Combination of baseline FDG PET/CT total metabolic tumour volume and gene expression profile have a robust predictive value in patients with diffuse large B-cell lymphoma

Toledano, Mathieu Nessim; Desbordes, Paul; Banjar, Ayman Abdulhakim; Gardin, I.; Véra, Pierre; Ruminy, Philippe; Jardin, Fabrice; Tilly, Hervé; Becker, Stéphanie

doi:10.1007/s00259-017-3907-x

Cited by 82 publications

(90 citation statements)

References 35 publications

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“…39 Recent research provided preliminary but promising evidence that the integration of quantitative PET parameters with some clinical, biological, and molecular features may allow a more accurate prognostication in DLBCL. 29,40,41 The main aim of this study was to test the ability of baseline functional PET parameters in predicting the efficacy of immunochemotherapy in a cohort of DLBCL patients treated with R-CHOP14 in the prospective clinical trial SAKK 38/07. A separate validation cohort of DLBCL patients treated with the standard RCHOP21 was also analyzed.…”

Section: Introductionmentioning

confidence: 99%

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model

et al. 2020

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Several functional parameters from baseline (18)F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography have been proposed as promising biomarkers of treatment efficacy in diffuse large B-cell lymphoma (DLBCL). We tested their ability to predict outcome in 2 cohorts of DLBCL patients receiving conventional immunochemotherapy (rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, and prednisone [R-CHOP] regimen), either every 14 (R-CHOP14) or 21 days (R-CHOP21). Baseline PET analysis was performed in 141 patients with DLBCL treated with R-CHOP14 in the prospective SAKK38/07 study (NCT00544219) of the Swiss Group for Clinical Cancer Research (testing set). Reproducibility was examined in a validation set of 113 patients treated with R-CHOP21. In the SAKK38/07 cohort, progression-free survival (PFS) at 5 years was 83% for patients with low metabolic tumor volume (MTV) and 59% for those with high MTV (hazard ratio [HR], 3.4; 95% confidence interval [CI], 1.6-7.0; P = .0005), whereas overall survival (OS) was 91% and 64%, respectively (HR, 4.4; 95% CI, 1.9-10; P = .0001). MTV was the most powerful predictor of outcome also in the validation set. Elevated metabolic heterogeneity (MH) significantly predicted poorer outcomes in the subgroups of patients with elevated MTV. A model integrating MTV and MH identified high-risk patients with shorter PFS (testing set: HR, 5.6; 95% CI, 1.8-17; P < .0001; validation set: HR, 5.6; 95% CI, 1.7-18; P = .0002) and shorter OS (testing set: HR, 9.5; 95% CI, 1.7-52; P < .0001; validation set: HR, 7.6; 95% CI, 2.0-28; P = .0003). This finding was confirmed by an unsupervised regression tree analysis indicating that prognostic models based on MTV and MH may allow early identification of refractory patients who might benefit from treatment intensification. This trial was registered at www.clinicaltrials.gov as #NCT00544219.

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Section: Introductionmentioning

confidence: 99%

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model

et al. 2020

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“…High-risk patients are not accurately identified by the current prognostic scoring systems, such as the International Prognostic Index (IPI) (5), the revised IPI (6), or the National Comprehensive Cancer Network IPI (7). Over the last 5 y, the prognostic role of quantitative PET parameters, in particular the metabolic tumor volume (MTV), has been demonstrated in many lymphoma subtypes (8,9), including DLBCL (10)(11)(12). MTV reflects the total volume of 18 F-FDG-avid tumor regions within the whole body and hence provides a more comprehensive tumor burden evaluation than previous surrogates such as lactate dehydrogenase levels.…”

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¹⁸F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome

et al. 2019

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We assessed the predictive value of new radiomic features characterizing lesion dissemination in baseline 18 F-FDG PET and tested whether combining them with baseline metabolic tumor volume (MTV) could improve prediction of progression-free survival (PFS) and overall survival (OS) in diffuse large B-cell lymphoma (DLBCL) patients. Methods: From the LNH073B trial (NCT00498043), patients with advancedstage DLCBL and 18 F-FDG PET/CT images available for review were selected. MTV and several radiomic features, including the distance between the 2 lesions that were farthest apart (Dmax patient), were calculated. Receiver-operating-characteristic analysis was used to determine the optimal cutoff for quantitative variables, and Kaplan-Meier survival analyses were performed. Results: With a median age of 46 y, 95 patients were enrolled, half of them treated with R-CHOP biweekly (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and the other half with R-ACVBP (rituximab, doxorubicin, cyclophosphamide, vindesine, bleomycin, and prednisone), with no significant impact on outcome. Median MTV and Dmax patient were 375 cm 3 and 45 cm, respectively. The median follow-up was 44 mo. High MTV and Dmax patient were adverse factors for PFS (P 5 0.027 and P 5 0.0003, respectively) and for OS (P 5 0.0007 and P 5 0.0095, respectively). In multivariate analysis, only Dmax patient was significantly associated with PFS (P 5 0.0014) whereas both factors remained significant for OS (P 5 0.037 and P 5 0.0029, respectively). Combining MTV (.384 cm 3) and Dmax patient (.58 cm) yielded 3 risk groups for PFS (P 5 0.0003) and OS (P 5 0.0011): high with 2 adverse factors (4-y PFS and OS of 50% and 53%, respectively, n 5 18), low with no adverse factor (94% and 97%, n 5 36), and an intermediate category with 1 adverse factor (73% and 88%, n 5 41). Conclusion: Combining MTV with a parameter reflecting the tumor burden dissemination further improves DLBCL patient risk stratification at staging.

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“…[18,39] Some has also found that integrating models can improve prediction power by combining genes and images such as magnetic resonance imaging and computerized tomography. [40][41][42] On account of the changes in cancer genetic mechanisms are often re ected in cell morphology, pathological images have better insight into other medical images.…”

Section: Discussionmentioning

confidence: 99%

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Zeng

Chen

et al. 2020

Preprint

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Background: Colon adenocarcinoma (COAD) is one of the highest morbidity cancers all over the world. Its 5-year survival is no more than 60% even in European countries with the highest survival rates. The histopathological information is crucial for the prognosis and therapy of COAD. Application of the digital whole slide imaging system enables us to read histopathological sections digitally. Apart from that, cancer genomics is also an important prognostic factor.Methods: To identify prognosis biomarkers of COAD, we downloaded whole-slide histopathological images from TCIA database. After processing these images, histopathological features were extracted by CellProfiler. Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination were followed applied, screening out 5 prognosis-related features. Weighted gene co-expression network analysis (WGCNA) was operated to find co-expression gene module correlated with prognosis-related features. The samples were divided into a training set and a testing set on a scale of 70% and 30%. Random forest was applied to construct histopathologic-genomic prognosis factor (HGPF) using prognosis-related features and genomic data. After that, we combined HGPF and clinical characteristics with nomogram and verify its predictive efficacy.Results: The time-dependent ROC was drawn to evaluate the efficacy of prognostic model. In the training set, 1-year, 3-year and 5-year AUCs are respectively 0.948, 0.916, 0.933. In the testing set, 1-year, 3-year and 5-year AUCs are respectively 0.913, 0.894, 0.924. In addition, patients were separated into high-risk survival group and low-risk survival group by HGPF. Survival analysis indicates that the low-risk patients’ survival was significantly better than high-risk patients’ in both training set and testing set. It is suggested that histopathological image features have certain ability to predict COAD survival, which can be further improved by means of multi-omics combination.Conclusions: In conclusion, this study constructs an integrative prognosis model based on histopathological and genomic features, which may have some guidance effect on prognosis and clinical decision of COAD patients. Furthermore, the underlying biological mechanisms of this multi-omics model require further study.

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Combination of baseline FDG PET/CT total metabolic tumour volume and gene expression profile have a robust predictive value in patients with diffuse large B-cell lymphoma

Abstract: This integrated risk model could lead to more accurate selection of patients that would allow better individualization of therapy.

Cited by 82 publications

References 35 publications

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model

¹⁸F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

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Combination of baseline FDG PET/CT total metabolic tumour volume and gene expression profile have a robust predictive value in patients with diffuse large B-cell lymphoma

Abstract: This integrated risk model could lead to more accurate selection of patients that would allow better individualization of therapy.

Cited by 82 publications

References 35 publications

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model

18F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

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¹⁸F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome