scholarly journals Precision medicine in human heart modeling

2021 ◽  
Author(s):  
M. Peirlinck ◽  
F. Sahli Costabal ◽  
J. Yao ◽  
J. M. Guccione ◽  
S. Tripathy ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Treatment Planning ◽  
Human Heart ◽  
Cardiovascular Health ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Ventricular Assist Devices ◽  
Device Design ◽  
Heart Modeling ◽  
Medical Histories

AbstractPrecision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

The Johns Hopkins Molecular Tumor Board Precision Oncology elective for Medical Oncology fellows.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11035-11035
Author(s):  
Kristen Marrone ◽  
Jessica Tao ◽  
Jenna VanLiere Canzoniero ◽  
Paola Ghanem ◽  
Emily Nizialek ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Cancer Genomics ◽  
Medical Oncology ◽  
Clinical Decision ◽  
Tumor Board ◽  
Precision Oncology ◽  
Adaptive Expertise ◽  
Molecular Tumor Board

11035 Background: The accelerated impact of next generation sequencing (NGS) in clinical decision making requires the integration of cancer genomics and precision oncology focused training into medical oncology education. The Johns Hopkins Molecular Tumor Board (JH MTB) is a multi-disciplinary effort focused on integration of NGS findings with critical evidence interpretation to generate personalized recommendations tailored to the genetic footprint of individual patients. Methods: The JH MTB and the Medical Oncology Fellowship Program have developed a 3-month precision oncology elective for fellows in their research years. Commencing fall of 2020, the goals of this elective are to enhance the understanding of NGS platforms and findings, advance the interpretation and characterization of molecular assay outputs by use of mutation annotators and knowledgebases and ultimately master the art of matching NGS findings with available therapies. Fellow integration into the MTB focuses on mentored case-based learning in mutation characterization and ranking by levels of evidence for actionability, with culmination in form of verbal presentations and written summary reports of final MTB recommendations. A mixed methods questionnaire was administered to evaluate progress since elective initiation. Results: Three learners who have participated as of February 2021 were included. Of the two who had completed the MTB elective, each have presented at least 10 cases, with at least 1 scholarly publication planned. All indicated strong agreement that MTB elective had increased their comfort with interpreting clinical NGS reports as well as the use of knowledgebases and variant annotators. Exposure to experts in the field of molecular precision oncology, identification of resources necessary to interpret clinical NGS reports, development of ability to critically assess various NGS platforms, and gained familiarity with computational analyses relevant to clinical decision making were noted as strengths of the MTB elective. Areas of improvement included ongoing initiatives that involve streamlining variant annotation and transcription of information for written reports. Conclusions: A longitudinal elective in the JHU MTB has been found to be preliminarily effective in promoting knowledge mastery and creating academic opportunities related to the clinical application of precision medicine. Future directions will include leveraging of the MTB infrastructure for research projects, learner integration into computational laboratory meetings, and expansion of the MTB curriculum to include different levels of learners from multiple medical education programs. Continued elective participation will be key to understanding how best to facilitate adaptive expertise in assigning clinical relevance to genomic findings, ultimately improving precision medicine delivery in patient care and trial development.

scholarly journals Patient attrition in Molecular Tumour Boards: A Review

2021 ◽  
Author(s):  
Hannah Frost ◽  
Donna M. Graham ◽  
Louise Carter ◽  
Paul O’Regan ◽  
Donal Landers ◽  
...  
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Decision ◽  
Clinical Deterioration ◽  
Treatment Data ◽  
Patient Attrition ◽  
Receive Treatment

AbstractMolecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision making within precision medicine. Though these meetings are in use globally reporting often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment. A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 56 papers were reviewed spanning a 6 year period from 11 different countries. 20% of patients received treatment through the MTB process. Of those that did not receive treatment the main reasons were no mutations identified (26%), no actionable mutations (22%) and clinical deterioration (15%). However, the data was often incomplete due to inconsistent reporting of MTBs with only 54% reporting on patients having no mutations, 48% reporting on presence of actionable mutations and 57% reporting on clinical deterioration. Patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data.

scholarly journals Precision medicine in inflammatory bowel disease: concept, progress and challenges

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 54 ◽  
Author(s):  
Simon P. Borg-Bartolo ◽  
Ray Kiran Boyapati ◽  
Jack Satsangi ◽  
Rahul Kalla
Keyword(s):  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Underlying Disease ◽  
Response To Therapy ◽  
Current Evidence ◽  
Disease Biology ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
The Individual

Crohn’s disease and ulcerative colitis are increasingly prevalent, relapsing and remitting inflammatory bowel diseases (IBDs) with variable disease courses and complications. Their aetiology remains unclear but current evidence shows an increasingly complex pathophysiology broadly centring on the genome, exposome, microbiome and immunome. Our increased understanding of disease pathogenesis is providing an ever-expanding arsenal of therapeutic options, but these can be expensive and patients can lose response or never respond to certain therapies. Therefore, there is now a growing need to personalise therapies on the basis of the underlying disease biology and a desire to shift our approach from “reactive” management driven by disease complications to “proactive” care with an aim to prevent disease sequelae. Precision medicine is the tailoring of medical treatment to the individual patient, encompassing a multitude of data-driven (and multi-omic) approaches to foster accurate clinical decision-making. In IBD, precision medicine would have significant benefits, enabling timely therapy that is both effective and appropriate for the individual. In this review, we summarise some of the key areas of progress towards precision medicine, including predicting disease susceptibility and its course, personalising therapies in IBD and monitoring response to therapy. We also highlight some of the challenges to be overcome in order to deliver this approach.

scholarly journals Precision Medicine in the Context of Artificial Intelligence

2019 ◽  
Author(s):  
Rehab A. Rayan
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Decision Support Techniques ◽  
Initial Step ◽  
Clinical Decision ◽  
Moral Accountability ◽  
Legal Obligations ◽  
Ibm Watson ◽  
Selection Of

Similar to all fields of study, growth occurs with breakthrough or developmental research spanning to the progress and intimate utility of technology. Dilemmas of various sectors have been favorably resolved by adopting artificial intelligence (AI) algorithms. Applying Precision Medicine profoundly depends on AI algorithms to work Precision Medicine queries like; predicting or detecting, diagnosing the disease properly, and optimizing therapy, hence, the selection of the algorithm is affected by its capacity and practicality. Nevertheless, it is yet in its initial step and fronts some hurdles crucial to the flourishing deployment of precision medicine like research, adoption values, and authority controls. Notwithstanding, Precision Medicine also pretends some difficulties like; modifying the health discipline and profession to the fact that automata and algorithms could displace most of the healthcare professional tasks they act now. Ultimately, effective employment of precision medicine would rescue countless lives and improve the health profession. This review examines the present state of AI applications in precision medicine and future opportunities. It discusses major AI systems like IBM Watson, examining moral accountability and legal obligations when applying it in clinical decision-making, advantages and boundaries of employing Watson and different AI clinical decision support techniques, and considerations before consulting AI systems.

Radiomics

2018 ◽  
pp. 191-217
Author(s):  
Julie Constanzo ◽  
Issam El Naqa
Keyword(s):  
Treatment Planning ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Quantitative Information ◽  
Imaging Modalities ◽  
Clinical Endpoints ◽  
Recent Advances ◽  
Multiple Imaging ◽  
Hidden Knowledge ◽  
Cancer Types

Recent advances in image-guided and adaptive radiotherapy have ushered new requirements for using single and/or multiple-imaging modalities in staging, treatment planning, and predicting response of different cancer types. Quantitative information analysis from multi-imaging modalities, known as ‘radiomics', have generated great promises to unravel hidden knowledge embedded in imaging for mining it and its association with observed clinical endpoints and/or underlying biological processes. In this chapter, we will review recent advances and discuss current challenges for using radiomics in radiotherapy. We will discuss issues related to image acquisition, registration, contouring, feature extraction and fusion, statistical modeling, and combination with other imaging modalities and other ‘omics' for developing robust models of treatment outcomes. We will provide examples based on our experience and others for predicting cancer outcomes in radiotherapy generally and brain cancer specifically, and their application in personalizing treatment planning and clinical decision-making.

Pathways to radiomics-aided clinical decision-making for precision medicine

2019 ◽  
pp. 193-201
Author(s):  
Tianye Niu ◽  
Xiaoli Sun ◽  
Pengfei Yang ◽  
Guohong Cao ◽  
Khin K. Tha ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision
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