ORIGINAL RESEARCH article
Front. Cell Dev. Biol.
Sec. Cancer Cell Biology
Volume 13 - 2025 | doi: 10.3389/fcell.2025.1679782
This article is part of the Research TopicAdvancements in Solid Tumor Immunotherapy: Enhancing Efficacy and Overcoming ResistanceView all 6 articles
Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemoimmunotherapy in Patients with Resectable Non-Small Cell Lung Cancer
Provisionally accepted
Wenyi Liu1,2
Zhilin Sui1Chunguang Wang1Youjun Deng1
Songhua Cai1Ran Jia1
Yu Zhentao1*
Mingqiang Kang2*Baihua Zhang1,3*- 1National Cancer Center, Cancer Hospital Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
- 2Fujian Medical University Union Hospital, Fuzhou, China
- 3Hunan Cancer Hospital, Changsha, China
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Objectives: Neoadjuvant chemoimmunotherapy is increasingly employed in resectable non-small cell lung cancer (NSCLC), with variable pathological complete response (pCR) rates. Currently, no reliable preoperative tool is available for predicting pCR. This study develops a nomogram based on clinical variables to predict pCR and guide individualized surgical decisions. Methods: We retrospectively analyzed data from 179 NSCLC patients (stages IIB-IIIB) who received neoadjuvant chemoimmunotherapy followed by resection (2019-2022). Variables included demographics, smoking history, comorbidities, treatment details, and pathology. Univariate and multivariate logistic regression identified pCR predictors, which were incorporated to build a nomogram. Performance was assessed via area under the curve (AUC), calibration, and decision curve analysis (DCA). Results: Of 179 patients, 92 (51.4%) achieved pCR. Multivariate analysis identified independent predictors: non-squamous histology (OR 0.344 (non-squamous vs. squamous), 95% CI 0.151-0.707, p=0.006), positive family history (OR 10.76 (positive vs. negative), 95% CI 1.903-203.3, p=0.027), shorter smoking cessation duration (defined as time in days from last cigarette to treatment start) (OR 0.999 (per day), 95% CI 0.999-0.999, p=0.033), older age (OR 1.053 (per year), 95% CI 1.005-1.106, p=0.032), and more treatment cycles (OR 1.621 (per cycle), 95% CI 1.007-2.661, p=0.049). The nomogram showed modest discrimination (AUC 0.709, 95% CI 0.633-0.785), good calibration, and net benefit on DCA, though it has not been externally validated and is limited by single-center data, small sample size, high pCR rate, and skewed demographics (95.5% male, 92.7% smokers), potentially limiting generalizability to diverse populations such as females or non-smokers. Conclusions: This nomogram, derived from routine clinical data, predicts pCR after neoadjuvant chemoimmunotherapy in NSCLC, offering a tool for thoracic surgeons to optimize treatment and surgical planning, despite its modest discriminative power, by serving as a complementary aid in resource-limited settings where biomarkers may not be readily available. External validation in larger, multi-center cohorts is essential.
Keywords: Non-small cell lung cancer, neoadjuvant chemoimmunotherapy, Pathological complete response, nomogram, Thoracic Surgery, Prediction model
Received: 05 Aug 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Liu, Sui, Wang, Deng, Cai, Jia, Zhentao, Kang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yu Zhentao, National Cancer Center, Cancer Hospital Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
Mingqiang Kang, Fujian Medical University Union Hospital, Fuzhou, China
Baihua Zhang, National Cancer Center, Cancer Hospital Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.