文章：

[18F]F-FDG PET/CT放射组学分类器在多种恶性肿瘤中鉴别组织学亚型与解剖学疾病起源：一项原理验证研究

The [18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study

原文发布日期：15 May 2024

DOI: 10.3390/cancers16101873

类型: Article

开放获取: 是

英文摘要：

We aimed to investigate whether [18F]F-FDG-PET/CT-derived radiomics can classify histologic subtypes and determine the anatomical origin of various malignancies. In this IRB-approved retrospective study, 391 patients (age = 66.7 ± 11.2) with pulmonary (n = 142), gastroesophageal (n = 128) and head and neck (n = 121) malignancies were included. Image segmentation and feature extraction were performed semi-automatically. Two models (all possible subset regression [APS] and recursive partitioning) were employed to predict histology (squamous cell carcinoma [SCC; n = 219] vs. adenocarcinoma [AC; n = 172]), the anatomical origin, and histology plus anatomical origin. The recursive partitioning algorithm outperformed APS to determine histology (sensitivity 0.90 vs. 0.73; specificity 0.77 vs. 0.65). The recursive partitioning algorithm also revealed good predictive ability regarding anatomical origin. Particularly, pulmonary malignancies were identified with high accuracy (sensitivity 0.93; specificity 0.98). Finally, a model for the synchronous prediction of histology and anatomical disease origin resulted in high accuracy in determining gastroesophageal AC (sensitivity 0.88; specificity 0.92), pulmonary AC (sensitivity 0.89; specificity 0.88) and head and neck SCC (sensitivity 0.91; specificity 0.92). Adding PET-features was associated with marginal incremental value for both the prediction of histology and origin in the APS model. Overall, our study demonstrated a good predictive ability to determine patients’ histology and anatomical origin using [18F]F-FDG-PET/CT-derived radiomics features, mainly from CT.

摘要翻译： 

本研究旨在探讨[18F]F-FDG-PET/CT影像组学特征是否能够对恶性肿瘤的组织学亚型进行分类并确定其解剖起源。这项经伦理委员会批准的回顾性研究共纳入391例患者（年龄66.7±11.2岁），包括肺部（n=142）、胃食管（n=128）及头颈部（n=121）恶性肿瘤。采用半自动方法进行图像分割与特征提取。通过全子集回归与递归分割两种模型预测组织学类型（鳞状细胞癌[n=219] vs. 腺癌[n=172]）、解剖起源及其联合特征。递归分割算法在组织学分类中表现更优（敏感性0.90 vs. 0.73；特异性0.77 vs. 0.65），同时在解剖起源判定中也展现出良好预测能力，尤其对肺部恶性肿瘤识别准确率较高（敏感性0.93；特异性0.98）。联合预测组织学类型与解剖起源的模型在胃食管腺癌（敏感性0.88；特异性0.92）、肺腺癌（敏感性0.89；特异性0.88）及头颈部鳞癌（敏感性0.91；特异性0.92）判定中均获得较高准确率。在全子集回归模型中，加入PET特征对组织学与起源的预测仅产生边际增量价值。总体而言，本研究证实基于[18F]F-FDG-PET/CT（主要源自CT）的影像组学特征在判定患者组织学类型与解剖起源方面具有良好的预测能力。

原文链接：

The [18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study