Rengo Marco, Onori Alessandro, Caruso Damiano, Bellini Davide, Carbonetti Francesco, De Santis Domenico, Vicini Simone, Zerunian Marta, Iannicelli Elsa, Carbone Iacopo, Laghi Andrea
Department of Medical-Surgical Sciences and Biotechnologies, Academic Diagnostic Imaging Division, I.C.O.T. Hospital, University of Rome Sapienza, Via F. Faggiana 1668, 04100 Latina, Italy.
Department of Radiological, Oncological and Pathological Sciences, Academic Diagnostic Imaging Division, I.C.O.T. Hospital, University of Rome Sapienza, Via F. Faggiana 1668, 04100 Latina, Italy.
J Pers Med. 2023 Apr 24;13(5):717. doi: 10.3390/jpm13050717.
preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST's prognosis as determined by the Miettinen classification.
patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated.
52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations.
the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs.
胃肠道间质瘤（GIST）的术前风险评估对于优化和个性化治疗方案至关重要。影像组学特征是预测风险评估的有前景的工具。本研究的目的是开发并验证一种基于CT特征的人工智能分类算法，以根据米耶蒂宁分类法确定GIST的预后。
回顾性纳入经组织学诊断为GIST且有CT检查的患者。从每个肿瘤中提取8个形态学和30个纹理CT特征，并将其组合以获得三个模型（形态学、纹理和组合模型）。使用机器学习分类（WEKA）对数据进行分析。对于每个分类过程，评估敏感性、特异性、准确性和曲线下面积。还计算了阅片者间和阅片者内的一致性。
共评估了52例患者。在验证人群中，组合模型的表现最佳（敏感性85.7%，特异性90.9%，准确性88.8%，曲线下面积0.954），其次是形态学模型（敏感性66.6%，特异性81.8%，准确性76.4%，曲线下面积0.742）和纹理模型（敏感性50%，特异性72.7%，准确性64.7%，曲线下面积0.613）。所有手动评估的可重复性都很高。
基于人工智能的使用CT特征的影像组学模型对GIST术前风险分层具有良好的预测性能。
