非小细胞肺癌是肺癌最常见的亚型。事实证明，使用机器学习 (ML) 和放射组学分析进行患者生存预测可以提供有希望的结果。然而，文献中报道的大多数研究都集中于从恶性病变中提取的信息。本研究旨在探索使用 ML 算法从健康器官和肿瘤组织中提取的信息的相关性和附加价值。本研究包括从可用在线数据库收集的 154 名患者的 PET/CT 图像。使用基于 nnU-Net 深度学习的分割对健康器官上定义的总肿瘤体积和 33 个感兴趣体积进行分割。随后，从 PET 和 CT 图像中提取了 107 个放射组学特征（有机组学）。考虑 19 种不同的输入组合，将临床信息与来自器官和大体肿瘤体积的 PET 和 CT 放射组学相结合。最后，在 3 倍数据分割交叉验证方案中测试了不同的特征选择（FS；5 种方法）和 ML（6 种算法）算法。模型的性能根据一致性指数（C-index）指标进行量化。对于所有放射组学信息的输入组合，大多数选定的特征属于 PET Organomics 和 CT Organomics。使用单变量 C 指数 FS 方法和使用 CT Organomics PET Organomics 作为输入的随机生存森林 ML 模型以及使用 PET Organomics 作为输入的最小深度 FS 方法和 CoxPH ML 模型实现了最高的 C 指数 (0.68)。考虑到 C 指数高于 0.65 的所有 17 种组合，来自 PET 或 CT 图像的有机组学被用作其中 16 种的输入。选定的特征和 C 指数表明，从 PET 和 CT 成像模式的健康器官中提取的附加信息提高了机器学习性能。器官组学可能是朝着利用多模态医学图像中的全部信息迈出的一步，为医疗保健中的数字双胞胎这一新兴领域做出贡献。版权所有 © 2024 作者。由 Wolters Kluwer Health, Inc. 出版

Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms.This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric.For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them.The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care.Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.