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Peer-reviewedCC BY-NC-ND 4.0World Journal of Surgical Oncology

Predicting long-term survival following lung cancer resection based on early perioperative immune-metabolic response patterns

  • Huilin Xu1
  • Yaqin Bie2
  • Yan Li3
  • Xinyu Wu4
  • Ying Huang1
  • Dedong Cao4
4 affiliations
  1. 1Department of Oncology, The Fifth Hospital of Wuhan, No. 122 Xianzheng Street, Hanyang District, Wuhan, Hubei 430000 China
  2. 2Department of Oncology, Taikang Tongji (Wuhan) Hospital, No. 322 Sixin North Road, Hanyang District, Wuhan, Hubei 430000 China
  3. 3Department of Reproductive Medicine, Taihe Hospital, Hubei University of Medicine, No. 32 Renmin South Road, Maojian District, Shiyan, Hubei 442000 China
  4. 4Department of Oncology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, Hubei 430060 China
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Abstract

Background: Lung cancer resection is usually associated with surgical stress and systemic immune-metabolic shifts. Existing prognostic tools largely depend on static parameters. We aimed to characterize early perioperative Systemic Immune-Inflammation Index (SII) response patterns and construct a predictive nomogram to risk-stratify patients undergoing lung cancer resection.

Methods: We selected patients with malignant lung neoplasms who underwent resection and had complete perioperative laboratory records from the INSPIRE database (2011–2020). Gaussian finite-mixture modeling within an LCGA framework was applied to classify early SII response patterns from baseline to 24 h postoperatively. Primary and secondary endpoints were overall survival (OS) and intensive care unit (ICU) length of stay, respectively. A perioperative immune-metabolic stress index (PIMSI) nomogram via multivariable logistic regression was established to predict the high-risk Severe Surge response pattern and was internally validated using bootstrap resampling.

Results: A total of 2,186 patients were included and classified into three early perioperative SII response patterns: Class 1 (Stable; 57.4%), Class 2 (Moderate Increase; 35.8%), and Class 3 (Severe Surge; 6.8%). Compared with the Stable group, patients in the Severe Surge group had longer ICU stays and a higher risk of 5-year mortality (Hazard Ratio [HR] = 2.38, 95% Confidence Interval [CI]: 1.69–3.35, p < 0.001). The PIMSI model, incorporating diabetes history, preoperative C-reactive protein, operative time, prognostic nutritional index, body weight, age, and ASA physical status, showed good discrimination for identifying patients at risk of a Severe Surge response pattern (apparent AUC, 0.828; optimism-corrected AUC, 0.820) and demonstrated clinical net benefit on decision curve analysis.

Conclusions: Early perioperative SII trajectories were associated with ICU dependency and prognosis after lung cancer resection. The PIMSI nomogram may support early risk stratification and perioperative care escalation, but it needs further validation with oncological staging and histological adjustment.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12957-026-04534-w.

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Cite

BibTeXRIS
BibTeX
@article{xu2026predicting,
  title = {{Predicting long-term survival following lung cancer resection based on early perioperative immune-metabolic response patterns}},
  author = {Huilin Xu and Yaqin Bie and Yan Li and Xinyu Wu and Ying Huang and Dedong Cao},
  journal = {World Journal of Surgical Oncology},
  year = {2026},
  month = aug,
  doi = {10.1186/s12957-026-04534-w},
  url = {https://doi.org/10.1186/s12957-026-04534-w},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Predicting long-term survival following lung cancer resection based on early perioperative immune-metabolic response patterns
AU  - Huilin Xu
AU  - Yaqin Bie
AU  - Yan Li
AU  - Xinyu Wu
AU  - Ying Huang
AU  - Dedong Cao
PY  - 2026
DA  - 2026/08/11
JO  - World Journal of Surgical Oncology
DO  - 10.1186/s12957-026-04534-w
UR  - https://doi.org/10.1186/s12957-026-04534-w
AB  - Background: Lung cancer resection is usually associated with surgical stress and systemic immune-metabolic shifts. Existing prognostic tools largely depend on static parameters. We aimed to characterize early perioperative Systemic Immune-Inflammation Index (SII) response patterns and construct a predictive nomogram to risk-stratify patients undergoing lung cancer resection.  Methods: We selected patients with malignant lung neoplasms who underwent resection and had complete perioperative laboratory records from the INSPIRE database (2011–2020). Gaussian finite-mixture modeling within an LCGA framework was applied to classify early SII response patterns from baseline to 24 h postoperatively. Primary and secondary endpoints were overall survival (OS) and intensive care unit (ICU) length of stay, respectively. A perioperative immune-metabolic stress index (PIMSI) nomogram via multivariable logistic regression was established to predict the high-risk Severe Surge response pattern and was internally validated using bootstrap resampling.  Results: A total of 2,186 patients were included and classified into three early perioperative SII response patterns: Class 1 (Stable; 57.4%), Class 2 (Moderate Increase; 35.8%), and Class 3 (Severe Surge; 6.8%). Compared with the Stable group, patients in the Severe Surge group had longer ICU stays and a higher risk of 5-year mortality (Hazard Ratio [HR] = 2.38, 95% Confidence Interval [CI]: 1.69–3.35, p < 0.001). The PIMSI model, incorporating diabetes history, preoperative C-reactive protein, operative time, prognostic nutritional index, body weight, age, and ASA physical status, showed good discrimination for identifying patients at risk of a Severe Surge response pattern (apparent AUC, 0.828; optimism-corrected AUC, 0.820) and demonstrated clinical net benefit on decision curve analysis.  Conclusions: Early perioperative SII trajectories were associated with ICU dependency and prognosis after lung cancer resection. The PIMSI nomogram may support early risk stratification and perioperative care escalation, but it needs further validation with oncological staging and histological adjustment.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12957-026-04534-w.
SN  - 1477-7819
ER  -