Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

Axenie, Cristian; Kurz, Daria

doi:10.1101/2020.06.08.140756

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Additionally, a personalized approach to predicting the results of reconstructive surgery for chronic otitis media has been developed, aiding in choosing surgical tactics based on individual patient groups. Furthermore, the use of machine learning and mechanistic models has been proposed for personalized chemotherapy and surgery sequencing in breast cancer, incorporating tumor cell growth and the effects of chemotherapy and surgery under cell-kill hypotheses ( 61 ). These examples illustrate the potential of predictive modeling and decision support systems in revolutionizing personalized surgery, ultimately improving patient outcomes.…”

Section: Data Visualization and Analytics In Surgery: A Transformativ...mentioning

confidence: 99%

Current and future applications of artificial intelligence in surgery: implications for clinical practice and research

Morris,

Fiocco,

Caneva

et al. 2024

Front. Surg.

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Surgeons are skilled at making complex decisions over invasive procedures that can save lives and alleviate pain and avoid complications in patients. The knowledge to make these decisions is accumulated over years of schooling and practice. Their experience is in turn shared with others, also via peer-reviewed articles, which get published in larger and larger amounts every year. In this work, we review the literature related to the use of Artificial Intelligence (AI) in surgery. We focus on what is currently available and what is likely to come in the near future in both clinical care and research. We show that AI has the potential to be a key tool to elevate the effectiveness of training and decision-making in surgery and the discovery of relevant and valid scientific knowledge in the surgical domain. We also address concerns about AI technology, including the inability for users to interpret algorithms as well as incorrect predictions. A better understanding of AI will allow surgeons to use new tools wisely for the benefit of their patients.

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Section: Data Visualization and Analytics In Surgery: A Transformativ...mentioning

confidence: 99%

Current and future applications of artificial intelligence in surgery: implications for clinical practice and research

Morris,

Fiocco,

Caneva

et al. 2024

Front. Surg.

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Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning

Kurz

aacutenchez

Axenie

2021

Preprint

Self Cite

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For decades, researchers have used the concepts of rate of change and differential equations to model and forecast neoplastic processes. This expressive mathematical apparatus brought significant insights in oncology by describing the unregulated proliferation and host interactions of cancer cells, as well as their response to treatments. Now, these theories have been given a new life and found new applications. With the advent of routine cancer genome sequencing and the resulting abundance of data, oncology now builds an "arsenal" of new modeling and analysis tools. Models describing the governing physical laws of tumor-host-drug interactions can be now challenged with biological data to make predictions about cancer progression. Our study joins the efforts of the mathematical and computational oncology community by introducing a novel machine learning system for data-driven discovery of mathematical and physical relations in oncology. The system utilizes computational mechanisms such as competition, cooperation, and adaptation in neural networks to simultaneously learn the statistics and the governing relations between multiple clinical data covariates. Targeting an easy adoption in clinical oncology, the solutions of our system reveal human-understandable properties and features hidden in the data. As our experiments demonstrate, our system can describe nonlinear conservation laws in cancer kinetics and growth curves, symmetries in tumor's phenotypic staging transitions, the pre-operative spatial tumor distribution, and up to the nonlinear intracellular and extracellular pharmacokinetics of neoadjuvant therapies. The primary goal of our work is to enhance or improve the mechanistic understanding of cancer dynamics by exploiting heterogeneous clinical data. We demonstrate through multiple instantiations that our system is extracting an accurate human-understandable representation of the underlying dynamics of physical interactions central to typical oncology problems. Our results and evaluation demonstrate that using simple - yet powerful - computational mechanisms, such a machine learning system can support clinical decision making. To this end, our system is a representative tool of the field of mathematical and computational oncology and offers a bridge between the data, the modeler, the data scientist, and the practising clinician.

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The Multiple Dimensions of Networks in Cancer: A Perspective

2021

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This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

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Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

Cited by 4 publications

References 26 publications

Current and future applications of artificial intelligence in surgery: implications for clinical practice and research

Current and future applications of artificial intelligence in surgery: implications for clinical practice and research

Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning

The Multiple Dimensions of Networks in Cancer: A Perspective

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