The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the “gold standard” information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), positive predictive value (PPV) and F1-score.

中文翻译：

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利用综合病理先验对 CT 图像上的肺癌亚型进行分类


肺癌病理亚型的准确诊断对于后续治疗和预后管理具有重要意义。在本文中，我们提出了自生成混合特征网络（SGHF-Net），用于在计算机断层扫描（CT）图像上准确分类肺癌亚型。受研究表明同一病例的 CT 图像与其病理图像之间的图像模式存在跨尺度关联的启发，我们创新地开发了病理特征合成模块（PFSM），该模块通过深度神经网络定量映射跨模态关联，以从CT图像中推导出相应病理图像中包含的“金标准”信息。此外，我们设计了放射特征提取模块（RFEM）来直接获取CT图像信息，并在有效的特征融合框架下将其与病理先验进行整合，使整个分类模型能够生成更多指示性和具体的病理相关特征，并最终输出更多准确的预测。该模型的优越性在于它能够基于单模态输入自行生成包含多模态图像信息的混合特征。为了评估我们模型的有效性、适应性和泛化能力，我们在大规模多中心数据集（即来自三个医院的 829 例病例）上进行了广泛的实验，以将我们的模型与一系列最新的模型进行比较。艺术（SOTA）分类模型。实验结果证明了我们的模型在肺癌亚型分类方面的优越性，在准确度（ACC）、曲线下面积（AUC）、阳性预测值（PPV）和F1分数方面显着提高。