Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits

Abstract

We aimed to evaluate whether radiomic feature-based fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits. Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict disease-free survival (DFS) and overall survival (OS) and were validated in the validation cohort. A radiomics nomogram with the radiomic signature incorporated was constructed to demonstrate the incremental value of the radiomic signature to the TNM staging system for individualized survival estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. The performance was assessed with concordance index (C-index) and integrated Brier scores. Results: Significant differences were found between the high- and low-radiomic score (Rad-score) patients in 5-year DFS and OS in training and validation cohorts. Multivariate analysis revealed that the Rad-score was an independent prognostic factor. Incorporating the Rad-score into the radiomics-based nomogram resulted in better performance (C-index: DFS, 0.800; OS, 0.786; in the training cohort) than TNM staging system and clinicopathologic nomogram. Further analysis revealed that patients with higher Rad-scores were prone to benefit from chemotherapy. Conclusion: The newly developed radiomic signature was a powerful predictor of OS and DFS. Moreover, the radiomic signature could predict which patients could benefit from chemotherapy.

Keywords: PET/CT; chemotherapy; gastric cancer; predictive signature; prognosis.

Figure 1

Rad-score measured by time-dependent ROC curves and Kaplan-Meier survival in the training and validation cohorts. (A) Training cohort. (B) Validation cohort. We used AUCs at 1, 3, and 5 years to assess prognostic accuracy in the training and validation cohorts. We calculated P-values using the log-rank test. Data are AUC or P-value. AUC: area under the curve; HR: hazard ratio; ROC: receiver operator characteristic; RS: radiomic score.

Figure 2

Rad-score analysis of 214 GC patients in the combined training and validation cohorts (n = 214). (A) Rad-score distribution. (B) Recurrence status of GC patients. (C) Survival status of GC patients. (D) Color-gram of the expression profiles of 3 radiomic features in GC patients. Rows represent 3 radiomic features and columns represent patients. Magenta dotted line represents the Rad-score cutoff dividing the patients into high- and low-Rad-score groups.

Figure 3

Use of the constructed radiomics nomogram to estimate DFS and OS for GC, along with the assessment of the model calibration. (A) Radiomics nomogram to estimate DFS (left) and OS (right). To determine how many points toward the probability of DFS and OS the patient receives for his or her Rad-score, locate the patient's Rad-score on the Rad-score axis, draw a line straight upward to the point axis, repeat this process for each variable, sum the points achieved for each of the risk factors, locate the final sum on the Total Point axis, and draw a line straight down to find the patient's probability of DFS and OS. Calibration curves for the radiomics nomograms of DFS (left, (B, D)) and OS (right, (C, E)) show the calibration of each model in terms of the agreement between the estimated and the observed 1-, 3-, and 5-year outcomes. Nomogram-estimated DFS or OS is plotted on the x-axis; the observed DFS or 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 line is the performance of the nomogram: a closer alignment with the diagonal dotted line represents a better estimation.

Figure 4

Prediction error curves for each model in the study for stratifying DFS and OS in the training and validation cohorts. Prediction error curves for the (A, C) training cohort and the (B, D) validation cohort (lower prediction errors indicate higher model accuracy).

Figure 5

Chemotherapy benefits in gastric cancer compared using disease-free survival (DFS) and overall survival (OS). Kaplan-Meier survival curves for patients with gastric cancer in different Rad-score subgroups, which were stratified by the receipt of chemotherapy. (A) Training cohort (n=132), (B) validation cohort (n=82), (C) combined cohort (n=214). CT: chemotherapy; RS: radiomic score.