Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study
Open accessAbstract
BACKGROUND: This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction. METHODS: 274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR. To reveal the underlying biological basis of the deep learning score, a genetic analysis was conducted based on 25 patients with RNA-sequencing data. FINDINGS: MPR was achieved in 54.0% (n = 148) patients. The area under the curve (AUC) of the deep learning score to predict MPR was 0.73 (95% confidence interval [CI]: 0.58-0.86) and 0.72 (95% CI: 0.58-0.85) in the internal validation and external validation cohorts, respectively. After integrating the clinical characteristic into the deep learning score, the combined model achieved satisfactory performance in the internal validation (AUC: 0.77, 95% CI: 0.64-0.89) and external validation cohorts (AUC: 0.75, 95% CI: 0.62-0.87). In the biological basis exploration for the deep learning score, a high deep learning score was associated with the downregulation of pathways mediating tumour proliferation and the promotion of antitumour immune cell infiltration in the microenvironment. INTERPRETATION: The proposed deep learning model could effectively predict MPR in NSCLC patients treated with neoadjuvant chemoimmunotherapy. FUNDING: This study was supported by National Key Research and Development Program of China, China (2017YFA0205200); National Natural Science Foundation of China, China (91959126, 82022036, 91959130, 81971776, 81771924, 6202790004, 81930053, 9195910169, 62176013, 8210071009); Beijing Natural Science Foundation, China (L182061); Strategic Priority Research Program of Chinese Academy of Sciences, China (XDB38040200); Chinese Academy of Sciences, China (GJJSTD20170004, QYZDJ-SSW-JSC005); Shanghai Hospital Development Center, China (SHDC2020CR3047B); and Science and Technology Commission of Shanghai Municipality, China (21YF1438200).
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Interpreting deep learning models remains a challenge for clinical applications.
“Interpreting deep learning models remains a challenge for clinical applications (29).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [29]
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- Authors
- She, Y., He, B., Wang, F., Zhong, Y., Wang, T., Liu, Z., Yang, M., Yu, B., Deng, J., Sun, X., Wu, C., Hou, L., Zhu, Y., Yang, Y., Hu, H., Dong, D., Chen, C., & Tian, J.
- Affiliations
- Tongji University (CN)
- Chinese Academy of Sciences (CN)
- Sir Run Run Shaw Hospital (CN)
- First Affiliated Hospital of Nanchang University (CN)
- Xidian University (CN)
- Corresponding author
- Hu, Hongjie
- Dong, Di
- Chen, Chang
- Tian, Jie
- ORCID
- Venue
- eBioMedicine
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 86
- Pages
- 104364
- ISSN
- 2352-3964 (ISSN-L)
- PMC
- PMC9672965
- OpenAlex
- W4308946414
- Cited by
- 91
- References
- 26
- Citations by year
- 2026: 15 · 2025: 37 · 2024: 26 · 2023: 13
- FWCI
- 10.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EBioMedicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2017YFA0205200)
- National Natural Science Foundation of China (6202790004, 62176013, 81771924, 81930053, 81971776, 82022036, 8210071009, 9195910169, 91959126, 91959130)
- Chinese Academy of Sciences (GJJSTD20170004, QYZDJ-SSW-JSC005, XDB38040200)
- Science and Technology Commission of Shanghai Municipality (21YF1438200)
- Natural Science Foundation of Beijing Municipality (L182061)
- Shanghai Hospital Development Center (SHDC2020CR3047B)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- non-small cell lung cancer, neoadjuvant chemoimmunotherapy, major pathological response, deep learning prediction, computed tomography, ShuffleNetV2, tumor immune microenvironment, RNA sequencing, tumor suppressor, prognostic factors, treatment response prediction
- MeSH terms
- Deep Learning, Carcinoma, Non-Small-Cell Lung, China, Humans, Lung Neoplasms, Retrospective Studies, Neoadjuvant Therapy, Tumor Microenvironment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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