scholarly journals MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Gilmer Valdes ◽  
José Marcio Luna ◽  
Eric Eaton ◽  
Charles B. Simone ◽  
Lyle H. Ungar ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Patient Stratification

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 Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1409 ◽  
Author(s):  
Chiesa-Estomba ◽  
Echaniz ◽  
Larruscain ◽  
Gonzalez-Garcia ◽  
Sistiaga-Suarez ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Texture Analysis ◽  
Laryngeal Cancer ◽  
Clinical Decision Making ◽  
Functional Anatomy ◽  
Functional Results ◽  
Predictive Biomarker ◽  
Diagnostic Tools ◽  
Imaging Data

Radiomics and texture analysis represent a new option in our biomarkers arsenal. These techniques extract a large number of quantitative features, analyzing their properties to incorporate them in clinical decision-making. Laryngeal cancer represents one of the most frequent cancers in the head and neck area. We hypothesized that radiomics features can be included as a laryngeal cancer precision medicine tool, as it is able to non-invasively characterize the overall tumor accounting for heterogeneity, being a prognostic and/or predictive biomarker derived from routine, standard of care, imaging data, and providing support during the follow up of the patient, in some cases avoiding the need for biopsies. The larynx represents a unique diagnostic and therapeutic challenge for clinicians due to its complex tridimensional anatomical structure. Its complex regional and functional anatomy makes it necessary to enhance our diagnostic tools in order to improve decision-making protocols, aimed at better survival and functional results. For this reason, this technique can be an option for monitoring the evolution of the disease, especially in surgical and non-surgical organ preservation treatments. This concise review article will explain basic concepts about radiomics and discuss recent progress and results related to laryngeal cancer.

scholarly journals Plasminogen Activator System and Breast Cancer: Potential Role in Therapy Decision Making and Precision Medicine

2016 ◽  
Vol 11 ◽  
pp. BMI.S33372 ◽  
Author(s):  
Adel Gouri ◽  
Aoulia Dekaken ◽  
Khalid El Bairi ◽  
Arifa Aissaoui ◽  
Nihad Laabed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Precision Medicine ◽  
Plasminogen Activator ◽  
Critical Role ◽  
Multiple Cancer ◽  
Dismal Prognosis ◽  
Plasminogen Activator System ◽  
Pai 1

Shifting from the historical TNM paradigm to the determination of molecular and genetic subtypes of tumors has been a major improvement to better picture cancerous diseases. The sharper the picture is, the better will be the possibility to develop subsequent strategies, thus achieving higher efficacy and prolonged survival eventually. Recent studies suggest that urokinase-type plasminogen activator (uPA), uPA receptor (uPAR), and plasminogen activator inhibitor-1 (PAI-1) may play a critical role in cancer invasion and metastasis. Consistent with their role in cancer dissemination, high levels of uPA, PAI-1, and uPAR in multiple cancer types correlate with dismal prognosis. In this respect, upfront determination of uPA and PAI-1 as invasion markers has further opened up the possibilities for individualized therapy of breast cancer. Indeed, uPA and PAI-1 could help to classify patients on their risk for metastatic spreading and subsequent relapse, thus helping clinicians in their decision-making process to propose, or not propose, adjuvant therapy. This review covers the implications for cancer diagnosis, prognosis, and therapy of uPA and PAI-1, and therefore how they could be major actors in the development of a precision medicine in breast cancer.

scholarly journals INNV-34. PATTERNS OF FAILURE AND DEVELOPMENT OF A NOVEL PROGNOSTIC SCORING SYSTEM IN ELDERLY PATIENTS WITH GLIOBLASTOMA – FOLLOW UP ON 10 YEAR ANALYSIS OF THE BC CANCER AGENCY POPULATION

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi137-vi137
Author(s):  
Jonathan Zeng ◽  
Kimberly DeVries ◽  
Andra Krauze
Keyword(s):  
Decision Making ◽  
Scoring System ◽  
Clinical Decision Making ◽  
Radiographic Progression ◽  
Clinical Decision ◽  
Patient Stratification ◽  
Patterns Of Failure ◽  
Resection Status ◽  
Cancer Agency

Abstract PURPOSE Glioblastomas (GBM) are the most common primary brain tumour recurring in most patients despite maximal management. Patient selection for appropriate treatment modality remains challenging resulting in heterogeneity in management. We examined the patterns of failure and developed a scoring system for patient stratification to optimise clinical decision making. METHODS 822 adults (BC Cancer Agency registry) diagnosed 2005–2015 age ≥60 with histologically confirmed GBM ICD-O-3 codes (9440/3, 9441/3, 9442/3) were reviewed. Univariate and Kaplan-Meier analysis were performed. Performance status (PS), age and resection status were assigned a score, cummulative maximal (favorable) score of 10 and minimum (unfavorable) score of 3. Patterns of failure were further analysed in the subset of patients with radiographic follow-up. RESULTS PS score of 3(KPS >80, ECOG 0/1), 2 (KPS 60–70, ECOG 2), 1 (KPS < 60, ECOG 3/4) (median OS 11, 6, 3 months respectively), age score and resection status were prognostic for OS with PS resulting in the most significant curve separation (p< 0.0001). Biopsy as compared to STR/GTR resulted in poorer OS in patients over 70 (age score 1/2) but had less impact in patients younger than 70 (age scores 3/4). The median OS for cumulative scores of 9/10 (123 patients), 7/8 (286 patients), 5/6 (313 patients), and 3/4 (55 patients) were 14, 8, 4 and 2 months respectively (p< 0.0001) allowing for stratification into 4 prognostic groups. 133 patients had >3 MRIs following diagnosis allowing for clinical and radiographic analysis of progression. Clinical/radiographic progression occurred within 3 months (29%/45%), 6 months (50%/66%), 9 months (70%/81%). Progression type (radiographic, clinical, both was not associated with OS. CONCLUSION Our novel prognostic scoring system is effective in achieving patient stratification and may guide clinical decision making. Early radiographic progression appears to precede clinical deterioration and may represent true progression in the elderly.

scholarly journals Generic Cost-Effectiveness Models: A Proof of Concept of a Tool for Informed Decision-Making for Public Health Precision Medicine

2018 ◽  
Vol 21 (5-6) ◽  
pp. 217-227 ◽  
Author(s):  
Susan R. Snyder ◽  
Jing Hao ◽  
Larisa H. Cavallari ◽  
Zhi Geng ◽  
Amanda Elsey ◽  
...  
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Cost Effectiveness ◽  
Precision Medicine ◽  
Informed Decision ◽  
Informed Decision Making ◽  
Proof Of Concept

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.

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
Sign in / Sign up

Export Citation Format

Share Document