Yuan Enyu, Chen Yuntian, Ye Lei, He Ben, He ChunLei, Ma Junchao, Yang Ting, Zeng Hao, Yang Ling, Yao Jin, Song Bin
Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
Department of Urology, The Third People's Hospital of Chengdu/The Affiliated Hospital of Southwest Jiaotong University, Chengdu, 610014, China.
NPJ Digit Med. 2025 May 24;8(1):305. doi: 10.1038/s41746-025-01723-x.
Preoperative detection of pT3a invasion in non-metastatic renal cell carcinoma (RCC) remains challenging with CT. This study developed and validated radiomic models using preoperative CT to identify pT3a invasions. Six models were trained and internally validated via nested cross-validation on 999 patients from one hospital. External validation included 313 patients from two hospitals and 204 patients from four TCIA datasets. A multi-reader multi-case study with seven radiologists evaluated the model's incremental value. The morphology model achieved the highest internal AUC (0.867, 95% CI: 0.866-0.869) and maintained performance in external validations (AUC = 0.895 and 0.842). When used as a second reader, it significantly improved junior radiologists' sensitivity and discrimination (AUC: 0.790 vs. 0.831, p < 0.001) without compromising specificity. This study demonstrates that CT-based radiomic models, particularly the morphology model, can reliably detect pT3a invasion and enhance diagnostic accuracy for junior radiologists, offering potential clinical utility in preoperative staging.
对于非转移性肾细胞癌（RCC），术前通过CT检测pT3a浸润仍然具有挑战性。本研究开发并验证了使用术前CT识别pT3a浸润的放射组学模型。通过对一家医院的999例患者进行嵌套交叉验证，训练并内部验证了六个模型。外部验证包括来自两家医院的313例患者和来自四个TCIA数据集的204例患者。一项由七名放射科医生参与的多读者多病例研究评估了该模型的增量价值。形态学模型在内部验证中获得了最高的AUC（0.867，95%CI：0.866 - 0.869），并在外部验证中保持了性能（AUC = 0.895和0.842）。当作为第二读者使用时，它显著提高了初级放射科医生的敏感性和辨别力（AUC：0.790对0.831，p < 0.001），而不影响特异性。本研究表明，基于CT的放射组学模型，特别是形态学模型，能够可靠地检测pT3a浸润并提高初级放射科医生的诊断准确性，在术前分期中具有潜在的临床应用价值。
