(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38–87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.
（1）背景与（2）方法：本项回顾性、观察性、单中心研究纳入了2014年1月至2020年12月期间确诊的85例患者（年龄范围38-87岁，男性51例），所有患者均经新发诊断发现直径小于4厘米的肾脏小肿块（SRM），后续接受肾切除术（部分或全切）或肿瘤切除术，并伴有病灶组织病理学检查。研究使用三台不同型号的CT扫描仪获取门静脉期腹部CT图像，从中提取85个SRM的影像组学特征（RFs）。所有病灶均由腹部放射科医师手动分割标注，采用3D-Slicer软件的Pyradiomics库进行图像分析，每个病灶体积共提取108个影像组学特征。研究构建了基于影像组学特征的机器学习模型以区分良恶性肾脏小肿块，技术流程包括冗余特征剔除、特征标准化、数据集平衡处理、不可重复特征排除、特征选择、模型训练、参数调优及未见数据验证。（3）结果：研究队列包含51例肾细胞癌和34例良性病变（25例嗜酸细胞瘤、7例乏脂型血管平滑肌脂肪瘤及2例肾平滑肌瘤）。最终构建的影像组学特征标签包含10个关键特征。模型在未见数据上的平均性能表现为：ROC曲线下面积0.79±0.12，准确率0.73±0.12，灵敏度0.78±0.19，特异度0.63±0.15。（4）结论：通过严谨的技术流程，本研究发现构建的影像组学特征标签能够有效区分肾细胞癌与良性肾脏肿瘤。
肿瘤(癌症)患者之家