Areeba Patel

Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes

Darui Jin, Artem Shmatko, Areeba Patel, Samuel Rutz et autres

Molecular testing is essential for classifying central nervous system (CNS) tumors, with methylation profiling providing the highest diagnostic granularity. However, this requires more resources and time than conventional hematoxylin and eosin (H&E) histopathology, which is widely available globally. Here we propose Hetairos, …

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Whole-Genome Long-Read Sequencing for Rapid Comprehensive Molecular Diagnostics of Brain Tumors

Skarphéðinn Halldórsson, Richard Mark Nagymihaly, Christian Domilongo Bope, Marius Lund‐Iversen et autres

Abstract Background Classification of central nervous system (CNS) tumors has become increasingly complex over the past decade, raising concerns about the availability, feasibility and sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to …

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The prognostic impact of CDKN2A/B hemizygous deletions in meningioma

Franziska M Ippen, Thomas Hielscher, Areeba Patel, Dennis Friedel et autres

BACKGROUND: Meningiomas are the most common adult brain tumors. While homozygous deletions of CDKN2A/B are linked to early recurrence and hence serve as CNS WHO grade 3 criterion, the clinical impact of hemizygous deletions remains unclear-especially since distinguishing between hemi- and homozygous …

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A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification

Sybren L. N. Maas, Yiheng Tang, Eric Y. Stutheit-Zhao, Ramin Rahmanzade et autres

Classification of tumors in neuro-oncology today relies on molecular patterns (mostly DNA methylation) and their machine learning-supported interpretation. Understanding the process of algorithmic interpretation is essential for safe application in clinical routine. This is paradigmatically true for the most common primary intracranial …

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Advancing CNS tumor diagnostics with expanded DNA methylation-based classification

Martin Sill, Daniel Schrimpf, Areeba Patel, Dominik Sturm et autres

Advancing CNS tumor diagnostics with expanded DNA methylation-based classification

Martin Sill, Daniel Schrimpf, Areeba Patel, Dominik Sturm et autres

DNA methylation-based classification is now central to contemporary neuro-oncology, as highlighted by the World Health Organization (WHO) classification of central nervous system (CNS) tumors. We present the Heidelberg CNS Tumor Methylation Classifier version 12.8 (v12.8), trained on 7,495 methylation profiles, which expands …

de, dk, us, ch, ca, se, fr, at, it, au, gb, nl, lu (code pays fourni par la source)

Employing nanopore sequencing on FFPE-derived DNA for CNS tumor diagnostics

Paul Kerbs, Daniel Haag, Henri Bogumil, Areeba Patel et autres

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PATH-75. Hetairos enables AI-based histopathological classification of CNS tumours

Areeba Patel, Darui Jin, Artem Shmatko, Ramin Rahmanzade et autres

Abstract Central nervous system (CNS) tumours encompass a diverse group of neoplasms with complex histological and molecular profiles. DNA methylation profiling has revolutionised their classification, enabling diagnosis at granular resolution. However, this approach remains limited by high costs, specialised infrastructure requirements, long …

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PATH-60. Advancing CNS tumor diagnostics with expanded DNA methylation-based classification

Martin Sill, Daniel Schrimpf, Areeba Patel, Dominik Sturm et autres

Abstract DNA methylation-based classification is integral to contemporary neuro-oncological diagnostics, as highlighted by the current World Health Organization (WHO) classification of central nervous system (CNS) tumors. We introduce the Heidelberg CNS Tumor Methylation Classifier version 12.8 (v12.8), trained using 7,495 methylation profiles, …

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EPCO-52. MACHINE LEARNING AND MULTI-OMIC ANALYSIS IDENTIFY A MICROENVIRONMENT-DRIVEN MENINGIOMA RISK CONTINUUM UNDERLYING MOLECULAR CLASSIFICATIONS

Sybren L. N. Maas, Yiheng Tang, Eric Stutheit-Zhao, Ramin Rahmanzade et autres

Abstract Machine learning-based molecular classifications, particularly those using DNA methylation data, have greatly advanced diagnostics for meningioma, the most common type of primary intracranial tumor. Meningiomas have historically been classified into NF2-mutant and NF2-wild-type groups, while additional mutations and copy-number variations associated …

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P07.16.B A NOVEL METHOD ENABLING THE DETECTION OF INTRACELLULAR DRUGS BY SEQUENCING ON SINGLE-CELL LEVEL IN GLIOBLASTOMA

Obada Alhalabi, Emma Phillips, Irmengard Sax, Areeba Patel et autres

Abstract BACKGROUND Glioblastoma is an aggressive and incurable primary brain tumor, primarily due to its resistance to standard therapies and imminent recurrence. The increasing importance of Single-Cell RNA Sequencing (scRNA-Seq) in deciphering tumor heterogeneity of glioblastoma (GB) unravels a further dimension to …

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OS12.4.A AI-BASED HISTOPATHOLOGICAL CLASSIFICATION OF CENTRAL NERVOUS SYSTEM TUMOURS

Dayong Jin, Artem Shmatko, Areeba Patel, Ramin Rahmanzade et autres

Abstract BACKGROUND DNA methylation profiling is central to CNS tumour diagnostics, providing molecular classification beyond conventional histopathology. However, its use is constrained by extended turnaround time, high costs and the need for specialised equipment. Recent developments in computational pathology have shown that …

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