Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG

Since the late eighties, Superparamagnetic Iron Oxide Nanoparticles (SPIONs) have been extensively studied for their exceptional ability in targeted drug delivery, typically coerced by magnetic fields and delivered at chemotherapy sites.

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Publication Details

Journal
Annals of Biostatistics & Biometric Applications
Published
2025-03-27
DOI
https://doi.org/10.33552/abba.2025.06.000639
Primary Topic
Mathematical Biology Tumor Growth
Type
article
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article

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG

Amit Chattopadhyay
Annals of Biostatistics & Biometric Applications
Mathematical Biology Tumor Growth
article

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG

Amit Chattopadhyay
article en

Abstract

Since the late eighties, Superparamagnetic Iron Oxide Nanoparticles (SPIONs) have been extensively studied for their exceptional ability in targeted drug delivery, typically coerced by magnetic fields and delivered at chemotherapy sites.

Annals of Biostatistics & Biometric ApplicationsVol. 6(3)
National College of Ireland (IE)
Openalex Percentile: Top 79%
Mathematical Biology Tumor Growth
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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG — Amit Chattopadhyay · Annals of Biostatistics & Biometric Applications (2025) | TGRS Research Map | TGRS