SYNERGY-AI: Artificial intelligence based precision oncology clinical trial matching and registry.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. TPS717-TPS717
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla ◽  
Jason Lawrence Freedman ◽  
Belisario Augusto Arango ◽  
Kristin Johnston ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Hematological Malignancies ◽  
Machine Learning Algorithms ◽  
Precision Oncology ◽  
Level Of Evidence ◽  
Dynamic Matching ◽  
Oncology Clinical Trial ◽  
Tumor Boards ◽  
The Impact

TPS717 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo (~24-mo enrollment followed by 12 mo of data collection, to occur every 3 mo). The primary analysis will be performed 12 mo after last pt enrolled. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; cancer-related biomarkers. Key exclusion criteria: ECOG PS > 2; abnormal organ function; hospice enrollment Clinical trial information: NCT03452774.

scholarly journals SYNERGY-AI: Artificial intelligence-based precision oncology clinical trial matching and registry.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 22-22
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Hematological Malignancies ◽  
Machine Learning Algorithms ◽  
Precision Oncology ◽  
Level Of Evidence ◽  
Dynamic Matching ◽  
Oncology Clinical Trial ◽  
Tumor Boards ◽  
The Impact

22 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; Pts cancer-related biomarkers. Key exclusion: ECOG PS > 2; abnormal organ function; hospice. Results: To be presented. Conclusions: AI-based, patient-driven CTE is feasible, highly effective and paradigm-changing. Clinical trial information: NCT03452774.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

No-cost next generation sequencing of advanced cancer patients within the Strata Precision Oncology Network supports clinical trial enrollment.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3073-3073
Author(s):  
Marc Ryan Matrana ◽  
Scott A. Tomlins ◽  
Kat Kwiatkowski ◽  
Khalis Mitchell ◽  
Jennifer Marie Suga ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Next Generation Sequencing ◽  
Clinical Samples ◽  
Precision Oncology ◽  
Cancer Type ◽  
Next Generation ◽  
Match Trial ◽  
The Impact ◽  
Generation Sequencing

3073 Background: Widespread integration of systematized next generation sequencing (NGS)-based precision oncology is hindered by numerous barriers. Hence, we developed the Strata trial (NCT03061305), a screening protocol to determine the impact of scaled precision oncology. Methods: We implemented no-cost NGS on formalin fixed paraffin embedded (FFPE) clinical samples for all patients with advanced tumors, a common portfolio of partnered therapeutic clinical trials, and robust infrastructure development across the Strata Precision Oncology Network. Results: Across the network of 17 centers, specimens from 8673/9222 (94%) patients were successfully tested in the Strata CLIA/CAP/NCI-MATCH accredited laboratory using comprehensive amplicon-based DNA and RNA NGS. Patients were tested with one of three StrataNGS test versions; the most recent panel assesses all classes of actionable alterations (mutations, copy number alterations, gene fusions, microsatellite instability, tumor mutation burden and PD-L1 expression). Median surface area of received FFPE tumor samples was 25mm2 (interquartile range 9-95mm2), and the median turnaround time from sample receipt to report was 6 business days. 2577 (27.9%) patients had highly actionable alterations, defined as alterations associated with within-cancer type FDA approved or NCCN guideline recommended therapies (1072 patients), NCI-MATCH trial arms (1467 patients), Strata-partnered therapeutic trials (327 patients), or specific alteration-matched FDA approved therapies in patients with cancers of unknown primary (71 patients). Of the 1467 patients matched to an NCI-MATCH trial arm, 15 enrolled. Of the 327 patients matched to one of nine Strata-partnered clinical trials, 77 (24%) were screen failures, while 250 (76%) have either enrolled or are being actively followed for enrollment upon progression. Conclusions: Through streamlined consent methods, electronic medical record queries, and high throughput laboratory testing at no cost to patients, we demonstrate that scaled precision oncology is feasible across a diverse network of healthcare systems when paired with access to relevant clinical trials. Clinical trial information: NCT03061305.

Cancer

2021 ◽  
pp. 627-638
Author(s):  
Rashi Rai ◽  
Prudhvilal Bhukya ◽  
Muneesh Kumar Barman ◽  
Meenakshi Singh ◽  
Kailash Chand ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Success Rates ◽  
Design Strategies ◽  
Safety Level ◽  
Key Steps ◽  
The Impact

Clinical trials are essential to govern the impact of a new possible treatment. It is utilized to determine the safety level and efficacy of a certain treatment. Clinical trial studies in cancer have provided successful treatment leading to longer survival span in the patients. The design of clinical trials for cancer has been done to find new ways to prevent, diagnose, treat, and manage symptoms of the disease. This chapter will provide detailed information on different aspects of clinical trials in cancer research. Protocols outlining the design and method to conduct a clinical trial in each phase will be discussed. The process and the conditions applied in each phase (I, II, and III) will be described precisely. The design of trials done in every aspect such as prevention, immunochemotherapy, diagnosis, and treatment to combat cancer will be illustrated. Also, recent innovations in clinical design strategies and principles behind it as well as the use of recent advances in artificial intelligence in reshaping key steps of clinical trial design to increase trial success rates.

Delivering Precision Oncology in a Community Cancer Program: Results From a Prospective Observational Study

2018 ◽  
pp. 1-12
Author(s):  
Steven F. Powell ◽  
Elie G. Dib ◽  
Jonathan S. Bleeker ◽  
Michael D. Keppen ◽  
Miroslaw Mazurczak ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Survival Data ◽  
Care Delivery ◽  
Tumor Board ◽  
Precision Oncology ◽  
Advanced Tumor ◽  
Effective Delivery ◽  
Tumor Types ◽  
The Impact ◽  
Matched Treatment

Introduction Precision oncology (PO) is a growing treatment approach in the era of next-generation sequencing (NGS) and matched therapies. Effective delivery of PO in the community has not been extensively studied. Our program developed a virtual molecular tumor board (MTB) strategy to help guide PO care. Materials and Methods Over 18 months, eligible adult patients with advanced, incurable solid tumor malignancies were enrolled in a molecular profiling (MP) study using the Foundation Medicine NGS panel. Results were reviewed through a weekly, videoconferenced MTB conducted across our largely rural integrated health system. Recommendations from the MTB were used to identify actionable alterations (AAs). Feasibility of PO care delivery was assessed as the primary outcome. Secondary outcomes included the frequency of AAs, genomic matched treatments, genomic matched clinical trial enrollment, and clinical outcomes. Results A total of 120 participants with a variety of advanced tumor types were enrolled. Of these, 109 (90.8%) had successful MP. Treatment on the basis of an AA was recommended by the MTB in 58% of patients (63 of 109) who had a successful MP result. For those completing MP, treatments included enrollment in a genomic matched clinical trial (n = 16; 14.6%) and genomic matched treatment with a Food and Drug Administration–approved agent (n = 23; 21.1%). Response and survival data were similar regardless of the matched treatment option chosen. Conclusion A video-conferenced MTB-facilitated NGS testing and treatment delivery system was implemented in our integrated community oncology program. Continued use of this model aims to increase understanding of the impact of PO in this setting.

scholarly journals Development of intelligent agents through collaborative innovation

2019 ◽  
Vol 11 (3) ◽  
pp. 29-37
Author(s):  
Mateusz Kot ◽  
Grzegorz Leszczyński
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Machine Learning Algorithms ◽  
User Engagement ◽  
Collaborative Development ◽  
New Approach ◽  
Collaborative Innovation ◽  
The Impact ◽  
Depth Interviews ◽  
Agent Development

Abstract This study focuses on the development of a specific type of Intelligent Agents — Business Virtual Assistants (BVA). The paper aims to identify the scope of collaboration between users and providers in the process of agent development and to define the impact that user interpretations of a BVA agent have on this collaboration. This study conceptualises the collaboration between providers and users in the process of the BVA development. It uses the concept of the collaborative development of innovation and sensemaking. The empirical part presents preliminary exploratory in-depth interviews conducted with CEOs of BVA providers and analyses the use of the scheme offered by Miles and Hubermann (1994). The main results show the scope of the collaboration between BVA users and providers in the process of the BVA development. User engagement is crucial in the development of BVA agents since they are using machine learning algorithms. The user interpretation through sensemaking influences the process as their attitudes guide their behaviour. Apart from that, users have to adjust to this new kind of entity in the market and learn how to use it in line with savoir-vivre rules. This paper suggests the need to develop a new approach to the collaborative development of innovation when Artificial Intelligence is involved.

Is There Anything Wrong With Using AI Implantable Brain Devices to Prevent Convicted Offenders from Reoffending?

2020 ◽  
pp. 113-126
Author(s):  
Frédéric Gilbert ◽  
Susan Dodds
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Moral Autonomy ◽  
Moral Decision ◽  
Moral Decision Making ◽  
Moral Issues ◽  
Response Systems ◽  
Intelligent Devices ◽  
Advisory Systems ◽  
The Impact

The world’s first clinical trial using advisory brain implant operated by artificial intelligence (AI) has been completed with significant success. The tested devices predict a specific neuronal event (epileptic seizure), allowing people implanted with the device to be forewarned and to take steps to reduce or avoid the impact of the event. In principle, these kinds of artificially intelligent devices could be used to predict other neuronal events and allow those implanted with the device to take precautionary steps or to automate drug delivery so as to avoid unwanted outcomes. This chapter examines moral issues arising from the hypothetical situation where such devices controlled by AI are used to ensure that convicted criminal offenders are safe for release into society. We distinguish two types of predictive technologies controlled by AI: advisory systems and automated therapeutic response systems. The purpose of this chapter is to determine which of these two technologies would generate fewer ethical concerns. While there are moral similarities between the two technologies, the latter raises more concerns. In particular, it raises the possibility that individual moral decision-making and moral autonomy can be threatened by the use of automated implants.

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