Imaging-based biological age estimation predicts competing mortality risk in lung cancer screening

Abstract Lung cancer screening (LCS) participants have a high competing risk of non-lung cancer related death, which limits screening benefit. We hypothesized that imaging-based biological age can identify individuals at higher risk of non-lung cancer mortality. Low dose computed tomography scans from a large LCS trial were retrospectively analyzed. Bone density loss, muscular fat infiltration, vascular calcification, and visceral fat mass were calculated using segmentations from a nnU-Net based deep learning segmentation model. Each participant’s age gap, defined as the difference between their estimated biological age and chronological age, was estimated with disease course mapping using a Bayesian mixed-effects model. Disease course map construction used all available longitudinal scan data, whilst individual biological age prediction used baseline scan data only. The performance of age gap as a parameter in competing risk models for non-lung cancer related death versus lung cancer diagnosis was evaluated, following the TRIPOD + AI reporting guideline. We included 29,745 scans from 12,478 participants in the final analysis. Median follow-up was 5 years, representing 58,284 person-years at risk. Multivariable survival models of competing risks were constructed, including the estimated age gap. The subdistribution hazard ratio (sHR) for each decade of age gap for non-lung cancer related death was higher (sHR = 2.0, 95% CI 1.8–2.3), compared to lung cancer diagnosis (sHR = 1.3, 95% CI 1.2–1.5). Addition of age gap improved model concordance of non-lung cancer death compared to a model with clinical variables alone (pooled C-index = 0.69 vs 0.73, difference + 0.04, 95% CI 0.03–0.04, p < 0.001). Biological age estimation using an imaging-based disease progression model improves prediction of non-lung cancer related death in lung cancer screening.

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Publication Details

Journal
Scientific Reports
Published
2026-10-15
DOI
https://doi.org/10.1038/s41598-026-75832-2
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Imaging-based biological age estimation predicts competing mortality risk in lung cancer screening

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Scientific Reports
Lung Cancer Diagnosis and Treatment
article

Imaging-based biological age estimation predicts competing mortality risk in lung cancer screening

Georgia Bullock, Alberto Villanueva, Ethaar El‐Emir, Daryl Cheng, Hina Pervez, April Q Neville, Alice Cotton, Nick Beech, Navinah Nundlall, Sindhu Bhaarrati Naidu, Helen Kiconco, Antonette Andrews, Elodie Murali, Karen Parry-Billings, Kim Sorley, Dominique Arancon, Fahmida Hoque, Kate H.C. Gowers, Charlotte J. C. Cash, Mamta Ruparel, Andrew Perugia, Monica L.L. Mullin, Geoff J. Bellingan, Claire Levermore, Chuen Ryan Khaw, Adam Szmul, Kylie Gyertson, Burcu Özaltin, Jeannie Eng, Laura Farrelly, Fanta Bojang, James Rusius, Aashna Samson, Maureen Browne, Esther Arthur-Darkwa, Graham Robinson, Janine Zylstra, Robert Chapman, Derya Ovayolu, Kate Davies, Laura Green, Hasti Robbie, Nick Screaton, Mehran Azimbagirad, Karen Sennett, Charlie Sayer, Harriet Sorrell, Catherine Nestor, Marilyn Ijeomah-Orji, Nikita Sharma, Malavika Suresh, Jon W. Teague, Magali N. Taylor, Tanya Patrick, Shahab Aslani, Elena Stefan, Eleanor Hellier, Judy Airebamen, Christine Hosein, Andrew Crossingham, Kitty Chan, Lynsey Gallagher, Julian McKee, Moksud Miah, Ali Mohammed, Kaylene Phua, Tianqi Yang, Chimtom Nwaosu, Gulistan Keskinbicak, Domminique Arancon, Houda Benmebarek, Joseph Jacob, Nazim Miah, Fahiza Chowdhury, Celia Snell, Adefiola Olabode, Fabia Ruggiero, Columbus Ife, Henna Makwana, Anna Sikorska, Marie Calligaris, Milica Rajkov, Farhida Begum, Monika Sommerrey, Helen Victoria Bowyer, Lucas Clarke, Jhanara Begum, Neil Songsong, Gulten Geneci, Mark Clark, Laila Ahmed, Miguel Ferreira, Jane Rowlands, Camilla Mitchell, Anthony Edey, Shanshan Wang, Anjeli Ketkar, Alfred C. P. So, John McCabe
article en

Abstract

Abstract Lung cancer screening (LCS) participants have a high competing risk of non-lung cancer related death, which limits screening benefit. We hypothesized that imaging-based biological age can identify individuals at higher risk of non-lung cancer mortality. Low dose computed tomography scans from a large LCS trial were retrospectively analyzed. Bone density loss, muscular fat infiltration, vascular calcification, and visceral fat mass were calculated using segmentations from a nnU-Net based deep learning segmentation model. Each participant’s age gap, defined as the difference between their estimated biological age and chronological age, was estimated with disease course mapping using a Bayesian mixed-effects model. Disease course map construction used all available longitudinal scan data, whilst individual biological age prediction used baseline scan data only. The performance of age gap as a parameter in competing risk models for non-lung cancer related death versus lung cancer diagnosis was evaluated, following the TRIPOD + AI reporting guideline. We included 29,745 scans from 12,478 participants in the final analysis. Median follow-up was 5 years, representing 58,284 person-years at risk. Multivariable survival models of competing risks were constructed, including the estimated age gap. The subdistribution hazard ratio (sHR) for each decade of age gap for non-lung cancer related death was higher (sHR = 2.0, 95% CI 1.8–2.3), compared to lung cancer diagnosis (sHR = 1.3, 95% CI 1.2–1.5). Addition of age gap improved model concordance of non-lung cancer death compared to a model with clinical variables alone (pooled C-index = 0.69 vs 0.73, difference + 0.04, 95% CI 0.03–0.04, p < 0.001). Biological age estimation using an imaging-based disease progression model improves prediction of non-lung cancer related death in lung cancer screening.

Scientific Reports
University College London Hospitals NHS Foundation Trust (GB), University of Calgary (CA), Royal Free London NHS Foundation Trust (GB), Barts Health NHS Trust (GB), Papworth Hospital NHS Foundation Trust (GB), University College London (GB)
Oregon Health and Science University, Wellcome Trust, UK Research and Innovation, Cancer Research UK, National Institute for Health and Care Research, University College London, Rosetrees Trust, Robert Luff Foundation, University College London Hospitals NHS Foundation Trust, Knight Cancer Institute, Oregon Health and Science University
Good health and well-being
Openalex Percentile: Top 13%
Lung Cancer Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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