Joshua Roth, PhD, assistant member, Fred Hutchinson Cancer Research Center, discusses the design of a novel risk-prediction algorithm in the context of screening patients for lung cancer.

The database analysis used SEER data, which provided detailed information regarding patients with early stage non-small cell lung cancer (NSCLC). Looking at different factors, researchers investigated the likelihood of death within 30 days of surgery.

This data was then fed back to the screening process and can be a part of the conversation between the patient and physician as they discuss treatment options, explains Roth.

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The Design Behind the Development of a Novel-Risk Prediction Model in Lung Cancer Screening

Joshua Roth, PhD, assistant member, Fred Hutchinson Cancer Research Center, discusses the rationale behind the development of a novel risk-prediction algorithm in the context of screening patients for lung cancer. He discussed this during an interview at the IASLC 17th World Conference on Lung Cancer in Vienna, Austria.

Low-dose computed tomography (LDCT) lung cancer screening has been shown to increase detection of cases at an early stage and reduce lung cancer mortality, in comparison with X-ray or no screening at all. However, the benefits associated with such screening are reduced in patients who are considered to be poor candidates for curative intent surgery in the event of screen-detected, early-stage disease.

The rationale, says Roth, is to encourage shared decision-making between physicians and patients when the latter must undergo screening for lung cancer. The conversation should cover both potential benefits and harms associated with screening, such as false positives, or the adverse side effects of radiation. The main benefit lies in possible early detection of a tumor, according to Roth, which is then followed by early intervention with either surgery or radiation therapy.

Joshua Roth, PhD

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The Design Behind the Development of a Novel-Risk Prediction Model in Lung Cancer Screening

Dr. Roth on the Rationale Behind a Novel Risk-Prediction Model in Lung Cancer Screening