Gholizadeh Neda, Simpson John, Ramadan Saadallah, Denham Jim, Lau Peter, Siddique Sabbir, Dowling Jason, Welsh James, Chalup Stephan, Greer Peter B
School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia.
Radiation Oncology Department, Calvary Mater Newcastle, Newcastle, NSW, Australia.
J Appl Clin Med Phys. 2020 Oct;21(10):179-191. doi: 10.1002/acm2.12992. Epub 2020 Aug 8.
The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp-MRI), comprised of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters.
In this work, 191 radiomic features were extracted from mp-MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp-MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave-one-patient-out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps.
The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features.
Combination of noncontrast mp-MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.
本研究的目的是开发并评估一种监督式机器学习技术的性能，该技术使用由T2加权成像（T2WI）、扩散加权成像（DWI）和高级扩散张量成像（DTI）参数组成的非对比多参数磁共振成像（mp-MRI）将磁共振成像（MRI）体素分类为癌性或非癌性。
在本研究中，从前列腺癌患者的mp-MRI中提取了191个放射组学特征。基于新颖的贝叶斯参数优化方法，开发了一套用于T2WI和mp-MRI（T2WI + DWI、T2WI + DTI以及T2WI + DWI + DTI）的综合支持向量机（SVM）模型，并采用留一患者法进行验证以消除任何可能的过拟合。使用受试者操作特征曲线下面积（AUROC）评估每个模型的诊断性能。使用测试数据集和生成的相应二值图评估模型的平均灵敏度、特异性和准确性。最后，使用性能最高的模型的SVM加Sigmoid函数生成癌症概率图。
使用最佳特征子集的T2WI + DWI + DTI模型在前列腺癌检测中表现最佳，平均AUROC、灵敏度、特异性和准确性分别为0.93±0.03、0.85±0.05、0.82±0.07和0.83±0.04。使用最佳放射组学特征时，T2WI + DTI模型的平均诊断性能略高于T2WI + DWI模型（高3.52%）。
非对比mp-MRI（T2WI、DWI和DTI）特征与监督分类技术框架及贝叶斯优化方法相结合，能够在不使用造影剂的情况下高精度地区分癌性与非癌性体素。癌症概率图的添加为图像解读、病变异质性评估和治疗管理提供了额外功能。
