Multimodal Integration of Radiomics, Genomics, and Clinical Data Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

BI Lingfeng ^1,2 , LI Jiayi ^1,2 , WANG Xin ^1,2 , HOU Wang ^1,2,3,4 , WANG Zhoufeng ^1,2 , WANG Jing ⁵ ,  LI Weimin ^1,2,3,4

1. Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R.China;
2. Institute of Respiratory Health, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R.China;
3. Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R.China;
4. The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, Sichuan 610041, P.R.China;
5. Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P.R.China;

Corresponding?author：

LI Weimin, Email: weimi003@scu.edu.cn

Keywords：

Non-small-cell lung cancer; Tumour biomarkers; radiology; Machine learning

DOI：

10.7507/1671-6205.202510027

Video：

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Objective To develop a multimodal recurrence prediction model for stage I NSCLC by integrating radiomics, genomics, and clinical data through hierarchical feature engineering and cross-omics interaction algorithms.Methods This study pioneered a hierarchical multimodal integration framework, synergizing radiomics (2 265 nodule features), genomics (116 whole-exome sequencing profiles), and clinical data from 323 stage I NSCLC patients. A two-stage feature engineering pipeline (correlation analysis + Random Forest selection) optimized discriminative features, and a cross-omics interaction algorithm dynamically quantified spatial associations between imaging phenotypes and genomic alterations. Results Single-modality model performance: The radiomics model (XGBoost, AUC=0.896±0.088) demonstrated the best predictive efficacy among single-modality models, significantly outperforming the genomics-based model (Random Forest, AUC=0.644±0.196) and the clinical model (Random Forest, AUC=0.742±0.160). Dual-modality model performance: The radiomics-genomics integrated model achieved the best performance (XGBoost, AUC=0.942±0.077) among dual-modality models, superior to the radiomics-clinical model (XGBoost, AUC=0.929±0.086) and the genomics-clinical model (Random Forest, AUC=0.733±0.164).Trimodal integration performance: The trimodal model integrating radiomics, genomics, and clinical data achieved the peak performance (XGBoost, AUC=0.971±0.082), which was significantly better than all single-modality and dual-modality benchmarks.Conclusions This work establishes a methodological framework for predicting postoperative recurrence in stage I NSCLC through multimodal feature integration guided by domain-specific machine learning. Its superior performance highlights the advantage of multimodal integration, offering actionable insights for personalized surveillance strategies.

Citation： BI Lingfeng, LI Jiayi, WANG Xin, HOU Wang, WANG Zhoufeng, WANG Jing, LI Weimin. Multimodal Integration of Radiomics, Genomics, and Clinical Data Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer. Chinese Journal of Respiratory and Critical Care Medicine, 2025, 24(12): 850-858. doi: 10.7507/1671-6205.202510027 Copy

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2.	Detterbeck FC, Woodard GA, Bader AS, et al. The Proposed Ninth Edition TNM Classification of Lung Cancer. Chest, 2024, 166(4): 882-895.
3.	Catania F, Chapron T, Crincoli E, et al. Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging. Acta Ophthalmol, 2024, 102(7): e984-e993.
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23.	O'Connor JP, Rose CJ, Waterton JC, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res, 2015, 21(2): 249-57.
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25.	Parmar C, Leijenaar RT, Grossmann P, et al. Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Sci Rep, 2015, 5: 11044.
26.	Jamal-Hanjani M, Wilson GA, McGranahan N, et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med, 2017, 376(22): 2109-2121.
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28.	Zhang J, Mucs D, Norinder U, et al. LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity-Application to the Tox21 and Mutagenicity Data Sets. J Chem Inf Model, 2019, 59(10): 4150-4158.
29.	?orgi? D, Stefanovi? A, Keckarevi? D, et al. XGBoost as a reliable machine learning tool for predicting ancestry using autosomal STR profiles - Proof of method. Forensic Sci Int Genet, 2025, 76: 103183.
30.	Bai Y, Li Y, Shen Y, et al. AutoDC: an automatic machine learning framework for disease classification. Bioinformatics, 2022, 38(13): 3415-3421.
31.	Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 2006, 7: 91.
32.	Schnell PM. Controlling the false-discovery rate when identifying the subgroup benefiting from treatment. Clin Trials, 2023, 20(4): 394-404.
33.	Zhang D, Qian Y, Akula N, et al. Accuracy of CNV Detection from GWAS Data. PLoS One, 2011, 6(1): e14511.
34.	Zhou D, Jiang Y, Zhong X, et al. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet, 2020, 52(11): 1239-1246.
35.	de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med, 2020, 382(6): 503-513.
36.	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
37.	Prentice RL, Zhao S. Regression Models and Multivariate Life Tables. J Am Stat Assoc, 2021, 116(535): 1330-1345.
38.	Li Y, Wu X, Yang P, et al. Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis. Genomics Proteomics Bioinformatics, 2022, 20(5): 850-866.
39.	Kirkpatrick A, Onyeze C, Kartchner D, et al. Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2. 0. Big Data Cogn Comput, 2022, 6(1): 27.
40.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology, 2016, 278(2): 563-577.
41.	Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature, 2014, 511(7511): 543-550.
42.	Bodalal Z, Trebeschi S, Nguyen-Kim TDL, et al. Radiogenomics: bridging imaging and genomics. Abdom Radiol (NY), 2019, 44(6): 1960-1984.
43.	Grossmann P, Stringfield O, El-Hachem N, et al. Defining the biological basis of radiomic phenotypes in lung cancer. Elife, 2017, 6: e23421.
44.	Pignon JP, Tribodet H, Scagliotti GV, et al. Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group. J Clin Oncol, 2008, 26(21): 3552-3559.
45.	Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A, 2018, 115(13): E2970-E2979.
46.	Sullivan R, Peppercorn J, Sikora K, et al. Delivering affordable cancer care in high-income countries. Lancet Oncol, 2011, 12(10): 933-80.
47.	Janke AT, Neumar RW. Academic emergency medicine: Common practice or underdeveloped? Acad Emerg Med, 2024, 31(8): 835-836.
48.	Heitzer E, Haque IS, Roberts CES, et al. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet, 2019, 20(2): 71-88.
49.	Abbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature, 2017, 545(7655): 446-451.
50.	St?hl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 2016, 353(6294): 78-82.
51.	MacParland SA, Liu JC, Ma XZ, et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun, 2018, 9(1): 4383.

1. Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol, 2016, 11(1): 39-51.
2. Detterbeck FC, Woodard GA, Bader AS, et al. The Proposed Ninth Edition TNM Classification of Lung Cancer. Chest, 2024, 166(4): 882-895.
3. Catania F, Chapron T, Crincoli E, et al. Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging. Acta Ophthalmol, 2024, 102(7): e984-e993.
4. Reeve R, Borley DW, Maree FF, et al. Tracking the Antigenic Evolution of Foot-and-Mouth Disease Virus. PLoS One, 2016, 11(7): e0159360.
5. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
6. Delasos L, Madabhushi A, Patil PD. Can Radiomics Bridge the Gap Between Immunotherapy and Precision Medicine in Lung Cancer? J Thorac Oncol, 2023, 18(6): 686-688.
7. Machlowska J, Baj J, Sitarz M, et al. Gastric Cancer: Epidemiology, Risk Factors, Classification, Genomic Characteristics and Treatment Strategies. Int J Mol Sci, 2020, 21(11): 4012.
8. Antony V, Sun T, Dolezal D, et al. Comprehensive Molecular Profiling of Metastatic Pancreatic Adenocarcinomas. Cancers (Basel), 2025, 17(3): 335.
9. Galperin MY, Kristensen DM, Makarova KS, et al. Microbial genome analysis: the COG approach. Brief Bioinform, 2019, 20(4): 1063-1070.
10. Avanzo M, Stancanello J, Pirrone G, et al. Radiomics and deep learning in lung cancer. Strahlenther Onkol, 2020, 196(10): 879-887.
11. She Y, He B, Wang F, et al. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study. EBioMedicine, 2022, 86: 104364.
12. Zhao Y, Xiong S, Ren Q, et al. Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study. Lancet Oncol, 2025, 26(1): 136-146.
13. Jiang B, Li N, Shi X, et al. Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT. Radiology, 2022, 303(1): 202-212.
14. Rakaee M, Tafavvoghi M, Ricciuti B, et al. Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer. JAMA Oncol, 2025, 11(2): 109-118.
15. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res, 2017, 77(21): e104-e107.
16. Zwanenburg A, Vallières M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology, 2020, 295(2): 328-338.
17. Liao WW, Asri M, Ebler J, et al. A draft human pangenome reference. Nature, 2023, 617(7960): 312-324.
18. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res, 2010, 38(16): e164.
19. DePristo MA, Banks E, Poplin R, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet, 2011, 43(5): 491-8.
20. Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res, 2019, 47(D1): D941-D947.
21. Jusakul A, Cutcutache I, Yong CH, et al. Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma. Cancer Discov, 2017, 7(10): 1116-1135.
22. Kontschieder P, Bulò SR, Pelillo M, et al. Structured Labels in Random Forests for Semantic Labelling and Object Detection. IEEE Trans Pattern Anal Mach Intell, 2014, 36(10): 2104-16.
23. O'Connor JP, Rose CJ, Waterton JC, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res, 2015, 21(2): 249-57.
24. Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol, 2009, 70(2): 232-41.
25. Parmar C, Leijenaar RT, Grossmann P, et al. Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Sci Rep, 2015, 5: 11044.
26. Jamal-Hanjani M, Wilson GA, McGranahan N, et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med, 2017, 376(22): 2109-2121.
27. Leijenaar RT, Nalbantov G, Carvalho S, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep, 2015, 5: 11075.
28. Zhang J, Mucs D, Norinder U, et al. LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity-Application to the Tox21 and Mutagenicity Data Sets. J Chem Inf Model, 2019, 59(10): 4150-4158.
29. ?orgi? D, Stefanovi? A, Keckarevi? D, et al. XGBoost as a reliable machine learning tool for predicting ancestry using autosomal STR profiles - Proof of method. Forensic Sci Int Genet, 2025, 76: 103183.
30. Bai Y, Li Y, Shen Y, et al. AutoDC: an automatic machine learning framework for disease classification. Bioinformatics, 2022, 38(13): 3415-3421.
31. Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 2006, 7: 91.
32. Schnell PM. Controlling the false-discovery rate when identifying the subgroup benefiting from treatment. Clin Trials, 2023, 20(4): 394-404.
33. Zhang D, Qian Y, Akula N, et al. Accuracy of CNV Detection from GWAS Data. PLoS One, 2011, 6(1): e14511.
34. Zhou D, Jiang Y, Zhong X, et al. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet, 2020, 52(11): 1239-1246.
35. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med, 2020, 382(6): 503-513.
36. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
37. Prentice RL, Zhao S. Regression Models and Multivariate Life Tables. J Am Stat Assoc, 2021, 116(535): 1330-1345.
38. Li Y, Wu X, Yang P, et al. Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis. Genomics Proteomics Bioinformatics, 2022, 20(5): 850-866.
39. Kirkpatrick A, Onyeze C, Kartchner D, et al. Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2. 0. Big Data Cogn Comput, 2022, 6(1): 27.
40. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology, 2016, 278(2): 563-577.
41. Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature, 2014, 511(7511): 543-550.
42. Bodalal Z, Trebeschi S, Nguyen-Kim TDL, et al. Radiogenomics: bridging imaging and genomics. Abdom Radiol (NY), 2019, 44(6): 1960-1984.
43. Grossmann P, Stringfield O, El-Hachem N, et al. Defining the biological basis of radiomic phenotypes in lung cancer. Elife, 2017, 6: e23421.
44. Pignon JP, Tribodet H, Scagliotti GV, et al. Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group. J Clin Oncol, 2008, 26(21): 3552-3559.
45. Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A, 2018, 115(13): E2970-E2979.
46. Sullivan R, Peppercorn J, Sikora K, et al. Delivering affordable cancer care in high-income countries. Lancet Oncol, 2011, 12(10): 933-80.
47. Janke AT, Neumar RW. Academic emergency medicine: Common practice or underdeveloped? Acad Emerg Med, 2024, 31(8): 835-836.
48. Heitzer E, Haque IS, Roberts CES, et al. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet, 2019, 20(2): 71-88.
49. Abbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature, 2017, 545(7655): 446-451.
50. St?hl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 2016, 353(6294): 78-82.
51. MacParland SA, Liu JC, Ma XZ, et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun, 2018, 9(1): 4383.

Chinese Journal of Respiratory and Critical Care Medicine

Multimodal Integration of Radiomics, Genomics, and Clinical Data Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer

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