10% 至 50% 的早期肺腺癌患者会出现局部或远处复发。组织学参数（例如实性或微乳头生长模式）是明确描述的复发风险因素。然而，并非所有出现这种模式的患者都会复发。设计一个可以更准确地预测小活检样本复发的模型，可以帮助对患者进行手术、（新）辅助治疗和随访分层。在这项研究中，利用早期和晚期的组织学数据建立了一个活检统计模型。晚期肺腺癌的开发是为了预测手术切除后的复发。此外，基于卷积神经网络 (CNN) 的人工智能 (AI) 分类模型（称为基于 AI 的肺腺癌复发预测器 (AILARP)）经过训练来预测复发，并使用 ImageNet 预训练的 EfficientNet 进行微调。使用迁移学习进行肺腺癌活检。两种模型均使用相同的活检数据集进行验证，以确保进行准确的比较。仅使用组织学数据时，统计模型对所有患者的准确度为 0.49。 AI 分类模型的测试准确度为 0.70 和 0.82，针对补丁方式和患者方式的苏木精和曙红 (H) 的曲线下面积 (AUC) 分别为 0.74 和 0.87。

Between 10 and 50% of early-stage lung adenocarcinoma patients experience local or distant recurrence. Histological parameters such as a solid or micropapillary growth pattern are well-described risk factors for recurrence. However, not every patient presenting with such a pattern will develop recurrence. Designing a model which can more accurately predict recurrence on small biopsy samples can aid the stratification of patients for surgery, (neo-)adjuvant therapy, and follow-up.In this study, a statistical model on biopsies fed with histological data from early and advanced-stage lung adenocarcinomas was developed to predict recurrence after surgical resection. Additionally, a convolutional neural network (CNN)-based artificial intelligence (AI) classification model, named AI-based Lung Adenocarcinoma Recurrence Predictor (AILARP), was trained to predict recurrence, with an ImageNet pre-trained EfficientNet that was fine-tuned on lung adenocarcinoma biopsies using transfer learning. Both models were validated using the same biopsy dataset to ensure that an accurate comparison was demonstrated.The statistical model had an accuracy of 0.49 for all patients when using histology data only. The AI classification model yielded a test accuracy of 0.70 and 0.82 and an area under the curve (AUC) of 0.74 and 0.87 on patch-wise and patient-wise hematoxylin and eosin (H&E) stained whole slide images (WSIs), respectively.AI classification outperformed the traditional clinical approach for recurrence prediction on biopsies by a fair margin. The AI classifier may stratify patients according to their recurrence risk, based only on small biopsies. This model warrants validation in a larger lung biopsy cohort.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.