Combined laboratory and imaging indicators to construct risk models for predicting immunotherapy efficacy and prognosis in non-small cell lung cancer: An observational study (STROBE compliant)
Bibliographic record
Abstract
This study aimed to investigate the correlations between short- and long-term efficacy of immune checkpoint inhibitors (ICIs) and pretreatment laboratory/imaging parameters in advanced non-small cell lung cancer (NSCLC), and to construct risk prediction models. We enrolled 137 NSCLC patients with stage IIIB-IV disease who completed 4 cycles of PD-1/PD-L1 inhibitor monotherapy or combination therapy. All participants underwent pretreatment laboratory assessments encompassing inflammatory markers, lymphocyte subsets, tumor biomarkers, coagulation profiles, and contrast-enhanced computed tomography (CE-CT) scans. The primary endpoints were objective response rate (ORR) and overall survival (OS), with progression-free survival (PFS) as the secondary endpoint. Univariate and multivariate logistic regression analyses were performed to identify significant predictors of short-term treatment response and develop an efficacy prediction model. For long-term outcomes, univariate and multivariate Cox proportional hazards regression analyses were conducted to establish a prognostic risk model. The final models were presented as nomograms and validated through receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). CD4+ T-cell count (P = .007), fibrinogen (FIB, P = .047), and mediastinal lymph node enlargement (P = .028) emerged as independent predictors of ORR. The prediction model demonstrated an area under the ROC curve (AUC) of 0.838, with bootstrap validation (1000 resamples) yielding a mean AUC of 0.867. Calibration analysis, DCA, and clinical impact curve (CIC) collectively confirmed the model's robust predictive performance. For OS, metastatic site (P = .007), neutrophil-to-lymphocyte ratio (NLR, P = .025), carbohydrate antigen 125 (CA125, P = .020), cytokeratin 19 fragment (CYFRA 21-1, P = .004), FIB (P < .001), and pleural effusion (P < .001) were identified as significant prognostic determinants. The model achieved AUC values of 0.858 and 0.860 for 1- and 2-year survival prediction, respectively. Calibration plots revealed excellent concordance between predicted and observed survival probabilities at both timepoints. Furthermore, DCA indicated superior net clinical benefit of the prognostic model compared to random chance models across threshold probability ranges. Comprehensive prediction models integrating clinical characteristics, laboratory biomarkers, and imaging parameters were developed for both short- and long-term efficacy evaluation of immunotherapy, offering clinically actionable guidance for personalizing treatment strategies in advanced NSCLC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".