Synergistic Effect of 18F-FDG PET Radiomics and International Prognostic Index on Outcome Prediction in Diffuse Large B-cell Lymphoma
Conditions: Lymphoma, Large B-Cell, Diffuse
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University

Official title: Synergistic effect of 18F-FDG PET radiomics and International Prognostic 
Index on outcome prediction in diffuse large B-cell lymphoma 
 
NCT number: 04317313 
 
Document date: July 29, 2021 
 

Study Protocol and Statistical Analysis Plan 
Background: Previous studies have shown that 18F-FDG PET radiomics is predictive 
of survival in DLBCL. However, to the best of the investigator's knowledge, a multi-
feature radiomic signature for prognosis assessment of DLBCL has not yet been 
described. Furthermore, it remains unclear whether PET-based radiomics could add 
more prognostic values to the IPI in DLBCL. 
 
Objectives: This study aims to develop 18F-FDG PET radiomic signatures, and 
investigate whether the RS could improve the prognostic value of the IPI in predicting 
progression-free survival (PFS) and overall survival (OS) in DLBCL. 
 
Study design and methods: 
Study population 
We retrospectively included patients with newly-diagnosed DLBCL between July 
2013 and July 2019 in our institution. The inclusion criteria were: 1) histopathologically 
confirmed DLBCL, 2) over 18 years old, 3) underwent pre-treatment 18F-FDG PET/CT, 
and 4) initial treatment with R-CHOP (rituximab, cyclophosphamide, doxorubicin, 
vincristine, and prednisone) or R-EPOCH (rituximab, etoposide, prednisone, 
vincristine, cyclophosphamide, and doxorubicin). Patients were excluded if they had 
coexistent central nervous system lymphoma or second primary cancer, underwent 
surgical resection, or had an incomplete follow-up.  
Clinical variables including gender, age at diagnosis, cell of origin, performance 
status, B symptoms, Ann Arbor stage, serum lactate dehydrogenase level, serum β2-
microglobulin (β2-MG) level, extranodal involvement and treatment regimens of each 
patient were recorded. The IPI score was calculated. 
 
Patient Treatment and Follow-up 
Patients were initially treated with standard first-line chemotherapy every 21 days. 
Response to treatment was assessed according to the Lugano classification.  

Follow-up was performed every 3 months after the completion of treatment, and 
ended in July 2021. The endpoints included OS (defined as the period from the initial 
diagnosis to the death from any cause) and PFS (defined as the period from the initial 
diagnosis to the progression, relapse or death from any cause). 
 
PET Image Segmentation and Feature Extraction 
PET images were analyzed by two experienced nuclear medicine physicians who 
were blind to the patients’ outcome. The volumes of interest (VOIs) were 
semiautomatically 
delineated 
using 
the 
LIFEx 
software 
(https://www.lifexsoft.org/index.php) with a fixed threshold of 41% SUVmax. A total of 
1245 radiomic features were extracted via PyRadiomics software. The radiomics 
features consisted of four groups: (1) first-order statistics features; (2) shape 
descriptors features; (3) texture features calculated by gray level co-occurrence matrix 
(GLCM), gray level dependence matrix (GLDM), gray level run length matrix (GLRLM), 
and gray level size zone matrix (GLSZM); (4) filter and wavelet features obtained by 
filter and wavelet transformation of the original image, such as exponential, logarithm, 
square, square root and eight frequency band combinations (low-high-low [LHL], low-
high-high [LHH], high-low-low [HLL], low-low-high [LLH], high-low-high [HLH], high-
high-high [HHH], high-high-low [HHL] and low-low-low [LLL]). 
 
Radiomics feature selection 
First, intra-class correlation coefficients (ICCs) were used to evaluate the inter- 
and intra-observer agreement for 1248 features, including 3 conventional and 1245 
radiomics features. Features with both inter- and intra-observer ICCs greater than 0.75 
were retained. Then, the least absolute shrinkage and selection operator (LASSO) Cox 
regression algorithm with 10-fold cross-validation was applied to select the optimal 
features with non-zero coefficients in the training set. The radiomics signature was 
developed by linear combination of the selected features weighted by their respective 
coefficients. The cut-off value of the radiomics signature was identified by X-tile 
software. Based on the cut-off value of the R-signature, patients were divided into high- 

and low-risk groups. Survival function of the R-signature was estimated by Kaplan-
Meier analysis, and survival distributions were compared using log-rank test. 
 
Model construction 
Univariate Cox regression analysis was performed to investigate the prognostic 
value of the R-signature and clinical variables. Then, all significant variables were 
enrolled into a stepwise multivariate Cox regression to build an integrated radiomics-
based model for PFS and OS prediction. 
 
Model performance assessment 
Time-dependent ROC curve analysis was conducted to investigate the predictive 
accuracy of the R-signature, clinical variables and the integrated radiomics-based 
model. The 95% confidence interval (CI) of the area under the curves (AUCs) were 
calculated by bootstrapping with 1000 resampling. The AUC between the radiomics-
based model and clinical variables was compared by using the Delong’s test. The 
calibration plots and the Hosmer-Lemeshow test were used to compare the predicted 
and observed probabilities of the radiomics-based model. The decision curve analysis 
(DCA) was applied to determine the clinical utility by quantifying the net benefits under 
different threshold probabilities in the whole cohort. 
 
Statistical analysis 
The chi-square test and independent t-test were performed using SPSS software 
(version 25.0, IBM). ICC, LASSO regression analysis, time-dependent ROC analysis, 
Delong’s test, calibration plots, Hosmer-Lemeshow test, DCA and Kaplan-Meier 
survival analysis were performed using R software (version 3.6.1, http://www.r-
project.org). A two-sided P value of < 0.05 was considered statistically significant.