Ben Shofty1,2, Moran Artzi2,3, Dafna Ben Bashat4,5,6, Gilad Liberman7, Oz Haim1, Alon Kashanian1,2, Felix Bokstein2,8, Deborah T Blumenthal2,8, Zvi Ram1,2, Tal Shahar1,9. 1. Division of Neurosurgery, Tel Aviv Sourasky Medical Center, 6 Weizman St., 64239, Tel Aviv, Israel. 2. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. 3. The Functional Brain Center, Tel Aviv Sourasky Medical Center, 6 Weizman St., 64239, Tel Aviv, Israel. 4. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. dafnab@tlvmc.gov.il. 5. The Functional Brain Center, Tel Aviv Sourasky Medical Center, 6 Weizman St., 64239, Tel Aviv, Israel. dafnab@tlvmc.gov.il. 6. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. dafnab@tlvmc.gov.il. 7. Department of Chemical Physics, Weizmann Institute of Science, Rehovot, Israel. 8. Neuro-Oncology Service, Tel-Aviv Medical Center, Tel Aviv, Israel. 9. Department of Neurosurgery, Shaare Zedek Medical Center, Jerusalem, Israel.
Abstract
PURPOSE: Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tumor sampling via neurosurgical procedures. The aim of this study was to evaluate the radiomics approach for noninvasive classification of patients with LGG and IDH mutation, based on their 1p/19q codeletion status, by testing different classifiers and assessing the contribution of the different MR contrasts. METHODS: Preoperative MRI scans of 47 patients diagnosed with LGG with IDH1-mutated tumors and a genetic analysis for 1p/19q deletion status were included in this study. A total of 152 features, including size, location and texture, were extracted from fluid-attenuated inversion recovery images, [Formula: see text]-weighted images (WI) and post-contrast [Formula: see text]. Classification was performed using 17 machine learning classifiers. Results were evaluated by a fivefold cross-validation analysis. RESULTS: Radiomic analysis differentiated tumors with 1p/19q intact ([Formula: see text]; astrocytomas) from those with 1p/19q codeleted ([Formula: see text]; oligodendrogliomas). Best classification was obtained using the Ensemble Bagged Trees classifier, with sensitivity [Formula: see text] 92%, specificity [Formula: see text] 83% and accuracy [Formula: see text] 87%, and with area under the curve [Formula: see text] 0.87. Tumors with 1p/19q intact were larger than those with 1p/19q codeleted ([Formula: see text] vs. [Formula: see text] cc, respectively; [Formula: see text]) and predominantly located to the left insula ([Formula: see text]). CONCLUSION: The proposed method yielded good discrimination between LGG with and without 1p/19q codeletion. Results from this study demonstrate the great potential of this method to aid decision-making in the clinical management of patients with LGG.
PURPOSE: Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tumor sampling via neurosurgical procedures. The aim of this study was to evaluate the radiomics approach for noninvasive classification of patients with LGG and IDH mutation, based on their 1p/19q codeletion status, by testing different classifiers and assessing the contribution of the different MR contrasts. METHODS: Preoperative MRI scans of 47 patients diagnosed with LGG with IDH1-mutated tumors and a genetic analysis for 1p/19q deletion status were included in this study. A total of 152 features, including size, location and texture, were extracted from fluid-attenuated inversion recovery images, [Formula: see text]-weighted images (WI) and post-contrast [Formula: see text]. Classification was performed using 17 machine learning classifiers. Results were evaluated by a fivefold cross-validation analysis. RESULTS: Radiomic analysis differentiated tumors with 1p/19q intact ([Formula: see text]; astrocytomas) from those with 1p/19q codeleted ([Formula: see text]; oligodendrogliomas). Best classification was obtained using the Ensemble Bagged Trees classifier, with sensitivity [Formula: see text] 92%, specificity [Formula: see text] 83% and accuracy [Formula: see text] 87%, and with area under the curve [Formula: see text] 0.87. Tumors with 1p/19q intact were larger than those with 1p/19q codeleted ([Formula: see text] vs. [Formula: see text] cc, respectively; [Formula: see text]) and predominantly located to the left insula ([Formula: see text]). CONCLUSION: The proposed method yielded good discrimination between LGG with and without 1p/19q codeletion. Results from this study demonstrate the great potential of this method to aid decision-making in the clinical management of patients with LGG.
Authors: K Yamashita; A Hiwatashi; O Togao; K Kikuchi; R Hatae; K Yoshimoto; M Mizoguchi; S O Suzuki; T Yoshiura; H Honda Journal: AJNR Am J Neuroradiol Date: 2015-09-24 Impact factor: 3.825
Authors: Philipp Kickingereder; David Bonekamp; Martha Nowosielski; Annekathrin Kratz; Martin Sill; Sina Burth; Antje Wick; Oliver Eidel; Heinz-Peter Schlemmer; Alexander Radbruch; Jürgen Debus; Christel Herold-Mende; Andreas Unterberg; David Jones; Stefan Pfister; Wolfgang Wick; Andreas von Deimling; Martin Bendszus; David Capper Journal: Radiology Date: 2016-09-16 Impact factor: 11.105
Authors: M C Zlatescu; A TehraniYazdi; H Sasaki; J F Megyesi; R A Betensky; D N Louis; J G Cairncross Journal: Cancer Res Date: 2001-09-15 Impact factor: 12.701
Authors: David N Louis; Arie Perry; Guido Reifenberger; Andreas von Deimling; Dominique Figarella-Branger; Webster K Cavenee; Hiroko Ohgaki; Otmar D Wiestler; Paul Kleihues; David W Ellison Journal: Acta Neuropathol Date: 2016-05-09 Impact factor: 17.088
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
Authors: D Williams Parsons; Siân Jones; Xiaosong Zhang; Jimmy Cheng-Ho Lin; Rebecca J Leary; Philipp Angenendt; Parminder Mankoo; Hannah Carter; I-Mei Siu; Gary L Gallia; Alessandro Olivi; Roger McLendon; B Ahmed Rasheed; Stephen Keir; Tatiana Nikolskaya; Yuri Nikolsky; Dana A Busam; Hanna Tekleab; Luis A Diaz; James Hartigan; Doug R Smith; Robert L Strausberg; Suely Kazue Nagahashi Marie; Sueli Mieko Oba Shinjo; Hai Yan; Gregory J Riggins; Darell D Bigner; Rachel Karchin; Nick Papadopoulos; Giovanni Parmigiani; Bert Vogelstein; Victor E Velculescu; Kenneth W Kinzler Journal: Science Date: 2008-09-04 Impact factor: 47.728
Authors: Biqi Zhang; Ken Chang; Shakti Ramkissoon; Shyam Tanguturi; Wenya Linda Bi; David A Reardon; Keith L Ligon; Brian M Alexander; Patrick Y Wen; Raymond Y Huang Journal: Neuro Oncol Date: 2016-06-26 Impact factor: 13.029
Authors: Minjae Kim; So Yeong Jung; Ji Eun Park; Yeongheun Jo; Seo Young Park; Soo Jung Nam; Jeong Hoon Kim; Ho Sung Kim Journal: Eur Radiol Date: 2019-12-11 Impact factor: 5.315
Authors: Evi J van Kempen; Max Post; Manoj Mannil; Benno Kusters; Mark Ter Laan; Frederick J A Meijer; Dylan J H A Henssen Journal: Cancers (Basel) Date: 2021-05-26 Impact factor: 6.639