Preoperative Assessment of Extraprostatic Extension in Prostate Cancer Using an Interpretable Tabular Prior-Data Fitted Network-Based Radiomics Model From MRI.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
- Department of Pathology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
- Department of Pathology, Third Medical Center of Chinese PLA General Hospital, Beijing, China.
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China.
- GE Healthcare, MR Research China, Beijing, China.
- Department of Radiology, Second Medical Center of Chinese PLA General Hospital, Beijing, China.
- Department of Urology, Third Medical Center of Chinese PLA General Hospital, Beijing, China.
Abstract
MRI assessment for extraprostatic extension (EPE) of prostate cancer (PCa) is challenging due to limited accuracy and interobserver agreement. To develop an interpretable Tabular Prior-data Fitted Network (TabPFN)-based radiomics model to evaluate EPE using MRI and explore its integration with radiologists' assessments. Retrospective. Five hundred and thirteen consecutive patients who underwent radical prostatectomy. Four hundred and eleven patients from center 1 (mean age 67 ± 7 years) formed training (287 patients) and internal test (124 patients) sets, and 102 patients from center 2 (mean age 66 ± 6 years) were assigned as an external test set. Three Tesla, fast spin echo T2-weighted imaging (T2WI) and diffusion-weighted imaging using single-shot echo planar imaging. Radiomics features were extracted from T2WI and apparent diffusion coefficient maps, and the TabRadiomics model was developed using TabPFN. Three machine learning models served as baseline comparisons: support vector machine, random forest, and categorical boosting. Two radiologists (with > 1500 and > 500 prostate MRI interpretations, respectively) independently evaluated EPE grade on MRI. Artificial intelligence (AI)-modified EPE grading algorithms incorporating the TabRadiomics model with radiologists' interpretations of curvilinear contact length and frank EPE were simulated. Receiver operating characteristic curve (AUC), Delong test, and McNemar test. p < 0.05 was considered significant. The TabRadiomics model performed comparably to machine learning models in both internal and external tests, with AUCs of 0.806 (95% CI, 0.727-0.884) and 0.842 (95% CI, 0.770-0.912), respectively. AI-modified algorithms showed significantly higher accuracies compared with the less experienced reader in internal testing, with up to 34.7% of interpretations requiring no radiologist input. However, no difference was observed in both readers in the external test set. The TabRadiomics model demonstrated high performance in EPE assessment and may improve clinical assessment in PCa. 4. Stage 2.