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MONDAY, Aug. 12, 2019 (HealthDay News) — Deep natural language processing may be able to estimate the presence of active cancer, cancer worsening or progression, and cancer improvement or response from radiologic reports, according to a study published online July 25 in JAMA Oncology<\/i>.<\/p>\n
Kenneth L. Kehl, M.D., from the Dana-Farber Cancer Institute in Boston, and colleagues evaluated whether deep natural language processing can extract relevant cancer outcomes from radiologic reports in electronic health records. A retrospective cohort of 1,112 patients with a lung cancer diagnosis who underwent tumor genotyping were divided into the curation set, with an additional 109 in the test subset.<\/p>\n
The researchers found that in the test subset, deep learning models identified the presence of cancer, improvement\/response, and worsening\/progression with accurate discrimination (area under the curve >0.90). Machine and human analysis resulted in similar measurements of disease-free survival (hazard ratio [HR], 1.18; 95 percent confidence interval [CI], 0.71 to 1.95), progression-free survival (HR, 1.11; 95 percent CI, 0.71 to 1.71), and time to improvement\/response (HR, 1.03; 95 percent CI, 0.65 to 1.64). Using a reserve set of 1,294 patients (15,000 additional radiologic reports), algorithm-detected cancer worsening\/progression was associated with decreased overall survival (HR for mortality, 4.04; 95 percent CI, 2.78 to 5.85) and improvement\/response was associated with increased overall survival (HR, 0.41; 95 percent CI, 0.22 to 0.77).<\/p>\n
“Deep natural language processing appears to speed curation of relevant cancer outcomes and facilitate rapid learning from electronic health record data,” the authors write.<\/p>\n
Several authors disclosed financial ties to medical systems and the pharmaceutical industry.<\/p>\n
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