PURPOSE: Although BRCA1/2 testing in ovarian cancer improves outcomes, it is vastly underutilized. Scalable approaches are urgently needed to improve genomically guided care. METHODS: We developed a Natural Language Processing (NLP) pipeline to extract electronic medical record information to identify recipients of BRCA testing. We applied the NLP pipeline to assess testing status in 308 patients with ovarian cancer receiving care at a National Cancer Institute Comprehensive Cancer Center (main campus [MC] and five affiliated clinical network sites [CNS]) from 2017 to 2019. We compared characteristics between (1) patients who had/had not received testing and (2) testing utilization by site. RESULTS: We found high uptake of BRCA testing (approximately 78%) from 2017 to 2019 with no significant differences between the MC and CNS. We observed an increase in testing over time (67%-85%), higher uptake of testing among younger patients (mean age tested = 61 years v untested = 65 years, P = .01), and higher testing among Hispanic (84%) compared with White, Non-Hispanic (78%), and Asian (75%) patients (P = .006). Documentation of referral for an internal genetics consultation for BRCA pathogenic variant carriers was higher at the MC compared with the CNS (94% v 31%). CONCLUSION: We were able to successfully use a novel NLP pipeline to assess use of BRCA testing among patients with ovarian cancer. Despite relatively high levels of BRCA testing at our institution, 22% of patients had no documentation of genetic testing and documentation of referral to genetics among BRCA carriers in the CNS was low. Given success of the NLP pipeline, such an informatics-based approach holds promise as a scalable solution to identify gaps in genetic testing to ensure optimal treatment interventions in a timely manner.
PURPOSE: Although BRCA1/2 testing in ovarian cancer improves outcomes, it is vastly underutilized. Scalable approaches are urgently needed to improve genomically guided care. METHODS: We developed a Natural Language Processing (NLP) pipeline to extract electronic medical record information to identify recipients of BRCA testing. We applied the NLP pipeline to assess testing status in 308 patients with ovarian cancer receiving care at a National Cancer Institute Comprehensive Cancer Center (main campus [MC] and five affiliated clinical network sites [CNS]) from 2017 to 2019. We compared characteristics between (1) patients who had/had not received testing and (2) testing utilization by site. RESULTS: We found high uptake of BRCA testing (approximately 78%) from 2017 to 2019 with no significant differences between the MC and CNS. We observed an increase in testing over time (67%-85%), higher uptake of testing among younger patients (mean age tested = 61 years v untested = 65 years, P = .01), and higher testing among Hispanic (84%) compared with White, Non-Hispanic (78%), and Asian (75%) patients (P = .006). Documentation of referral for an internal genetics consultation for BRCA pathogenic variant carriers was higher at the MC compared with the CNS (94% v 31%). CONCLUSION: We were able to successfully use a novel NLP pipeline to assess use of BRCA testing among patients with ovarian cancer. Despite relatively high levels of BRCA testing at our institution, 22% of patients had no documentation of genetic testing and documentation of referral to genetics among BRCA carriers in the CNS was low. Given success of the NLP pipeline, such an informatics-based approach holds promise as a scalable solution to identify gaps in genetic testing to ensure optimal treatment interventions in a timely manner.
Authors: Bella Kaufman; Ronnie Shapira-Frommer; Rita K Schmutzler; M William Audeh; Michael Friedlander; Judith Balmaña; Gillian Mitchell; Georgeta Fried; Salomon M Stemmer; Ayala Hubert; Ora Rosengarten; Mariana Steiner; Niklas Loman; Karin Bowen; Anitra Fielding; Susan M Domchek Journal: J Clin Oncol Date: 2014-11-03 Impact factor: 44.544
Authors: Kathryn Alsop; Sian Fereday; Cliff Meldrum; Anna deFazio; Catherine Emmanuel; Joshy George; Alexander Dobrovic; Michael J Birrer; Penelope M Webb; Colin Stewart; Michael Friedlander; Stephen Fox; David Bowtell; Gillian Mitchell Journal: J Clin Oncol Date: 2012-06-18 Impact factor: 44.544
Authors: Jonathan A Ledermann; Philipp Harter; Charlie Gourley; Michael Friedlander; Ignace Vergote; Gordon Rustin; Clare Scott; Werner Meier; Ronnie Shapira-Frommer; Tamar Safra; Daniela Matei; Anitra Fielding; Stuart Spencer; Philip Rowe; Elizabeth Lowe; Darren Hodgson; Mika A Sovak; Ursula Matulonis Journal: Lancet Oncol Date: 2016-09-09 Impact factor: 41.316
Authors: Kathleen Moore; Nicoletta Colombo; Giovanni Scambia; Byoung-Gie Kim; Ana Oaknin; Michael Friedlander; Alla Lisyanskaya; Anne Floquet; Alexandra Leary; Gabe S Sonke; Charlie Gourley; Susana Banerjee; Amit Oza; Antonio González-Martín; Carol Aghajanian; William Bradley; Cara Mathews; Joyce Liu; Elizabeth S Lowe; Ralph Bloomfield; Paul DiSilvestro Journal: N Engl J Med Date: 2018-10-21 Impact factor: 91.245
Authors: Robert L Coleman; Amit M Oza; Domenica Lorusso; Carol Aghajanian; Ana Oaknin; Andrew Dean; Nicoletta Colombo; Johanne I Weberpals; Andrew Clamp; Giovanni Scambia; Alexandra Leary; Robert W Holloway; Margarita Amenedo Gancedo; Peter C Fong; Jeffrey C Goh; David M O'Malley; Deborah K Armstrong; Jesus Garcia-Donas; Elizabeth M Swisher; Anne Floquet; Gottfried E Konecny; Iain A McNeish; Clare L Scott; Terri Cameron; Lara Maloney; Jeff Isaacson; Sandra Goble; Caroline Grace; Thomas C Harding; Mitch Raponi; James Sun; Kevin K Lin; Heidi Giordano; Jonathan A Ledermann Journal: Lancet Date: 2017-09-12 Impact factor: 79.321