Noninvasive identification of HER2 status by integrating multiparametric MRI-based radiomics model with the vesical imaging-reporting and data system (VI-RADS) score in bladder urothelial carcinoma.

IF 2.3 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Cheng Luo, Shurong Li, Yichao Han, Jian Ling, Xuanling Wu, Lingwu Chen, Daohu Wang, Junxing Chen
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引用次数: 0

Abstract

Purpose: HER2 expression is crucial for the application of HER2-targeted antibody-drug conjugates. This study aims to construct a predictive model by integrating multiparametric magnetic resonance imaging (mpMRI) based multimodal radiomics and the Vesical Imaging-Reporting and Data System (VI-RADS) score for noninvasive identification of HER2 status in bladder urothelial carcinoma (BUC).

Methods: A total of 197 patients were retrospectively enrolled and randomly divided into a training cohort (n = 145) and a testing cohort (n = 52). The multimodal radiomics features were derived from mpMRI, which were also utilized for VI-RADS score evaluation. LASSO algorithm and six machine learning methods were applied for radiomics feature screening and model construction. The optimal radiomics model was selected to integrate with VI-RADS score to predict HER2 status, which was determined by immunohistochemistry. The performance of predictive model was evaluated by receiver operating characteristic curve with area under the curve (AUC).

Results: Among the enrolled patients, 110 (55.8%) patients were demonstrated with HER2-positive and 87 (44.2%) patients were HER2-negative. Eight features were selected to establish radiomics signature. The optimal radiomics signature achieved the AUC values of 0.841 (95% CI 0.779-0.904) in the training cohort and 0.794 (95%CI 0.650-0.938) in the testing cohort, respectively. The KNN model was selected to evaluate the significance of radiomics signature and VI-RADS score, which were integrated as a predictive nomogram. The AUC values for the nomogram in the training and testing cohorts were 0.889 (95%CI 0.840-0.938) and 0.826 (95%CI 0.702-0.950), respectively.

Conclusion: Our study indicated the predictive model based on the integration of mpMRI-based radiomics and VI-RADS score could accurately predict HER2 status in BUC. The model might aid clinicians in tailoring individualized therapeutic strategies.

将基于多参数mri的放射组学模型与膀胱影像报告和数据系统(VI-RADS)评分相结合,无创识别膀胱尿路上皮癌的HER2状态。
目的:HER2的表达对于HER2靶向抗体-药物偶联物的应用至关重要。本研究旨在通过整合基于多参数磁共振成像(mpMRI)的多模态放射组学和膀胱成像报告和数据系统(VI-RADS)评分,构建一种预测模型,用于无创识别膀胱尿路上皮癌(BUC)的HER2状态。方法:回顾性纳入197例患者,随机分为训练组(n = 145)和测试组(n = 52)。多模态放射组学特征来自mpMRI,也用于VI-RADS评分评估。采用LASSO算法和6种机器学习方法进行放射组学特征筛选和模型构建。选择最佳放射组学模型,结合VI-RADS评分预测HER2状态,免疫组织化学测定HER2状态。采用带曲线下面积(AUC)的受试者工作特征曲线评价预测模型的性能。结果:入组患者中,her2阳性110例(55.8%),her2阴性87例(44.2%)。选取8个特征建立放射组学特征。最佳放射组学特征在训练组和测试组的AUC值分别为0.841 (95%CI 0.779-0.904)和0.794 (95%CI 0.650-0.938)。选择KNN模型来评估放射组学特征和VI-RADS评分的意义,并将其整合为预测nomogram。训练组和测试组的nomogram AUC值分别为0.889 (95%CI 0.840 ~ 0.938)和0.826 (95%CI 0.702 ~ 0.950)。结论:我们的研究表明,基于mpmri放射组学和VI-RADS评分相结合的预测模型可以准确预测BUC的HER2状态。该模型可以帮助临床医生制定个性化的治疗策略。
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来源期刊
Abdominal Radiology
Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.20
自引率
8.30%
发文量
334
期刊介绍: Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits
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