U-Net Based Segmentation and Characterization of Gliomas

Kihira, Shingo; Mei, Xueyan; Mahmoudi, Keon; Liu, Zelong; Dogra, Siddhant; Belani, Puneet; Tsankova, Nadejda M.; Hormigo, Adı́lia; Fayad, Zahi A.; Doshi, Amish; Nael, Kambiz

doi:10.3390/cancers14184457

Cited by 21 publications

(14 citation statements)

References 57 publications

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“…When applying the trained model to the test set, our best classification performance dropped (AUC = 0.59). Similarly, previous studies that performed a validation phase obtained AUC values between 0.62 and 0.67 [25,[35][36][37].…”

Section: Discussionmentioning

confidence: 57%

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

Doniselli,

Pascuzzo,

Agrò

et al. 2023

IJMS

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The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients’ age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8–74.6%) accuracy and 0.572 (0.439–0.705) for AUC. The performances did not change when the patients’ age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

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Section: Discussionmentioning

confidence: 57%

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

Doniselli,

Pascuzzo,

Agrò

et al. 2023

IJMS

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“…Traditional lesion segmentation is performed manually, which is time-consuming and labor-intensive. Progress in the automatic segmentation of gliomas has been reported more frequently [ 28 – 31 ], but only few studies have attempted to segment spinal cord tumor automatically due to the lack of sufficient training in such rare tumors [ 8 , 10 ]. This study employed a deep learning strategy based on transformer architecture to segment IMGs, and relatively satisfactory segmentation results were obtained (the DSC was 0.8697 for SAG and 0.8738 for TRA).…”

Section: Discussionmentioning

confidence: 99%

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Wang

Song

et al. 2023

BMC Med

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Background Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. Methods A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. Results The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I–II or WHO III–IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. Conclusions This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.

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“…In September 2022 Shingo Kihira et al [32] developed a symmetric Deep Learning-based U-Net framework based on FLAIR's 512 _ 512 segmented maps as the ground truth mask. Their ndings: The nal group included 208 patients with an average _ standard deviation of age (years) of fty-six _ fteen and an M/F ratio of 130/78.…”

Section: K-means Algorithmmentioning

confidence: 99%

Brain tumor Radiogenomic Classification and Prediction of Glioblastoma Status using MRI Scans with Deep Learning Algorithms

AbdelAziz,

Dawood,

Hassan

et al. 2024

Preprint

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Accurate Radiogenomic classification of brain tumors play an essential role in improving prognosis, diagnosis, and therapy strategy in glioblastoma patients. In this paper, we provide a novel MGMT Promoter Methylation Prediction (MGMT-PMP) approach that extracts latent features fused with radiomic data to predict the genetic subtype of glioblastoma. The novice rejection method has been shown to be highly effective at picking and isolating negative training cases from the original dataset. In this study, the fused feature vectors are used to train and test CNN classifiers. We examined classification performance for the first time in published form, using metrics such as accuracy, F1-score, and Matthews’s correlation coefficient. Jackknife tenfold cross-validation was used to train and evaluate the BraTS-2021 dataset validation. The maximum classification accuracy for detecting MGMT methylation status in glioblastoma patients is 99.13 percent. Deep learning feature extraction with Radiogenomic characteristics, merging imaging phenotypes and molecular structure, and using a rejection method were found to outperform in diagnosing the MGMT methylation status of glioblastoma patients. The approach connects genetic variance to radiomic characteristics, bridging two areas of research that may be valuable for clinical treatment planning and leading to better outcomes.

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U-Net Based Segmentation and Characterization of Gliomas

Cited by 21 publications

References 57 publications

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Brain tumor Radiogenomic Classification and Prediction of Glioblastoma Status using MRI Scans with Deep Learning Algorithms

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