Radiomics analysis of CT imaging for predicting MIT family translocation renal cell carcinoma: a multicenter retrospective clinical study.

OBJECTIVE

This study aimed to investigate the imaging characteristics of Microphthalmia Transcription Factor (MIT) Family Translocation Renal Cell Carcinoma (MIT-RCC), assess the relationship between CT-based radiomics and MIT-RCC, and develop a predictive model to improve preoperative non-invasive diagnosis.

MATERIALS AND

METHODS

A retrospective analysis was conducted using preoperative computed tomography (CT) images and clinical data from 746 RCC patients (77 MIT-RCC and 669 other RCC subtypes) across multiple centers. Regions of interest (ROIs) in both tumor and normal renal parenchymal during non-contrast and arterial phases were manually segmented using ITK-SNAP. Radiomics features were extracted via Python, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was applied for feature selection. A combined model incorporating radiomics scores and clinical predictors (age, stage) was developed using logistic regression and visualized as a nomogram. Patients from two centers formed the training cohort (n = 539), and three other centers served as the validation cohort (n = 207).

RESULTS

From 1316 features per ROI, 22 tumor and 24 normal renal parenchymal features were selected. The nomogram integrating Tumor_Radscores, Kidney_Radscores, age, and clinical stage showed strong predictive performance in the training cohort (area under the curve [AUC] = 0.952, 95% confidence interval [CI]: 0.927-0.971) and validation cohort (AUC = 0.914, 95% CI: 0.879-0.942), with good calibration and clinical utility.

CONCLUSION

By integrating radiomics and clinical data, the proposed nomogram achieved accurate, non-invasive prediction of MIT-RCC, offering valuable support for preoperative diagnosis and personalized treatment planning.