A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

Xiaohui Zhang, Lin Chen, Han Jiang, Xuexin He, Liu Feng, Miaoqi Ni, Mindi Ma, Jing Wang, Teng Zhang, Shuang Wu, Rui Zhou, Chentao Jin, Kai Zhang, Wenbin Qian, Zexin Chen, Cheng Zhuo, Hong Zhang, Mei Tian, Xiaohui Zhang, Lin Chen, Han Jiang, Xuexin He, Liu Feng, Miaoqi Ni, Mindi Ma, Jing Wang, Teng Zhang, Shuang Wu, Rui Zhou, Chentao Jin, Kai Zhang, Wenbin Qian, Zexin Chen, Cheng Zhuo, Hong Zhang, Mei Tian

Abstract

Purpose: This study aimed to develop a novel analytic approach based on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) radiomic signature (RS) and International Prognostic Index (IPI) to predict the progression-free survival (PFS) and overall survival (OS) of patients with diffuse large B-cell lymphoma (DLBCL).

Methods: We retrospectively enrolled 152 DLBCL patients and divided them into a training cohort (n = 100) and a validation cohort (n = 52). A total of 1245 radiomic features were extracted from the total metabolic tumor volume (TMTV) and the metabolic bulk volume (MBV) of pre-treatment PET/CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to develop the RS. Cox regression analysis was used to construct hybrid nomograms based on different RS and clinical variables. The performances of hybrid nomograms were evaluated using the time-dependent receiver operator characteristic (ROC) curve and the Hosmer-Lemeshow test. The clinical utilities of prediction nomograms were determined via decision curve analysis. The predictive efficiency of different RS, clinical variables, and hybrid nomograms was compared.

Results: The RS and IPI were identified as independent predictors of PFS and OS, and were selected to construct hybrid nomograms. Both TMTV- and MBV-based hybrid nomograms had significantly higher values of area under the curve (AUC) than IPI in training and validation cohorts (all P < 0.05), while no significant difference was found between TMTV- and MBV-based hybrid nomograms (P > 0.05). The Hosmer-Lemeshow test showed that both TMTV- and MBV-based hybrid nomograms calibrated well in the training and validation cohorts (all P > 0.05). Decision curve analysis indicated that hybrid nomograms had higher net benefits than IPI.

Conclusion: The hybrid nomograms combining RS with IPI could significantly improve survival prediction in DLBCL. Radiomic analysis on MBV may serve as a potential approach for prognosis assessment in DLBCL.

Trial registration: NCT04317313. Registered March 16, 2020. Public site: https://ichgcp.net/clinical-trials-registry/NCT04317313.

Keywords: Diffuse large B-cell lymphoma; Glucose metabolism; Positron emission tomography (PET); Prognosis; Radiomics.

Conflict of interest statement

The authors declare no competing interests.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Representative [18F]FDG PET images of metabolic bulk volume (MBV) and total metabolic tumor volume (TMTV) delineation. a Anterior maximum intensity projection image. b The VOIs of cervical (red), iliac (yellow and purple), and inguinal (green) lymph nodes were semiautomatically delineated using the 41% SUVmax threshold method. The VOI of inguinal lymph node (green) represents the MBV. c TMTV (blue) was constructed using the “save all in one” function in LIFEx
Fig. 2
Fig. 2
Hybrid nomograms combining radiomic signatures (RS) and IPI score based on a MBV and b TMTV for PFS and OS prediction
Fig. 3
Fig. 3
Decision curve analysis (DCA) of a PFS and b OS for hybrid nomograms (HN), radiomic signatures (RS), and IPI score in the whole cohort
Fig. 4
Fig. 4
Kaplan–Meier estimates of PFS and OS according to IPI score in a the training cohort and b the validation cohort. Hazard ratio (HR) with 95% confidence interval and log-rank P value are reported
Fig. 5
Fig. 5
Kaplan–Meier estimates of PFS and OS according to MBV- and TMTV-based radiomic signatures (RS) in a the training cohort and b the validation cohort. Hazard ratio (HR) with 95% confidence interval and log-rank P value are reported
Fig. 6
Fig. 6
Kaplan–Meier estimates of PFS and OS according to MBV- and TMTV-based hybrid nomograms (HN) in a the training cohort and b the validation cohort. Hazard ratio (HR) with 95% confidence interval and log-rank P value are reported

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