Dataset Information

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma.

ABSTRACT: The objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-year time to progression (TTP). The metabolic tumor volume (MTV), maximum distance between the largest lesion and another lesion (Dmaxbulk), and peak standardized uptake value (SUVpeak) were extracted. The predictive values of the IPI and clinical PET model (MTV, Dmaxbulk, SUVpeak, performance status, and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance, using the positive predictive value (PPV). The IPI yielded an AUC of 0.62. The clinical PET model yielded a significantly higher AUC of 0.71 (P < .001). Patients with high-risk IPI had a 2-year PFS of 61.4% vs 51.9% for those with high-risk clinical PET, with an increase in PPV from 35.5% to 49.1%, respectively. A total of 66.4% of patients with high-risk IPI were free from progression or relapse vs 55.5% of patients with high-risk clinical PET scores, with an increased PPV from 33.7% to 44.6%, respectively. The clinical PET model remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.

SUBMITTER: Eertink JJ

PROVIDER: S-EPMC10646814 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma.

Eertink J J JJ Zwezerijnen G J C GJC Heymans M W MW Pieplenbosch S S Wiegers S E SE Dührsen U U Hüttmann A A Kurch L L Hanoun C C Lugtenburg P J PJ Barrington S F SF Mikhaeel N G NG Ceriani L L Zucca E E Czibor S S Györke T T Chamuleau M E D MED Hoekstra O S OS de Vet H C W HCW Boellaard R R Zijlstra J M JM

Blood 20230601 25

The objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-yea ...[more]

PMID: 37001036

Similar Datasets

Project description:BackgroundDiffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL.ResultsA total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403).ConclusionsRadiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.

Project description:ObjectivesAccurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients.Material and methodsGiven the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans. Consequently, our radiomics analysis focused on these 31 patients who underwent [18F]FDG PET-CT scans before and after R-CHOP chemotherapy. Expert manual lesion segmentation was conducted on their scans for delta radiomics analysis, along with an additional 19 EoT scans, totaling 50 segmented scans for single time point analysis. Radiomics features (on PET and CT), along with maximum and mean standardized uptake values (SUVmax and SUVmean), total metabolic tumor volume (TMTV), tumor dissemination (Dmax), total lesion glycolysis (TLG), and the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH) were calculated. We additionally applied longitudinal analysis using radial mean intensity (RIM) changes. For prediction of relapse/progression, we utilized the individual coefficient approximation for risk estimation (ICARE) and machine learning (ML) techniques (K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)) including sequential feature selection (SFS) following correlation analysis for feature selection. For TTP, ICARE and CoxNet approaches were utilized. In all models, we used nested cross-validation (CV) (with 10 outer folds and 5 repetitions, along with 5 inner folds and 20 repetitions) after balancing the dataset using Synthetic Minority Oversampling TEchnique (SMOTE).ResultsTo predict relapse/progression using Delta radiomics between the baseline (staging) and EoT scans, the best performances in terms of accuracy and F1 score (F1 score is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives) were achieved with ICARE (accuracy = 0.81 ± 0.15, F1 = 0.77 ± 0.18), RF (accuracy = 0.89 ± 0.04, F1 = 0.87 ± 0.04), and LDA (accuracy = 0.89 ± 0.03, F1 = 0.89 ± 0.03), that are higher compared to the predictive power achieved by using only EoT radiomics features. For the second category of our analysis, TTP prediction, the best performer was CoxNet (LASSO feature selection) with c-index = 0.67 ± 0.06 when using baseline + Delta features (inclusion of both baseline and Delta features). The TTP results via Delta radiomics were comparable to the use of radiomics features extracted from EoT scans for TTP analysis (c-index = 0.68 ± 0.09) using CoxNet (with SFS). The performance of Deauville Score (DS) for TTP was c-index = 0.66 ± 0.09 for n = 50 and 0.67 ± 03 for n = 31 cases when using EoT scans with no significant differences compared to the radiomics signature from either EoT scans or baseline + Delta features (p-value> 0.05).ConclusionThis work demonstrates the potential of Delta radiomics and the importance of using EoT scans to predict progression and TTP from PMBCL [18F]FDG PET-CT scans.

Project description:ObjectiveThe existing Central Nervous System-International Prognostic Index (CNS-IPI) provides insufficient guidance for predicting central nervous system (CNS) relapse in individuals with primary breast diffuse large B-cell lymphoma (DLBCL). This retrospective cohort study sought to examine the potential of the stage-modified IPI in predicting CNS relapse within this specific patient population.Patients and methodsWe examined the baseline characteristics of 76 consecutive patients diagnosed with primary breast DLBCL, calculating the stage-modified IPI score for each individual. Utilizing a competing risk regression (CRR) model, we conducted both univariate and multivariate analyses to explore the relationship between potential prognostic factors and the occurrence of CNS relapse.ResultsIn our cohort, the rates of CNS disease at 2 and 5 years since the diagnosis of primary breast DLBCL are 3.9% and 7.8%, respectively. Among patients experiencing CNS relapse, 80% presented with a parenchymal brain mass. Individuals with a high stage-modified IPI score (1-3 points) had a significantly higher incidence of CNS relapse (p = 0.031), a shorter time from the initial diagnosis of primary breast DLBCL to the first CNS relapse (p = 0.010), as well as relapse at any site (p = 0.012), compared to those with a low score (0 points). Univariate analysis identified stage (Hazard Ratio (HR): 4.098, p = 0.024), stage-modified IPI score (HR: 11.582, p = 0.012), and radiation therapy (HR: 5.784, p = 0.026) as significant risk factors. In multivariate analysis, in addition to radiation therapy (HR: 7.258, p = 0.012), the stage-modified IPI score (1-3 points versus 0 points) emerged as an independent and reliable predictor for CNS relapse (HR: 12.945, p = 0.016).ConclusionOur study underscores the significance of stage-modified IPI scores in predicting CNS relapse for patients with primary breast DLBCL. Validation of these findings through further research is essential, along with exploring potential prevention and intervention approaches.

Dataset Information

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma.

Publications

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets