Preoperative CT-based radiomics nomogram for progression-free survival prediction in pediatric posterior mediastinal malignancies

TL;DR

This study creates a CT-based radiomics tool to predict survival outcomes in children with posterior mediastinal tumors before surgery.

Contribution

The first preoperative CT-based radiomics nomogram for predicting progression-free survival in pediatric posterior mediastinal malignancies.

Findings

The radiomics nomogram achieved an AUC of 0.87 in predicting progression-free survival.

Ki-67 index and metastasis were identified as independent clinical predictors.

The nomogram outperformed other models with a brier score of 0.22 in the test set.

Abstract

Progression-free survival (PFS) prediction plays a pivotal role in developing personalized treatment strategies and ensuring favorable long-term outcomes in pediatric posterior mediastinal malignant tumors. This study developed and validated the first preoperative contrast-enhanced computed tomography (CT)-based radiomics nomogram to forecast PFS in posterior mediastinal malignancies patients. The aim was to provide a clinically applicable prognostic tool to stratify high-risk populations.

Medical data from 306 patients with posterior mediastinal malignancies were analyzed retrospectively and randomly divided into training (n = 215) and test sets (n = 91). The clinical model was built using conventional clinical data and CT signs. Selection of the radiomic features was performed using maximum relevance minimum redundancy and the least absolute shrinkage and selection operator. To overcome class imbalance, the synthetic minority over-sampling technique was used in the training set. Radiomics signature was derived using logistic regression algorithm, and we developed a nomogram by integrating the clinical model and the radiomics signature. The predictive efficiency of the nomogram was assessed using the area under the curve (AUC), brier score (BS), decision curve analysis, and calibration.

The Ki-67 index and metastasis were identified as independent predictors, with the test set achieving an AUC of 0.82 (0.647–0.964) and a BS of 0.21 (0.181–0.239). Nineteen radiomics features most relevant to PFS were retained, with the logistic regression algorithm achieving an AUC of 0.77 (0.589–0.896) and a BS of 0.26 (0.215–0.292) in the test set. The radiomics nomogram demonstrated best predictive capability in the test set, achieving an AUC of 0.87 (0.733–0.968) and a BS of 0.22 (0.177–0.255), compared with remaining prediction models. Both calibration curves and decision curve analysis demonstrated good fit and clinical benefit.

Our contrast-enhanced CT-based radiomics nomogram may be a dependable, precise, and noninvasive prognostic tool to predict PFS in pediatric posterior mediastinal malignancies preoperatively.

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