Journal of magnetic resonance imaging : JMRI · 2025
Preoperative Assessment of Extraprostatic Extension in Prostate Cancer Using an Interpretable Tabular Prior-Data Fitted Network-Based Radiomics Model From MRI.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Bai-Chuan, Ding Xiao-Hui, Xu Hong-Hao, Bai Xu, Zhang Xiao-Jing, Cui Meng-Qiu, Guo Ai-Tao, Mu Xue-Tao, Xie Li-Zhi, Kang Huan-Huan, Zhou Shao-Peng, Zhao Jian, Wang Bao-Jun, Wang Hai-Yi
Аннотация
BACKGROUND: MRI assessment for extraprostatic extension (EPE) of prostate cancer (PCa) is challenging due to limited accuracy and interobserver agreement.
PURPOSE: 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.
STUDY TYPE: Retrospective.
POPULATION: 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.
FIELD STRENGTH/SEQUENCE: Three Tesla, fast spin echo T2-weighted imaging (T2WI) and diffusion-weighted imaging using single-shot echo planar imaging.
ASSESSMENT: 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.
STATISTICAL TESTS: Receiver operating characteristic curve (AUC), Delong test, and McNemar test. p < 0.05 was considered significant.
RESULTS: 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.
DATA CONCLUSIONS: The TabRadiomics model demonstrated high performance in EPE assessment and may improve clinical assessment in PCa.
EVIDENCE LEVEL: 4.
TECHNICAL EFFICACY: Stage 2.
PURPOSE: 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.
STUDY TYPE: Retrospective.
POPULATION: 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.
FIELD STRENGTH/SEQUENCE: Three Tesla, fast spin echo T2-weighted imaging (T2WI) and diffusion-weighted imaging using single-shot echo planar imaging.
ASSESSMENT: 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.
STATISTICAL TESTS: Receiver operating characteristic curve (AUC), Delong test, and McNemar test. p < 0.05 was considered significant.
RESULTS: 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.
DATA CONCLUSIONS: The TabRadiomics model demonstrated high performance in EPE assessment and may improve clinical assessment in PCa.
EVIDENCE LEVEL: 4.
TECHNICAL EFFICACY: Stage 2.