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[2602.21613] Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Summary
This article presents a novel Virtual Biopsy framework for diagnosing intracranial tumors using MRI, addressing the challenges of traditional biopsy methods and achieving over 90% accuracy in tumor localization and diagnosis.
Why It Matters
The development of a non-invasive diagnostic method for intracranial tumors is crucial due to the risks associated with traditional biopsies. This research contributes to improving clinical decision-making and patient outcomes by providing a reliable alternative that leverages advanced machine learning techniques.
Key Takeaways
- Introduces the ICT-MRI dataset, the first public biopsy-verified benchmark for intracranial tumors.
- Proposes a Virtual Biopsy framework that enhances MRI diagnostics through advanced machine learning techniques.
- Achieves over 90% accuracy in tumor localization, significantly outperforming existing methods.
- Addresses data scarcity and sampling bias challenges in tumor diagnosis.
- Highlights the importance of non-invasive techniques in modern clinical practices.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21613 (cs) [Submitted on 25 Feb 2026] Title:Virtual Biopsy for Intracranial Tumors Diagnosis on MRI Authors:Xinzhe Luo, Shuai Shao, Yan Wang, Jiangtao Wang, Yutong Bai, Jianguo Zhang View a PDF of the paper titled Virtual Biopsy for Intracranial Tumors Diagnosis on MRI, by Xinzhe Luo and 5 other authors View PDF HTML (experimental) Abstract:Deep intracranial tumors situated in eloquent brain regions controlling vital functions present critical diagnostic challenges. Clinical practice has shifted toward stereotactic biopsy for pathological confirmation before treatment. Yet biopsy carries inherent risks of hemorrhage and neurological deficits and struggles with sampling bias due to tumor spatial heterogeneity, because pathological changes are typically region-selective rather than tumor-wide. Therefore, advancing non-invasive MRI-based pathology prediction is essential for holistic tumor assessment and modern clinical decision-making. The primary challenge lies in data scarcity: low tumor incidence requires long collection cycles, and annotation demands biopsy-verified pathology from neurosurgical experts. Additionally, tiny lesion volumes lacking segmentation masks cause critical features to be overwhelmed by background noise. To address these challenges, we construct the ICT-MRI dataset - the first public biopsy-verified benchmark with 249 cases across four categories. We propose a Virtual Biopsy framew...
