Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Hollon, Todd C.; Jiang, Cheng; Chowdury, Asadur; Nasir-Moin, Mustafa; Kondepudi, Akhil; Aabedi, Alexander A; Adapa, Arjun; Al-Holou, Wajd N.; Heth, Jason; Sagher, Oren; Löwenstein, Pedro R.; Castro, Maria R H; Wadiura, Lisa Irina; Widhalm, Georg; Neuschmelting, Volker; Reinecke, David; Spreckelsen, Niklas von; Berger, Mitchel S.; Hervey-Jumper, Shawn L.; Golfinos, John G.; Snuderl, Matija; Camelo-Piragua, Sandra; Freudiger, Christian W.; Lee, Honglak; Orringer, Daniel A.

doi:10.1038/s41591-023-02252-4

Cited by 40 publications

(16 citation statements)

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“…The pursuit of precision medicine has involved more than a century-long effort to comprehend the human brain -the most enigmatic and intricate human organ 1 . The advent of artificial intelligence (AI) has made significant progress in its ability to decipher various aspects of human brain health 2,3 , such as normal brain aging 4 , neurodegenerative disorders like Alzheimer's disease (AD) 5 , and brain cancer 6 . Utilizing magnetic resonance imaging (MRI), the AI-derived human brain age [7][8][9] has emerged as a valuable biomarker for the evaluation of brain health.…”

Section: Mainmentioning

confidence: 99%

“…The advent of artificial intelligence (AI) has provided novel approaches to investigate various aspects of human brain health 1,2 , such as normal brain aging 3 , neurodegenerative disorders such as Alzheimer's disease (AD) 4 , and brain cancer 5 . Based on magnetic resonance imaging (MRI), AI-derived measures of the human brain age [6][7][8] have emerged as a valuable biomarker for evaluating brain health.…”

Section: Mainmentioning

confidence: 99%

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The Genetic Architecture of Multimodal Human Brain Age

Wen

Zhao

Yang

et al. 2023

Preprint

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The complex biological mechanisms underlying human brain aging remain incompletely understood. To investigate this, we utilized multimodal magnetic resonance imaging and artificial intelligence (AI) to examine the genetic heterogeneity of the brain age gap (BAG) derived from gray matter volume (GM-BAG), white matter tract (WM-BAG), and functional connectivity (FC-BAG). Sixteen significant genomic loci were identified, with GM-BAG loci showing abundant associations with neurodegenerative and neuropsychiatric traits, WM-BAG for cancer and Alzheimer's disease (AD), and FC-BAG for only insomnia. The gene-drug-disease network further corroborated these associations by highlighting genes linked to GM-BAG for the treatment of neurodegenerative and neuropsychiatric disorders, and WM-BAG genes for cancer therapy. GM-BAG showed the highest enrichment of heritability in conserved regions, while in WM-BAG, the 5' untranslated regions exhibited the highest heritability enrichment; oligodendrocytes and astrocytes showed significant heritability enrichment in WM and FC-BAG, respectively. Notably, Mendelian randomization identified risk causal effects of triglyceride-to-lipid ratio in VLDL and type 2 diabetes on GM-BAG, and AD on WM-BAG. These findings suggest that interventions targeting these factors and diseases may ameliorate human brain health. Overall, our results provide valuable insights into the genetic heterogeneity of human brain aging, with potential implications for lifestyle and therapeutic interventions.

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Section: Mainmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

The Genetic Architecture of Multimodal Human Brain Age

Wen

Zhao

Yang

et al. 2023

Preprint

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“…The third channel combines the two Raman shift wave numbers to produce an SRH image. By acquiring a mosaic out of 400 × 400 μm SRH field of views at a speed of 2 s per frame and subsequent image calculation, a virtual H&E like image in the size of 3.06 mm 2 was created ( 17 – 25 ). The two-photon fluorescence signal is detected in reflection of the sample ( Figure 1B ).…”

Section: Methodsmentioning

confidence: 99%

Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence

Fürtjes,

Reinecke,

von Spreckelsen

et al. 2023

Front. Oncol.

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IntroductionThe intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence.MethodsWith this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured.ResultsIn healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum.DiscussionIn conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

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“…Therefore, non-invasive imaging-based tumor molecular subtyping, if accurate and reliable, could enable proper selection of patients for BRAF-targeted therapies and clinical trials. In recent years, deep learning (DL) has emerged as the forefront technology for analyzing medical images (6,7), and has demonstrated numerous successful applications, encompassing tumor segmentation (8–10), outcome prediction (11,12), tumor and molecular classification (13,14). However, DL performance degrades dramatically in limited data scenarios, due to instability, overfitting, and shortcut learning (15), and a key barrier to applying DL to pLGG imaging, is the lack of training data available for these rare tumor cases.…”

Section: Introductionmentioning

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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Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Cited by 40 publications

References 30 publications

The Genetic Architecture of Multimodal Human Brain Age

The Genetic Architecture of Multimodal Human Brain Age

Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence

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

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