[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127548-en":3,"doc-seo-127548-105":31,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127548,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning","A retrospective MRI study evaluates fractal dimension (FD) measures as quantitative neuroimaging biomarkers for glioma grading, separating low-grade glioma (LGG) from high-grade glioma (HGG) by analyzing tumor and non-tumorous gray matter and white matter regions. FD features are computed from general structure, boundary, and skeleton representations, and are compared with texture features. Among 15 FD measures, enhanced tumor-region general-structure FD achieves high diagnostic performance (accuracy 93%, sensitivity 97%, specificity 98%, AUC 98%), while non-tumorous gray-matter skeleton FD also discriminates grades with strong sensitivity. FD features differ significantly between LGG and HGG (p \u003C 0.05) and complement texture-based findings.","TYPE Original Research PUBLISHED 17 July 2023  \nDOI 10.3389/fphys.2023.1201617  \nOPEN ACCESS  \nEDITED BY  \nRishi Raj Sharma,  \nDefence Institute of Advanced Technology (DIAT), India  \nREVIEWED BY  \nKandala N. V. P. S. Rajesh, VIT-AP University, India Rahul Dubey,  \nMadhav Institute of Technology and Science Gwalior, India  \n*CORRESPONDENCE  \nVenkateswaran Rajagopalan,  \n venkateswaran@hyderabad. bits[pilani.ac.in](pilani.ac.in)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 06 April 2023  \nACCEPTED 28 June 2023  \nPUBLISHED 17 July 2023  \nCITATION  \nBattalapalli D, Vidyadharan S, Prabhakar Rao BVVSN, Yogeeswari P, Kesavadas C and Rajagopalan V (2023), Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning. Front. Physiol. 14:1201617 .  \ndoi: 10.3389/fphys.2023.1201617  \nCOPYRIGHT  \n© 2023 Battalapalli, Vidyadharan, Prabhakar Rao, Yogeeswari, Kesavadasand Rajagopalan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nFractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning  \nDheerendranath Battalapalli 1†, Sreejith Vidyadharan 1†,  \nB. V. V. S. N. Prabhakar Rao 1, P. Yogeeswari 2, C. Kesavadas 3 and Venkateswaran Rajagopalan 1*  \n1Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India, 2Department of Pharmacy, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India, 3Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India  \nPurpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions.  \nMethods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and nontumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients.  \nResults: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%) . Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p \u003C 0. 05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy","cbCaivXIrDyWUUpU","https://ap.wps.com/l/cbCaivXIrDyWUUpU","pdf",1633500,2,1,12,"English","en",105,"# Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction\n# Highlights","[{\"question\":\"What is the study purpose regarding fractal dimension and glioma grading?\",\"answer\":\"The study investigates whether fractal dimension measures can discriminate gliomas into low-grade (LGG) and high-grade (HGG) by analyzing tumor tissue and non-tumorous gray matter and white matter regions.\"},{\"question\":\"How were the features extracted and evaluated?\",\"answer\":\"Retrospective MRI data from 42 glioma patients were used. Different FD measures were calculated for tumor and non-tumorous regions, and texture features were also measured; statistical inference and machine learning methods were applied to distinguish LGG from HGG.\"},{\"question\":\"Which results showed the best performance and what were the main findings?\",\"answer\":\"Enhanced tumor-region general-structure FD produced the highest performance (93% accuracy, 97% sensitivity, 98% specificity, 98% AUC). Non-tumorous gray-matter skeleton FD also performed well, and FD features significantly differed between LGG and HGG (p \\u003c 0.05).\"},{\"question\":\"How do FD features compare with texture features in classification insight?\",\"answer\":\"A comparison between texture and FD features indicates that FD analysis not only supports high-accuracy classification with statistical significance but also provides improved insights into glioma grade classification, suggesting FD features can serve as potential neuroimaging biomarkers.\"}]","Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning | PDF",1785939897,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":91,"head_meta":93,"extra_data":95,"updated_unix":29},"fractal-dimension-analyzing-its-potential-as-a-neuroimaging-biomarker-for-brain-tumor-diagnosis-using-machine-learning","",{"@graph":37,"@context":90},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/fractal-dimension-analyzing-its-potential-as-a-neuroimaging-biomarker-for-brain-tumor-diagnosis-using-machine-learning/127548/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What is the study purpose regarding fractal dimension and glioma grading?","Question",{"text":76,"@type":77},"The study investigates whether fractal dimension measures can discriminate gliomas into low-grade (LGG) and high-grade (HGG) by analyzing tumor tissue and non-tumorous gray matter and white matter regions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the features extracted and evaluated?",{"text":81,"@type":77},"Retrospective MRI data from 42 glioma patients were used. Different FD measures were calculated for tumor and non-tumorous regions, and texture features were also measured; statistical inference and machine learning methods were applied to distinguish LGG from HGG.",{"name":83,"@type":74,"acceptedAnswer":84},"Which results showed the best performance and what were the main findings?",{"text":85,"@type":77},"Enhanced tumor-region general-structure FD produced the highest performance (93% accuracy, 97% sensitivity, 98% specificity, 98% AUC). Non-tumorous gray-matter skeleton FD also performed well, and FD features significantly differed between LGG and HGG (p \u003C 0.05).",{"name":87,"@type":74,"acceptedAnswer":88},"How do FD features compare with texture features in classification insight?",{"text":89,"@type":77},"A comparison between texture and FD features indicates that FD analysis not only supports high-accuracy classification with statistical significance but also provides improved insights into glioma grade classification, suggesting FD features can serve as potential neuroimaging biomarkers.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,127,132,135,139],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":126},"research-report",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":47,"category_name":141,"show_sort_weight":111,"slug":142},19,"General","general"]