ORCID
0000-0003-4162-0276 (Colen)
Document Type
Article
Publication Date
2026
DOI
10.1038/s41598-026-57670-4
Publication Title
Scientific Reports
Volume
Advance online publication
Pages
22 pp.
Abstract
CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low malignancy rates in early-development PNs and variable image acquisition hinder development of radiomic models for diagnosing these PNs. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We examine early-development benign and malignant PNs (n = 106) below the sensitivity of standard-of-care diagnosis. Classifiers predicting malignancy performed near chance when trained on ComBat-harmonized radiomic features from only early-development PNs. We then augmented training with later-development benign and malignant PNs (n = 225). We evaluated whether harmonization must incorporate biology that impacts acquisition effects in added training data. To correct variability from four acquisition protocols, we compared: (1) biology-unaware harmonization, (2) harmonizing with a covariate distinguishing early-development, later-development benign, later-development malignant datasets, (3) harmonizing each dataset separately. Models trained using augmentation, but biology-unaware harmonization, failed to improve consistently. Augmented training data harmonized with a covariate (ROC-AUC 0.74 [0.69-0.79]) or separately (ROC-AUC 0.71 [0.66-0.77]) yielded higher test ROC-AUC (Delong, p ≤ 0.05) and PR-AUC (Wilcoxon, p ≤ 0.05). In a proof-of-principle methodological study, we demonstrate with a small single-center dataset that combining radiomic features from later-development benign and malignant PNs requires biology-aware harmonization.
Rights
© 2026 The Authors.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Data Availability
Article states: "The code used to generate the primary results of this study is available at https://github.com/chuchthausen/HarmonizationLungRadiomics. This version of the manuscript used code with the commit tag 165be17. The Optimized Nested ComBat code by Hannah Horng (MIT license, 2022) used in this study with a minor modification is available with the commit tag b090546. The original Optimized Nested ComBat code is available with examples at https://github.com/hannah-horng/opnested-combat."
Original Publication Citation
Huchthausen, C., Shi, M., de Sousa, G., Larner, J., Janowski, E., Colen, J., & Wijesooriya, K. (2026). Training set augmentation and biology-aware harmonization improve radiomic models for lung cancer prediction in indeterminate nodules. Scientific Reports. https://doi.org/10.1038/s41598-026-57670-4
Repository Citation
Huchthausen, C., Shi, M., de Sousa, G., Larner, J., Janowski, E., Colen, J., & Wijesooriya, K. (2026). Training set augmentation and biology-aware harmonization improve radiomic models for lung cancer prediction in indeterminate nodules. Scientific Reports. https://doi.org/10.1038/s41598-026-57670-4
Supplementary Information