Integrating deep learning of low-dose computed tomography with clinical data for lung cancer risk prediction

EClinicalMedicine 2026 Open access

Read full text
Authors: Renzo Phellan Aro, Stephen Lam, Matthew T. Warkentin, Geoffrey Liu, Brenda Diergaarde, David O. Wilson, Jian-Min Yuan, Hamad Al-Sawaihey, Kiera Murison, Elham Khodayari Moez, Yonathan Brhane, Rafael Meza
+2 more Renelle Myers, Rayjean J. Hung
DOI: 10.1016/j.eclinm.2026.103948
Cited by: 1

Abstract

Lung cancer is the leading cause of cancer deaths globally. Low-dose computed tomography (LDCT) screening reduces lung cancer mortality. Within screening programs, efficient patient management requires accurate lung cancer risk assessment. Segmentation-free deep learning (DL) models such as Sybil can improve screening efficiency but require extensive validation and possible improvement. Based on 4 LDCT screening cohorts, we investigated whether integration of deep learning based on LDCT scans and clinical data can improve lung cancer risk prediction.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging Lung Cancer Diagnosis and Treatment Advanced Radiotherapy Techniques

Publication details

Journal / source
EClinicalMedicine
Publisher
Elsevier BV
Volume
94
Pages
103948–103948
Publication date
1 April 2026
Language
EN