Building a national repository of dural-based lesions: clinical, pathological, and demographic insights from the Indian population.
Authors
Affiliations (24)
Affiliations (24)
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India. Electronic address: [email protected].
- Department of Neuroradiology, All India Institute of Medical Sciences, New Delhi, India.
- Department of Neuropathology, All India Institute of Medical Sciences, New Delhi, India.
- Department of Computational Data Science, Indian Institute of Science (IISc), Bengaluru, India.
- Department of Neurosurgery, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
- Department of Neurosurgery, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.
- Department of Neurosurgery, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India.
- Department of Neurosurgery, King George's Medical University (KGMU), Lucknow, India.
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhubaneswar, India.
- Department of Neurosurgery, Sree Chitra Tirunal Institute of Medical Sciences & Technology, Thiruvananthapuram, Kerala, India.
- Indian Council of Medical Research, Delhi, India.
- Department of Neuropathology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
- Department of Neuroradiology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
- Department of Neuroradiology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.
- Department of Pathology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.
- Department of Pathology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India.
- Department of Radiodiagnosis, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India.
- Department of Neuroradiology, King George's Medical University (KGMU), Lucknow, India.
- Department of Neuropathology, King George's Medical University (KGMU), Lucknow, India.
- Department of Neuropathology, All India Institute of Medical Sciences, Bhubaneswar, India.
- Department of Neuroradiology, All India Institute of Medical Sciences, Bhubaneswar, India.
- Department of Radiology, Sree Chitra Tirunal Institute of Medical Sciences & Technology (SCTIMST), Thiruvananthapuram, Kerala, India.
- Department of Pathology, Sree Chitra Tirunal Institute of Medical Sciences & Technology (SCTIMST), Thiruvananthapuram, Kerala, India.
Abstract
Meningiomas are the most common dural-based intracranial tumors, yet Indian literature is predominantly composed of limited single-center studies, restricting nationwide representation and data-driven decision making. With artificial intelligence (AI) becoming increasingly relevant in neuro-oncology for diagnosis, segmentation, and outcome prediction, the lack of a large, standardized national dataset poses a major barrier. The Medical Imaging Datasets for India (MIDAS) initiative, a collaborative national effort involving ICMR, IISc, and ARTPARK, aims to create high-quality, annotated medical imaging repositories that can support clinical research and AI model development. As a part of this initiative, we developed a multicenter national repository of dural-based lesions. This ambispective study included patients with radiologically suspected and histopathologically confirmed dural-based lesions from seven neurosurgical centers across India (January 2022-July 2025). Standardized de-identified demographic, clinical, imaging, and pathological data were collected. Imaging was archived in DICOM format and annotated using ITK-SNAP, while histopathology followed WHO-2021 CNS tumor guidelines. Statistical analysis was performed using descriptive and comparative measures. Among 586 patients, women constituted two-thirds of the cohort, with a mean age of 47.2 years. Meningiomas accounted for 98.3 % of cases and were predominantly WHO Grade I, most commonly of transitional and meningothelial subtypes. Convexity, parasagittal, and falcine locations were most frequently involved. A small but important proportion of lesions were non-meningiomatous, including schwannomas, solitary fibrous tumors, granulomatous, and metastatic lesions. Simpson Grade II resection was the most common surgical outcome, and a subset of patients underwent postoperative adjuvant radiosurgery. This MIDAS-linked national repository represents the largest structured dataset of dural-based lesions from India, integrating standardized clinical, imaging, and pathological information across multiple centers. In addition to defining national disease patterns, the availability of curated imaging and volumetric segmentations provides a strong translational platform for future artificial intelligence-based research, including automated segmentation, diagnostic classification, and outcome prediction.