Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.
[BACKGROUND] Low-dose computed tomography (LDCT) imaging screening reduces lung cancer mortality, the leading cause of cancer deaths globally.
- 95% CI 0.81-0.85
- 추적기간 7 years
APA
Aro RP, Lam S, et al. (2026). Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.. Chest. https://doi.org/10.1016/j.chest.2026.02.022
MLA
Aro RP, et al.. "Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.." Chest, 2026.
PMID
41833807
Abstract
[BACKGROUND] Low-dose computed tomography (LDCT) imaging screening reduces lung cancer mortality, the leading cause of cancer deaths globally. Segmentation-free deep learning (DL) models such as Sybil can improve screening efficiency but require extensive validation and possible improvement.
[RESEARCH QUESTION] Can the integration of DL based on LDCT scans and clinical data improve lung cancer risk prediction?
[STUDY DESIGN AND METHODS] Retrospective cohort data from 4 different screening programs, 1 used for model training and 3 used for external validation. Data were collected between 2002 and 2021. The median follow-up period was 7 years. All participants had a history of either current or former smoking, with at least 10 pack-years of smoking or who smoked over 20 years. The area under the receiver operating characteristic curve (AUC) was calculated for lung cancer risk within 1 to 6 years, stratified by pulmonary nodule presence and size. Key clinical and epidemiologic factors were evaluated for their added predictive value.
[RESULTS] This analysis used 52,482 LDCT scan series from 22,469 participants. Sybil's AUC ranged from 0.93 in year 1 and reduced to 0.79 in year 6 in the independent cohorts. The predictive performance was suboptimal in the absence of documented nodules (AUC, 0.64) and for small nodules (AUC, 0.61) in year 6. Our new model, Sybil-Epi, trained with baseline scans, achieved higher predictive performance (AUC, 0.83; 95% CI, 0.81-0.85) compared with Sybil (AUC, 0.80; 95% CI, 0.78-0.82) in year 6. The difference is most notable when nodules are absent. Sybil-Epi's AUC was 0.76 (95% CI, 0.70-0.82) and Sybil's AUC was 0.64 (95% CI, 0.57-0.70).
[INTERPRETATION] Our results show that Sybil performs better for short-term lung cancer risk, but the predictive accuracy was suboptimal when nodules were absent. Our integrated Sybil-Epi model with DL and clinical and epidemiologic factors significantly improved model predictive performance.
[RESEARCH QUESTION] Can the integration of DL based on LDCT scans and clinical data improve lung cancer risk prediction?
[STUDY DESIGN AND METHODS] Retrospective cohort data from 4 different screening programs, 1 used for model training and 3 used for external validation. Data were collected between 2002 and 2021. The median follow-up period was 7 years. All participants had a history of either current or former smoking, with at least 10 pack-years of smoking or who smoked over 20 years. The area under the receiver operating characteristic curve (AUC) was calculated for lung cancer risk within 1 to 6 years, stratified by pulmonary nodule presence and size. Key clinical and epidemiologic factors were evaluated for their added predictive value.
[RESULTS] This analysis used 52,482 LDCT scan series from 22,469 participants. Sybil's AUC ranged from 0.93 in year 1 and reduced to 0.79 in year 6 in the independent cohorts. The predictive performance was suboptimal in the absence of documented nodules (AUC, 0.64) and for small nodules (AUC, 0.61) in year 6. Our new model, Sybil-Epi, trained with baseline scans, achieved higher predictive performance (AUC, 0.83; 95% CI, 0.81-0.85) compared with Sybil (AUC, 0.80; 95% CI, 0.78-0.82) in year 6. The difference is most notable when nodules are absent. Sybil-Epi's AUC was 0.76 (95% CI, 0.70-0.82) and Sybil's AUC was 0.64 (95% CI, 0.57-0.70).
[INTERPRETATION] Our results show that Sybil performs better for short-term lung cancer risk, but the predictive accuracy was suboptimal when nodules were absent. Our integrated Sybil-Epi model with DL and clinical and epidemiologic factors significantly improved model predictive performance.