Optimizing the future: how mathematical models inform treatment schedules for cancer

Mathur, Deepti; Barnett, Ethan S.; Scher, Howard I.; Xavier, João B.

doi:10.1016/j.trecan.2022.02.005

Cited by 20 publications

(11 citation statements)

References 68 publications

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“…Mathematical modeling is of great help to medicine [ 30 , 31 ]. Through effective mathematical model analysis, the risk of disease occurrence or intervention can be predicted [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Risk Prediction of Ureaplasma urealyticum Affecting Sperm Quality Based on Mathematical Model and Cross-Sectional Study

Liu

Yang

He³

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. To explore the risk of Ureaplasma urealyticum (UU) affecting sperm quality. Methods. Prospective cross-sectional study was conducted. In total, 340 semen samples were collected. According to whether they were infected with UU, the samples were divided into the UU-positive group (observation group) and UU-negative group (control group). The patients with UU-positive were followed up to obtain treatment and collected the semen again after treatment. The semen characteristics and sperm parameters were detected and compared, and the relationship of UU and the sperm quality was analyzed by mathematical models. Results. There were 104 UU-positive semen samples in all, with an overall infection rate of 30.6%, which was highest in 31 to 40-year-old men, and over 40-year-old men were the lowest. The pH, PR, VCL, VSL, and STR in the observation group were significantly lower than those in the control group (all P < 0.001 ), while SV, NP, and WOB were significantly higher (all P < 0.001 ). After treatment, the pH, VSL, LIN, WOB, and STR in the observation group were significantly higher than before (all P < 0.001 ), while SV and VCL were significantly lower (all P < 0.001 ). UU infection was closely correlated with pH, PR, NP, VCL, VSL, WOB, and STR. During the treatment, pH, PR, VSL, WOB, and STR increased, but NP and VCL decreased. 7 major factors that would affect SQ were extracted, of which VAP, LIN, and UU were the first three main factors. The risk of SQ declining after UU infection increased nearly twice with the change of PR and VCL and increased 0.08 times with STR. Conclusion. UU may approximately double the risk of altering the sperm’s curvilinear movement rate and straightness to affect the sperm quality.

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“…Mathematical modeling is of great help to medicine [ 30 , 31 ]. Through effective mathematical model analysis, the risk of disease occurrence or intervention can be predicted [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Risk Prediction of Ureaplasma urealyticum Affecting Sperm Quality Based on Mathematical Model and Cross-Sectional Study

Liu

Yang

He³

et al. 2022

Computational and Mathematical Methods in Medicine

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“…To overcome this challenge, evolutionary dosing and game theory-based approaches operate by the principle of tumour management based on therapy that manages the population of drug-sensitive tumour cells versus drug-resistant cells. [ 65 66 67 68 69 70 71 72 73 74 75 76 ] Other strategies have included adaptive therapeutic approaches. For instance, optimal adaptive therapeutic schedules were identified through the combination of mathematical modelling and dynamic optimisation, and this approach can precisely pinpoint the optimal personal drug doses and respective schedules for prostate cancer.…”

Section: Need For Dynamic and Optimal Treatment At The N-of-1 Levelmentioning

confidence: 99%

N-of-1 medicine

Wang,

Leong,

Lau

et al. 2024

Singapore Medical Journal

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The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient’s own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

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“…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.…”

Section: Mathematical Modelingmentioning

confidence: 99%

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

Ramaswamy,

Keidar

2023

Applied Sciences

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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.

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Optimizing the future: how mathematical models inform treatment schedules for cancer

Cited by 20 publications

References 68 publications

Risk Prediction of Ureaplasma urealyticum Affecting Sperm Quality Based on Mathematical Model and Cross-Sectional Study

Risk Prediction of Ureaplasma urealyticum Affecting Sperm Quality Based on Mathematical Model and Cross-Sectional Study

N-of-1 medicine

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

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