Broader concerns
Indeed, potential impediments extend beyond screening capacity itself. Emerging discussions around how and whether healthcare systems can manage significant increases in lung cancer cases overshadow the prevailing momentum. An installed base capable of LCS procedures does not negate ongoing crises such as high rates of radiologist burnout, shortages of trained staff, and surging energy costs. Ensuring that influxes of lung cancer patients do not exacerbate these problems could present a significant political obstacle in some countries.

Another challenge is the enduring stigma around lung cancer. Sometimes overlooked, this is the perception that incidences of lung cancer, relative to other cancers, are primarily caused by lifestyle choices rather than genetic predisposition. The reluctance to seek treatment continues to hinder early interventions. In this regard, new and existing screening programmes could benefit from framing checkups as dedicated to general lung health (CT scans can identify several pulmonary and cardiovascular diseases). They may also promote complementary smoking cessation initiatives.
Market trends to watch
With the American Cancer Society forecasting 238,000 new lung cancer cases in the U.S. alone this year, the proliferation of LCS promises to augment sales of CT equipment significantly over the next decade. Though from a hardware perspective at least, this shouldn’t prompt an overhaul of vendors’ product portfolios: almost the entire installed base is suitable for performing LCS scans. Aside from bolstering demand for workhorse solutions, the most notable hardware impact will be the rising influence of mobile CT, which remains niche in much of the global market. Therefore, product differentiation will be achieved principally through marketing and innovation of non-hardware features. Examples of this are already emerging. One such case, at ECR 2023, was Canon Medical’s new 160-slice system and the promotion of its ability to integrate into the LCS workflow with dedicated and standardised software protocols for lung scanning. This is despite the system being a far higher spec than clinically required.

Lung cancer AI is a market that will see more substantive change. Yet to reach maturity, lung cancer AI fits into the broader CT AI trend of workflow efficiency and image analysis, but its evolution could drastically affect LCS in years to come. For example, MIT’s Sybil is a deep-learning model that can detect signs of lung cancer at earlier stages than some doctors. While still pending FDA approval, preliminary results suggest it can identify if cancerous lesions will develop in a patient within a year, with 90% accuracy. The recently approved Aview LCS, developed by Coreline Soft, also supports image interpretation, substantially cutting read times and reducing incidences of false positives. Such advances will accelerate the adoption of LCS globally, particularly as strains on healthcare systems, including severe radiologist shortages, have been prohibitive in many cases.

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