Jing Guodong, Xing Pengyi, Li Zhihui, Ma Xiaolu, Lu Haidi, Shao Chengwei, Lu Yong, Lu Jianping, Shen Fu
Department of Radiology, Changhai Hospital, Shanghai, China.
Department of Radiology, 989th Hospital of the joint logistic support force of the Chinese People's Liberation Army, Luoyang, China.
Front Oncol. 2022 Jul 15;12:918830. doi: 10.3389/fonc.2022.918830. eCollection 2022.
To develop and validate a multimodal MRI-based radiomics nomogram for predicting clinically significant prostate cancer (CS-PCa).
Patients who underwent radical prostatectomy with pre-biopsy prostate MRI in three different centers were assessed retrospectively. Totally 141 and 60 cases were included in the training and test sets in cohort 1, respectively. Then, 66 and 122 cases were enrolled in cohorts 2 and 3, as external validation sets 1 and 2, respectively. Two different manual segmentation methods were established, including lesion segmentation and whole prostate segmentation on T2WI and DWI scans, respectively. Radiomics features were obtained from the different segmentation methods and selected to construct a radiomics signature. The final nomogram was employed for assessing CS-PCa, combining radiomics signature and PI-RADS. Diagnostic performance was determined by receiver operating characteristic (ROC) curve analysis, net reclassification improvement (NRI) and decision curve analysis (DCA).
Ten features associated with CS-PCa were selected from the model integrating whole prostate (T2WI) + lesion (DWI) for radiomics signature development. The nomogram that combined the radiomics signature with PI-RADS outperformed the subjective evaluation alone according to ROC analysis in all datasets (all <0.05). NRI and DCA confirmed that the developed nomogram had an improved performance in predicting CS-PCa.
The established nomogram combining a biparametric MRI-based radiomics signature and PI-RADS could be utilized for noninvasive and accurate prediction of CS-PCa.
开发并验证一种基于多模态磁共振成像（MRI）的影像组学列线图，用于预测具有临床意义的前列腺癌（CS-PCa）。
回顾性评估在三个不同中心接受前列腺穿刺活检前进行前列腺MRI检查并随后接受根治性前列腺切除术的患者。队列1的训练集和测试集分别纳入了141例和60例患者。然后，队列2和队列3分别纳入66例和122例患者，作为外部验证集1和外部验证集2。建立了两种不同的手动分割方法，分别是在T2WI和DWI扫描上进行病灶分割和全前列腺分割。从不同的分割方法中获取影像组学特征，并选择这些特征构建影像组学特征模型。最终的列线图用于评估CS-PCa，将影像组学特征模型与前列腺影像报告和数据系统（PI-RADS）相结合。通过受试者操作特征（ROC）曲线分析、净重新分类改善（NRI）和决策曲线分析（DCA）来确定诊断性能。
从整合全前列腺（T2WI）+病灶（DWI）的模型中选择了10个与CS-PCa相关的特征用于构建影像组学特征模型。根据ROC分析，在所有数据集中，将影像组学特征模型与PI-RADS相结合的列线图的表现均优于单独的主观评估（所有P<0.05）。NRI和DCA证实，所开发的列线图在预测CS-PCa方面具有更好的性能。
所建立的结合基于双参数MRI的影像组学特征模型和PI-RADS的列线图可用于无创且准确地预测CS-PCa。
