Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer
Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer
复制标题
开发和验证基于多参数 MRI 的放射组学特征，用于区分惰性前列腺癌和侵袭性前列腺癌
DOI：
10.1259/bjr.20210191
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发表时间：
2022-01-01
影响因子：
2.6
通讯作者：
Pang, Jun
中科院分区：
文献类型：
--
作者：
Zhang, Liuhui;Jiang, Donggen;Pang, Jun
目的：为了开发和验证一个非侵入性的MRI为基础的放射组学签名区分惰性和侵袭性前列腺癌（PCa）之前therapeutic.Methods：在所有，139个合格的和病理证实的PCa患者分为训练集（n = 93）和验证集（n = 46）。从T2 WI（n = 788）和弥散加权成像（n = 788）中提取了每位患者的1576个放射组学特征。使用Select K Best和最小绝对收缩和选择算子回归算法在训练集中构建放射组学特征。在训练集中评估放射组学特征的预测性能，然后通过受试者工作特征曲线分析在验证集中进行验证。我们计算的校准曲线和决策曲线，以评估的校准和临床实用性的signature.Results：九个放射组学特征被确定为形成放射组学签名。放射组学评分（Radscore）在惰性和侵袭性PCa之间有显著差异（p < 0.001）。放射组学特征在训练集（AUC：0.853，95%CI：0.766至0.941）和验证集（AUC：0.901，95%CI：0.793至1.000）中表现出惰性和侵袭性PCa组之间的有利区分。决策曲线分析表明，当阈值概率在20%~ 90%之间时，可以获得更大的净效益。结论：基于多参数MRI的放射组学特征可以作为一种潜在的无创性工具，用于在治疗前区分惰性和侵袭性PCa。基于多参数MRI的放射组学特征具有非侵入性区分惰性和侵袭性PCa的潜力，这可能有助于临床医生做出个性化的治疗决策。
Objective: To develop and validate a non-invasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy.Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and diffusion-weighted imaging (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature.Results: Nine radiomics features were identified to form the radiomics signature. The radiomics score (Radscore) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95%CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95%CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%.Conclusion: The multiparametric MRI-based radiomics signature can potentially serve as a non-invasive tool for distinguishing between indolent and aggressive PCa prior to therapy.Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to non-invasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.