基于CT的常规放射素学和量身内异质性的定量，以预测良性和恶性肾脏病变
CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions
影响因子:3.50000
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
摘要
随着肾脏病变的发生率的增加，良性和恶性病变之间的预处理分化对于优化管理至关重要。这项研究旨在开发一种机器学习模型，该模型利用了从各个兴趣区域（ROI）提取的放射性特征，肿瘤内生态多样性特征和临床因素对肾脏病变进行分类。CT图像（动脉期），为1,795个肾脏病变，通过三个医院现场确认的病理学的肾脏病变，分为开发（1184，66％）和66％（66％）（66％）（66％），34％（34）。从八个动脉相图像的ROI中提取常规放射线特征。肿瘤内生态多样性特征来自肿瘤内子区域。开发了结合这些特征与临床因素的组合模型，并将其性能与放射科医生的解释进行了比较。在CT扫描中提取的所有特征组合中，在所有特征组合中均产生了肿瘤内和周围的放射线特征，以及生态多样性特征。在将临床因子纳入从CT图像中提取的特征中后，我们的合并模型在整个（AUC = 0.946 vs 0.823，p <0.001）和小肾脏病变（AUC = 0.935 vs 0.745 vs 0.745，p <0.001）测试同胞中的整体模型（AUC = 0.946 vs 0.823，p <0.001）。此外，合并的模型表现出有利的一致性，并在超过60％的阈值概率上提供了最高的净益处。在整个肾脏病变测试群中，预测风险低于或高于95％的敏感性和特异性截止的亚组的AUC分别为0.974和0.978。结合模型，结合了肿瘤内和周次肿瘤内部和临床范围的良好性效果，良好的效果，与临床的良好效果相比，散发出了良好的范围，这表现出了良好的范围。全肾脏病变的诊断。它有可能使患者免于不必要的入侵活检/手术，并增强临床决策。
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.