基于CT的常规放射组学与肿瘤内异质性定量在良恶性肾脏病变预测中的应用

CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions

CANCER IMAGING

影响因子:3.5

分区:医学2区 / 肿瘤学2区核医学2区

发表日期:2024 Oct 02

作者: Shuanbao Yu, Yang Yang, Zeyuan Wang, Haoke Zheng, Jinshan Cui, Yonghao Zhan, Junxiao Liu, Peng Li, Yafeng Fan, Wendong Jia, Meng Wang, Bo Chen, Jin Tao, Yuhong Li, Xuepei Zhang

DOI: 10.1186/s40644-024-00775-8

摘要

随着肾脏病变发生率的增加，术前区分良恶性病变对于优化管理策略至关重要。本研究旨在建立一种利用从不同感兴趣区域（ROI）提取的放射组学特征、肿瘤内生态多样性特征及临床因素的机器学习模型，以分类肾脏病变。研究对象为来自三家医院的1795例经病理确认的肾脏病变的CT（动脉期）影像，按手术日期分为开发组（1184例，占66%）和测试组（611例，占34%）。从动脉期图像的八个ROI中提取常规放射组学特征，从肿瘤内亚区域获得生态多样性特征，构建结合这些特征与临床因素的联合模型，并将其性能与放射科医师的解读进行比较。结果显示，结合肿瘤内和肿瘤周围的放射组学特征及生态多样性特征，获得的最高曲线下面积（AUC）为0.929。将临床因素纳入CT影像特征后，模型在全部（AUC=0.946对比0.823，P<0.001）及小型肾脏病变（AUC=0.935对比0.745，P<0.001）测试组中的表现优于放射科医师的解读。此外，该模型在超过60%的阈值概率下展现出较好的符合性和最高净收益。在全部及小型肾脏病变测试组中，风险预测低于或高于95%的子组的AUC分别为0.974和0.978。综上所述，结合肿瘤内外放射组学特征、生态多样性和临床因素的模型在良恶性肾脏病变的鉴别中表现良好，优于放射科医师诊断，具有减少不必要侵入性活检/手术的潜力，有助于临床决策优化。

Abstract

With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions.CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation.Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively.The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.