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.
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.
A new European Radiology study provided what might be the first insights into whether AI can allow radiographers to independently read lung cancer screening exams, while alleviating the resource challenges that have slowed LDCT screening program rollouts.
This is the type of study that makes some radiologists uncomfortable, but its results suggest that rads’ role in lung cancer screening remains very secure.
The researchers had two trained UK-based radiographers read 716 LDCT exams using a computer-assisted detection AI solution (158 w/ significant pulmonary nodules), and compared them with interpretations from radiologists who didn’t have CADe assistance.
The radiographers had significantly lower sensitivity than the radiologists (68% & 73.7%; p < 0.001), leading to 61 false negative exams. However, the two CADe-assisted radiographers did achieve:
- Good sensitivity with cancers confirmed from baseline scans – 83.3% & 100%
- Relatively high specificity – 92.1% & 92.7%
- Low false-positive rates – 7.9% and 7.3%
The CADe AI solution might have both helped and hurt the radiographers’ performance, as CADe missed 20 of the radiographers’ 40 false negative nodules, and four of their seven false negative malignant nodules.
Even as LDCT CADe tools become far more accurate, they might not be able to fill in radiographers’ incidental findings knowledge gap. The radiographers achieved either “good” or “fair” interobserver agreement rates with radiologists for emphysema and CAC findings, but the variety of other incidental pathologies was “too broad to reasonably expect radiographers to detect and interpret.”
Although CADe-assisted radiographer studies might concern some radiologists, this seems like an important aspect of AI to understand given the workload demands that come with lung cancer screening programs, and the need to better understand how clinicians and AI can work together.
Good thing for any concerned radiologists, this study shows that LDCT reporting is too complex and current CADe solutions are too limited for CADe-equipped radiographers to independently read LDCTs… “at least for the foreseeable future.”
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.
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.
A team of Dutch radiologists analyzed Aidence’s Veye Chest lung nodule detection tool, finding that it works “very well,” while outlining some areas for improvement.
The Study – After using Veye Chest for 1.5 years, the researchers analyzed 145 chest CTs with the AI tool and compared its performance against three radiologists’ consensus reads, finding that:
- Veye Chest detected 130 nodules (80 true positive, 11 false negative, 39 false positives)
- That’s 88% sensitivity, a 1.04 mean FP per-scan rate, and 95% negative predictive value
- The radiologists and Veye Chest had different size measurements for 23 nodules
- Veye Chest tended to overestimate nodule size (bigger than rads w/ 19 of the 23)
- Veye Chest and the rads’ nodule composition measurements had a 95% agreement rate
The Verdict – The researchers found that Veye Chest “performs very well” and matched Aidence’s specifications. They also noted that the tool is “not good enough to replace the radiologist” and its nodule size overestimations could lead to unnecessary follow-up exams.
The Takeaway – This is a pretty positive study, considering how poorly many recent commercial AI studies have gone and understanding that no AI vendor would dare propose that their AI tools “replace the radiologist.” Plus, it provides the feedback that Aidence and other AI developers need to keep getting better. Given the lack of AI clinical evidence, let’s hope we see a lot more studies like this.