Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

ABSTRACT:

Aims

This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and to identify key clinical and immunogenetic predictors.

Materials and methods

A dataset of 30,917 lung transplant recipients with no prior cancer history was analyzed using pre-, peri-, and post-transplant variables. Multiple ML algorithms-gradient boosting, random forest, neural networks, and logistic regression-were applied to predict: (1) overall de novo malignancies (DNM), (2) skin versus non-skin cancers, and (3) skin cancer subtypes, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC).

Results

Gradient boosting achieved the highest AUC for overall malignancies (0.746) and skin versus non-skin canc

SUBMITTER: Nosoudi N 

PROVIDER: S-EPMC12826725 | biostudies-literature | 2026 Dec

REPOSITORIES: biostudies-literature