Wednesday, May 17, 2023 ALC5 analysis Othus M, Thomas I, Wang X, Ariti C, Mehta P, Sydenham M, Hills RK, Burnett AK, Nand S, Assouline S, Michaelis LC, Erba HP, Russell N, Kerr KF, Walter RB, Dennis M. Early mortality risk with non-intensive acute myeloid leukemia (AML) therapies: analysis of 1336 patients from MRC/NCRI and SWOG. Leuk Lymphoma 64: 250-2, 2023. https://doi.org/10.1080/10428194.2022.2131416 Several scoring systems have been developed aimed at identifying patients at high risk of poor outcome after intensive chemotherapy, including the Wheatley Score which has been prospectively validated [Citation4–9]. Similar tools for use with non-intensive therapies are currently not available. As such therapies are increasingly effective and more widely utilized, the analysis sought to develop tools to estimate the risk of early death for adults treated with non-intensive AML therapies. MA21 meta-analysis Early Breast Cancer Trialists' Collaborative Group (EBCTCG). Anthracycline-containing and taxane-containing chemotherapy for early-stage operable breast cancer: a patient-level meta-analysis of 100,000 women from 86 randomised trials. The Lancet 401: 1277-92, 2023. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(23)00285-4/fulltext Anthracycline–taxane chemotherapy for early-stage breast cancer substantially improves survival compared with no chemotherapy. However, concerns about short-term and long-term side-effects of anthracyclines have led to increased use of taxane chemotherapy without anthracycline, which could compromise efficacy. The aim is to better characterise the benefits and risks of including anthracycline, and the comparative benefits of different anthracycline–taxane regimens. CO17-CO20 assessment Nicholls DL, Xu M, Kaneswaran L, Grant B, Brown MC, Shapiro J, Karapetis CS, Simes J, Jonker D, Tu DS, O'Callaghan C, Liu G. Assessment of successful randomization through a machine learning and visualization tool for pre-treatment symptoms: Examples from CCTG/AGITG CO.17 and CO.20 trials. Revue d'Intelligence Artificielle 36: 913-8, 2022. https://doi.org/10.18280/ria.360612 Patients in randomized controlled trials (RCTs) must be successfully randomized to reduce or eliminate bias. Because pre-treatment symptoms have prognostic significance in cancer patients, qualitative and quantitative tools were developed to assess similarity of baseline pre-treatment symptoms across different treatment arms of RCTs as one measure of randomization success. Clinician-reported symptom data from two colorectal cancer RCTs, CO.20 and CO.17, were used to demonstrate the utility of a qualitative visualization tool and quantitative machine learning K-means tool, which grouped patients into clusters using baseline symptoms.
