scholarly journals Personalized mathematical oncology: Challenges and opportunities

2021 ◽  
Author(s):  
Michael C. Luo ◽  
Elpiniki Nikolopoulou ◽  
Jana Gevertz
Keyword(s):  
Treatment Response ◽  
Clinical Care ◽  
Population Level ◽  
Personalized Treatment ◽  
Patient Specific ◽  
Individual Treatment ◽  
Specific Data ◽  
Level Statistics ◽  
Challenges And Opportunities ◽  
The Individual

An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. In this work, we employ two fitting methodologies to personalize treatment in a mathematical model of murine cancer immunotherapy. Unexpectedly, we found that the predicted personalized treatment response is sensitive to the fitting methodology utilized. This raises concerns about the ability of mathematical models, even relatively simple ones, to make reliable predictions about individual treatment response. Our analyses shed light onto why it can be challenging to make personalized treatment recommendations from a model, but also suggest ways we can increase our confidence in personalized mathematical predictions.

Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity

2022 ◽  
pp. 0272989X2110728
Author(s):  
Anna Heath ◽  
Petros Pechlivanoglou
Keyword(s):  
Personalized Medicine ◽  
Patient Outcomes ◽  
Clinical Care ◽  
Treatment Strategies ◽  
Treatment Decisions ◽  
Personalized Treatment ◽  
Sensitivity Analyses ◽  
Individual Treatment ◽  
Potential Value ◽  
The Individual

Background Clinical care is moving from a “one size fits all” approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. Methods Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. Results We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. Conclusion We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. Highlights It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment. The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area. These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.

scholarly journals Birthweight: EN-BIRTH multi-country validation study

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Stefanie Kong ◽  
◽  
Louise T. Day ◽  
Sojib Bin Zaman ◽  
Kimberly Peven ◽  
...  
Keyword(s):  
Low Birthweight ◽  
Clinical Care ◽  
Qualitative Interviews ◽  
Population Level ◽  
Exit Interview ◽  
Actual Weight ◽  
Individual Level ◽  
Survey Report ◽  
Interview Survey ◽  
The Individual

Abstract Background Accurate birthweight is critical to inform clinical care at the individual level and tracking progress towards national/global targets at the population level. Low birthweight (LBW) < 2500 g affects over 20.5 million newborns annually. However, data are lacking and may be affected by heaping. This paper evaluates birthweight measurement within the Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study. Methods The EN-BIRTH study took place in five hospitals in Bangladesh, Nepal and Tanzania (2017–2018). Clinical observers collected time-stamped data (gold standard) for weighing at birth. We compared accuracy for two data sources: routine hospital registers and women’s report at exit interview survey. We calculated absolute differences and individual-level validation metrics. We analysed birthweight coverage and quality gaps including timing and heaping. Qualitative data explored barriers and enablers for routine register data recording. Results Among 23,471 observed births, 98.8% were weighed. Exit interview survey-reported weighing coverage was 94.3% (90.2–97.3%), sensitivity 95.0% (91.3–97.8%). Register-reported coverage was 96.6% (93.2–98.9%), sensitivity 97.1% (94.3–99%). Routine registers were complete (> 98% for four hospitals) and legible > 99.9%. Weighing of stillbirths varied by hospital, ranging from 12.5–89.0%. Observed LBW rate was 15.6%; survey-reported rate 14.3% (8.9–20.9%), sensitivity 82.9% (75.1–89.4%), specificity 96.1% (93.5–98.5%); register-recorded rate 14.9%, sensitivity 90.8% (85.9–94.8%), specificity 98.5% (98–99.0%). In surveys, “don’t know” responses for birthweight measured were 4.7%, and 2.9% for knowing the actual weight. 95.9% of observed babies were weighed within 1 h of birth, only 14.7% with a digital scale. Weight heaping indices were around two-fold lower using digital scales compared to analogue. Observed heaping was almost 5% higher for births during the night than day. Survey-report further increased observed birthweight heaping, especially for LBW babies. Enablers to register birthweight measurement in qualitative interviews included digital scale availability and adequate staffing. Conclusions Hospital registers captured birthweight and LBW prevalence more accurately than women’s survey report. Even in large hospitals, digital scales were not always available and stillborn babies not always weighed. Birthweight data are being captured in hospitals and investment is required to further improve data quality, researching of data flow in routine systems and use of data at every level.

338 Variation in the Cost/Benefit of Spine Surgery at the Individual Patient Level: Re-examining the Definition of Healthcare Value

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 275-276
Author(s):  
Scott L Parker ◽  
Silky Chotai ◽  
Clinton J Devin ◽  
Lindsay Tetreault ◽  
Matthew J McGirt
Keyword(s):  
Health Benefit ◽  
Population Level ◽  
Cost Effective ◽  
Patient Specific ◽  
Individual Patient Level ◽  
Patient Level ◽  
Patient Reported ◽  
Specific Care ◽  
The Individual ◽  
Prospective Longitudinal

Abstract INTRODUCTION Currently, specific spine treatments are deemed altogether cost-effective vs not based on group average costs and group average health benefit. These approaches ignore that certain treatments may be highly cost-effective in subsets of patients while group averages suggest otherwise, leading to disadvantageous policy for some highly valuable spine care. We evaluate the variability in outcomes and cost for patients undergoing surgery for degenerative lumbar spine pathology at the individual patient-level. METHODS Retrospective analysis of prospective longitudinal spine registry data was conducted. Baseline and postoperative 1-year patient-reported outcomes (PROs) were recorded. Previously published MCID for ODI (14.9) was used. Back-related resource utilization and quality-adjusted life years (QALYs) were assessed. Variations in outcomes and cost were analyzed at population level and at the individual-patient level. RESULTS >Total 1454 patients were analyzed. There was significant improvement in PROs at postoperative 1-year (P < 0.0001). For patients demonstrating health benefit at population level, 12.5%, n = 182 of patients experienced no gain from surgery and 38%, n = 554 failed to achieve MCID. Mean 1-year QALY-gained was 0.29,18% of patients failed to report gain in QALY. For patients with 2-year follow-up, surgery resulted in 0.62 QALY-gained at average direct cost of $28,953. A wide variation in both QALY-gained and cost was observed. CONCLUSION Spine treatments that on average are cost-effective may have wide variability in value at the individual patient-level. The variability demonstrated here represents an opportunity, through registries, to identify specific care that may be less effective, and refine patient-specific care delivery and indications to drive overall group-level treatment value.

scholarly journals Creation and application of virtual patient cohorts of heart models

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190558 ◽  
Author(s):  
S. A. Niederer ◽  
Y. Aboelkassem ◽  
C. D. Cantwell ◽  
C. Corrado ◽  
S. Coveney ◽  
...  
Keyword(s):  
State Of The Art ◽  
Clinical Care ◽  
Virtual Patient ◽  
Patient Specific ◽  
Theme Issue ◽  
Large Numbers ◽  
Cardiac Modelling ◽  
Cardiac Models ◽  
The Individual ◽  
Cardiovascular Modelling

Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

scholarly journals Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Sara Hamis ◽  
Gibin G. Powathil ◽  
Mark A.J. Chaplain
Keyword(s):  
Mathematical Models ◽  
In Silico ◽  
Population Level ◽  
Patient Specific ◽  
Cancer Treatments ◽  
Bottom Up ◽  
Treatment Protocols ◽  
Oncology Practice ◽  
The Individual

Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line–specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.

A proof-of-concept application of a novel scoring approach for personalized medicine in multiple sclerosis

2019 ◽  
Vol 26 (9) ◽  
pp. 1064-1073
Author(s):  
Fabio Pellegrini ◽  
Massimiliano Copetti ◽  
Francesca Bovis ◽  
David Cheng ◽  
Robert Hyde ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Personalized Medicine ◽  
Treatment Response ◽  
Short Form ◽  
Combined Treatment ◽  
Patient Specific ◽  
Individual Treatment ◽  
Treatment Groups ◽  
Relapsing Remitting ◽  
Relapsing Remitting Multiple Sclerosis

Background: Stratified medicine methodologies based on subgroup analyses are often insufficiently powered. More powerful personalized medicine approaches are based on continuous scores. Objective: We deployed a patient-specific continuous score predicting treatment response in patients with relapsing-remitting multiple sclerosis (RRMS). Methods: Data from two independent randomized controlled trials (RCTs) were used to build and validate an individual treatment response (ITR) score, regressing annualized relapse rates (ARRs) on a set of baseline predictors. Results: The ITR score for the combined treatment groups versus placebo detected differential clinical response in both RCTs. High responders in one RCT had a cross-validated ARR ratio of 0.29 (95% confidence interval (CI) = 0.13–0.55) versus 0.62 (95% CI = 0.47–0.83) for all other responders (heterogeneity p = 0.038) and were validated in the other RCT, with the corresponding ARR ratios of 0.31 (95% CI = 0.18–0.56) and 0.61 (95% CI = 0.47–0.79; heterogeneity p = 0.036). The strongest treatment effect modifiers were the Short Form-36 Physical Component Summary, age, Visual Function Test 2.5%, prior MS treatment and Expanded Disability Status Scale. Conclusion: Our modelling strategy detects and validates an ITR score and opens up avenues for building treatment response calculators that are also applicable in routine clinical practice.

The Development of Language-Training Programs for Postrubella Hearing-Impaired Children

1973 ◽  
Vol 38 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Linda Lynch ◽  
Annette Tobin
Keyword(s):  
Hearing Impaired ◽  
Treatment Programs ◽  
Individual Treatment ◽  
Target Behaviors ◽  
Systematic Fashion ◽  
Development Of Language ◽  
The Individual ◽  
Impaired Children ◽  
Hearing Impaired Children

This paper presents the procedures developed and used in the individual treatment programs for a group of preschool, postrubella, hearing-impaired children. A case study illustrates the systematic fashion in which the clinician plans programs for each child on the basis of the child’s progress at any given time during the program. The clinician’s decisions are discussed relevant to (1) the choice of a mode(s) for the child and the teacher, (2) the basis for selecting specific target behaviors, (3) the progress of each program, and (4) the implications for future programming.

Evaluation of a computerized cardiovascular simulation system using patient-specific data

2013 ◽  
Vol 61 (S 01) ◽  
Author(s):  
M Kaur ◽  
N Sprunk ◽  
U Schreiber ◽  
R Lange ◽  
J Weipert ◽  
...  
Keyword(s):  
Simulation System ◽  
Patient Specific ◽  
Specific Data ◽  
Cardiovascular Simulation

scholarly journals Genetic biomarkers in the VEGF pathway predicting response to anti-VEGF therapy in age-related macular degeneration

2019 ◽  
Vol 4 (1) ◽  
pp. e000273
Author(s):  
Irina Balikova ◽  
Laurence Postelmans ◽  
Brigitte Pasteels ◽  
Pascale Coquelet ◽  
Janet Catherine ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Macular Degeneration ◽  
Treatment Response ◽  
Multiple Testing ◽  
Age Related Macular Degeneration ◽  
Patient Specific ◽  
Multiple Testing Correction ◽  
Anti Vegf ◽  
Age Related ◽  
Vegf Pathway

ObjectiveAge-related macular degeneration (ARMD) is a leading cause of visual impairment. Intravitreal injections of anti-vascular endothelial growth factor (VEGF) are the standard treatment for wet ARMD. There is however, variability in patient responses, suggesting patient-specific factors influencing drug efficacy. We tested whether single nucleotide polymorphisms (SNPs) in genes encoding VEGF pathway members contribute to therapy response.Methods and analysisA retrospective cohort of 281 European wet ARMD patients treated with anti-VEGF was genotyped for 138 tagging SNPs in the VEGF pathway. Per patient, we collected best corrected visual acuity at baseline, after three loading injections and at 12 months. We also registered the injection number and changes in retinal morphology after three loading injections (central foveal thickness (CFT), intraretinal cysts and serous neuroepithelium detachment). Changes in CFT after 3 months were our primary outcome measure. Association of SNPs to response was assessed by binomial logistic regression. Replication was attempted by associating visual acuity changes to genotypes in an independent Japanese cohort.ResultsAssociation with treatment response was detected for seven SNPs, including in FLT4 (rs55667289: OR=0.746, 95% CI 0.63 to 0.88, p=0.0005) and KDR (rs7691507: OR=1.056, 95% CI 1.02 to 1.10, p=0.005; and rs2305945: OR=0.963, 95% CI 0.93 to 1.00, p=0.0472). Only association with rs55667289 in FLT4 survived multiple testing correction. This SNP was unavailable for testing in the replication cohort. Of six SNPs tested for replication, one was significant although not after multiple testing correction.ConclusionIdentifying genetic variants that define treatment response can help to develop individualised therapeutic approaches for wet ARMD patients and may point towards new targets in non-responders.

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