Shai Shrot1,2, Moshe Salhov3, Nir Dvorski3, Eli Konen4,5, Amir Averbuch3, Chen Hoffmann4,5. 1. Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel, 2 Sheba Rd, 52621, Ramat Gan, Israel. Shai.Shrot@Sheba.health.gov.il. 2. Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel. Shai.Shrot@Sheba.health.gov.il. 3. School of Computer Science, Tel Aviv University, Tel Aviv, Israel. 4. Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel, 2 Sheba Rd, 52621, Ramat Gan, Israel. 5. Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Abstract
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors. METHODS: The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention. RESULTS: A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively. CONCLUSION: A machine learning scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a scheme can be integrated into clinical decision support systems to optimize tumor classification.
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors. METHODS: The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention. RESULTS: A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively. CONCLUSION: A machine learning scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a scheme can be integrated into clinical decision support systems to optimize tumor classification.
Authors: G I Cassinelli Petersen; J Shatalov; T Verma; W R Brim; H Subramanian; A Brackett; R C Bahar; S Merkaj; T Zeevi; L H Staib; J Cui; A Omuro; R A Bronen; A Malhotra; M S Aboian Journal: AJNR Am J Neuroradiol Date: 2022-03-31 Impact factor: 3.825
Authors: Norbert Galldiks; Frank Angenstein; Jan-Michael Werner; Elena K Bauer; Robin Gutsche; Gereon R Fink; Karl-Josef Langen; Philipp Lohmann Journal: Brain Pathol Date: 2022-03 Impact factor: 6.508
Authors: Darius Kalasauskas; Michael Kosterhon; Naureen Keric; Oliver Korczynski; Andrea Kronfeld; Florian Ringel; Ahmed Othman; Marc A Brockmann Journal: Cancers (Basel) Date: 2022-02-07 Impact factor: 6.639