the training (HR = 3.279, 95% CI: 2.865-3.693, p < 0.0001) and validation cohorts (HR = 2.247, 95% CI: 1.735-2.759, p < 0.0001). Otherwise, the combined nomogram, integrating the Rad-score and patient's age, hemoglobin, white blood cell, and lymph vascular space invasion, demonstrated prominent discrimination, yielding an AUC of 0.879 (95% CI, 0.811-0.947) in the training cohort and 0.820 (95% CI, 0.668-0.971) in the validation cohort. The Delong test verified that the combined nomogram showed better performance in estimating PFS than the clinical model and Rad-score in the training cohort (p = 0.038, p = 0.043).

Conclusion

The radiomics nomogram performed well in individualized PFS estimation for the patients with LACC, which might guide individual treatment decisions.

SUBMITTER: Cai M

PROVIDER: S-EPMC8712932 | biostudies-literature |

REPOSITORIES: biostudies-literature

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