基于机器学习的多参数磁共振成像放射组学提名图,用于预测透明细胞肾细胞癌的 WHO/ISUP 核分级。

IF 3.5 3区 医学 Q2 ONCOLOGY Frontiers in Oncology Pub Date : 2024-11-07 eCollection Date: 2024-01-01 DOI:10.3389/fonc.2024.1467775
Yunze Yang, Ziwei Zhang, Hua Zhang, Mengtong Liu, Jianjun Zhang
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引用次数: 0

摘要

目的探索基于机器学习的多参数 MRI 放射组学提名图在手术前预测透明细胞肾细胞癌(ccRCC)WHO/ISUP 核分级的有效性:回顾性收集了86例接受术前肾脏MRI扫描(包括平扫和增强扫描)并确诊为ccRCC患者的数据。根据2016年WHO/ISUP分级标准,患者被分为低级别组(I级和II级)和高级别组(III级和IV级),并按7:3的比例随机分为训练集和测试集。从 FS-T2WI、DWI 和 CE-T1WI 序列中提取放射组学特征。使用曼-惠特尼 U 检验、斯皮尔曼相关性分析和最小绝对收缩和选择算子(LASSO)选择最佳特征。五种机器学习分类器--逻辑回归(LR)、奈夫贝叶斯(NB)、k-近邻(KNN)、自适应提升(AdaBoost)和多层感知器(MLP)--被用来建立预测ccRCC WHO/ISUP核分级的模型。测试集中曲线下面积(AUC)最大的模型被选为最佳放射组学模型。通过单变量和多变量逻辑回归确定了独立的临床风险因素,从而建立了临床模型,并将该模型与放射组学评分(rad-score)相结合,建立了提名图。该模型的有效性通过接收者操作特征曲线(ROC)进行评估,其校准通过校准曲线进行评估,其临床实用性通过决策曲线分析进行分析:结果:最终选出了六个放射组学特征。MLP 分类器在测试集中显示出最高的诊断性能(AUC=0.933)。皮质髓质增强水平(P=0.020)和肾静脉侵犯(P=0.011)被确定为预测WHO/ISUP核分类的独立风险因素,并与rad-score一起被纳入提名图中。ROC曲线显示,提名图模型具有很强的诊断性能,训练集的AUC值为0.964,测试集的AUC值为0.933:基于机器学习的多参数磁共振成像放射组学提名图为ccRCC患者术前预测WHO/ISUP核分级提供了一种高预测性的无创工具。
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Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma.

Objective: To explore the effectiveness of a machine learning-based multiparametric MRI radiomics nomogram for predicting the WHO/ISUP nuclear grading of clear cell renal cell carcinoma (ccRCC) before surgery.

Methods: Data from 86 patients who underwent preoperative renal MRI scans (both plain and enhanced) and were confirmed to have ccRCC were retrospectively collected. Based on the 2016 WHO/ISUP grading standards, patients were divided into a low-grade group (Grade I and II) and a high-grade group (Grade III and IV), and randomly split into training and testing sets at a 7:3 ratio. Radiomics features were extracted from FS-T2WI, DWI, and CE-T1WI sequences. Optimal features were selected using the Mann-Whitney U test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Five machine learning classifiers-logistic regression (LR), naive bayes (NB), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and multilayer perceptron (MLP)-were used to build models to predict ccRCC WHO/ISUP nuclear grading. The model with the highest area under the curve (AUC) in the testing set was chosen as the best radiomics model. Independent clinical risk factors were identified using univariate and multivariate logistic regression to create a clinical model, which was combined with radiomics score (rad-score) to develop a nomogram. The model's effectiveness was assessed using the receiver operating characteristic (ROC) curve, its calibration was evaluated using a calibration curve, and its clinical utility was analyzed using decision curve analysis.

Results: Six radiomics features were ultimately selected. The MLP classifier showed the highest diagnostic performance in the testing set (AUC=0.933). Corticomedullary enhancement level (P=0.020) and renal vein invasion (P=0.011) were identified as independent risk factors for predicting the WHO/ISUP nuclear classification and were included in the nomogram with the rad-score. The ROC curves indicated that the nomogram model had strong diagnostic performance, with AUC values of 0.964 in the training set and 0.933 in the testing set.

Conclusion: The machine learning-based multiparametric MRI radiomics nomogram provides a highly predictive, non-invasive tool for preoperative prediction of WHO/ISUP nuclear grading in patients with ccRCC.

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来源期刊
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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