Prediction of pathological complete response to neoadjuvant chemo-immunotherapy in locally advanced lung adenocarcinoma: a multimodal machine learning approach
- 1Lung Cancer Center, Yunnan Qujing Traditional Chinese Medicine Hospital
- 2Department of Medical Oncology, Qujing Central Hospital of Yunnan Province
- 3The First Department of Thoracic Surgery, Yunnan Cancer Hospital
Abstract
Predicting pathological complete response (pCR) following neoadjuvant chemo-immunotherapy in locally advanced lung adenocarcinoma (LUAD) remains clinically challenging. We aimed to develop an explainable machine learning framework to predict pCR and stratify patient prognosis. This retrospective study included 627 driver-negative LUAD patients (2020–2025). Fifteen baseline clinicopathological, metabolic, and inflammatory features were integrated to evaluate four algorithms. The optimal model was interpreted using SHapley Additive exPlanations (SHAP) and validated for progression-free survival (PFS). The Extreme Gradient Boosting (XGBoost) model exhibited superior discriminative performance (AUC = 0.872), significantly outperforming traditional logistic regression (P < 0.001). SHAP analysis identified PET-CT SUVmax, CEA, and PD-L1 expression as the primary predictors, unveiling their non-linear thresholds and synergistic interactions. Decision curve analysis confirmed its clinical net benefit. Furthermore, patients stratified into the high-probability group by the AI model demonstrated significantly prolonged PFS, even within highly malignant sub-cohorts (P < 0.001). This SHAP-enhanced XGBoost framework shows significant potential as a proof-of-concept model for predicting pCR and stratifying long-term survival in LUAD. However, given the artificially enriched cohort, prospective multicenter external validation is required before it can be considered a definitive clinical tool for optimizing individualized neoadjuvant therapeutic strategies.
Related
- Prediction of Pathological Complete Response to Neoadjuvant Immunotherapy-Based Regimens for Non-Metastatic Colon and Rectal Cancer
- Neoadjuvant immunotherapy for locally advanced resectable gastric and gastroesophageal junction adenocarcinoma: a systematic review and meta-analysis.
- Multimodal prediction of neoadjuvant chemotherapy response in locally advanced gastric cancer: a multicenter cohort study
- Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.
- A multimodal deep learning radiomics nomogram integrating clinicopathological features for predicting pathological complete response after neoadjuvant therapy in triple-negative breast cancer: a multicenter retrospective study
BibTeX
@article{liu2026prediction,
title = {{Prediction of pathological complete response to neoadjuvant chemo-immunotherapy in locally advanced lung adenocarcinoma: a multimodal machine learning approach}},
author = {Bin Liu and Yanyan Fu and Yuan Ma},
journal = {Scientific Reports},
year = {2026},
month = sep,
doi = {10.1038/s41598-026-68739-5},
url = {https://doi.org/10.1038/s41598-026-68739-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - Prediction of pathological complete response to neoadjuvant chemo-immunotherapy in locally advanced lung adenocarcinoma: a multimodal machine learning approach
AU - Bin Liu
AU - Yanyan Fu
AU - Yuan Ma
PY - 2026
DA - 2026/09/01
JO - Scientific Reports
DO - 10.1038/s41598-026-68739-5
UR - https://doi.org/10.1038/s41598-026-68739-5
AB - Predicting pathological complete response (pCR) following neoadjuvant chemo-immunotherapy in locally advanced lung adenocarcinoma (LUAD) remains clinically challenging. We aimed to develop an explainable machine learning framework to predict pCR and stratify patient prognosis. This retrospective study included 627 driver-negative LUAD patients (2020–2025). Fifteen baseline clinicopathological, metabolic, and inflammatory features were integrated to evaluate four algorithms. The optimal model was interpreted using SHapley Additive exPlanations (SHAP) and validated for progression-free survival (PFS). The Extreme Gradient Boosting (XGBoost) model exhibited superior discriminative performance (AUC = 0.872), significantly outperforming traditional logistic regression (P < 0.001). SHAP analysis identified PET-CT SUVmax, CEA, and PD-L1 expression as the primary predictors, unveiling their non-linear thresholds and synergistic interactions. Decision curve analysis confirmed its clinical net benefit. Furthermore, patients stratified into the high-probability group by the AI model demonstrated significantly prolonged PFS, even within highly malignant sub-cohorts (P < 0.001). This SHAP-enhanced XGBoost framework shows significant potential as a proof-of-concept model for predicting pCR and stratifying long-term survival in LUAD. However, given the artificially enriched cohort, prospective multicenter external validation is required before it can be considered a definitive clinical tool for optimizing individualized neoadjuvant therapeutic strategies.
SN - 2045-2322
ER -