CT Detects Early Lung Cancer

A massive CT lung cancer screening program launched in Taiwan has been effective in detecting early lung cancer. Research presented at this week’s World Conference on Lung Cancer (WCLC) in Singapore offers more support for lung screening, which has seen the lowest uptake of the major population-based screening programs. 

Previous randomized clinical trials like the National Lung Screening Trial and the NELSON study have shown that LDCT lung cancer screening can reduce lung cancer mortality by at least 20%. But screening adherence rates remain low, ranging from the upper single digits to as high as 21% in a recent US study. 

Meanwhile, lung cancer remains the leading cause of cancer death worldwide. To reduce this burden, Taiwan in July 2022 launched the Lung Cancer Early Detection Program, which offers biennial screening nationwide to people at high risk of lung cancer.

The Taiwan program differs from screening programs in the US and South Korea by including family history of lung cancer in the eligibility criteria, rather than just focusing on people who smoke. 

Researchers at WCLC 2023 presented the first preliminary results from the program, covering almost 50k individuals screened from July 2022 to June 2023; 29k had a family history of lung cancer and 19k were people who smoked heavily. Researchers found …

  • 4.4k individuals receive a positive screening result for a positive rate of 9.2%
  • 531 people were diagnosed with lung cancer for a detection rate of 1.1%
  • 85% of cancers were diagnosed at an early stage, either stage 0 or stage 1

This last finding is perhaps the most significant, as part of the reason for lung cancer’s high mortality rate is that it’s often discovered at a late stage, when it’s far more difficult to treat. As such, lung cancer’s five-year survival rate is about 25% – far lower than breast cancer at 91%.

The Takeaway

Taiwan is setting an example to other countries for how to conduct a nationwide LDCT lung cancer screening program, even as some critics take aim at population-based screening. Taiwan’s approach is broader and more proactive than that of the US, for example, which has erected screening barriers like shared decision-making.

Although it’s still early days for the Taiwan program, future results will be examined closely to determine screening’s impact on lung cancer mortality – and respond to screening’s critics.

RadNet’s UK Lung Cancer Screening Acquisition

RadNet advanced its AI-led cancer screening strategy, acquiring a 75% stake in Heart & Lung Health, a UK-based teleradiology network with a direct connection to the NHS’ lung cancer screening program.

Heart & Lung Health (HLH) has a network of over 70 cardiothoracic radiologists, and provides teleradiology reporting services for the NHS and a variety of UK hospitals and academic institutions.

Acquiring a UK telerad company might seem out of character for RadNet, which has historically focused its M&A on US-based imaging centers (and more recently global AI developers), only mentioned Europe once in its 2021 annual report, and exited the teleradiology business in 2020. However…

  • HLH is the leading reporting provider for NHS England Targeted Lung Health Check (TLHC), an AI-enabled lung cancer screening pilot program that might pave the way for a UK-wide program. 
  • TLHC requires all radiologists to use AI with their LDCT screening interpretations, suggesting that AI might also be required in a future UK-wide program.
  • HLH uses RadNet’s Aidence subsidiary’s lung cancer AI tools, and HLH will work with Aidence to further develop its solutions.

The Takeaway

RadNet started 2022 by acquiring two major cancer screening AI companies (Aidence and Quantib), which combined with its DeepHealth breast cancer AI business to support its ambitious new strategy to become a population-scale cancer screening leader. 

That goal might have seemed like a longshot to some, given AI’s uncertain path forward and RadNet’s geographic concentration in just seven US states. However, last week’s HLH acquisition showed that RadNet remains very committed to AI-driven cancer screening leadership, and its strategy might not be as geographically-challenged as some initially thought.

RevealDx & contextflow’s Lung CT Alliance

RevealDx and contextflow announced a new alliance that should advance the companies’ product and distribution strategies, and appears to highlight an interesting trend towards more comprehensive AI solutions.

The companies will integrate RevealDx’s RevealAI-Lung solution (lung nodule characterization) with contextflow’s SEARCH Lung CT software (lung nodule detection and quantification), creating a uniquely comprehensive lung cancer screening offering. 

contextflow will also become RevealDx’s exclusive distributor in Europe, adding to RevealDx’s global channel that includes a distribution alliance with Volpara (exclusive in Australia/NZ, non-exclusive in US) and a platform integration deal with Sirona

The alliance highlights contextflow’s new partner-driven strategy to expand SEARCH Lung CT beyond its image-based retrieval roots, coming just a few weeks after announcing an integration with Oxipit’s ChestEye Quality AI solution to identify missed lung nodules.

In fact, contextflow’s AI expansion efforts appear to be part of an emerging trend, as AI vendors work to support multiple steps within a given clinical activity (e.g. lung cancer assessments) or spot a wider range of pathologies in a given exam (e.g. CXRs):

  • Volpara has amassed a range of complementary breast cancer screening solutions, and has started to build out a similar suite of lung cancer screening solutions (including RevealDx & Riverain).
  • A growing field of chest X-ray AI vendors (Annalise.ai, Lunit, Qure.ai, Oxipit, Vuno) lead with their ability to detect multiple findings from a single CXR scan and AI workflow. 
  • Siemens Healthineers’ AI-RAD Companion Chest CT solution combines these two approaches, automating multiple diagnostic tasks (analysis, quantification, visualization, results generation) across a range of different chest CT exams and organs.

The Takeaway

contextflow and RevealDx’s European alliance seems to make a lot of sense, allowing contextflow to enhance its lung nodule detection/quantification findings with characterization details, while giving RevealDx the channel and lung nodule detection starting points that it likely needs.

The partnership also appears to represent another step towards more comprehensive and potentially more clinically valuable AI solutions, and away from the narrow applications that have dominated AI portfolios (and AI critiques) before now.

MD Anderson’s Lung Cancer Blood Test

MD Anderson researchers developed a blood and risk-based test that could improve how we identify lung cancer screening candidates, potentially bringing more high-risk patients into screening while keeping more low-risk patients out.

The Blood + Risk Test – The test combines MD Anderson’s blood-based protein biomarker test with a lung cancer risk model that analyzes patient smoking history (the PLCOm2012 model). This combined test would be used to identify patients who should enroll in LD-CT screening programs.

The Study – MD Anderson researchers used the test to analyze 10k blood samples from 2,745 people with a +10 pack-year smoking history (including 1,299 samples from 552 people who developed cancer), finding that the blood + risk test:

  • Identified 105 of the 119 people diagnosed with cancer within one year
  • Beat the USPSTF 2021 criteria’s sensitivity (88.4% vs. 78.5%) and specificity (56.2% vs. 49.3%)
  • Identified 9.2% more lung cancer cases than the USPSTF criteria
  • Referred 13.7% fewer unnecessary screening patients than the USPSTF criteria

Blood-Based Momentum – Blood-based tests appear to be gaining momentum as a first-line cancer screening method, as the last 6 months brought a promising new MGH lung cancer test and a key validation milestone for the multi-cancer early detection blood test (MCED; detects 50 types of cancer).

The Takeaway – Although there’s still more research to be done, blood-based tests could bring more high-risk patients into LD-CT lung cancer screening programs, while reducing screening participation among patients who don’t actually need it. In other words, blood tests like these could address lung cancer screening’s two biggest challenges.

Volpara’s Lung Cancer Push

Breast imaging AI leader Volpara Health took a big step into the lung cancer AI segment last week, launching partnerships with Riverain Technologies and RevealDx. Here are some details.

Volpara & Riverain – Volpara and Riverain announced plans to integrate Riverain ClearRead CT (AI-based lung nodule detection) and the Volpara Lung platform (lung cancer screening reporting, tracking, and risk assessment), giving Volpara a market-leading detection partner and extending the clinical value of both tools.

Volpara & RevealDx – Within days, Volpara announced a $250k strategic investment in AI-based lung nodule diagnosis startup RevealDx, that will allow Volpara to sell RevealDx’s RevealAI-Lung tool (CE-marked, FDA pending) in the US and make Volpara its exclusive distributor in Australia / New Zealand. 

Not That Surprising – Volpara’s lung cancer screening expansion isn’t as surprising as some might think. Volpara first entered the lung cancer screening segment through its 2019 acquisition of MRS Systems, which likely targeted MRS’ breast cancer screening management software but also included its lung cancer screening platform (used w/ 8% of US LC screenings). Volpara also built its business around supporting population-scale cancer screening workflows and it has a long history of complementary partnerships within its breast imaging business.

The Takeaway – Lung cancer screening volumes are about to significantly increase in the US (and potentially globally), creating new bandwidth and workflow constraints, and driving demand for comprehensive solutions that support the entire screening and patient management pathway. With these alliances, Volpara, Riverain, and RevealDx are far better positioned to support that pathway.

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