May 29, 2020. Chicago, IL – Altis Labs, Inc. (“Altis”) is announcing the results of a study being presented by Felipe S. Torres M.D., Ph.D. of Toronto General Hospital’s Joint Department of Medical Imaging at the 2020 ASCO annual meeting.
The study illustrates how deep learning can be applied to pretreatment computed tomography (CT) imaging to enhance 2-year survival prediction in lung cancer patients beyond traditional tumor size measurements.
In the retrospective study, a fully automated imaging-based prognostication (IPRO) model was designed to localize the entire three-dimensional (3D) space comprising the lungs and heart, and to learn deep prognostic features using a 3D convolutional neural network. The model was trained and validated using pretreatment CTs of 1,184 stage I-IV lung cancer patients with known 2-year survival outcomes.
The model significantly increased prognostic accuracy (C-index = 0.73, p < 0.01) compared to that of clinical TNM staging (C-index = 0.63, p < 0.01).
More accurate prognostic insight derived from standard-of-care imaging not only has the potential to facilitate optimal treatment decisions but also offers cancer researchers more sophisticated tools to refine clinical trial design and quantify treatment effect.
View the full publication here.
Altis is a clinical information company providing computational imaging tools to advance precision medicine. Altis’ software platform Nota enables researchers to operationalize imaging data and leverage predictive imaging insights at scale. Life sciences companies use Nota to accelerate and optimize R&D of their most promising therapies across all stages of clinical development. Altis is headquartered in Toronto, a city recognized for its deep learning research and medical institutions.
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