A Decision Making Approach for Chemotherapy Planning based on Evolutionary Processing

• URL: http://arxiv.org/abs/2303.10535v1
• Date: Sun, 19 Mar 2023 02:26:50 GMT
• Title: A Decision Making Approach for Chemotherapy Planning based on Evolutionary Processing
• Authors: Mina Jafari, Behnam Ghavami, Vahid Sattari Naeini
• Abstract summary: In this paper, a multi-objective meta-heuristic method is provided for cancer chemotherapy. The proposed method uses mathematical models in order to measure the drug concentration, tumor growth and the amount of toxicity. Results show that the proposed method achieve to a better therapeutic performance compared to a more recent similar method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The problem of chemotherapy treatment optimization can be defined in order to minimize the size of the tumor without endangering the patient's health; therefore, chemotherapy requires to achieve a number of objectives, simultaneously. For this reason, the optimization problem turns to a multi-objective problem. In this paper, a multi-objective meta-heuristic method is provided for cancer chemotherapy with the aim of balancing between two objectives: the amount of toxicity and the number of cancerous cells. The proposed method uses mathematical models in order to measure the drug concentration, tumor growth and the amount of toxicity. This method utilizes a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to optimize cancer chemotherapy plan using cell-cycle specific drugs. The proposed method can be a good model for personalized medicine as it returns a set of solutions as output that have balanced between different objectives and provided the possibility to choose the most appropriate therapeutic plan based on some information about the status of the patient. Experimental results confirm that the proposed method is able to explore the search space efficiently in order to find out the suitable treatment plan with minimal side effects. This main objective is provided using a desirable designing of chemotherapy drugs and controlling the injection dose. Moreover, results show that the proposed method achieve to a better therapeutic performance compared to a more recent similar method [1].

Related papers

• Automating proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning [0.7519872646378836]
  We propose an automatic treatment planning model using the proximal policy optimization (PPO) algorithm and a dose distribution-based reward function. A set of empirical rules is used to create auxiliary planning structures from target volumes and organs-at-risk. A decision-making policy network trained using PPO is developed to iteratively adjust the involved planning objective parameters in a continuous action space.
  arXiv  Detail & Related papers  (2024-09-17T22:01:56Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Learning-Based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties [0.0]
  It is shown that dash-boards can be built in the 2D-space of the most influent state components that summarize the outcomes' probabilities and the associated drug usage as iso-values curves in the reduced state space. It is shown that dash-boards can be built in the 2D-space of the most influent state components that summarize the outcomes' probabilities and the associated drug usage as iso-values curves in the reduced state space.
  arXiv  Detail & Related papers  (2022-05-16T07:08:50Z)
• A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior [13.368491963797151]
  personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging. We formulate drug screening study as a "contextual bandit" problem, in which an algorithm selects anticancer therapeutics based on contextual information about cancer cell lines. We propose using a novel deep Bayesian bandits framework that uses functional prior to approximate posterior for drug response prediction based on multi-modal information consisting of genomic features and drug structure.
  arXiv  Detail & Related papers  (2022-05-05T21:56:14Z)
• Conditional Generation Net for Medication Recommendation [73.09366442098339]
  Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. We propose Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-02-14T10:16:41Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• Optimizing Radiotherapy Plans for Cancer Treatment with Tensor Networks [0.0]
  Intensity-Modulated Radiation Therapy (IMRT) technique allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs. We map the dose optimization problem into the search of the ground state of an Ising-like Hamiltonian, describing a system of long-range interacting qubits. A similar approach can be applied to future hybrid classical-quantum algorithms, paving the way for the use of quantum technologies in future medical treatments.
  arXiv  Detail & Related papers  (2020-10-19T14:28:21Z)
• Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology [0.0]
  Current strategies comprise model-informed dosing tables or are based on maximum a-posteriori estimates. We propose three novel approaches for MIPD employing Bayesian data assimilation and/or reinforcement learning to control neutropenia. These approaches have the potential to substantially reduce the incidence of life-threatening grade 4 and subtherapeutic grade 0 neutropenia.
  arXiv  Detail & Related papers  (2020-06-01T16:38:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.