Technology | Treatment Planning | October 24, 2018

New software introduces real-time prescriptive contouring to enable faster, smarter radiation therapy treatment planning and better outcomes

Siris Medical Releases PlanMD Decision Support Software

October 24, 2018 — Siris Medical Inc. announced the release of its new artificial intelligence (AI) treatment decision-support system for use in radiation oncology. The new PlanMD software enables radiation oncology clinical teams to leverage patient data and artificial intelligence to efficiently empower their treatment decisions. The software is the first and only tool, according to the company, that enables the physician to see the effect of editing contours in real time, without re-optimizing or replanning.

PlanMD complements Siris Medical’s QuickMatch software, which accelerates the treatment planning process by using AI to quickly identify the most similar cases previously treated to inform planning decisions and help reduce dose to critical structures. Siris Medical President and CEO Colin Carpenter, Ph.D., said the software has been shown to enable clinical teams to save up to 70 percent of the time required to plan treatment.

This announcement follows a recent publication, “Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making,” in the Journal of Radiotherapy and Oncology in December 2017.1 This study demonstrates the ability of the QuickMatch software to draw upon historical clinical insights for patient-specific decision making. The study was a collaborative effort between the University of California San Francisco (UCSF), University of Pennsylvania, University of Maryland and Siris Medical.

For more information: www.siris-medical.com

Reference

1. Valdes G., Simone C.B., Chen J., et al. Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making. Journal of Radiotherapy and Oncology, Nov. 20, 2017. https://doi.org/10.1016/j.radonc.2017.10.014

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