IPD-Brain: An Indian histopathology dataset for glioma subtype classification.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-19 DOI:10.1038/s41597-024-04225-9

Ekansh Chauhan, Amit Sharma, Megha S Uppin, Manasa Kondamadugu, C V Jawahar, P K Vinod

{"title":"IPD-Brain: An Indian histopathology dataset for glioma subtype classification.","authors":"Ekansh Chauhan, Amit Sharma, Megha S Uppin, Manasa Kondamadugu, C V Jawahar, P K Vinod","doi":"10.1038/s41597-024-04225-9","DOIUrl":null,"url":null,"abstract":"<p><p>The effective management of brain tumors relies on precise typing, subtyping, and grading. We present the IPD-Brain Dataset, a crucial resource for the neuropathological community, comprising 547 high-resolution H&E stained slides from 367 patients for the study of glioma subtypes and immunohistochemical biomarkers. Scanned at 40x magnification, this dataset is one of the largest in Asia, specifically focusing on the Indian demographics. It encompasses detailed clinical annotations, including patient age, sex, radiological findings, diagnosis, CNS WHO grade, and IHC biomarker status (IDH1R132H, ATRX and TP53 along with proliferation index, Ki67), providing a rich foundation for research. The dataset is open for public access and is designed for various applications, from machine learning model training to the exploration of regional and ethnic disease variations. Preliminary validations utilizing Multiple Instance Learning for tasks such as glioma subtype classification and IHC biomarker identification underscore its potential to significantly contribute to global collaboration in brain tumor research, enhancing diagnostic precision and understanding of glioma variability across different populations.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1403"},"PeriodicalIF":5.8000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04225-9","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

The effective management of brain tumors relies on precise typing, subtyping, and grading. We present the IPD-Brain Dataset, a crucial resource for the neuropathological community, comprising 547 high-resolution H&E stained slides from 367 patients for the study of glioma subtypes and immunohistochemical biomarkers. Scanned at 40x magnification, this dataset is one of the largest in Asia, specifically focusing on the Indian demographics. It encompasses detailed clinical annotations, including patient age, sex, radiological findings, diagnosis, CNS WHO grade, and IHC biomarker status (IDH1R132H, ATRX and TP53 along with proliferation index, Ki67), providing a rich foundation for research. The dataset is open for public access and is designed for various applications, from machine learning model training to the exploration of regional and ethnic disease variations. Preliminary validations utilizing Multiple Instance Learning for tasks such as glioma subtype classification and IHC biomarker identification underscore its potential to significantly contribute to global collaboration in brain tumor research, enhancing diagnostic precision and understanding of glioma variability across different populations.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.