CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study.

IF 4.1 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Huancheng Yang, Yueyue Zhang, Fan Li, Weihao Liu, Haoyang Zeng, Haoyuan Yuan, Zixi Ye, Zexin Huang, Yangguang Yuan, Ye Xiang, Kai Wu, Hanlin Liu
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引用次数: 0

Abstract

Purpose: To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict pathological grade and Ki67 index in clear cell renal cell carcinoma (ccRCC).

Methods: In this multicenter retrospective study, a total of 1073 pathologically confirmed ccRCC patients from seven cohorts were split into internal cohorts (training and validation sets) and an external test set. The AI framework comprised an image processor, a 3D-kidney and tumor segmentation model by 3D-UNet, a multi-scale features extractor built upon unsupervised learning, and a multi-task classifier utilizing XGBoost. A quantitative model interpretation technique, known as SHapley Additive exPlanations (SHAP), was employed to explore the contribution of multi-scale features.

Results: The 3D-UNet model showed excellent performance in segmenting both the kidney and tumor regions, with Dice coefficients exceeding 0.92. The proposed multi-scale features model exhibited strong predictive capability for pathological grading and Ki67 index, with AUROC values of 0.84 and 0.87, respectively, in the internal validation set, and 0.82 and 0.82, respectively, in the external test set. The SHAP results demonstrated that features from radiomics, the 3D Auto-Encoder, and dimensionality reduction all made significant contributions to both prediction tasks.

Conclusions: The proposed AI framework, leveraging multi-scale features, accurately predicts the pathological grade and Ki67 index of ccRCC.

Critical relevance statement: The CT-based AI framework leveraging multi-scale features offers a promising avenue for accurately predicting the pathological grade and Ki67 index of ccRCC preoperatively, indicating a direction for non-invasive assessment.

Key points: Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. The AI framework enables non-invasive preoperative detection of high-risk tumors, assisting clinical decision-making.

基于ct的AI框架利用多尺度特征预测透明细胞肾细胞癌的病理分级和Ki67指数:一项多中心研究。
目的:探讨基于ct的AI框架,利用多尺度特征,是否可以提供无创方法准确预测透明细胞肾细胞癌(ccRCC)的病理分级和Ki67指数。方法:在这项多中心回顾性研究中,来自7个队列的1073例病理确诊的ccRCC患者被分为内部队列(训练和验证组)和外部测试组。人工智能框架包括图像处理器、3D-UNet的3d肾脏和肿瘤分割模型、基于无监督学习的多尺度特征提取器和利用XGBoost的多任务分类器。采用SHapley加性解释(SHAP)定量模型解释技术来探讨多尺度特征的贡献。结果:3D-UNet模型在肾脏和肿瘤区域的分割上均表现优异,Dice系数均超过0.92。提出的多尺度特征模型对病理分级和Ki67指数具有较强的预测能力,内部验证集的AUROC值分别为0.84和0.87,外部测试集的AUROC值分别为0.82和0.82。SHAP结果表明,来自放射组学、3D自动编码器和降维的特征都对这两项预测任务做出了重大贡献。结论:提出的AI框架利用多尺度特征,能准确预测ccRCC的病理分级和Ki67指数。关键相关性声明:基于ct的人工智能框架利用多尺度特征,为术前准确预测ccRCC的病理分级和Ki67指数提供了一条有希望的途径,为无创评估指明了方向。重点:无创检测ccRCC的病理分级和Ki67指数可指导治疗决策。AI框架集成了分割、分类和模型解释,实现了全自动分析。AI框架可实现高危肿瘤的无创术前检测,辅助临床决策。
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来源期刊
Insights into Imaging
Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
7.30
自引率
4.30%
发文量
182
审稿时长
13 weeks
期刊介绍: Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.
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