Exploring prognostic precision: a nomogram approach for malignant pleural effusion in lung cancer

Figures

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  Fig. 1. The comprehensive flowchart, illustrating the process from patient selection to nomogram establishment

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  Fig. 2. Selection of clinical features. A-B: The optimal parameter (lambda) selection in the LASSO was performed through tenfold cross-validation, resulting in the selection of the optimal lambda value of 0.0259. This choice led to 17 features with nonzero coefficients. C: Further screening of clinical features using logistic regression and plotting their Odds Ratio (OR) in a forest plot. Note: The OR of continuous variables indicates the multiplicative change in the probability of an event occurring with each unit increase

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  Fig. 3. Comparison of different model performances. A: ROC curves for the clinical features model, radscore model, and combined model; B: DCA for the clinical features model, radscore model, and combined model. The threshold probability for the clinical features model ranges from 0.01 to 0.95, for the radscore model it ranges from 0.01 to 0.66, and for the combined model it ranges from 0.01 to 1.00; C: Calibration curves for the radscore model; D: Calibration curves for the clinical features model; E. Calibration curves for the combined model

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  Fig. 4. Establishment and validation of the nomogram. A: A nomogram formulated utilizing 5 predictive factors; B: ROC curves of the nomogram in the modeling group and external validation group; C-D: Calibration curves of the nomogram in the modeling group and external validation group; E-F: Clinical decision curves of the nomogram in the modeling group and external validation group. The threshold probability in the modeling group ranges from 0.01 to 1.00, and in the external validation group, it ranges from 0.02 to 1.00

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  Fig. 5. Predictive performance of the nomogram for prognosis of different lung cancer types and patient risk stratification. A: Predictive performance of the nomogram for adenocarcinoma, squamous cell carcinoma, and small cell lung cancer. B: Distribution of deceased and surviving patients among four risk groups: low, moderate, high, and very high risk. Scores less than 75 points represent the low-risk group, 75–115 points (excluding 115 points) represent the moderate-risk group, 115–166 points represent the high-risk group (excluding 166 points), and scores greater than or equal to 116 points represent the very high-risk group. C: Prognosis of patients in the four risk groups