Evaluating the impact of an artificial intelligence tool: Improving high-risk patient identification for hereditary breast and ovarian cancer genetic testing

2021 ◽  
Vol 132 ◽  
pp. S52-S53
Author(s):  
Melissa Maisenbacher ◽  
Kathryn Young ◽  
Asaf Sadowsky ◽  
Paul Billings ◽  
Sheetal Parmar
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Genetic Testing ◽  
High Risk ◽  
High Risk Patient ◽  
Risk Patient ◽  
Cancer Genetic ◽  
Patient Identification ◽  
Breast And Ovarian Cancer ◽  
The Impact

scholarly journals Oncology Clinic-Based Hereditary Cancer Genetic Testing in a Population-Based Health Care System

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 338 ◽  
Author(s):  
Matthew Richardson ◽  
Hae Jung Min ◽  
Quan Hong ◽  
Katie Compton ◽  
Sze Wing Mung ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Health Care ◽  
Genetic Testing ◽  
Population Based ◽  
Gene Panel ◽  
Wait Times ◽  
Genetic Counsellor ◽  
Care Providers ◽  
Cancer Genetic ◽  
Breast And Ovarian Cancer

New streamlined models for genetic counseling and genetic testing have recently been developed in response to increasing demand for cancer genetic services. To improve access and decrease wait times, we implemented an oncology clinic-based genetic testing model for breast and ovarian cancer patients in a publicly funded population-based health care setting in British Columbia, Canada. This observational study evaluated the oncology clinic-based model as compared to a traditional one-on-one approach with a genetic counsellor using a multi-gene panel testing approach. The primary objectives were to evaluate wait times and patient reported outcome measures between the oncology clinic-based and traditional genetic counselling models. Secondary objectives were to describe oncologist and genetic counsellor acceptability and experience. Wait times from referral to return of genetic testing results were assessed for 400 patients with breast and/or ovarian cancer undergoing genetic testing for hereditary breast and ovarian cancer from June 2015 to August 2017. Patient wait times from referral to return of results were significantly shorter with the oncology clinic-based model as compared to the traditional model (403 vs. 191 days; p < 0.001). A subset of 148 patients (traditional n = 99; oncology clinic-based n = 49) completed study surveys to assess uncertainty, distress, and patient experience. Responses were similar between both models. Healthcare providers survey responses indicated they believed the oncology clinic-based model was acceptable and a positive experience. Oncology clinic-based genetic testing using a multi-gene panel approach and post-test counselling with a genetic counsellor significantly reduced wait times and is acceptable for patients and health care providers.

The impact of the medicare benefits schedule on genetic testing in hereditary breast and ovarian cancer: An audit of test request practices in Western Australia

Pathology ◽  
2018 ◽  
Vol 50 ◽  
pp. S100
Author(s):  
Sarah L. Nickerson ◽  
Jamie-Lee Ricciardi ◽  
Ratna Dubey ◽  
Deborah Norman ◽  
Natasha Buzzacott ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Genetic Testing ◽  
Western Australia ◽  
Breast And Ovarian Cancer ◽  
The Impact ◽  
Test Request

Thrombocytopenia Is a Strong Adverse Feature in Transplant-Eligible Patients with Myelofibrosis: Utility for an Improved CBC-Based Scoring System.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2584-2584
Author(s):  
David Dingli ◽  
Susan M. Schwager ◽  
Ruben A. Mesa ◽  
Chin-Yang Li ◽  
Ayalew Tefferi
Keyword(s):  
Multivariate Analysis ◽  
Platelet Count ◽  
High Risk ◽  
Scoring System ◽  
High Risk Patient ◽  
Risk Patient ◽  
Poor Survival ◽  
Hematopoietic Stem ◽  
Myeloid Metaplasia ◽  
The Impact

Abstract Background: Allogeneic hematopoietic stem cell transplantation is potentially curative in agnogenic myeloid metaplasia (AMM) but is associated with substantial mortality and morbidity that necessitates accurate identification of patients in whom benefit outweighs risk. The current single institutional study investigates prognostic variables in transplant-eligible patients with AMM with the main objective of improved discrimination between intermediate- and high-risk patient categories. Methods: Patients diagnosed with AMM before the age of 60 years and seen at Mayo Clinic were identified and the diagnosis confirmed. Relevant demographic, clinical and laboratory characteristics were abstracted and the impact of various parameters on overall survival was evaluated with univariate and multivariate analysis. Results: A cohort of 159 patients (median age 52 years, range 18–60; 89 males) with AMM is described. Median follow-up from initial diagnosis was 63 months (range 0–300). During this period, 102 patients have died; overall median survival 79 months. Multivariate analysis of parameters measured in all study patients at diagnosis identified thrombocytopenia (platelet count &lt; 100 x 109/L) as the strongest predictor of inferior survival (p=0.002). In addition, a hemoglobin level of &lt;10 g/dL (p=0.003), white blood cell count of either &lt;4 or &gt;30 x 109/L (p=0.03), and older age (p=0.02) were also found to be independent indicators of poor prognosis. However, when the analysis included parameters that were measured in variable proportion of the study population, the independent prognostic factors for poor survival were thrombocytopenia (p=0.0001), anemia (p=0.01), and the presence of unfavorable cytogenetic abnormalities (0.001). Based on the above findings, we constructed a new complete blood count (CBC)-based prognostic scoring system (Figure 1) that performed better than the Dupriez scoring system in discriminating intermediate- from high-risk patient categories(Figure 2). Figure Figure Conclusions: Thrombocytopenia is a strong predictor of poor survival in transplant-eligible patients with AMM. The incorporation of platelet count into the Dupriez prognostic scoring system might allow the construction of an improved, CBC-based scoring system that can accurately identify high-risk as well as intermediate-risk patients with AMM.

scholarly journals Psychological care program for participants of cancer genetic counseling with high risk of breast and ovarian cancer

2020 ◽  
Vol 19 (4) ◽  
Author(s):  
Reyna J. Martínez-Arriaga ◽  
Leivy P. González-Ramírez ◽  
Azucena Del Toro-Valero ◽  
Rebeca Robles-García ◽  
Antonio Oceguera-Villanueva ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Genetic Counseling ◽  
High Risk ◽  
Care Program ◽  
Cancer Genetic Counseling ◽  
Cancer Genetic ◽  
Psychological Care ◽  
Breast And Ovarian Cancer

scholarly journals Clinical Considerations ofBRCA1- andBRCA2-Mutation Carriers: A Review

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
O. Bougie ◽  
J. I. Weberpals
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Genetic Testing ◽  
High Risk ◽  
Significant Risk ◽  
Surgical Prophylaxis ◽  
Breast And Ovarian Cancer ◽  
Screening Guidelines ◽  
Mutation Carriers ◽  
Clinical Considerations

Individuals who carry an inherited mutation in the breast cancer 1 (BRCA1) andBRCA2genes have a significant risk of developing breast and ovarian cancer over the course of their lifetime. As a result, there are important considerations for the clinician in the counseling, followup and management of mutation carriers. This review outlines salient aspects in the approach to patients at high risk of developing breast and ovarian cancer, including criteria for genetic testing, screening guidelines, surgical prophylaxis, and chemoprevention.

scholarly journals Non-BRCA1/2 Variants Detected in a High-Risk Chilean Cohort With a History of Breast and/or Ovarian Cancer

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Christina Adaniel ◽  
Francisca Salinas ◽  
Juan Manuel Donaire ◽  
Maria Eugenia Bravo ◽  
Octavio Peralta ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Genetic Testing ◽  
High Risk ◽  
Genetic Predisposition ◽  
Cancer Syndrome ◽  
Breast And Ovarian Cancer ◽  
Brca1 And Brca2 ◽  
Pathogenic Variants ◽  
Cancer Program ◽  
Genetic Test Results

PURPOSE Little is known about the genetic predisposition to breast and ovarian cancer among the Chilean population, in particular genetic predisposition beyond BRCA1 and BRCA2 mutations. In the current study, we aim to describe the germline variants detected in individuals who were referred to a hereditary cancer program in Santiago, Chile. METHODS Data were retrospectively collected from the registry of the High-Risk Breast and Ovarian Cancer Program at Clínica Las Condes, Santiago, Chile. Data captured included index case diagnosis, ancestry, family history, and genetic test results. RESULTS Three hundred fifteen individuals underwent genetic testing during the study period. The frequency of germline pathogenic and likely pathogenic variants in a breast or ovarian cancer predisposition gene was 20.3%. Of those patients who underwent testing with a panel of both high- and moderate-penetrance genes, 10.5% were found to have pathogenic or likely pathogenic variants in non- BRCA1/2 genes. CONCLUSION Testing for non- BRCA1 and -2 mutations may be clinically relevant for individuals who are suspected to have a hereditary breast or ovarian cancer syndrome in Chile. Comprehensive genetic testing of individuals who are at high risk is necessary to further characterize the genetic susceptibility to cancer in Chile.

scholarly journals Skin cancer high-risk patient screening from dermoscopic images via Artificial Intelligence: an online study

2021 ◽  
Author(s):  
David Coronado-Gutiérrez ◽  
Carlos López ◽  
Xavier P. Burgos-Artizzu
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Skin Lesion ◽  
High Risk Patient ◽  
Area Under The Curve ◽  
Skin Lesions ◽  
Risk Patient ◽  
Online Study ◽  
Patient Screening ◽  
Malignant Skin

ABSTRACTObjectivesTo evaluate a novel Artificial Intelligence (AI) method for the detection of malignant skin lesions from dermoscopic images.Methods58,457 dermoscopic images available online from the International Skin Imaging Collaboration (ISIC) Archive were downloaded. These images were acquired from different centers worldwide by recognized dermatologists and show varied clinical outcomes belonging to different types of benign and malign skin lesions. A state-of-the-art AI skin lesion classifier based on Deep Learning was designed. The method, fully automated, first locates and segments the nevus in the image and then classifies it into either benign or malign type.Results1,631 images (2.8%) were discarded due to bad quality. A total of 56,826 images were finally used. Two thirds of the images (37,688) were used to train the classifier, leaving the remaining 19,138 images for validation. In this set, malignant lesions had a prevalence of 15.4% (2,956/19,138). The AI skin lesion classifier reached an area under the curve (AUC) of 87.4%. Optimal cut-off point in terms of accuracy resulted in an 85.9% accuracy (16,439/19,138) and sensibility of 89.6% (2,648/ 2,956) at 85.2% (13,791/16,182) specificity. Negative predictive value (NPV) was 97.8% (13,791/14,099). Other training/validation splits were also evaluated, showing similar results.ConclusionsA novel AI method showed promising results as skin lesion classifier from dermoscopic images. Its high NPV value could make it suited for high-risk patient screening. A large clinical study to confirm these results is needed and will be pursued.

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