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      Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology

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          Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer.

          The 21-gene recurrence score (RS) assay quantifies the likelihood of distant recurrence in women with estrogen receptor-positive, lymph node-negative breast cancer treated with adjuvant tamoxifen. The relationship between the RS and chemotherapy benefit is not known. The RS was measured in tumors from the tamoxifen-treated and tamoxifen plus chemotherapy-treated patients in the National Surgical Adjuvant Breast and Bowel Project (NSABP) B20 trial. Cox proportional hazards models were utilized to test for interaction between chemotherapy treatment and the RS. A total of 651 patients were assessable (227 randomly assigned to tamoxifen and 424 randomly assigned to tamoxifen plus chemotherapy). The test for interaction between chemotherapy treatment and RS was statistically significant (P = .038). Patients with high-RS (> or = 31) tumors (ie, high risk of recurrence) had a large benefit from chemotherapy (relative risk, 0.26; 95% CI, 0.13 to 0.53; absolute decrease in 10-year distant recurrence rate: mean, 27.6%; SE, 8.0%). Patients with low-RS (< 18) tumors derived minimal, if any, benefit from chemotherapy treatment (relative risk, 1.31; 95% CI, 0.46 to 3.78; absolute decrease in distant recurrence rate at 10 years: mean, -1.1%; SE, 2.2%). Patients with intermediate-RS tumors did not appear to have a large benefit, but the uncertainty in the estimate can not exclude a clinically important benefit. The RS assay not only quantifies the likelihood of breast cancer recurrence in women with node-negative, estrogen receptor-positive breast cancer, but also predicts the magnitude of chemotherapy benefit.
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            Evolving artificial neural networks

             XIN YAO (1999)
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              Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

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                Author and article information

                Journal
                Nature Reviews Clinical Oncology
                Nat Rev Clin Oncol
                Springer Science and Business Media LLC
                1759-4774
                1759-4782
                August 9 2019
                Article
                10.1038/s41571-019-0252-y
                © 2019

                http://www.springer.com/tdm

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