Modeling the impact of novel systemic treatments on lung cancer screening benefits

Abstract

Background Since low‐dose computed tomography (LDCT) screening was shown to be effective in the National Lung Screening Trial (NLST), novel targeted therapies and immunotherapies for advanced lung cancer have become available. This study investigated the impact of these treatment advances on the expected benefits of LDCT screening. Methods A microsimulation model of LDCT screening for high‐risk individuals under standard systemic treatments (chemotherapy and radiation therapy) and novel treatments (immunotherapy and targeted therapy) was used. The model assumed a reduction in advanced‐stage disease consistent with the NLST, and given the stage at diagnosis, it projected survival. The disease‐specific relative mortality reduction (MR) due to LDCT screening was projected in the trial setting and in a population eligible for LDCT screening under the current US Preventive Services Task Force (USPSTF) recommendations. Results The availability of novel treatments reduced the MR in the LDCT arm of the NLST from 15% to 13.5% and the number of lung cancer deaths prevented from 310 to 224 per 100,000 persons screened. Over 10 years, population LDCT screening based on USPSTF recommendations prevented 374 lung cancer deaths per 100,000 under standard treatments (13.3% MR) and 236 per 100,000 under fully adopted novel treatments (10.6% MR). The number needed to screen to avert one death over 10 years was 270 under standard treatments and 440 under novel treatments. Conclusions The transition from standard systemic treatments to novel treatments is expected to reduce the relative and absolute mortality benefits of LDCT screening. Benefit–harm tradeoffs of LDCT screening are likely to change as novel treatments become widespread.

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

“…Early detection of lung cancer significantly reduces the risk of fatality [1] , with Computer Tomography (CT) scans being the predominant imaging diagnosis technology for this purpose. Consequently, computer-aided automatic lung cancer classification from CT scans has emerged as a vibrant and rapidly growing research area.…”