scholarly journals Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics

2020 ◽  
Author(s):  
Yoon Seong Choi ◽  
Sohi Bae ◽  
Jong Hee Chang ◽  
Seok-Gu Kang ◽  
Se Hoon Kim ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Hybrid Approach ◽  
University Hospital ◽  
Tumor Segmentation ◽  
Idh Mutation ◽  
Mr Images ◽  
Mutation Status ◽  
Severance Hospital ◽  
National University ◽  
Fluid Attenuated Inversion Recovery

Abstract Background Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics. Methods We reviewed 1166 preoperative MR images of gliomas (grades II–IV) from Severance Hospital (n = 856), Seoul National University Hospital (SNUH; n = 107), and The Cancer Imaging Archive (TCIA; n = 203). The Severance set was subdivided into the development (n = 727) and internal test (n = 129) sets. Based on T1 postcontrast, T2, and fluid-attenuated inversion recovery images, a fully automated model was developed that comprised a CNN for tumor segmentation (Model 1) and CNN-based classifier for IDH status prediction (Model 2) that uses a hybrid approach based on 2D tumor images and radiomic features from 3D tumor shape and loci guided by Model 1. The trained model was tested on internal (a subset of the Severance set) and external (SNUH and TCIA) test sets. Results The CNN for tumor segmentation (Model 1) achieved a dice coefficient of 0.86–0.92 across datasets. Our hybrid model achieved accuracies of 93.8%, 87.9%, and 78.8%, with areas under the receiver operating characteristic curves of 0.96, 0.94, and 0.86 and areas under the precision-recall curves of 0.88, 0.82, and 0.81 in the internal test, SNUH, and TCIA sets, respectively. Conclusions Our fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

scholarly journals A Novel Fully Automated Mri-Based Deep Learning Method for Classification of Idh Mutation Status in Brain Gliomas

2019 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Bhavya R Shah ◽  
Maryam Vejdani-Jahromi ◽  
Sahil S Nalawade ◽  
Gowtham K Murugesan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Brain Mri ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Idh Mutation ◽  
Mr Images ◽  
Post Contrast ◽  
Mutation Status ◽  
Brain Gliomas

Abstract Background Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly-accurate, MRI-based, voxel-wise deep-learning IDH-classification network using T2-weighted (T2w) MR images and compare its performance to a multi-contrast network. Methods Multi-parametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Two separate networks were developed including a T2w image only network (T2-net) and a multi-contrast (T2w, FLAIR, and T1 post-contrast) network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the networks’ performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy. Results T2-net demonstrated a mean cross-validation accuracy of 97.14% ±0.04 in predicting IDH mutation status, with a sensitivity of 0.97 ±0.03, specificity of 0.98 ±0.01, and an AUC of 0.98 ±0.01.  TS-net achieved a mean cross-validation accuracy of 97.12% ±0.09, with a sensitivity of 0.98 ±0.02, specificity of 0.97 ±0.001, and an AUC of 0.99 ±0.01. The mean whole tumor segmentation Dice-scores were 0.85 ±0.009 for T2-net and 0.89 ±0.006 for TS-net. Conclusion We demonstrate high IDH classification accuracy using only T2-weighted MR images. This represents an important milestone towards clinical translation.

scholarly journals A Novel Fully Automated MRI-Based Deep Learning Method for Classification of IDH Mutation Status in Brain Gliomas

2019 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Bhavya R. Shah ◽  
Maryam Vejdani-Jahromi ◽  
Sahil S. Nalawade ◽  
Gowtham K. Murugesan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Cross Validation ◽  
Clinical Translation ◽  
Tumor Segmentation ◽  
Idh Mutation ◽  
Wild Type ◽  
Mr Images ◽  
Mutation Status ◽  
Important Milestone

ABSTRACTBackgroundIsocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly-accurate, MRI-based, voxel-wise deep-learning IDH-classification network using T2-weighted (T2w) MR images and compare its performance to a multi-contrast network.MethodsMulti-parametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Two separate networks were developed including a T2w image only network (T2-net) and a multi-contrast (T2w, FLAIR, and T1 post-contrast), network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D-Dense-UNets. A three-fold cross-validation was performed to generalize the networks’ performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy.ResultsT2-net demonstrated a mean cross-validation accuracy of 97.14% +/-0.04 in predicting IDH mutation status, with a sensitivity of 0.97 +/-0.03, specificity of 0.98 +/-0.01, and an AUC of 0.98 +/-0.01. TS-net achieved a mean cross-validation accuracy of 97.12% +/-0.09, with a sensitivity of 0.98 +/-0.02, specificity of 0.97 +/-0.001, and an AUC of 0.99 +/-0.01. The mean whole tumor segmentation Dice-scores were 0.85 +/-0.009 for T2-net and 0.89 +/-0.006 for TS-net.ConclusionWe demonstrate high IDH classification accuracy using only T2-weighted MRI. This represents an important milestone towards clinical translation.Keypoints – 1IDH status is an important prognostic marker for gliomas. 2. We developed a non-invasive, MRI based, highly accurate deep-learning method for the determination of IDH status 3. The deep-learning networks utilizes only T2 weighted MR images to predict IDH status thereby facilitating clinical translation.IMPORTANCE OF THE STUDYOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation status as a marker for therapy and prognosis. The mutated form of the gene confers a better prognosis and treatment response than gliomas with the non-mutated or wild-type form. Currently, the only reliable way to determine IDH mutation status is to obtain glioma tissue either via an invasive brain biopsy or following open surgical resection. The ability to non-invasively determine IDH mutation status has significant implications in determining therapy and predicting prognosis. We developed a highly accurate, deep learning network that utilizes only T2-weighted MR images and outperforms previously published methods. The high IDH classification accuracy of our T2w image only network (T2-net) marks an important milestone towards clinical translation. Imminent clinical translation is feasible because T2-weighted MR imaging is widely available and routinely performed in the assessment of gliomas.

scholarly journals A Novel Fully Automated MRI-Based Deep Learning Method for Classification of 1P/19Q Co-Deletion Status in Brain Gliomas

2020 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Bhavya R. Shah ◽  
Frank F. Yu ◽  
Marco C. Pinho ◽  
Sahil S. Nalawade ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Tumor Segmentation ◽  
Idh Mutation ◽  
Mr Images ◽  
Mutation Status ◽  
Learning Network ◽  
Genomic Marker ◽  
Deep Learning Network ◽  
Brain Gliomas

ABSTRACTBackgroundOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network.MethodsMulti-parametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 1p/19 co-deletions were present in 130 subjects. 238 subjects were non co-deleted. A T2w image only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Threefold cross-validation was performed to generalize the network performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy.Results1p/19q-net demonstrated a mean cross validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, standard dev=0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ±0.003 and 0.95 ±0.01, respectively and a mean AUC of 0.95 ±0.01. The whole tumor segmentation mean Dice-score was 0.80 ± 0.007.ConclusionWe demonstrate high 1p/19q co-deletion classification accuracy using only T2-weighted MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.Keypoints1. 1p/19 co-deletion status is an important genetic marker for gliomas. 2. We developed a non-invasive, MRI based, highly accurate deep-learning method for the determination of 1p/19q co-deletion status that only utilizes T2 weighted MR imagesIMPORTANCE OF THE STUDYOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Currently, the only reliable way to determine 1p/19q mutation status requires analysis of glioma tissue obtained either via an invasive brain biopsy or following open surgical resection. The ability to non-invasively determine 1p/19q co-deletion status has significant implications in determining therapy and predicting prognosis. We developed a highly accurate, deep learning network that utilizes only T2-weighted MR images and outperforms previously published imagebased methods. The high classification accuracy of our T2w image only network (1p/19q-net) in predicting 1p/19q co-deletion status marks an important step towards image-based stratification of brain gliomas. Imminent clinical translation is feasible because T2-weighted MR imaging is widely available and routinely performed in the assessment of gliomas.

scholarly journals Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 691
Author(s):  
Nhu-Tai Do ◽  
Sung-Taek Jung ◽  
Hyung-Jeong Yang ◽  
Soo-Hyung Kim
Keyword(s):  
Malignant Tumor ◽  
Bone Tumor ◽  
Tumor Detection ◽  
University Hospital ◽  
Tumor Segmentation ◽  
Bone Lesions ◽  
Global Context ◽  
Liver Cancers ◽  
National University ◽  
Radiographic Interpretation

Tumor classification and segmentation problems have attracted interest in recent years. In contrast to the abundance of studies examining brain, lung, and liver cancers, there has been a lack of studies using deep learning to classify and segment knee bone tumors. In this study, our objective is to assist physicians in radiographic interpretation to detect and classify knee bone regions in terms of whether they are normal, begin-tumor, or malignant-tumor regions. We proposed the Seg-Unet model with global and patched-based approaches to deal with challenges involving the small size, appearance variety, and uncommon nature of bone lesions. Our model contains classification, tumor segmentation, and high-risk region segmentation branches to learn mutual benefits among the global context on the whole image and the local texture at every pixel. The patch-based model improves our performance in malignant-tumor detection. We built the knee bone tumor dataset supported by the physicians of Chonnam National University Hospital (CNUH). Experiments on the dataset demonstrate that our method achieves better performance than other methods with an accuracy of 99.05% for the classification and an average Mean IoU of 84.84% for segmentation. Our results showed a significant contribution to help the physicians in knee bone tumor detection.

scholarly journals NI-12 The ratio of T1-Weighted to T2-Weighted Signal Intensity and IDH mutation in glioma

2021 ◽  
Vol 3 (Supplement_6) ◽  
pp. vi20-vi20
Author(s):  
Takahiro Sanada ◽  
Shota Yamamoto ◽  
Hirotaka Sato ◽  
Mio Sakai ◽  
Masato Saito ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Region Of Interest ◽  
University Hospital ◽  
Lower Grade ◽  
Idh Mutation ◽  
Mutation Status ◽  
Weighted Image ◽  
Clinically Significant ◽  
Tcga Dataset ◽  
T2 Weighted Images

Abstract Introduction: Prediction of IDH mutation status for Lower-grade glioma (LrGG) is clinically significant. The purpose of this study is to test the hypothesis that the T1-weighted image/T2-weighted image ratio (rT1/T2), an imaging surrogate developed for myelin integrity, is a useful MRI biomarker for predicting the IDH mutation status of LrGG. Methods: Twenty-five LrGG patients (IDHwt: 8, IDHmt: 17) at Asahikawa Medical University Hospital (AMUH) were used as an exploratory cohort. Twenty-nine LrGG patients (IDHwt: 13, IDHmt: 16) from Osaka International Cancer Institute (OICI) and 103 patients from the Cancer Imaging Archive (TCIA) / Cancer Genome Atlas (TCGA) dataset (IDHwt: 19, IDHmt: 84) were used as validation cohorts. rT1/T2 images were calculated from T1- and T2-weighted images using a recommended signal correction. The region-of-interest was defined on T2-weighted images, and the relationship between the mean rT1/T2 (mrT1/T2) and the IDH mutation status was investigated. Results: The mrT1/T2 was able to significantly predict the IDH mutation status for the AMUH exploratory cohort (AUC = 0.75, p = 0.048). The ideal cut-off for detecting mutant IDH was mrT1/T2 < 0.666 ~ 0.677, with a sensitivity of 58.8% and a specificity of 87.5%. This result was further validated by the OICI validation cohort (AUC = 0.75, p = 0.023) with a sensitivity of 56.3% and a specificity of 69.2%. On the other hand, the sensitivity was 42.9% and the specificity was 68.4 % for the TCIA validation cohort (AUC = 0.63, p = 0.068). Conclusion: Our results supported the hypothesis that mrT1/T2 could be a useful image surrogate to predict the IDH mutation status of LrGG using two domestic cohorts. The decline of the accuracy for the TCIA cohort should be further investigated.

scholarly journals COMP-03. QUANTITATIVE IMAGE FEATURE ANALYSIS IN DIFFUSE GLIOMA – A VALUABLE MR IMAGING BIOMARKER FOR PREOPERATIVE IDH MUTATION CLASSIFICATION

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi61-vi61
Author(s):  
Eric Carver ◽  
James Snyder ◽  
Brent Griffith ◽  
Ning Wen
Keyword(s):  
Mr Imaging ◽  
Image Features ◽  
Image Feature ◽  
Feature Analysis ◽  
Imaging Features ◽  
Idh Mutation ◽  
Mr Images ◽  
Mutation Status ◽  
Non Invasive ◽  
Quantitative Image

Abstract INTRODUCTION Pre-operative differentiation of IDH mutant gliomas from similar appearing pathologies on imaging prior to definitive surgical diagnosis may aid treatment navigation, maximize the surgical approach, and provide diagnostic support for inoperable tumors. Quantitative image feature analysis offers a potential non-invasive method to identify diagnostic, prognostic, and predictive imaging biomarkers. We investigated the use of radiomic MR imaging features to classify tumors based on IDH mutation status. METHOD Pre-operative T1-weighted (T1W), T2-weighted (T2W), T1-contrast enhanced (T1CE), and fluid attenuated inversion recovery (FLAIR) MR brain images, along with patient IDH mutation status (mutant/wildtype) were obtained for 128 glioma patients from The Cancer Genome Atlas (TCGA). Enhancing tumor was delineated by GLISTRboost. GlistrBoost is a hybrid-discriminative model that segments tumors based on an expectation-maximization framework with a classification scheme and uses a probabilistic Bayesian strategy for segmentation refinement. MR studies for 78 glioma patients from six institutions were used for training and 50 glioma patients from a different institution were used for validation. Pre-processing included registration, resampling, and normalization. Cancer Imaging Phenomics Toolkit (CaPTK) extracted 938 radiomic image features per sequence for the enhancing tumor contour. Relevance of each individual feature was determined by the least absolute shrinkage and selection operator (LASSO). The ability of relevant radiomic image features to identify mutation status of IDH was assessed by logistic regression. RESULTS LASSO identified one highly informative radiomic imaging feature, the minimum of the mean absolute histogram deviation on T1 MR images, which was able to predict IDH mutation status with an accuracy of 0.74, precision of 1.0, and recall of 0.32. CONCLUSION Non-invasive prediction of IDH mutation status from pre-surgical MR images offers potential diagnostic, therapeutic, and prognostic benefits for glioma patients. Quantitative image feature analysis is a feasible method for identifying potential radiomic imaging features.

scholarly journals Segmentation of Brain Gliomas Based on a Multi-modal Multi-scale Double-pathway 3D Residual CNN

2020 ◽  
Author(s):  
Mingyuan Pan ◽  
Yonghong Shi ◽  
Zhijian Song
Keyword(s):  
Conditional Random Field ◽  
Radiotherapy Planning ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Pathway Network ◽  
Multi Scale ◽  
Fluid Attenuated Inversion Recovery ◽  
Fully Connected ◽  
Brain Gliomas

Abstract Background: The automated segmentation of brain gliomas regions in magnetic resonance (MR) images plays an important role in the early diagnosis, intraoperative navigation, radiotherapy planning and prognosis of brain tumors. It is very challenging to segment gliomas and intratumoral structures since the location, size, shape, edema range and boundary of gliomas are heterogeneous, and multimodal brain gliomas images (such as T1, T2, fluid-attenuated inversion recovery (FLAIR), and T1c images) are collected from multiple radiation centers. Methods: This paper presents a multimodal, multi-scale, double-pathway, 3D residual convolution neural network (CNN) for automatic gliomas segmentation. First, a robust gray-level normalization method is proposed to solve the multicenter problem, such as very different intensity ranges due to different imaging protocols. Second, a multi-scale, double-pathway network based on DeepMedic toolkit is trained with different combinations of multimodal MR images for gliomas segmentation. Finally, a fully connected conditional random field (CRF) is used as a post-processing strategy to optimize the segmentation results for addressing the isolated segmentations and holes. Results: Experiments on the Multimodal Brain Tumor Segmentation (BraTS) 2017 and 2019 challenge data show that our methods achieve a good performance in delineating the whole tumor with a Dice coefficient, a sensitivity and a positive predictive value (PPV) of 0.88, 0.89 and 0.88, respectively. Regarding the segmentation of the tumor core and the enhancing area, the sensitivity reached 0.80. Conclusions: Experiments show that our method can accurately segment gliomas and intratumoral structures from multimodal MR images, and it is of great significance to clinical neurosurgery.

Intestinal parasite survey of Kyungpook National University Hospital patients

1971 ◽  
Vol 9 (2) ◽  
pp. 47 ◽  
Author(s):  
Dong Wik Choi ◽  
Sung Deok Park ◽  
Jae Woun Kim ◽  
Doo Hong Ahn ◽  
Young Myung Kim
Keyword(s):  
Intestinal Parasite ◽  
University Hospital ◽  
Hospital Patients ◽  
National University

Intraoperative assessment of isocitrate dehydrogenase mutation status in human gliomas using desorption electrospray ionization–mass spectrometry

2020 ◽  
Vol 132 (1) ◽  
pp. 180-187 ◽  
Author(s):  
Clint M. Alfaro ◽  
Valentina Pirro ◽  
Michael F. Keating ◽  
Eyas M. Hattab ◽  
R. Graham Cooks ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Isocitrate Dehydrogenase ◽  
Tumor Resection ◽  
Desorption Electrospray Ionization ◽  
Extent Of Resection ◽  
Ionization Mass ◽  
Idh Mutation ◽  
Mutation Status ◽  
Desi Ms

OBJECTIVEThe authors describe a rapid intraoperative ambient ionization mass spectrometry (MS) method for determining isocitrate dehydrogenase (IDH) mutation status from glioma tissue biopsies. This method offers new glioma management options and may impact extent of resection goals. Assessment of the IDH mutation is key for accurate glioma diagnosis, particularly for differentiating diffuse glioma from other neoplastic and reactive inflammatory conditions, a challenge for the standard intraoperative diagnostic consultation that relies solely on morphology.METHODSBanked glioma specimens (n = 37) were analyzed by desorption electrospray ionization–MS (DESI-MS) to develop a diagnostic method to detect the known altered oncometabolite in IDH-mutant gliomas, 2-hydroxyglutarate (2HG). The method was used intraoperatively to analyze tissue smears obtained from glioma patients undergoing resection and to rapidly diagnose IDH mutation status (< 5 minutes). Fifty-one tumor core biopsies from 25 patients (14 wild type [WT] and 11 mutant) were examined and data were analyzed using analysis of variance and receiver operating characteristic curve analysis.RESULTSThe optimized DESI-MS method discriminated between IDH-WT and IDH-mutant gliomas, with an average sensitivity and specificity of 100%. The average normalized DESI-MS 2HG signal was an order of magnitude higher in IDH-mutant glioma than in IDH-WT glioma. The DESI 2HG signal intensities correlated with independently measured 2HG concentrations (R2 = 0.98). In 1 case, an IDH1 R132H–mutant glioma was misdiagnosed as a demyelinating condition by frozen section histology during the intraoperative consultation, and no resection was performed pending the final pathology report. A second craniotomy and tumor resection was performed after the final pathology provided a diagnosis most consistent with an IDH-mutant glioblastoma. During the second craniotomy, high levels of 2HG in the tumor core biopsies were detected.CONCLUSIONSThis study demonstrates the capability to differentiate rapidly between IDH-mutant gliomas and IDH-WT conditions by DESI-MS during tumor resection. DESI-MS analysis of tissue smears is simple and can be easily integrated into the standard intraoperative pathology consultation. This approach may aid in solving differential diagnosis problems associated with low-grade gliomas and could influence intraoperative decisions regarding extent of resection, ultimately improving patient outcome. Research is ongoing to expand the patient cohort, systematically validate the DESI-MS method, and investigate the relationships between 2HG and tumor heterogeneity.

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