Generation and validation of a PET radiomics model that predicts survival in diffuse large B cell lymphoma treated with R-CHOP14: A SAKK 38/07 trial post-hoc analysis.
Aged
Antineoplastic Combined Chemotherapy Protocols
/ therapeutic use
Clinical Trials as Topic
Cyclophosphamide
/ administration & dosage
Female
Fluorodeoxyglucose F18
/ metabolism
Follow-Up Studies
Humans
Lymphoma, Large B-Cell, Diffuse
/ diagnostic imaging
Male
Middle Aged
Positron Emission Tomography Computed Tomography
/ methods
Prednisone
/ administration & dosage
Prognosis
Radiopharmaceuticals
/ metabolism
Retrospective Studies
Rituximab
/ administration & dosage
Survival Rate
Vincristine
/ administration & dosage
18FDG-PET/CT
DLBCL
PET metrics
prognostic models
radiomics
Journal
Hematological oncology
ISSN: 1099-1069
Titre abrégé: Hematol Oncol
Pays: England
ID NLM: 8307268
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
revised:
01
10
2021
received:
15
07
2021
accepted:
04
10
2021
pubmed:
30
10
2021
medline:
15
2
2022
entrez:
29
10
2021
Statut:
ppublish
Résumé
Functional parameters from positron emission tomography (PET) seem promising biomarkers in various lymphoma subtypes. This study investigated the prognostic value of PET radiomics in diffuse large B-cell lymphoma (DLBCL) patients treated with R-CHOP given either every 14 (testing set) or 21 days (validation set). Using the PyRadiomics Python package, 107 radiomics features were extracted from baseline PET scans of 133 patients enrolled in the Swiss Group for Clinical Cancer Research 38/07 prospective clinical trial (SAKK 38/07) [ClinicalTrial.gov identifier: NCT00544219]. The international prognostic indices, the main clinical parameters and standard PET metrics, together with 52 radiomics uncorrelated features (selected using the Spearman correlation test) were included in a least absolute shrinkage and selection operator (LASSO) Cox regression to assess their impact on progression-free (PFS), cause-specific (CSS), and overall survival (OS). A linear combination of the resulting parameters generated a prognostic radiomics score (RS) whose area under the curve (AUC) was calculated by receiver operating characteristic analysis. The RS efficacy was validated in an independent cohort of 107 DLBCL patients. LASSO Cox regression identified four radiomics features predicting PFS in SAKK 38/07. The derived RS showed a significant capability to foresee PFS in both testing (AUC, 0.709; p < 0.001) and validation (AUC, 0.706; p < 0.001) sets. RS was significantly associated also with CSS and OS in testing (CSS: AUC, 0.721; p < 0.001; OS: AUC, 0.740; p < 0.001) and validation (CSS: AUC, 0.763; p < 0.0001; OS: AUC, 0.703; p = 0.004) sets. The RS allowed risk classification of patients with significantly different PFS, CSS, and OS in both cohorts showing better predictive accuracy respect to clinical international indices. PET-derived radiomics may improve the prediction of outcome in DLBCL patients.
Substances chimiques
Radiopharmaceuticals
0
Fluorodeoxyglucose F18
0Z5B2CJX4D
Rituximab
4F4X42SYQ6
Vincristine
5J49Q6B70F
Cyclophosphamide
8N3DW7272P
Prednisone
VB0R961HZT
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
11-21Subventions
Organisme : Ente Ospedaliero Cantonale
ID : ABREOC 22008-262
Organisme : Amgen
Organisme : Oncosuisse
ID : OCS-02270-08-2008
Informations de copyright
© 2022 John Wiley & Sons Ltd.
Références
Sehn LH, Salles G. Diffuse large B-cell lymphoma. N Engl J Med. 2021;384(9):842-858.
Cheson BD, Fisher RI, Barrington SF, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32(27):3059-3067.
Barrington SF, Mikhaeel NG, Kostakoglu L, et al. Role of imaging in the staging and response assessment of lymphoma: consensus of The International Conference on Malignant Lymphomas Imaging Working Group. J Clin Oncol. 2014;32(27):3048-3058.
Delarue R, Tilly H, Mounier N, et al. Dose-dense rituximab-CHOP compared with standard rituximab-CHOP in elderly patients with diffuse large B-cell lymphoma (the LNH03-6B study): a randomised phase 3 trial. Lancet Oncol. 2013;14(6):525-533.
Cunningham D, Hawkes EA, Jack A, et al. Rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone in patients with newly diagnosed diffuse large B-cell non-Hodgkin lymphoma: a phase 3 comparison of dose intensification with 14-day versus 21-day cycles. Lancet (London, England). 2013;381(9880):1817-1826.
Ruppert AS, Dixon JG, Salles G, et al. International prognostic indices in diffuse large B-cell lymphoma: a comparison of IPI, R-IPI, and NCCN-IPI. Blood. 2020;135(23):2041-2048.
Kim CY, Hong CM, Kim DH, et al. Prognostic value of whole-body metabolic tumour volume and total lesion glycolysis measured on (1)(8)F-FDG PET/CT in patients with extranodal NK/T-cell lymphoma. Eur J Nucl Med Mol Imaging. 2013;40(9):1321-1329.
Kanoun S, Rossi C, Berriolo-Riedinger A, et al. Baseline metabolic tumour volume is an independent prognostic factor in Hodgkin lymphoma. Eur J Nucl Med Mol Imaging. 2014;41(9):1735-1743.
Ceriani L, Martelli M, Zinzani PL, et al. Utility of baseline 18FDG-PET/CT functional parameters in defining prognosis of primary mediastinal (thymic) large B-cell lymphoma. Blood. 2015;126(8):950-956.
Cottereau AS, Lanic H, Mareschal S, et al. Molecular profile and FDG-PET/CT total metabolic tumor volume improve risk classification at diagnosis for patients with diffuse large B-cell lymphoma. Clin Cancer Res. 2016;22(15):3801-3809.
Zhou M, Chen Y, Huang H, Zhou X, Liu J, Huang G. Prognostic value of total lesion glycolysis of baseline 18F-fluorodeoxyglucose positron emission tomography/computed tomography in diffuse large B-cell lymphoma. Oncotarget. 2016;7(50):83544-83553.
Sasanelli M, Meignan M, Haioun C, et al. Pretherapy metabolic tumour volume is an independent predictor of outcome in patients with diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging. 2014;41(11):2017-2022.
Chang CC, Cho SF, Chuang YW, et al. Prognostic significance of total metabolic tumor volume on (18)F-fluorodeoxyglucose positron emission tomography/computed tomography in patients with diffuse large B-cell lymphoma receiving rituximab-containing chemotherapy. Oncotarget. 2017;8(59):99587-99600.
Kim J, Hong J, Kim SG, et al. Prognostic value of metabolic tumor volume estimated by (18) F-FDG positron emission tomography/computed tomography in patients with diffuse large B-cell lymphoma of stage II or III disease. Nuclear Medicine and Molecular Imaging. 2014;48(3):187-195.
Mikhaeel NG, Smith D, Dunn JT, et al. Combination of baseline metabolic tumour volume and early response on PET/CT improves progression-free survival prediction in DLBCL. Eur J Nucl Med Mol Imaging. 2016;43(7):1209-1219.
Cottereau AS, Nioche C, Dirand AS, et al. F-FDG PET dissemination features in diffuse large B-cell lymphoma are predictive of outcome. J Nucl Med. 2020;61(1):40-45.
Cottereau AS, Meignan M, Nioche C, et al. Risk stratification in diffuse large B-cell lymphoma using lesion dissemination and metabolic tumor burden calculated from baseline PET/CT(†). Ann Oncol. 2021;32(3):404-411.
Senjo H, Hirata K, Izumiyama K, et al. High metabolic heterogeneity on baseline 18FDG-PET/CT scan as a poor prognostic factor for newly diagnosed diffuse large B-cell lymphoma. Blood Adv. 2020;4(10):2286-2296.
Ceriani L, Milan L, Martelli M, et al. Metabolic heterogeneity on baseline 18FDG-PET/CT scan is a predictor of outcome in primary mediastinal B-cell lymphoma. Blood. 2018;132(2):179-186.
Ceriani L, Gritti G, Cascione L, et al. SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic models. Blood Advances. 2020;4(6):1082-1092.
Zucca E, Cascione L, Ruberto T, et al. Prognostic models integrating quantitative parameters from baseline and interim positron emission computed tomography in patients with diffuse large B-cell lymphoma: post-hoc analysis from the SAKK38/07 clinical trial. Hematol Oncol. 2020;38(5):715-725.
Zhang YY, Song L, Zhao MX, Hu K. A better prediction of progression-free survival in diffuse large B-cell lymphoma by a prognostic model consisting of baseline TLG and %DeltaSUVmax. Cancer medicine. 2019;8(11):5137-5147.
Schmitz C, Huttmann A, Muller SP, et al. Dynamic risk assessment based on positron emission tomography scanning in diffuse large B-cell lymphoma: post-hoc analysis from the PETAL trial. Eur J Cancer. 2020;124:25-36.
Toledano MN, Desbordes P, Banjar A, et al. 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. Eur J Nucl Med Mol imaging. 2018;45(5):680-688.
Shagera QA, Cheon GJ, Koh Y, et al. Prognostic value of metabolic tumour volume on baseline (18)F-FDG PET/CT in addition to NCCN-IPI in patients with diffuse large B-cell lymphoma: further stratification of the group with a high-risk NCCN-IPI. Eur J Nucl Med Mol imaging. 2019;46(7):1417-1427.
Vercellino L, Cottereau AS, Casasnovas O, et al. High total metabolic tumor volume at baseline predicts survival independent of response to therapy. Blood. 2020;135(16):1396-1405.
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762.
Cook GJR, Azad G, Owczarczyk K, Siddique M, Goh V. Challenges and promises of PET radiomics. Int J Radiat Oncol Biol Phys. 2018;102(4):1083-1089.
Milgrom SA, Elhalawani H, Lee J, et al. A PET radiomics model to predict refractory mediastinal Hodgkin lymphoma. Sci Rep. 2019;9(1):1322.
Mamot C, Klingbiel D, Hitz F, et al. Final results of a prospective evaluation of the predictive value of interim positron emission tomography in patients with diffuse large B-cell lymphoma treated with R-CHOP-14 (SAKK 38/07). J Clin Oncol official J Am Soc Clin Oncol. 2015;33(23):2523-2529.
Tzankov A, Leu N, Muenst S, et al. Multiparameter analysis of homogeneously R-CHOP-treated diffuse large B cell lymphomas identifies CD5 and FOXP1 as relevant prognostic biomarkers: report of the prospective SAKK 38/07 study. J Hematol Oncol. 2015;8:70.
Hans CP, Weisenburger DD, Greiner TC, et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood. 2004;103(1):275-282.
Barrington SF, Zwezerijnen BG, de Vet HC, et al. Automated segmentation of baseline metabolic total tumor burden in diffuse large B-cell lymphoma: which method is most successful? J Nucl Med. 2020.
Burggraaff CN, Rahman F, Kaßner I, et al. Optimizing workflows for fast and reliable metabolic tumor volume measurements in diffuse large B cell lymphoma. Mol Imag Biol. 2020;22(4):1102-1110.
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104-e107.
Soussan M, Orlhac F, Boubaya M, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One. 2014;9(4):e94017.
Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328-338.
Abdi H. Z-scores. In: Salkind NJ. ed. Encyclopedia of measurement and statistics. Vol 3. SAGE Publications, Inc.; 2007:1055-1058.
Mukaka MM. Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24(3):69-71.
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385-395.
Cheson BD, Pfistner B, Juweid ME, et al. Revised response criteria for malignant lymphoma. J Clin Oncol official J Am Soc Clin Oncol. 2007;25(5):579-586.
Harrell FE, Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy,and measuring and reducing errors. Stat Med. 1996;15(4):361-387.
Posada D, Buckley TR. Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Syst Biol. 2004;53(5):793-808.
R Core Team. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2020.
Parvez A, Tau N, Hussey D, Maganti M, Metser U. 18 F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin's lymphoma as predictors of treatment outcome and survival. Ann Nucl Med. 2018;32(6):410-416.
Aide N, Fruchart C, Nganoa C, Gac AC, Lasnon C. Baseline (18)F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy. Eur Radiol. 2020.
Lue K-H, Wu Y-F, Lin H-H, et al. Prognostic value of baseline radiomic features of 18F-FDG PET in patients with diffuse large B-cell lymphoma. Diagnostics. 2021;11(1):36.
Barrington SF, Meignan M. Time to prepare for risk adaptation in lymphoma by standardizing measurement of metabolic tumor burden. J Nucl Med. 2019;60(8):1096-1102.
Fornacon-Wood I, Mistry H, Ackermann CJ, et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur Radiol. 2020;30(11):6241-6250.
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present any future? Eur J Nucl Med Mol imaging. 2017;44(1):151-165.