It is critical for patients who cannot undergo eradicable surgery to predict the risk of lung
cancer recurrence and metastasis; therefore, the physicians can design the appropriate …

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 …

Simple Summary Pathology images are vital for understanding solid cancers. In this study,
we created DeepRePath using multi-scale pathology images with two-channel deep …

Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical
courses and outcomes, even within the same tumor stage. This study explores deep …

Simple Summary The high-grade pattern (micropapillary or solid pattern, MPSol) in lung
adenocarcinoma affects the patient's poor prognosis. We aimed to develop a deep learning …

Simple Summary Lung cancer is one of the most common and deadly malignancies
worldwide. Microscopic examination of histological and cytological lung specimens can be a …

Objective. The detection of epidermal growth factor receptor (EGFR) mutation and
programmed death ligand‐1 (PD‐L1) expression status is crucial to determine the treatment …

To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT
(LDCT) lung cancer screening, we propose a fusion algorithm that combines handcrafted …

Background A preoperative CT-based deep learning (DL) prediction model was proposed to
estimate disease-free survival in patients with resected lung adenocarcinoma. However, the …

Tumor histology is an important predictor of therapeutic response and outcomes in lung
cancer. Tissue sampling for pathologist review is the most reliable method for histology …