The application of cognitive computing technology in genomics in precision oncological medicine: The Sistemas Genomicos Experience.

Peña-Pérez, Lucía; Cantalapiedra, Diego; Valero-Hervas, Diana; Durán, Ignacio; Calvete, Oriol; Wang, Ching-Kun; Martínez‐Laperche, Carolina; González‐Neira, Anna; Felipe-Ponce, Vanesa; Olmo, Sergio Lois; García, J. Martínez; Mico, Carmen Collado; Pedrosa, Victoria Fernandez; Miñambres, Rebeca; Borde, Ismael Buño; Santillán, Sonia; Urman, Alexandra; Suárez, Fernando; Moya, Christian M.

doi:10.1200/jco.2018.36.15_suppl.e18544

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“…27 A recent WfG review in Spain found investigational options were identified in most cases from a variety of samples. 28 There were several limitations of the study which might affect generalization of the findings. We focused on a small subset of breast cancer test cases, namely post-menopausal earlier stage patients, in line with the most frequently identified in clinical practice.…”

Section: Discussionmentioning

confidence: 97%

“…The companion solution, Watson for Genomics (WfG), which analyzes and categorizes genetic alterations that are related to cancer progression and suggests potential treatments, was able to identify actionable mutations in one‐third of patients compared to a university molecular tumor board . A recent WfG review in Spain found investigational options were identified in most cases from a variety of samples …”

Section: Discussionmentioning

confidence: 99%

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Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists

McNamara

Goldberg²,

Latts

et al. 2019

Cancer Medicine

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Introduction Cognitive computing point‐of‐care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown. Methods Electronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform. Results The cognitive computing “recommended” option was concordant with selection by breast cancer experts in 78.5% and “for consideration” option was selected in 9.4%, yielding agreements in 87.9%. Fifty‐nine percent of non‐concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with “recommended,” 11.4% “for consideration”, and 19.3% “not recommended.” Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non‐recommended option (P < .01) without WfO/Cota RWE and changed decisions in 39% cases. Conclusions Watson for Oncology with Cota RWE options were largely concordant with disease expert judged best oncology practices, and was able to improve treatment decisions among breast cancer novices. The observation that nearly a fifth of patients with similar disease characteristics received non‐recommended options in a real world database highlights a need for decision support.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%