Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.
Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.
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对前列腺多参数MRI的可疑病变的临床基因组风险组分类。
DOI：
10.3390/cancers15215240
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发表时间：
2023-10-31
期刊：
影响因子：
5.2
通讯作者：
Pollack, Alan
中科院分区：
文献类型：
--
作者：
Stoyanova, Radka;Zavala-Romero, Olmo;Kwon, Deukwoo;Breto, Adrian L.;Xu, Isaac R.;Algohary, Ahmad;Alhusseini, Mohammad;Gaston, Sandra M.;Castillo, Patricia;Kryvenko, Oleksandr N.;Davicioni, Elai;Nahar, Bruno;Spieler, Benjamin;Abramowitz, Matthew C.;Dal Pra, Alan;Parekh, Dipen J.;Punnen, Sanoj;Pollack, Alan
在这项研究中，我们建立了基于临床和放射组学的模型，以结合临床-基因组分类系统来预测病变/低风险患者。对78名男性的83次多参数MRI检查进行了分析。使用最小临床变量子集和病变和正常组织的放射学特征建立了几个病变分类模型。还对模型进行了评估，以进行患者分类。在所有情况下，放射组学特征都改善了性能。就我们所知，这是第一项证明基于机器学习的放射组学模型可以使用临床-基因组分类来预测患者风险的研究。多参数磁共振成像(MpMRI)在前列腺癌患者治疗的临床决策中的应用最近有所增加。活检后，临床医生可以使用国家综合癌症网络(NCCN)的风险分层方案和商业上可用的基因组分类器(如Decpher)来评估风险。我们建立了基于放射组学的模型，在活检前预测病变/低风险患者，基于已建立的三级临床基因组分类系统。在T2加权和弥散加权成像上，从阳性活检区域和正常组织(NAT)中提取放射学特征。仅使用活检前现有的临床信息，评估了五种预测低风险病变/患者的模型，其基础是：1：临床变量；2：基于病变的放射组学特征；3：病变和NAT放射组学；4：临床和基于病变的放射组学；以及5：临床、病变和NAT放射组学特征。对78名男性的83次mpMRI检查进行了分析。模型1和模型2的表现相似(受试者工作特征曲线下的面积分别为0.835和0.838)，但放射组学在直肠指诊阴性患者的子集分析中显著改善了模型基于病变的性能。加入正常组织放射组学显著改善了所有病例的表现。在患者水平的模型上也观察到了类似的模式。就我们所知，这是第一项证明基于机器学习的放射组学模型可以使用临床-基因组分类来预测患者风险的研究。
In this study, we built clinical- and radiomics-based models to predict lesions/patients at low risk based on a combined clinical-genomic classification system. Eighty-three multi-parametric MRI exams from 78 men were analyzed. Several models for lesion classification were built using a minimal clinical variables subset and radiomic features from the lesion and normal tissues. The models were also evaluated for patient classification. In all cases, the radiomic features improved the performance. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients’ risk using combined clinical-genomic classification. The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients’ management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients’ risk using combined clinical-genomic classification.
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DOI：
10.1073/pnas.1505935112
发表时间：
2015-11-17
影响因子：
11.1
作者：
Fehr, Duc;Veeraraghavan, Harini;Deasy, Joseph O.
通讯作者：
Deasy, Joseph O.
影响因子：
6.6
作者：
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影响因子：
2.4
作者：
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Madabhushi, Anant
影响因子：
8.8
作者：
Cuzick, J.;Berney, D. M.;Fisher, G.;Mesher, D.;Moller, H.;Reid, J. E.;Perry, M.;Park, J.;Younus, A.;Gutin, A.;Foster, C. S.;Scardino, P.;Lanchbury, J. S.;Stone, S.
通讯作者：
Stone, S.
DOI：
10.1016/j.ijrobp.2018.08.030
发表时间：
2019-01-01
影响因子：
7
作者：
Berlin, Alejandro;Murgic, Jure;Chua, Melvin L. K.
通讯作者：
Chua, Melvin L. K.