Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study

Kazerooni, Anahita Fathi; Arif, Sherjeel; Madhogarhia, Rachel; Khalili, Nastaran; Haldar, Debanjan; Bagheri, Sina; Familiar, Ariana; Anderson, Hannah; Haldar, Shuvanjan; Tu, Wenxin; Kim, Meen Chul; Viswanathan, Karthik; Müller, Sabine; Prados, Michael; Kline, Cassie; Vidal, Lorenna; Aboian, Mariam; Storm, Phillip B.; Resnick, Adam; Ware, Jeffrey B.; Vossough, Arastoo; Davatzikos, Christos; Nabavizadeh, Ali

doi:10.1101/2023.01.02.22284037

Cited by 5 publications

(6 citation statements)

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“…43,44 The results on the external cohort were similar for WT, an indication of model generalizability and robustness when applied to independent data with different imaging and patient characteristics. Although results were inferior for TC segmentation for the external cohort (mean Dice=0.74), they are comparable to the 0.62–0.74 Dice scores reported in a recent study of automatic segmentation of subregions of pediatric brain tumors 26 . Our results are also comparable with results of the winning method (TC Dice=0.78, WT Dice=0.82) 38 for pediatric brain tumor segmentation challenge 2023 39…”

Section: Discussionsupporting

confidence: 79%

“…The mean Dice scores of 0.91 for TC and 0.88 for WT of cross-validation on the internal cohort were comparable to those reported for adult GBM segmentation using state-of-the-art deep learning models. 42,43 Although worse than the internal cohort, TC segmentation for the external cohort (mean Dice=0.74) is still comparable to the 0.62–0.74 Dice scores reported in a recent study of automatic segmentation of subregions of pediatric brain tumors 26 .…”

Section: Discussionsupporting

confidence: 76%

“…Such tools should be automatic, objective, and easy to use in multi-institutional clinical trials. With the vast advancements in deep learning techniques, there has been tremendous success in automatic segmentation of brain tumors from MRI, including adult and 23,24 pediatric brain tumors, 25,26 including our previous work focused on the segmentation of pediatric DMG 27,28 . These advancements have the potential to create a fully automatic, image-based radiomic analysis and DMG prognostic tool.…”

Section: Introductionmentioning

confidence: 99%

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Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study

Liu,

Jiang,

Roth

et al. 2023

Preprint

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BackgroundDiffuse midline gliomas (DMG) are aggressive pediatric brain tumors. MRI is the standard non-invasive tool for DMG diagnosis and monitoring. We developed an automatic pipeline to segment subregions of DMG and select radiomic features to predict patient overall survival (OS).MethodsWe acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, and T2 FLAIR) and manual segmentations of 53 (internal cohort) and 16 (external cohort) DMG patients. We pretrained a deep learning model on an adult brain tumor dataset, and finetuned the model on our internal cohort to segment tumor core (TC) and whole tumor (WT). PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic features (baseline study) and the other used both diagnostic and post-RT features (post-RT study).ResultsFor segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12/0.74 (0.83)±0.32 for TC and 0.88 (0.91)±0.07/0.86 (0.89)±0.06 for WT of internal/external cohorts. For OS prediction, accuracy was 77%/81% for the baseline study and 85%/78% for the post-RT study of internal/external cohorts. Our results suggest post-RT features are more discriminative and reliable compared with diagnostic features. Smaller post-RT TC/WT volume ratio indicates longer OS. Our model predicts with high accuracy which patients have short OS.ConclusionsWe demonstrated how a fully automatic approach to compute imaging biomarkers of DMG from multisequence MRI can accurately and non-invasively predict overall survival for impacted pediatric patients.KEYPOINTSThis is the first fully automatic deep learning/machine learning MRI study to predict DMG survival.Post-radiation therapy features are more discriminative and reliable than diagnostic features. Smaller post-radiation therapy tumor core/whole tumor volume ratio indicates better prognosis.IMPORTANCE OF STUDYPrevious studies on pediatric DMG prognostication relied on manual tumor segmentation, which is time-consuming and has high inter-operator variability. There is a great need for non-invasive prognostic imaging tools that can be universally used. Such tools should be automatic, objective, and easy to use in multi-institutional clinical trials. We developed a fully automatic imaging tool to segment subregions of DMG and select radiomic features to predict patient overall survival (OS). Our acquired 4 sequences of MRI for each patient, at both diagnostic and post-radiation therapy from 2 institutions, were more comprehensive than previous studies. The proposed method achieved high accuracy in DMG segmentation and survival prediction, especially for patients having short OS. The proposed method will be the foundation of increasing the utility of MRI as a tool for predicting clinical outcome, stratifying patients into risk-groups for improved therapeutic management and monitoring therapeutic response with greater sensitivity and an opportunity to adapt treatment.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

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Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study

Liu,

Jiang,

Roth

et al. 2023

Preprint

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“…We report our results in accordance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines (18). A portion of patients from the CBTN dataset (n=140) and an additional subset from the BCH dataset (n=100) had been utilized in two previous studies (10,19). It’s worth highlighting that these prior investigations were centered around tumor segmentation, whereas the present study was primarily dedicated to identifying BRAF mutational subtypes.…”

Section: Methodsmentioning

confidence: 99%

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning

Tak

Zapaishchykova

et al. 2023

Preprint

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PurposeTo develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG.Materials and MethodsWe conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: Boston Children’s Hospital (development dataset, N=214), and Child Brain Tumor Network (CBTN) (external validation, N=112). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wild-type) from whole-scan input via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. We investigated knowledge-transfer approaches to prevent model overfitting with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist that quantifies the accuracy of model attention with respect to the tumor.ResultsA combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest AUC, taken as a weighted average across the three mutational classes, (0.82 [95% CI: 0.70-0.90], Accuracy: 77%) on internal validation and (0.73 [95% CI 0.68-0.88], Accuracy: 75%) on external validation. Training with TransferX also led to an AUC improvement of 17.7% and a COMDist Improvement of 6.42% over training from scratch on the development dataset.ConclusionTransfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario.Summary StatementThe authors developed and externally validated an automated, scan-to-prediction deep learning pipeline that classifies BRAF Mutational status in pediatric low-grade gliomas directly from T2-Weighted MRI scans.Key ResultsAn innovative training approach combining self-supervision and transfer learning (“TransferX”) is developed to boost model performance in low data settings;TransferX enables the development of a scan-to-prediction pipeline for pediatric LGG mutational status (BRAF V600E, fusion, or wildtype) with high accuracy and mild performance degradation on external validation;An evaluation metric “COMDist” is proposed to increase interpretability and quantify the accuracy of the model’s attention around the tumor.

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“…Such tools should be automatic, objective, and easy to use in multi-institutional clinical trials. With the vast advancements in deep learning techniques, there has been tremendous success in automatic segmentation of brain tumors from MRI, including adult, 23 , 24 pediatric brain tumors, 25 , 26 and our previous work of segmenting DMG. 27 , 28 These advancements have the potential to enable us to create a fully automatic, image-based radiomic analysis, and DMG prognostic tool.…”

mentioning

confidence: 99%

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study

Liu,

Jiang,

Roth

et al. 2024

Neuro-Oncology Advances

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Background Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS). Methods We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers: 53 from one center formed the internal cohort and 16 from the other center formed the external cohort. We pretrained a deep learning model on a public adult brain tumor dataset (BraTS 2021), and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 12-month survival from diagnosis. One model used only data obtained at diagnosis prior to any therapy (baseline study) and the other used data at both diagnosis and post-RT (post-RT study). Results OS prediction accuracy was 77% and 81% for the baseline study, and 85% and 78% for the post-RT study, for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS. Conclusions Machine learning analysis of MRI radiomics has potential to accurately and non-invasively predict which pediatric patients with DMG will survive less than 12 months from the time of diagnosis to provide patient stratification and guide therapy.

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Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study

Cited by 5 publications

References 41 publications

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study

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