Development and Validation of a Nomogram Based on 18F-FDG PET/CT Radiomics to Predict the Overall Survival in Adult Hemophagocytic Lymphohistiocytosis

Abstract

Purpose: Hemophagocytic lymphohistiocytosis (HLH) is a rare and severe disease with a poor prognosis. We aimed to determine if ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT-derived radiomic features alone or combination with clinical parameters could predict survival in adult HLH. Methods: This study included 70 adults with HLH (training cohort, n = 50; validation cohort, n = 20) who underwent pretherapeutic ¹⁸F-FDG PET/CT scans between August 2016 and June 2020. Radiomic features were extracted from the liver and spleen on CT and PET images. For evaluation of 6-month survival, the features exhibiting p < 0.1 in the univariate analysis between non-survivors and survivors were selected. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to develop a radiomics score (Rad-score). A nomogram was built by the multivariate regression analysis to visualize the predictive model for 3-month, 6-month, and 1-year survival, while the performance and usefulness of the model were evaluated by calibration curves, the receiver operating characteristic (ROC) curves, and decision curves. Results: The Rad-score was able to predict 6-month survival in adult HLH, with area under the ROC curves (AUCs) of 0.927 (95% CI: 0.878-0.974) and 0.869 (95% CI: 0.697-1.000) in the training and validation cohorts, respectively. The radiomics nomogram combining the Rad-score with the clinical parameters resulted in better performance for predicting 6-month survival than the clinical model or the Rad-score alone. Moreover, the nomogram displayed superior discrimination, calibration, and clinical usefulness in both the cohorts. Conclusion: The newly developed Rad-score is a powerful predictor for overall survival (OS) in adults with HLH. The nomogram has great potential for predicting 3-month, 6-month, and 1-year survival, which may timely guide personalized treatments for adult HLH.

Keywords: 18F-FDG PET/CT; hemophagocytic lymphohistiocytosis; nomogram; prognosis; radiomics.

Figure 1

Workflow of radiomics analysis.

Figure 2

Feature selection for the prediction using the least absolute shrinkage and selection operator (LASSO) regression model, tuning parameter (λ) selection in the LASSO model involved the use of tenfold cross-validation (A). In the coefficient profiles of the radiomics features for OS prediction, a value of Lambda = 0.044668 was selected as the optimal value (B).

Figure 3

Rad-score of patients in the training and validation cohorts (A,B) and time-dependent ROC analysis of Rad-score at 180 days (C). Kaplan-Meier survival analysis with the best cutoff value of the Rad-score in the training cohort (D) and validation cohort (E). We calculated p values using the log-rank test.

Figure 4

Decision-curve analysis for the radiomics model and clinical model. The threshold probability represents the predicted 180-day risk of death for recommending aggressive treatment.

Figure 5

The radiomics nomogram for predicting overall survival for adult HLH patients (A). Points for Rad-score, WBC, and CRP can be obtained by calibrating with the point caliper, and then combined to obtain a total score that can be calibrated with the patient's probability of survival at different time. Calibration curves of the radiomics nomogram in the training cohort (B) and validation cohort (C). Nomogram-estimated OS is plotted on the x-axis; the observed OS is plotted on the y-axis. The diagonal dotted line is a perfect estimation by an ideal model, in which the estimated outcome perfectly corresponds to the actual outcome. The solid red line represents performance of the nomogram: A smaller distance of the scatter points from the dotted line indicates better calibration.