A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma

Abstract

This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple instance learning with final FC layer’s features from a model from all patches. Analysis of the clinicopathological and genetic characteristics of the model-based risk group was performed. For predicting recurrence, the model had an area under the curve score of 0.763 with 0.750, 0.633 and 0.680 of sensitivity, specificity, and accuracy in the test set, respectively. High-risk cases for recurrence predicted by the model (HR group) were significantly associated with shorter recurrence-free survival and a higher stage (both, p < 0.001). The HR group was associated with specific histopathological features such as poorly differentiated components, complex glandular pattern components, tumor spread through air spaces, and a higher grade. In the HR group, pleural invasion, necrosis, and lymphatic invasion were more frequent, and the size of the invasion was larger (all, p < 0.001). Several genetic mutations, including TP53 (p = 0.007) mutations, were more frequently found in the HR group. The results of stages I-II were similar to those of the general cohort. DL-based model can predict the recurrence risk of LUAD and identify the presence of the TP53 gene mutation by analyzing histopathologic features.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images

“…This result is consistent with previous reports, showing a strong correlation between histological grade 3 adenocarcinoma and STAS. This result is consistent with previous reports showing a strong correlation between histological grade 3 adenocarcinoma and STAS [ 18 , 28 , 29 , 30 ].…”

“…Numerous studies have highlighted the efficacy of deep learning models in extracting critical information from routine pathological images, offering valuable clinical insights [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, deep learning has been utilized for quantitative image analysis to forecast disease progression patterns, prognoses, and other clinical outcomes [ 27 , 28 , 29 , 30 , 31 ]. Despite these advancements, there remains a paucity of study specifically addressing AI-based STAS prediction using histopathological images.…”

“…In contrast, deep learning approaches have shown promise in capturing intricate features within images more effectively. By employing end-to-end training, these methods enhance predictive capabilities and provide more accurate prognostic information [ 27 , 28 , 29 , 30 , 31 ]. For example, recent studies have demonstrated the application of deep learning models to various medical imaging tasks, achieving high performance metrics.…”