scholarly journals Molecular-based precision oncology clinical decision making augmented by artificial intelligence

2021 ◽  
Author(s):  
Jia Zeng ◽  
Md Abu Shufean
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Molecular Data ◽  
Molecular Testing ◽  
Precision Oncology ◽  
Current Application ◽  
Next Generation Sequencing Ngs ◽  
Molecular Sequencing

The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians’ decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.

scholarly journals INNV-28. MOLECULAR TESTING IN NEURO-ONCOLOGY – ASSESSMENT OF UTILITY AND PRACTICE PATTERNS. A SOCIETY FOR NEURO-ONCOLOGY MEMBERSHIP SURVEY

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi136-vi136
Author(s):  
Maciej Mrugala ◽  
Susan Chang
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Practice Patterns ◽  
Clinical Decision ◽  
Educational Programs ◽  
Molecular Testing ◽  
Targeted Agents ◽  
Younger Patients ◽  
Tumor Boards ◽  
The Subject

Abstract BACKGROUND Molecular testing (MT) is utilized in neuro-oncology with increasing frequency. Multiple molecular panels are available providing a spectrum of information. We were interested in learning how this information is acquired, what are the practice patterns regarding this type of testing, how are the results utilized in patient care and how prepared neuro-oncologists are to interpret these results. METHODS We conducted a survey using the Society for Neuro-Oncology membership database. We developed a set of 13 questions and administered the survey to 2022 members using the online platform. RESULTS We received 153 responses (7.5% of membership). 89% percent of responders routinely order MT. Of those who do not order MT on all patients, 50% test younger patients and 57% midline tumors. 83% use MT in recurrent glioma. Other common indications for MT included: metastatic tumors, meningioma, medulloblastoma, ATRT. Majority (60%) use in-house panels, followed by Foundation One (35%), TEMPUS (13%), CARIS (10%) and other panels (23%). For 57% of respondents, the data from MT was somewhat useful and for 41% it was very useful. 78% used the results of MT for clinical decision-making. BRAF, EGFR/ALK, H3K27 mutations were most commonly used for treatment decisions. 50% of respondents have molecular tumor boards at their institutions and a majority of practitioners share the results of MT with their patients (95%). Respondents would like to see SNO-endorsed official guidelines on MT, organized lists of targeted agents available for specific mutations, a database of targetable mutations and clinical trials and more educational programs on the subject. CONCLUSIONS Molecular testing is neuro-oncology is commonly done. Many providers rely on the information for clinical decision making where appropriate. In-house and commercial genetic panels are equally used in practice. There continues to be a need for more education on the subject and development of neuro-oncology specific guidelines.

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.

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

2011 ◽  
pp. 1017-1029
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Statistical Analysis ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Ct Scanning ◽  
Clinical Decision ◽  
Radiology Department ◽  
Self Organizing Map ◽  
One Year

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

A comparison of patients' and physicians' expectations regarding precision oncology tests.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1568-1568
Author(s):  
Navdeep Dehar ◽  
Tasnima Abedin ◽  
Patricia A. Tang ◽  
D. Gwyn Bebb ◽  
Winson Y. Cheung
Keyword(s):  
Decision Making ◽  
Genetic Testing ◽  
Cancer Biology ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Survey Study ◽  
Precision Oncology ◽  
Diagnosis And Prognosis ◽  
Genetic Test Results ◽  
Survey Responses

1568 Background: With the increasing number and frequency of biomarker and genetic tests that are offered to patients with cancer, it is important to ensure that they fully understand the implications of these tests. In this survey study, we aimed to compare the attitudes and expectations of patients and cancer physicians about the role of biomarker and genetic testing in clinical decision-making. Methods: Two separate, complimentary, self-administered questionnaires for cancer patients and their physicians, respectively, were collected in Calgary, Alberta, Canada. Survey responses from patients were subsequently matched with those of their corresponding oncologists to form patient–oncologist dyads. We determined the concordance rates between responses of patients and those of their oncologists. Results: A total of 113 patients and 15 physicians participated in the study from July to September 2019. Patients demonstrated good understanding of general cancer biology (79%) and diagnostic processes (91%) associated with precision oncology. About 70% patients were willing to undergo minor procedures, and participate in research involving biomarker or genetic testing; however, this was over-estimated by their physicians in 82% of cases. Many patients felt that their tumor should be tested to guide treatment (70%) and were not bothered by potential delays in treatment due to testing (23%). These views from patients were largely shared by their oncologists (concordance 64%). While only 28% patients thought that they had enough knowledge to make informed decisions, majority (68%) said that they needed more information. Importantly, knowledge and expectations regarding the applications of biomarker or genetic test results on actual diagnosis and prognosis were grossly discrepant between patients and their oncologists (concordance 26% and 36%, respectively). Conclusions: Patients and cancer physicians tend to be aware of the advances in precision oncology and are willing to participate in biomarker and genetic testing and research. However, they do not consistently agree about the roles and applications of these tests, which may result in misplaced expectations. Strategies to improve education and communication are needed to align these expectations and improve the quality of clinical decision-making.

Molecular Diagnostics in Pediatric Brain Tumors: Impact on Diagnosis and Clinical Decision-Making — A Selected Case Series

2018 ◽  
Vol 230 (06) ◽  
pp. 305-313 ◽  
Author(s):  
Heidi Bächli ◽  
Jonas Ecker ◽  
Cornelis van Tilburg ◽  
Dominik Sturm ◽  
Florian Selt ◽  
...  
Keyword(s):  
Decision Making ◽  
Molecular Diagnostics ◽  
Clinical Management ◽  
Clinical Decision Making ◽  
Case Series ◽  
Clinical Decision ◽  
Cns Tumors ◽  
Molecular Testing ◽  
Cancer Predisposition Syndrome ◽  
Pediatric Cns Tumors

AbstractCentral nervous system (CNS) tumors account for the highest mortality among pediatric malignancies. Accurate diagnosis is essential for optimal clinical management. The increasing use of molecular diagnostics has opened up novel possibilities for more precise classification of CNS tumors. We here report a single-institutional collection of pediatric CNS tumor cases that underwent a refinement or a change of diagnosis after completion of molecular analysis that affected clinical decision-making including the application of molecularly informed targeted therapies. 13 pediatric CNS tumors were analyzed by conventional histology, immunohistochemistry, and molecular diagnostics including DNA methylation profiling in 12 cases, DNA sequencing in 8 cases and RNA sequencing in 3 cases. 3 tumors had a refinement of diagnosis upon molecular testing, and 6 tumors underwent a change of diagnosis. Targeted therapy was initiated in 5 cases. An underlying cancer predisposition syndrome was detected in 5 cases. Although this case series, retrospective and not population based, has its limitations, insight can be gained regarding precision of diagnosis and clinical management of the patients in selected cases. Accuracy of diagnosis was improved in the cases presented here by the addition of molecular diagnostics, impacting clinical management of affected patients, both in the first-line as well as in the follow-up setting. This additional information may support the clinical decision making in the treatment of challenging pediatric CNS tumors. Prospective testing of the clinical value of molecular diagnostics is currently underway.

scholarly journals The Role of Artificial Intelligence in Many Dental Specialties

2021 ◽  
Vol 41 ◽  
pp. 03005
Author(s):  
Choirunisa Nur Humairo ◽  
Aquarina Hapsari ◽  
Indra Bramanti
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Clinic Management ◽  
Pediatric Dentist ◽  
Dental Procedures ◽  
Evolution Of Technology ◽  
Near Future

Background: Technology has become a fundamental part of human living. The evolution of technology has been advantageous to science development, including dentistry. One of the latest technology that draw many attention is Artificial Intelligence (AI). Purpose: The aim of this review is to explain the use of AI in many disciplines of dental specialties and its benefit. Reviews: The application of Artificial Intelligence may be beneficial for all dental specialties, varying from pediatric dentist to oral surgeon. In dental clinic management, AI may assist in medical record as well as other paperwork. AI would also give a valuable contribution in important dental procedures, such as diagnosis and clinical decision making. It helps the dentist deliver the best treatment for the patients. Conclusion: The latest development of Artificial Intelligence is beneficial for dental practitioner in the near future. It is considered as a breakthrough of the 21st century to support the diagnostic procedure and decision making in clinical practice. The use of AI can be applied in most of dental specialties.

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