Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.
BRAF fusion
Machine learning
Magnetic resonance imaging
Pediatric low-grade glioma
Radiomics
Journal
BMC cancer
ISSN: 1471-2407
Titre abrégé: BMC Cancer
Pays: England
ID NLM: 100967800
Informations de publication
Date de publication:
11 Sep 2023
11 Sep 2023
Historique:
received:
25
10
2022
accepted:
25
08
2023
medline:
13
9
2023
pubmed:
12
9
2023
entrez:
11
9
2023
Statut:
epublish
Résumé
We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. This study was retrospectively registered at clinicaltrials.gov (NCT04217018).
Sections du résumé
BACKGROUND
BACKGROUND
We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI.
METHODS
METHODS
61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation.
RESULTS
RESULTS
We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set.
CONCLUSIONS
CONCLUSIONS
The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity.
TRIAL REGISTRATION
BACKGROUND
This study was retrospectively registered at clinicaltrials.gov (NCT04217018).
Identifiants
pubmed: 37697238
doi: 10.1186/s12885-023-11338-8
pii: 10.1186/s12885-023-11338-8
pmc: PMC10496393
doi:
Substances chimiques
Proto-Oncogene Proteins B-raf
EC 2.7.11.1
Transcription Factors
0
Banques de données
ClinicalTrials.gov
['NCT04217018']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
848Subventions
Organisme : the National Key R&D Program of China
ID : 2019YFC0117704
Organisme : the Science and Technology Program of Henan Province
ID : 202102310136, 202102310138, 202102310113, 202102310083
Organisme : the Science and Technology Program of Henan Province
ID : 202102310136, 202102310138, 202102310113, 202102310083
Organisme : the Science and Technology Program of Henan Province
ID : 202102310136, 202102310138, 202102310113, 202102310083
Organisme : the National Natural Science Foundation of China
ID : 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148
Organisme : the National Natural Science Foundation of China
ID : 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148
Organisme : the National Natural Science Foundation of China
ID : 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148
Organisme : the National Natural Science Foundation of China
ID : 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148
Organisme : the Key-Area Research and Development Program of Guangdong Province
ID : 2021B0101420006
Organisme : the Excellent Youth Talent Cultivation Program of Innovation in Health Science and Technology of Henan Province
ID : YXKC2022061
Organisme : the Key Program of Medical Science and Technique Foundation of Henan Province
ID : SBGJ202002062
Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
Références
Raabe E, Kieran MW, Cohen KJ. New strategies in pediatric gliomas: molecular advances in pediatric low-grade gliomas as a model. Clin Cancer Res. 2013;19(17):4553–8.
doi: 10.1158/1078-0432.CCR-13-0662
pubmed: 23881924
pmcid: 4696061
Yang RR, Aibaidula A, Wang WW, Chan AK, Shi ZF, Zhang ZY, Chan DTM, Poon WS, Liu XZ, Li WC, et al. Pediatric low-grade gliomas can be molecularly stratified for risk. Acta Neuropathol. 2018;136(4):641–55.
doi: 10.1007/s00401-018-1874-3
pubmed: 29948154
Sturm D, Pfister SM, Jones DTW. Pediatric Gliomas: current concepts on diagnosis, Biology, and Clinical Management. J Clin Oncol. 2017;35(21):2370–7.
doi: 10.1200/JCO.2017.73.0242
pubmed: 28640698
Wisoff JH, Sanford RA, Heier LA, Sposto R, Burger PC, Yates AJ, Holmes EJ, Kun LE. Primary neurosurgery for pediatric low-grade gliomas: a prospective multi-institutional study from the children’s Oncology Group. Neurosurgery. 2011;68(6):1548–54. discussion 1554 – 1545.
doi: 10.1227/NEU.0b013e318214a66e
pubmed: 21368693
Jones DT, Hutter B, Jäger N, Korshunov A, Kool M, Warnatz HJ, Zichner T, Lambert SR, Ryzhova M, Quang DA, et al. Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma. Nat Genet. 2013;45(8):927–32.
doi: 10.1038/ng.2682
pubmed: 23817572
pmcid: 3951336
Zhang J, Wu G, Miller CP, Tatevossian RG, Dalton JD, Tang B, Orisme W, Punchihewa C, Parker M, Qaddoumi I, et al. Whole-genome sequencing identifies genetic alterations in pediatric low-grade gliomas. Nat Genet. 2013;45(6):602–12.
doi: 10.1038/ng.2611
pubmed: 23583981
pmcid: 3727232
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127–57.
doi: 10.3322/caac.21552
pubmed: 30720861
pmcid: 6403009
Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, Kavouridis VK, Senders JT, Boaro A, Beers A, et al. Residual convolutional neural network for the determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging. Clin Cancer Res. 2018;24(5):1073–81.
doi: 10.1158/1078-0432.CCR-17-2236
pubmed: 29167275
Yan J, Zhang B, Zhang S, Cheng J, Liu X, Wang W, Dong Y, Zhang L, Mo X, Chen Q, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precis Oncol. 2021;5(1):72.
doi: 10.1038/s41698-021-00205-z
pubmed: 34312469
pmcid: 8313682
Macyszyn L, Akbari H, Pisapia JM, Da X, Attiah M, Pigrish V, Bi Y, Pal S, Davuluri RV, Roccograndi L, et al. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol. 2016;18(3):417–25.
doi: 10.1093/neuonc/nov127
pubmed: 26188015
Yan J, Zhang S, Sun Q, Wang W, Duan W, Wang L, Ding T, Pei D, Sun C, Wang W, et al. Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study. Lab Invest. 2022;102(2):154–9.
doi: 10.1038/s41374-021-00692-5
pubmed: 34782727
Wagner MW, Hainc N, Khalvati F, Namdar K, Figueiredo L, Sheng M, Laughlin S, Shroff MM, Bouffet E, Tabori U, et al. Radiomics of Pediatric Low-Grade Gliomas: toward a Pretherapeutic differentiation of BRAF-Mutated and BRAF-Fused tumors. AJNR Am J Neuroradiol. 2021;42(4):759–65.
doi: 10.3174/ajnr.A6998
pubmed: 33574103
pmcid: 8040992
Kursa MB, Rudnicki WR. Feature selection with the Boruta Package. J Stat Softw. 2010;36(11):1–13.
doi: 10.18637/jss.v036.i11
Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
doi: 10.1023/A:1010933404324
Shur JD, Doran SJ, Kumar S, Ap Dafydd D, Downey K, O’Connor JPB, Papanikolaou N, Messiou C, Koh DM, Orton MR. Radiomics in Oncology: a practical guide. Radiographics. 2021;41(6):1717–32.
doi: 10.1148/rg.2021210037
pubmed: 34597235
Ostrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro Oncol. 2015;17(Suppl 4):iv1–iv62.
doi: 10.1093/neuonc/nov189
pubmed: 26511214
pmcid: 4623240
Sievert AJ, Fisher MJ. Pediatric low-grade gliomas.
Dahiya S, Haydon DH, Alvarado D, Gurnett CA, Gutmann DH, Leonard JR. BRAF(V600E) mutation is a negative prognosticator in pediatric ganglioglioma. Acta Neuropathol. 2013;125(6):901–10.
doi: 10.1007/s00401-013-1120-y
pubmed: 23609006
Dimitriadis E, Alexiou GA, Tsotsou P, Simeonidi E, Stefanaki K, Patereli A, Prodromou N, Pandis N. BRAF alterations in pediatric low grade gliomas and mixed neuronal-glial tumors. J Neurooncol. 2013;113(3):353–8.
doi: 10.1007/s11060-013-1131-5
pubmed: 23612919
Dougherty MJ, Santi M, Brose MS, Ma C, Resnick AC, Sievert AJ, Storm PB, Biegel JA. Activating mutations in BRAF characterize a spectrum of pediatric low-grade gliomas. Neuro Oncol. 2010;12(7):621–30.
doi: 10.1093/neuonc/noq007
pubmed: 20156809
pmcid: 2940652
Tatevossian RG, Tang B, Dalton J, Forshew T, Lawson AR, Ma J, Neale G, Shurtleff SA, Bailey S, Gajjar A, et al. MYB upregulation and genetic aberrations in a subset of pediatric low-grade gliomas. Acta Neuropathol. 2010;120(6):731–43.
doi: 10.1007/s00401-010-0763-1
pubmed: 21046410
pmcid: 3066475
Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765–73.
doi: 10.1056/NEJMoa0808710
pubmed: 19228619
pmcid: 2820383
Smith JS, Perry A, Borell TJ, Lee HK, O’Fallon J, Hosek SM, Kimmel D, Yates A, Burger PC, Scheithauer BW, et al. Alterations of chromosome arms 1p and 19q as predictors of survival in oligodendrogliomas, astrocytomas, and mixed oligoastrocytomas. J Clin Oncol. 2000;18(3):636–45.
doi: 10.1200/JCO.2000.18.3.636
pubmed: 10653879
Eckel-Passow JE, Lachance DH, Molinaro AM, Walsh KM, Decker PA, Sicotte H, Pekmezci M, Rice T, Kosel ML, Smirnov IV, et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med. 2015;372(26):2499–508.
doi: 10.1056/NEJMoa1407279
pubmed: 26061753
pmcid: 4489704
Johnson A, Severson E, Gay L, Vergilio JA, Elvin J, Suh J, Daniel S, Covert M, Frampton GM, Hsu S, et al. Comprehensive genomic profiling of 282 Pediatric Low- and High-Grade Gliomas reveals genomic drivers, Tumor Mutational Burden, and Hypermutation Signatures. Oncologist. 2017;22(12):1478–90.
doi: 10.1634/theoncologist.2017-0242
pubmed: 28912153
pmcid: 5728033
Horbinski C, Nikiforova MN, Hagenkord JM, Hamilton RL, Pollack IF. Interplay among BRAF, p16, p53, and MIB1 in pediatric low-grade gliomas. Neuro Oncol. 2012;14(6):777–89.
doi: 10.1093/neuonc/nos077
pubmed: 22492957
pmcid: 3367847
Kieran MW. Targeting BRAF in pediatric brain tumors. Am Soc Clin Oncol Educ Book 2014:e436–440.
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
doi: 10.1038/ncomms5006
pubmed: 24892406
Baeßler B, Weiss K, Pinto Dos Santos D. Robustness and reproducibility of Radiomics in magnetic resonance imaging: a Phantom Study. Invest Radiol. 2019;54(4):221–8.
doi: 10.1097/RLI.0000000000000530
pubmed: 30433891
Rizzetto F, Calderoni F, De Mattia C, Defeudis A, Giannini V, Mazzetti S, Vassallo L, Ghezzi S, Sartore-Bianchi A, Marsoni S, et al. Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases. Eur Radiol Exp. 2020;4(1):62.
doi: 10.1186/s41747-020-00189-8
pubmed: 33169295
pmcid: 7652946
Peng J, Kim DD, Patel JB, Zeng X, Huang J, Chang K, Xun X, Zhang C, Sollee J, Wu J, et al. Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors. Neuro Oncol. 2022;24(2):289–99.
doi: 10.1093/neuonc/noab151
pubmed: 34174070
Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH. Quality of Radiomic features in Glioblastoma Multiforme: impact of semi-automated Tumor Segmentation Software. Korean J Radiol. 2017;18(3):498–509.
doi: 10.3348/kjr.2017.18.3.498
pubmed: 28458602
pmcid: 5390619
Lu CF, Hsu FT, Hsieh KL, Kao YJ, Cheng SJ, Hsu JB, Tsai PH, Chen RJ, Huang CC, Yen Y, et al. Machine learning-based Radiomics for Molecular Subtyping of Gliomas. Clin Cancer Res. 2018;24(18):4429–36.
doi: 10.1158/1078-0432.CCR-17-3445
pubmed: 29789422
Maynard J, Okuchi S, Wastling S, Busaidi AA, Almossawi O, Mbatha W, Brandner S, Jaunmuktane Z, Koc AM, Mancini L, et al. World Health Organization Grade II/III Glioma Molecular Status: prediction by MRI morphologic features and apparent diffusion coefficient. Radiology. 2021;298(1):E61.
doi: 10.1148/radiol.2020209024
pubmed: 33347400
Kihira S, Tsankova NM, Bauer A, Sakai Y, Mahmoudi K, Zubizarreta N, Houldsworth J, Khan F, Salamon N, Hormigo A, et al. Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion. Neurooncol Adv. 2021;3(1):vdab051.
pubmed: 34056604
pmcid: 8156980
Humphries PD, Sebire NJ, Siegel MJ, Olsen ØE. Tumors in pediatric patients at diffusion-weighted MR imaging: apparent diffusion coefficient and tumor cellularity. Radiology. 2007;245(3):848–54.
doi: 10.1148/radiol.2452061535
pubmed: 17951348