Predicting the Progression of Cancerous Tumors in Mice: A Machine and Deep Learning Intuition Article Swipe
Amit Chattopadhyay
,
Aimee Pascaline N Unkundiye
,
Gillian Pearce
,
Stephen R. Russell
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.19277
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.19277
The study explores Artificial Intelligence (AI) powered modeling to predict the evolution of cancer tumor cells in mice under different forms of treatment. The AI models are analyzed against varying ambient and systemic parameters, e.g. drug dosage, volume of the cancer cell mass, and time taken to destroy the cancer cell mass. The data required for the analysis have been synthetically extracted from plots available in both published and unpublished literature (primarily using a Matlab architecture called "Grabit"), that are then statistically standardized around the same baseline for comparison. Three forms of treatment are considered - saline (multiple concentrations used), magnetic nanoparticles (mNPs) and fluorodeoxyglycose iron oxide magnetic nanoparticles (mNP-FDGs) - analyzed using three Machine Learning (ML) algorithms, Decision Tree (DT), Random Forest (RF), Multilinear Regression (MLR), and a Deep Learning (DL) module, the Adaptive Neural Network (ANN). The AI models are trained on 60-80% data, the rest used for validation. Assessed over all three forms of treatment, ANN consistently outperforms other predictive models. Our models predict mNP-FDG as the most potent treatment regime that kills the cancerous tumor completely in ca 13 days from the start of treatment. The models can be generalized to other forms of cancer treatment regimens.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.19277
- https://arxiv.org/pdf/2407.19277
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4401201577Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.19277Digital Object Identifier
- Title
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Predicting the Progression of Cancerous Tumors in Mice: A Machine and Deep Learning IntuitionWork title
- Type
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-07-27Full publication date if available
- Authors
-
Amit Chattopadhyay, Aimee Pascaline N Unkundiye, Gillian Pearce, Stephen R. RussellList of authors in order
- Landing page
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https://arxiv.org/abs/2407.19277Publisher landing page
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https://arxiv.org/pdf/2407.19277Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2407.19277Direct OA link when available
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Intuition, Artificial intelligence, Computer science, Deep learning, Cognitive science, PsychologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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