Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists’ routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist’s routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
肺癌是全球范围内致死率最高的癌症之一，发病率居高不下，尤其在吸烟人群中更为显著。肺癌的精准诊断依赖于明确的组织学模式结合分子数据，以实现个体化治疗。仅凭单张H&E切片进行精确的肺癌分类对病理医生而言颇具挑战，通常需要借助组织化学及特殊免疫组化染色才能完成最终病理报告。根据世界卫生组织标准，约70%的晚期不可切除肺癌患者的诊断材料仅限于小活检标本和细胞学样本。因此，有限的诊断材料需要按照已发布的指南进行优化处理，以完成诊断和预测性检测。在数字病理学的新时代，深度学习为肺癌判读提供了潜力，有助于辅助病理医生的日常工作。本文系统综述了当前基于人工智能技术利用肺癌组织学和细胞学图像的研究方法。已发表文献多集中于肺腺癌、肺鳞状细胞癌和小细胞肺癌的鉴别诊断，这反映了病理医生实际工作中的常规需求。此外，部分研究开发了用于确定肺腺癌主要结构模式、预测预后、表征突变状态以及评估PD-L1表达水平的算法。
肿瘤(癌症)患者之家