MRI Radiomics Signature of Pediatric Medulloblastoma Improves Risk Stratification Beyond Clinical and Conventional MR Imaging Features.

Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9105850 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2586 (Electronic) Linking ISSN: 10531807 NLM ISO Abbreviation: J Magn Reson Imaging Subsets: MEDLINE

Publication: <2005-> : Hoboken , N.J. : Wiley-Liss
Original Publication: Chicago, IL : Society for Magnetic Resonance Imaging, c1991-

Medulloblastoma*/diagnostic imaging ; Cerebellar Neoplasms*/diagnostic imaging; Child ; Humans ; Child, Preschool ; Retrospective Studies ; Cohort Studies ; Magnetic Resonance Imaging/methods ; Risk Assessment

Background: Prognostic evaluation is important for personalized treatment in children with medulloblastoma (MB). Limited data are available for risk stratification using a radiomics-based model.
Purpose: To evaluate the incremental value of an MRI radiomics signature in stratifying the risk of pediatric MB in terms of overall survival (OS).
Study Type: Retrospective.
Subjects: A total of 111 children (mean age 5.82 years) with pathologically confirmed MB divided into training and validation cohorts (77 and 34 children, respectively).
Field Strength/sequence: A 3 T, contrast-enhanced T1-weighted imaging with inversion recovery.
Assessment: The study endpoint was OS defined as the time between the preoperative MRI study and death or last follow-up. The radiomics signature model and a clinical-MRI model were developed for personalized OS prediction. An integrative model, which combined the radiomics signature and clinical-MRI features, was also built using multivariable Cox regression model. The performance of the three models was evaluated with the C-index. The performance of integrative model was assessed by calibration curve and decision curve analysis (DCA).
Statistical Tests: Independent T-test, Mann-Whitney U test, Fisher's exact tests or chi-square test, logistic regression analysis, Kaplan-Meier survival analysis, C-index, intraclass correlation coefficients (ICC). P < 0.05 was considered statistically significant.
Results: The media OS was 2.83 years (3.87 ± 1.85 years). Two clinical and one conventional MR imaging features (remnant, adjuvant treatment, and peritumoral edema) were selected for clinical-MRI model building. The integrative model evaluated OS (C-index 0.823) better than either the radiomics signature (C-index 0.702) or the clinical-MRI model (C-index 0.771). And it also showed good performance in the validation cohort (C-indices: 0.786, 0.756, 0.721), which was validated by the good calibration (P > 0.05) and more benefit.
Data Conclusions: This study demonstrated that the integrative model, which combined radiomics signature, clinical, and conventional MRI features, showed best performance in OS evaluation for children with MB. The radiomics signature may confer incremental value over clinical-MRI features.
Evidence Level: 3.
Technical Efficacy: Stage 2.
(© 2022 International Society for Magnetic Resonance in Medicine.)

Ostrom QT, Patil N, Cioffi G, et al. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017. Neuro Oncol 2020;22(12 Suppl 2):iv1-iv96.
Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro Oncol 2021;23(8):1231-1251.
Holgado BL, Guerreiro Stucklin A, Garzia L, Daniels C, Taylor MD. Tailoring medulloblastoma treatment through genomics: Making a change, one subgroup at a time. Annu Rev Genomics Hum Genet 2017;18:143-166.
Schwalbe EC, Lindsey JC, Nakjang S, et al. Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: A cohort study. Lancet Oncol 2017;18:958-971.
Ellison DW, Kocak M, Dalton J, et al. Definition of disease-risk stratification groups in childhood medulloblastoma using combined clinical, pathologic, and molecular variables. J Clin Oncol 2011;29:1400-1407.
Ramaswamy V, Remke M, Bouffet E, et al. Risk stratification of childhood medulloblastoma in the molecular era: The current consensus. Acta Neuropathol 2016;131(6):821-831.
Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366(10):883-892.
Perreault S, Ramaswamy V, Achrol AS, et al. MRI surrogates for molecular subgroups of medulloblastoma. AJNR Am J Neuroradiol 2014;35(7):1263-1269.
Mata-Mbemba D, Zapotocky M, Laughlin S, Taylor MD, Ramaswamy V, Raybaud C. Characteristics of primary tumors and metastatic lesions in molecular subgroups of pediatric medulloblastoma: A single-center study. AJNR Am J Neuroradiol 2018;39(5):949-955.
Dasgupta A, Gupta T, Pungavkar S, et al. Nomograms based on pre-operative multi-parametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: Results from a radiogenomics study of 111 patients. Neuro Oncol 2019;21(1):115-124.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016;278:563-577.
Park H, Lim Y, Ko ES, et al. Radiomics signature on magnetic resonance imaging: Association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res 2018;24:4705-4714.
Yang L, Dong D, Fang M, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol 2018;5:2058-2067.
Lu CF, Hsu FT, Hsieh KL, et al. Machine learning-based radiomics for molecular subtyping of glioma. Clin Cancer Res 2018;24(18):4429-4436.
Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a based on texture analysis: a feasibility study. Eur Radiol 2018;28(11):4514-4523.
Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M. A new challenge for radiologists: Radiomics in breast cancer. Biomed Res Int 2018;2018:1-10.
Zhang M, Wong SW, Wright JN, et al. MRI Radiogenomics of pediatric medulloblastoma: A multicenter study. Radiology 2022;304(2):406-416.
Yan J, Zhang S, Li KK. Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma. EBioMedicine 2020;61:103093.
Lassaletta A, Ramaswamy V. Medulloblastoma in adults: They're not just big kids. Neuro Oncol 2016;18(7):895-897.
Chang FC, Wong TT, Wu KS, et al. Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric medulloblastoma. PLoS One 2021;16(7):e0255500.
Dasgupta A, Gupta T, Pungavkar S, et al. Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: Results from a radiogenomics study of 111 patients. Neuro Oncol 2019;21(1):115-124.
Mata-Mbemba D, Zapotocky M, Laughlin S, Taylor MD, Ramaswamy V, Raybaud C. MRI characteristics of primary tumors and metastatic lesions in molecular subgroups of pediatric medulloblastoma: A single-center study. AJNR Am J Neuroradiol 2018;39(5):949-955.
Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 2017;23:7253-7262.
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104-e107.
Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control 1974;19(6):716-723.
Gönen M, Heller G. Concordance probability and discriminatory power in proportional hazards regression. Biometrika 2005;92(4):965-970.
Massimino M, Biassoni V, Gandola L, et al. Childhood medulloblastoma. Crit Rev Oncol Hematol 2016;105:35-51.
Millard NE, De Braganca KC. Medulloblastoma. J Child Neurol 2016;31(12):1341-1353.
Kocakaya S, Beier CP, Beier D. Chemotherapy increases long-term survival in patients with adult medulloblastoma-A literature-based meta-analysis. Neuro Oncol 2016;18(3):408-416.
Rutkowski S, von Hoff K, Emser A, et al. Survival and prognostic factors of early childhood medulloblastoma: An international meta-analysis. J Clin Oncol 2010;28(33):4961-4968.
Pietsch T, Schmidt R, Remke M, et al. Prognostic significance of clinical, histopathological, and molecular characteristics of medulloblastomas in the prospective HIT2000 multicenter clinical trial cohort. Acta Neuropathol 2014;128(1):137-149.
Cavalli FMG, Remke M, Rampasek L, et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 2017;31(6):737-754.
Grossmann P, Narayan V, Chang K, et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 2017;12:1688-1697.
Kickingereder P, Burth S, Wick A, et al. Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016;280(3):880-889.
Han Y, Xie Z, Zang Y, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol 2018;140(2):297-306.
Iv M, Zhou M, Shpanskaya K, et al. MR imaging-based radiomic signatures of distinct molecular subgroups of medulloblastoma. AJNR Am J Neuroradiol 2019;40(1):154-161.
Liu ZM, Zhang H, Ge M, Hao XL, An X, Tian YJ. Radiomics signature for the prediction of progression-free survival and radiotherapeutic benefits in pediatric medulloblastoma. Childs Nerv Syst 2022;38(6):1085-1094.
Kuno H, Qureshi MM, Chapman MN, et al. CT texture analysis potentially predicts local failure in head and neck squamous cell carcinoma treated with chemoradiotherapy. AJNR Am J Neuroradiol 2017;38(12):2334-2340.
Yang Y, Park DS, Huang S, et al. Fusion of CT and MR images using an improved wavelet based method.J Xray. Sci Technol 2010;18(2):157-170.
Hu T, Wang S, Huang L, et al. A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectalcancer patients with indeterminate pulmonary nodules. Eur Radiol 2018;29:439-449.
Fan L, Fang M, Li Z, et al. Radiomics signature: A biomarker for the preoperative discrimination of lung invasiveadenocarcinoma manifesting as a ground-glass nodule. Eur Radiol 2018;29(2):889-897.

SHDC12021119 Shanghai Hospital Development Center; 20204Y0164 Shanghai Municipal Health Commission

Keywords: children; medulloblastoma; overall survival; radiomics

Date Created: 20221122 Date Completed: 20230612 Latest Revision: 20230612

20230612

10.1002/jmri.28537

36412264