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Review

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

by
Viswambari Devi Ramaswamy
†,‡ and
Michael Keidar
*,†
Micropropulsion and Nanotechnology Laboratory, School of Engineering and Applied Science, George Washington University, 800 22nd St. NW, Suite 3100, Washington, DC 20052, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
Current address: Independent Researcher, Chennai 600040, Tamil Nadu, India.
Appl. Sci. 2024, 14(1), 355; https://doi.org/10.3390/app14010355
Submission received: 19 October 2023 / Revised: 17 November 2023 / Accepted: 6 December 2023 / Published: 30 December 2023
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Abstract

Featured Application

Complexity, heterogeneity, and treatment resistance of cancers create challenges in achieving successful treatment outcomes. Personalized cold atmospheric plasma (CAP) therapy emerges as a prospective approach, fine-tuning parameters for individual patients. Self-adaptive CAP optimizes therapy in real-time, promising better outcomes in personalized cancer care.

Abstract

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species and electromagnetic emissions generated through gas plasma jets have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets or approaches within machine learning, such as reinforcement learning and deep learning. This review emphasizes the significant application of advanced algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of machine learning techniques in plasma medicine requires research efforts, data sharing, and interdisciplinary collaborations. Unraveling the complex mechanisms, developing real-time diagnostics, and optimizing advanced models will be crucial to harnessing the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.

1. Introduction

Plasma technology, a rapidly emerging field at the intersection of plasma physics and medicine, holds immense potential for biomedical applications [1,2,3,4,5,6,7,8,9]. Plasma, known as the fourth state of matter, is typically formed by heating gases such as inert gases like argon, helium, or air. When heated, the high-speed electrons collide with atoms or molecules, causing the removal of electrons and generating highly reactive positive and negative ions. These ions are conductive and can oscillate in response to electric and magnetic fields. The oscillations can give rise to the emission of various electromagnetic waves [10]. Additionally, there are also low-energy electrons that can only excite particles without causing ionization. These excited particles release energy in the form of photons. Plasma, or ionized gas, is a complex mixture comprising electrons, positively and negatively charged ions, radicals, neutral atoms and molecules, atoms in an excited state, electromagnetic waves, and electromagnetic fields [11]. The degree of ionization and the specific composition of plasma are dependent on factors such as temperature, pressure, energy supply, nature of the gas, electron density ranges, the device used, and operational conditions utilized to generate the plasma [12,13]. The most common type of low-temperature atmospheric pressure plasma is cold atmospheric plasma jets (CAPJ), which have been well established in the fields of biology and medicine [14].
Atmospheric plasma can self-organize into different patterns with modified plasma compositions in response to various operational parameters and interactions with the target environment [15,16]. This transition from a spatially homogeneous state to self-organized patterns in response to various parameters like discharge voltage [17,18,19] and the electrical permittivity of target cells [20] can play an important role in the therapeutic outcome of plasma treatment for cancer [21,22]. Self-organization might allow an adaptive and self-adaptive plasma system that can dynamically adjust and optimize plasma treatment conditions based on real-time feedback [16]. It aims to maximize the therapeutics on cancer cells while ensuring safety and efficacy.
This review delves into the pivotal role of mathematical modeling and machine learning (ML) approaches in achieving distinctive adaptive and self-adaptive features of CAP. CAP, with an adaptive feature, adjusts treatment parameters based on external feedback, while self-adaptive plasma autonomously optimizes treatment parameters using internal data and algorithms. These elements propel the application of CAP in precision oncology/personalized cancer treatment, where CAP doses are tailored to the unique characteristics of individual patients’ cancers.

2. Mathematical Modeling

In cancer research, mathematical modeling plays a pivotal role in comprehending the intricate dynamics of cancer in response to various treatments [23,24]. It complements cancer research by providing valuable quantitative predictions to unravel the complexity of cancer and open new avenues for developing effective treatments [25]. Numerous mathematical models, often rooted in ordinary differential equations (ODEs), have explored various aspects of cancer research, including treatment strategies [26,27,28,29,30,31,32,33,34], immune responses [35,36,37,38,39,40,41], treatment sensitivity and resistance [42], the effects of treatment combinations [43,44,45,46,47,48,49], habitat dynamics [50], tumor heterogeneity [51,52], and treatment optimization [53]. Their collective contribution lies in furnishing predictions for optimal dosing, treatment regimens, and scheduling, all while minimizing adverse side effects and maximizing therapeutic gains. This avenue holds promise for personalized cancer care, customizing treatments based on individual patient traits and diverse tumor characteristics, including size, type, growth rate, heterogeneity, genetics, and immune composition [37,54]. For mathematical models to operate with precision, the accurate identification of critical parameters hinges on the acquisition of frequent and precise data [55]. ODE-based equations are commonly employed in mechanistic and deterministic models where underlying mechanisms and deterministic relationships are of primary interest (Figure 1). Beyond the realm of ODEs, fractional calculus emerges as a potential frontier for refining cancer modeling, particularly in capturing memory effects and intricate aspects of tumor growth [56,57,58]. Fractional calculus enables the differentiation and integration of non-integer orders, extending beyond traditional calculus. Cancer research finds application by modeling anomalous diffusion processes, characterizing tumor behavior more accurately, and incorporating memory effects into models, deepening our understanding of tumor dynamics [59,60,61,62].
Several mathematical models have been validated through preclinical investigations and clinical trials, demonstrating their potential for shaping effective clinical trial designs [63,64,65,66,67,68,69]. Notably, Dean et al. [69] engineered a mathematical model tailored to refine radiation therapy schedules for glioblastoma, a formidable brain cancer. The model recommended an almost-optimal re-irradiation regimen for recurrent glioblastoma patients: 7 days of 3.96 Gy (administered once per day), followed by 9 days of 1.0 Gy (administered thrice per day). Strikingly, this regimen demonstrated both feasibility and safety in all 14 patients (100%) who received it, marking a notable achievement in glioblastoma treatment. Although preliminary results hint at enhanced tumor control, the imperative for larger-scale trials to substantiate its efficacy remains paramount. These findings prove the invaluable role of mathematical modeling in fine-tuning cancer treatment strategies, offering promising prospects for future clinical applications.
After the proposal of adaptive and self-adaptive plasma [70], an exponential growth model was developed [71] to predict the dynamic response of cancer cells (U-87 MG and MDA-MB-231) under CAP treatment [18]. This model successfully derived optimal treatment conditions for two distinct cancer cell lines: U-87 MG and MDA-MB-231. For U-87 MG, the study aimed for a desired ratio of cancer cell viability (rd) of 0.5, leading to optimal treatment parameters of ∆t (treatment duration) at 65.1 s and U (Plasma Discharge Voltage) at 3.367 kV. For MDA-MB-231, targeting an rd of 0.3 resulted in optimal settings of ∆t at 71.6 s and U at 3.315 kV. These specific values were attained through rigorous numerical optimization techniques applied to the mathematical model describing cancer cell responses to CAP exposure. Furthermore, the research extended the optimal control framework to employ an optimal feedback control strategy using Model Predictive Control (MPC). This approach facilitates real-time adaptation of treatment parameters based on actual cancer cell responses, effectively mitigating the impact of uncertainties and ensuring consistent maintenance of the desired level of cancer cell growth inhibition. The model thus offers a powerful tool for quantitatively optimizing CAP treatment parameters, accounting for dynamic cancer cell responses over time to achieve precise and targeted levels of growth inhibition. However, it is important to note that the model’s predictive accuracy is contingent upon the quality and representativeness of the experimental data, necessitating comprehensive validation through experiments. Additionally, the model may not encompass all biological complexities, potentially introducing uncertainties into its predictions (Figure 1).
In recent studies, indirect treatment using plasma-treated liquids (PTLs) [72,73,74] or plasma-treated hydrogels (PTHs) [75] has shown enhanced cytotoxicity and anticancer immune responses attributed to the long-lived reactive oxygen and nitrogen species (RONS) such as H2O2, NO2−, and NO3−. These treatments have demonstrated greater effectiveness than direct plasma treatment [76,77,78,79,80]. However, despite their advantages, concerns regarding the lack of consistent molecular-level analysis and precise control over their chemical compositions have been raised, limiting their clinical suitability. Mathematical modeling has been instrumental in investigating the behavior of RONS in cancer treatment with PTLs, allowing for parameter exploration to optimize treatment strategies. Bengston and Bogaerts [81,82] developed a valuable quantitative framework using mathematical modeling to investigate the behavior of RONS in cancer treatment with PTLs, enabling parameter exploration for treatment optimization. This model suggests that specific cell features, such as the H2O2 membrane diffusion rate constant and intracellular catalase concentration, influence the response to PTLs. It also proposes that certain reactions involving CO3•– (carbonate radical anion) [83] and catalase may be responsible for the enhanced anti-cancer effect observed when combining H2O2 and NO2− in PTLs.
The limitations associated with mathematical models in the context of CAP treatment are multifaceted and have implications for their reliability and applicability. These limitations include a strong reliance on the accuracy of input data, potential oversimplification of complex biological processes, the critical need for experimental validation, a confined scope of parameterization, potential variations across different cell types, assumptions of homogeneity, reliance on the literature-derived values, and inherent model uncertainty. Future advancements in this field should focus on developing patient-specific models, refining parameter estimation techniques, exploring the utility of biomarkers, enhancing data collection methods, and conducting rigorous validations. Notably, the integration of AI techniques holds promise for addressing these limitations by improving prediction accuracy, automating feature engineering, accommodating nonlinear relationships, handling noisy and incomplete data, enabling real-time adaptability, and incorporating diverse data types [84]. Mathematical modeling entails the creation of mathematical representations for real-world systems, while ML approaches use data-driven techniques to learn patterns and make predictions, often incorporating mathematical principles, formulas, and statistical methods within their frameworks [85,86]. By combining the strengths of mathematical modeling and ML, we can potentially offer improved patient care and treatment strategies [87], enhancing clinical trials and personalized medicine.

3. ML Techniques for Adaptive Plasma System

Artificial intelligence (AI) is about creating “thinking machines” that can make decisions on their own. Machine learning (ML) is a subset of AI that aims to create “learning machines”, in which machines/tools learn from data and perform tasks without explicit programming [88]. A tutorial review on widely used ML methods that can help discover patterns and relationships in datasets that may be challenging for human intuition is well described by Bonzanini et al. [89].
The evolution of AI, particularly with the integration of ML, has opened new possibilities in oncology, revolutionizing every facet of cancer care. It has significantly enhanced cancer diagnosis [90,91,92,93], prognosis [94,95,96,97,98], and the prediction of metastasis [99,100,101]. Treatment selection [102], efficacy [103,104,105], response assessment [106,107,108,109,110], and outcome prediction [111,112,113,114,115] have also seen remarkable enhancements. These advancements have paved the way for highly personalized cancer treatments, offering a ray of hope within the continually evolving healthcare landscape [116,117,118]. Although ML has been applied in various treatment approaches in cancer medicine, its use in plasma medicine is relatively limited (Figure 2). The potential of ML for accelerating plasma research towards personalized cancer treatment is well discussed [119,120,121,122].

3.1. Reinforcement Learning

In machine learning (ML), algorithms are used to train and construct models using large datasets containing relevant information, such as input features (e.g., varying operational parameters of the adaptive plasma system) and target labels (e.g., treatment responses by the cancer cells). The objective is to analyze the datasets, identify patterns and relationships between input features and target labels, and adjust the internal parameters of the algorithm iteratively to improve its predictive accuracy (e.g., optimizing parameters in the adaptive plasma system for selective cytotoxicity towards cancer cells). The goal is to minimize the difference between the model’s predictions and the actual outputs during training, making the model “trained” and capable of making accurate predictions.
Different ML approaches, such as reinforcement learning (RL) and deep learning (DL), have different methods to update their parameters during training. For example, in the RL process, the agent (the adaptive plasma system) learns, becomes trained, and improves its behavior in an environment (cancer cells) through its interaction experience [123]. It takes action, receives feedback in the form of rewards and penalties based on the effectiveness of its treatment strategies, and uses this feedback to update its internal parameters and improve its decision making. Rewards may be associated with positive outcomes, such as a successful reduction in cancer cell viability, while penalties could be linked to less desirable effects. The system uses this feedback to update its internal parameters and improve its decision-making processes, aiming to find an optimal strategy that maximizes cumulative rewards over time.
Hou et al. [119] developed an adaptive plasma framework for CAP cancer treatments using the integration of empirical dynamic modeling and reinforcement learning (Figure 3). The empirical dynamic model was constructed using data collected from in vitro experiments [18] that provided information on how cancer cell viability changes over time under various CAP treatment conditions (e.g., treatment duration from 0 to 180 s and the discharge voltage between 3.16 kV and 3.71 kV). The empirical data obtained from in vitro experiments may represent specific scenarios and treatment conditions. To estimate for a wide range of treatment scenarios, a Gaussian process, a type of statistical modeling tool that helps the model capture the variability and uncertainty inherent in real-world clinical settings, was incorporated. The CAP cancer treatment was then modeled as a Markov decision process (MDP) and conjugated with safe Q-learning. The MDP represents the sequence of states and actions in the treatment process. Each state corresponds to a specific situation or condition of the cancer cells (e.g., cancer cell viability), and each action represents a treatment strategy that the adaptive plasma system can take (e.g., treatment duration, discharge voltage). MDP helps in defining the states, actions, and transition probabilities in the treatment process, but it does not inherently provide the optimal policy for selecting actions that lead to the best long-term rewards. Q-learning, a specific algorithm designed for reinforcement learning tasks [124], helps the adaptive plasma system learn from its interactions with the environment and update its decision-making process by estimating the expected cumulative rewards for different actions in different states. This enables the system to make better decisions and optimize the treatment plan to achieve safer and more personalized cancer treatment. The study uses in vitro data to model cancer cell responses, potentially limiting their real-world applicability, but Gaussian processes aid in generalization. While the cancer treatment model as a Markov decision process lacks an in-depth exploration of its limitations, it offers structured optimization with effective Q-learning adaptability. Safe Q-learning focuses on avoiding risky actions, leaving a gap in addressing other safety aspects while still enhancing treatment safety. To enhance the model, future research should incorporate in vivo data, explore hybrid models, consider non-Markovian dynamics, investigate multi-agent systems, expand safety considerations, integrate real-time diagnostics, adopt personalized medicine, and validate through clinical trials for improved clinical relevance.

3.2. Gaussian Process Regression

Lin et al. [125] used Model Predictive Learning Control (MPLC), an extension of MPC that incorporates machine learning techniques for self-adaptive CAP jet optimization. While MPC typically uses a fixed mathematical model and optimization algorithm-based control strategy [71], MPLC integrates machine learning algorithms to capture system dynamics and adapt the control strategy based on real-time data. In the study, MPLC is implemented using GP regression, a statistical machine learning technique, to model the dynamic behavior of cancer cell viability in response to CAP treatment. After each treatment, the GP model is continuously updated with real-time feedback from the actual cancer cell responses, measured using real-time electrochemical impedance spectroscopy (EIS). Cancer cell response measurement after CAP treatment using EIS analyzes changes in the electrical impedance of the cells. The EIS response is directly proportional to cell viability, making it a quantitative indicator of the treatment’s effectiveness in affecting the health and viability of the targeted cancer cells. This real-time feedback enables the MPLC algorithm to adapt and improve its predictions and treatment plans over time (Figure 4).
By dynamically adjusting the plasma treatment parameters based on actual system responses, the self-adaptive plasma jet can optimize its treatment strategy for a wide range of biomedical applications and variations in the system.
While the study demonstrates its use for cancer cell viability in response to CAP treatment, the transition to clinical applications may require additional research and validation. Pros include the adaptability of MPLC with GP regression, which enhances precision and effectiveness through real-time learning and feedback via EIS. However, potential cons involve computational complexity and the reliability of GP models. Future directions should focus on clinical validation, scalability, and addressing computational challenges for practical healthcare adoption.

3.3. Deep Learning

Deep learning is proficient at autonomously extracting hierarchical features and patterns from raw data, rendering them highly effective in tasks like processing and decision making. It is composed of an input layer, multiple hidden layers, and an output layer, each containing interconnected nodes. These networks learn to execute tasks by fine-tuning the weights assigned to connections between nodes. The capacity to automatically uncover intricate patterns and representations in data without explicit programming is attained through a training process. In this process, the neural network learns from labeled examples and adjusts its parameters to minimize the disparity between predicted and actual outcomes. Deep learning uses artificial neural networks (ANNs) with multiple layers (deep neural networks) to make predictions from the labeled data (for instance, real-time spectra from optical emission spectroscopy). The self-adaptive plasma system empowered by deep learning holds great promise for advancing precision medicine and improving cancer treatment outcomes [122]. The self-adaptive plasma system is a plasma control system that can dynamically and autonomously diagnose plasma chemistry and adjust its operating parameters (e.g., gas and power inputs) to control and optimize plasma chemical composition in real time according to the required species/dosage to kill cancer cells. This system typically employs artificial neural networks and gradual mutation algorithms (GMA) to continuously monitor, analyze, control, and optimize the datasets of plasma chemistry and thus make real-time decisions on how to modify the plasma conditions. The real-time spectra of plasma chemical compositions and temperatures are obtained using spontaneous emission spectroscopy and processed via a diagnostic ANN. The diagnostic ANN provides valuable outputs on plasma chemistry, which are then used by a control ANN as inputs to adjust plasma parameters like gas injections and energy levels. The control ANN is trained with the GMA to adjust the production of specific RONS associated with the apoptosis of cancer cells. It receives feedback from the diagnostic ANN and dynamically adjusts the gas and power inputs to optimize the concentration of these species used in the plasma. The GMA is used to train both the diagnostic and control ANNs effectively, allowing the self-adaptive plasma system to diagnose, control, and optimize the plasma chemistry in real time based on the OES data and specific objectives. Such an empowered, self-adaptive plasma system allows personalized treatments tailored to the specific characteristics of the patient’s cancer.
The integration of diagnostic ANN, chemical process network (CPN), and control ANN creates an adaptive and intelligent system for effective and personalized cancer treatment [122,126] (Figure 5).
AI-driven gas plasma can continuously learn from treatment outcomes, adapt to changing conditions, and optimize treatment plans. This personalized and dynamic approach has the potential to improve the efficacy of cancer therapies, reduce side effects, and advance precision medicine.
The absence of performance metrics and considerations is a critical limitation in the above-discussed AI-based approaches. Performance evaluation is a fundamental aspect of any AI system. However, these research efforts primarily focus on the theoretical and conceptual aspects of AI-empowered plasma systems and their potential applications in cancer treatment. Given their early stage and limited access to real-world data, providing precise performance metrics is challenging. Addressing this gap necessitates future studies with interdisciplinary collaborations involving experts in computational science, machine learning, and medical engineering. These collaborations can facilitate the evaluation of the computational efficiency, scalability, adaptability, and real-time processing capabilities of AI-empowered plasma systems. Additionally, benchmarking AI models against existing standards in the field enables a comparative analysis of efficiency and effectiveness. Adherence to established reporting standards and guidelines for AI research is essential for enhancing the rigor and impact of future studies.
As artificial intelligence gains broader acceptance in clinical applications, we foresee the imminent emergence of an artificial intelligence-driven adaptive plasma system for personalized cancer treatment, revolutionizing medical practice and paving the way for transformative advancements in oncology.

4. Advanced Algorithms in Real-Time Diagnostics

To exploit the adaptive and self-adaptive properties of CAP in developing personalized treatment protocols, it is important to estimate the operational parameters of the CAP sources and measure the cell response to CAP treatment in real-time.

4.1. Real-Time Diagnosis of Operational Parameters of CAP Sources

Traditional diagnostics (e.g., laser-induced fluorescence, mass spectrometry, spontaneous Raman scattering) of the different operational parameters of CAP sources are complex and expensive, requiring sophisticated instrumentation setups and specialized analysis, making them impractical for real-time use [127,128] and making it challenging to control the plasma consistently and effectively. Using information-rich datasets collected from simpler diagnostic methods like optical emission spectroscopy (OES) [129] and electro-acoustic emission and enabling fast, automated data processing and analysis using AI techniques in real-time monitoring and control improves the feedback control, ensuring effective and optimal performance of CAP sources [127,128]. Simulated data [130,131], although not capturing all the complexities in real-world scenarios, complement real data by providing a controlled environment for analysis and experimentation.
The manual analysis of the datasets with traditional methods can be challenging due to the complex relations and patterns in the data. ML and other computational approaches (Table 1, Figure 6) are designed to handle such complexity and can identify patterns and trends that may not be apparent to human analysis [128].
ML algorithms can be trained on the operational parameters and their corresponding data, allowing them to learn and generalize their knowledge for making predictions on new, unseen data. As new data are received, the algorithms rapidly process and analyze the data, enabling quick estimation of critical operational parameters from the spectral data or other input data in real time. ML provides a promising pathway through the creation of input–output models that support real-time computations, thus enabling the implementation of more advanced feedback control strategies [120,138]. Constructing multivariable, nonlinear prediction models to characterize the complex interactions between CAP and cancer cells is crucial for guiding effective cancer treatment approaches [136]. AI models can thus be used for real-time monitoring and diagnostics [127,132,133,134], detecting any deviations from normal behavior [135,138], and predicting when maintenance or adjustment is required [122] for maintaining optimal real-time control of the operational parameters. Thus, AI-based approaches, specifically ML, can significantly improve the reliability and performance of CAP by adapting to dynamic and uncertain environments.

4.2. Real-Time Diagnosis of the Cell Responses to CAP Treatment

Collecting real-time data on cancer cell responses to different CAP treatment modalities is challenging. Studies discussed in Section 3.1 and Section 3.2 have utilized real-time EIS measurements [119,125] (Table 2) and have focused on incorporating automated feedback control systems into adaptive plasma systems to optimize performance and safety (Figure 7).
Yet, advanced real-time diagnostic systems are still needed to provide sensitive, accurate, and reproducible data for these feedback control systems. To achieve optimal results, a sophisticated diagnostic system with high sensitivity, accuracy, and reproducibility is imperative. These systems should seamlessly integrate with the biological target and have minimal interference with the plasma treatment process. By fulfilling these criteria, the feedback control loop can make well-informed and precise adjustments, optimizing treatment outcomes for diverse cancer cells and individual patients. Developing electronic and optical sensors suitable for addressing this technological gap and integrating these sensors into autonomous plasma systems must be focused on [140]. Bridging this gap could lead to the development of next-generation medical plasma technologies, offering superior healthcare outcomes.
The applications of ML in modeling and simulating different aspects of CAP processing are well reviewed by Treischmann et al. [141]. Future advancements in CAP modeling and simulation promise precise control, pattern discovery, transformative insights, and advancing plasma science and technology. Open data access and collaboration are pivotal for breakthroughs [141].

5. Conclusions

Cancer is a challenging disease to treat due to its complexity, heterogeneity, invasion (metastasis), and resistance to treatments. The intricate nature of cancer manifests in the variability of responses to therapies among patients owing to the individualized genetic makeup and biological characteristics of their tumors. This necessitates tailored treatment approaches to optimize the chances of successful outcomes [142]. To achieve personalized treatment, CAP is emerging as a promising approach, as the parameters of the plasma device can be fine-tuned during the treatment to suit the patient’s specific dosage. CAP has been shown to reprogram the tumor microenvironment [143,144,145], inhibit tumor growth with high selectivity, and even enhance the effectiveness of other treatment modalities like chemotherapy [146,147,148,149], immunotherapy [150,151], radiotherapy [152,153,154,155], and nanomedicine [156,157,158,159,160] with improved treatment outcomes and minimized side-effects [161]. The self-adaptive nature of the adaptive plasma system complements the power of ML. As CAP devices can dynamically adjust treatment parameters during the therapy session, they can respond to real-time changes in tumor characteristics or patient physiology. ML-driven feedback loops can enhance this self-adaptation, ensuring that CAP treatment remains optimized and effective throughout the course of therapy, making it a promising avenue for achieving optimal treatment outcomes in personalized cancer care.
Better cross-disciplinary collaboration with plasma physicists, mathematicians, AI engineers, data scientists, oncologists, and clinicians can make effective proposals in the future that can be tailored towards personalized cancer treatment strategies. Advancing diagnostic tools, exploring new combination treatment strategies with CAP treatment, and modeling with advanced algorithms will pave the way for more precise and personalized plasma-based therapies. AI is transforming plasma medicine, improving treatments with precise models, data analysis, real-time diagnostics, and protocols, despite ethical and transparency concerns [86]. The ongoing advancements at the frontiers of plasma technology in oncology are poised to revolutionize cancer treatment paradigms, providing new hope and improved outcomes for cancer patients worldwide. With continued efforts and interdisciplinary cooperation, plasma technology holds the potential to become an indispensable asset in the fight against cancer.

Author Contributions

Both the authors have equally contributed. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Mathematical models/approaches employed for the application of CAP in oncology, highlighting their respective advantages and disadvantages.
Figure 1. Mathematical models/approaches employed for the application of CAP in oncology, highlighting their respective advantages and disadvantages.
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Figure 2. A scatter chart comparing the increase in the application of machine learning in cancer and CAP, based on search results in PubMed and Google Scholar using two search strings, (“Machine Learning”) AND (Cancer Treatment) AND (“Cold Atmospheric Plasma”).
Figure 2. A scatter chart comparing the increase in the application of machine learning in cancer and CAP, based on search results in PubMed and Google Scholar using two search strings, (“Machine Learning”) AND (Cancer Treatment) AND (“Cold Atmospheric Plasma”).
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Figure 3. Adaptive Plasma Framework for Precision Cancer Treatment: integration of GP, MDP, Safe Q-Learning, and Softmax [119].
Figure 3. Adaptive Plasma Framework for Precision Cancer Treatment: integration of GP, MDP, Safe Q-Learning, and Softmax [119].
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Figure 4. Model Predictive Learning Control (MPLC) framework: merging real-time data with an adaptive plasma control system to modify the treatment parameters online, making treatment more effective and personalized to each cancer cell’s response [125].
Figure 4. Model Predictive Learning Control (MPLC) framework: merging real-time data with an adaptive plasma control system to modify the treatment parameters online, making treatment more effective and personalized to each cancer cell’s response [125].
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Figure 5. Artificial neural networks (ANNs) in real-time diagnostics of plasma composition and real-time control of plasma parameters to achieve optimized composition for personalized cancer treatment. The use of ANNs allows for intelligent analysis of real-time data, guiding decisions on the composition of plasma used in cancer treatment. This adaptive approach aims to enhance the effectiveness of the treatment while minimizing the impact on healthy tissues, contributing to a more personalized and precise cancer therapy [122,126].
Figure 5. Artificial neural networks (ANNs) in real-time diagnostics of plasma composition and real-time control of plasma parameters to achieve optimized composition for personalized cancer treatment. The use of ANNs allows for intelligent analysis of real-time data, guiding decisions on the composition of plasma used in cancer treatment. This adaptive approach aims to enhance the effectiveness of the treatment while minimizing the impact on healthy tissues, contributing to a more personalized and precise cancer therapy [122,126].
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Figure 6. Common ML components employed for the application of CAP in oncology, highlighting their respective advantages and disadvantages.
Figure 6. Common ML components employed for the application of CAP in oncology, highlighting their respective advantages and disadvantages.
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Figure 7. Incorporating automatic real-time feedback control strategy into adaptive plasma system.
Figure 7. Incorporating automatic real-time feedback control strategy into adaptive plasma system.
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Table 1. Selected parameters of the CAP sources for real-time diagnostics and techniques employed.
Table 1. Selected parameters of the CAP sources for real-time diagnostics and techniques employed.
Selected Parameters of the CAP Sources for Real-Time DiagnosticsInput Data Obtained FromML and Computational Techniques EmployedReference
Rotational and vibrational temperaturesOESLinear regression (supervised ML)[127]
Substrate characteristicsOESk-Means clustering (unsupervised ML)[127]
Separation distance between the electrodesElectro-acoustic EmissionGaussian process regression (supervised probabilistic ML)[127]
Electron energy distribution function (EEDF)OESGenetic algorithm (metaheuristic algorithm)[132]
EEDFOES, momentum-transfer cross-sectionVisible Bremmsstrahlung inversion (supervised ML)[133,134]
Time-series current signals from APPJ (discharge type and working gas)Sensors/probesConvolutional neural networks (DL)[135]
Plasma plume lengthVideo frames of the plasma plume captured using a camera (iPhone 11)Computer vision algorithms[136]
Temperature setpointSimulated data from thermal dynamics model of plasma–substrate interactionsReinforcement learning[130]
Self-adaptive plasma chemistry
Gas input densities and energy levels		OESArtificial neural networks (DL), gradual mutation algorithm[122]
Pulse discharge characteristics (current density and gap voltage)Simulated fluid model data of time and pulse rise rateDeep neural networks (DL)[131]
Plasma chemistry (tokamak)FTIRPhysics-informed neural networks[137]
Table 2. Cancer cell response measured in real-time in vitro studies.
Table 2. Cancer cell response measured in real-time in vitro studies.
Input DataReal-Time DiagnosticsAdvanced Control and Prediction MethodsReference
CAP treatment duration and discharge voltage appliedCell viability luminescence AssayModel Predictive Control
(MPC)		[71]
Cancer cell viability ratioElectrochemical impedance spectroscopy (EIS), operational parametersGP regression, MPLC[125]
Cancer cell viability ratioEIS, cell viability assays, operational parametersGP, safety Q—reinforcement learning[119]
Voltage applied, irradiation time, frequency of the plasma, and flow rate of the feed gas on the extent of DNA damageAgarose gel electrophoresis, UV fluorescence imagingArtificial neural networks (supervised DL)
Physics-guided neural network (supervised DL)		[139]
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Ramaswamy, V.D.; Keidar, M. Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches. Appl. Sci. 2024, 14, 355. https://doi.org/10.3390/app14010355

AMA Style

Ramaswamy VD, Keidar M. Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches. Applied Sciences. 2024; 14(1):355. https://doi.org/10.3390/app14010355

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Ramaswamy, Viswambari Devi, and Michael Keidar. 2024. "Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches" Applied Sciences 14, no. 1: 355. https://doi.org/10.3390/app14010355

APA Style

Ramaswamy, V. D., & Keidar, M. (2024). Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches. Applied Sciences, 14(1), 355. https://doi.org/10.3390/app14010355

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