MATHEMATICAL MODELING AND IMAGE PROCESSING TECHNIQUES FOR EARLY DETECTION OF TUMOR HETEROGENEITY IN MEDICAL IMAGING.
Tumor heterogeneity is a significant cause of treatment resistance and poor clinical outcomes, and the medical images used to diagnose and treat patients are routinely interpreted in a largely qualitative manner and may not reflect subtle patterns of heterogeneity early in disease progression. This...
| Publicado en: | Scientific Culture Vol. 12; no. 5 Part 1; pp. 160 - 171 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
University of the Aegean
2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=193975184&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 193975184 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 5 Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 193975184 10.5281/zenodo.12511014 ppf: 160 ppct: 11 formats: tig: atl: MATHEMATICAL MODELING AND IMAGE PROCESSING TECHNIQUES FOR EARLY DETECTION OF TUMOR HETEROGENEITY IN MEDICAL IMAGING. aug: au: Balaji, C. Aryan, Rishabh Ahuja, Rachit Bansal, Prabhat Bandyopadhyay, Krishnan Kumar, Prem affil: Assistant Professor, Department of Computer Applications, SRM Institute of Science and Technology Tiruchirappalli M.Tech (Artificial Intelligence and Data Science), Department of Computer Science and Engineering,, Indian Institute of Information Technology, Bhagalpur (Bihar) Assistant Professor, Department of Radiation Oncology, Shri Guru Ram Rai Institute of Medical and Health Sciences Assistant Professor, Institute of Applied Sciences, Mangalayatan University, Aligarh, Uttar Pradesh Assistant Professor, Department of Medical Electronics Engineering, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bangalore, Karnataka Assistant Professor Computer Science & Engineering (CS&DS) Brainware University, Kolkata su: Image processing Diagnostic imaging Entropy (Information theory) Tumor microenvironment Logistic regression analysis Mathematical models Detection algorithms Computer-assisted image analysis (Medicine) sug: subj: Image processing Diagnostic imaging Entropy (Information theory) Tumor microenvironment Logistic regression analysis Mathematical models Detection algorithms Computer-assisted image analysis (Medicine) keyword: Entropy mapping Mathematical modeling Medical imaging Radiomics Tumor heterogeneity ab: Tumor heterogeneity is a significant cause of treatment resistance and poor clinical outcomes, and the medical images used to diagnose and treat patients are routinely interpreted in a largely qualitative manner and may not reflect subtle patterns of heterogeneity early in disease progression. This study is aimed at proposing an integrated mathematical modeling and image processing framework for quantification of intra-tumoral heterogeneity and early detection using medical imaging. Paired magnetic resonance and computed tomography brain tumor images along with the expert-annotated segmentation masks were analyzed. Following the data ingestion and quality control process, images were standardized and modality specific preprocessing pipelines were followed. Tumor regions of interest were extracted in order to calculate morphological descriptors, first order radiomic features, texture measures, and multi-scale entropy maps. A composite heterogeneity index was developed from the combination of entropy statistics, dispersion measures, and spatial autocorrelation to describe both the variation of intensity and the organization of space in tumors. Slices-level heterogeneity measures were aggregated to patients level and an interpretable L1 regularized logistic regression model was applied for the early vs. advanced heterogeneity discrimination under patient-wise cross-validation. The proposed heterogeneity-informed model showed stable performance and obvious contributions of spatial heterogeneity components. The results show that mathematically derived heterogeneity indices can be robust and interpretable imaging biomarkers to aid in the early characterization of tumor complexity and provide motivation for their validation in larger, clinically annotated cohorts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
|---|