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Biodesix To Present New Data On Nodify Lung® Testing At CHEST 2023 Annual Meeting
Biodesix to Present New Data on Nodify Lung® Testing at CHEST 2023 Annual Meeting
Studies highlight the clinical utility and real-world impact of the Nodify CDT® and Nodify XL2® tests in managing both benign and malignant pulmonary nodules.
Biodesix, Inc. (Nasdaq: BDSX), a leading diagnostic solutions company with a focus in lung disease, today announced that new data will be presented at the CHEST Annual Meeting 2023 in Honolulu, Hawaiʻi. These presentations will provide new insight on the clinical utility and real-world impact of the Nodify CDT and Nodify XL2 tests in the comprehensive management of lung nodules.
The first abstract, titled "Real-World Impact of a Blood-Based Integrated Classifier on the Management of Benign Solid, Part-Solid, and Ground Glass Pulmonary Nodules," will be presented by Jonathan Kurman, MD, Director of Interventional Pulmonology at the Medical College of Wisconsin, on Tuesday, October 10 at 12:00 pm HT (6:00 pm ET). The presentation will delve into subgroup analyses from the prospective, real-world ORACLE study (NCT03766958), evaluating the clinical utility of the Nodify XL2 test across the spectrum of nodule types. The recently published primary endpoint of the study highlighted a 74% reduction in invasive procedures on benign nodules with use of the test. The analysis will demonstrate that this reduction is consistent across solid, part-solid, and ground glass lung nodules.
Dr. Kurman, remarked, "The data we're presenting at CHEST 2023 underscores the transformative potential of the Nodify XL2 test in managing all types of lung nodules. Clinical guidelines recommend different management strategies for solid versus non-solid nodules and this data indicates the test can be used broadly to avoid unnecessary invasive procedures."
The second abstract, titled "Comparison of a High Specificity Blood-Based Biomarker with PET/CT for Identifying Malignant Pulmonary Nodules," will be presented by Kathryn Long, MD, Pulmonary and Critical Care Fellow at the Medical University of South Carolina, on Wednesday, October 11 at 12:00 pm HT (6:00 pm ET). The presentation will demonstrate the complementary nature of the Nodify CDT test and PET/CT imaging results. PET/CT is a common imaging technique that detects abnormal levels of cellular metabolism and is clinically valuable in detecting cancer that has spread throughout the body. However, published data shows that the performance of PET/CT imaging alone is compromised in assessing small lung nodules for cancer. The analysis will demonstrate that the Nodify CDT test alongside PET/CT improves the performance, identifying malignant nodules with a high level of accuracy.
Additionally, independent studies by external investigators are expected at the event. "Novel Lung Cancer Biomarker Proteomic Testing on Lung Nodule Risk Stratification and Clinical Implications at a New York City Safety Net Hospital," is set to be presented in a rapid-fire format by Dr. Sonu Sahni, MD on Monday, October 9 at 12:00 pm HT (6:00 pm ET). Independent studies underscore the growing utility and adoption of Nodify Lung testing in the pulmonology community.
On Tuesday, October 10 at 1:00 pm HT (7:00 pm ET), Michael Pritchett, DO, and Jonathan Kurman, MD, will discuss the "Reduction of Diagnostic Interventions on Benign Lung Nodules: Results from the Nodify XL2® Clinical Utility Study" at Learning Theater 4. This presentation will highlight the ORACLE study's findings, covering primary and exploratory endpoints as well as multiple subgroup analyses from the study that have been published to date.
About Biodesix Biodesix is a leading diagnostic solutions company with a focus in lung disease. The Company develops diagnostic tests addressing important clinical questions by combining multi-omics through the power of artificial intelligence. Biodesix offers five Medicare-covered tests for patients with lung diseases. The blood based Nodify Lung® nodule risk assessment testing strategy, consisting of the Nodify XL2® and the Nodify CDT® tests, evaluates the risk of malignancy in pulmonary nodules, enabling physicians to better triage patients to the most appropriate course of action. The blood based IQLung™ strategy for lung cancer patients integrates the GeneStrat® targeted ddPCR™ test, the GeneStrat NGS™ test and the VeriStrat® test to support treatment decisions across all stages of lung cancer with results in an average of two to three business days, expediting the time to treatment. Biodesix also leverages the proprietary and advanced Diagnostic Cortex® AI (Artificial Intelligence) platform, to collaborate with many of the world's leading biotechnology and pharmaceutical companies to solve complex diagnostic challenges in lung disease. For more information about Biodesix, visit biodesix.Com.
Note Regarding Forward-Looking Statements This press release may contain forward-looking statements that involve substantial risks and uncertainties for purposes of the safe harbor provided by the Private Securities Litigation Reform Act of 1995. All statements contained in this press release other than statements of historical fact, are forward-looking statements. The words "believe," "may," "will," "estimate," "continue," "anticipate," "intend," "plan," "expect," "predict," "potential," "opportunity," "goals," or "should," and similar expressions are intended to identify forward-looking statements. Such statements are based on management's current expectations and involve risks and uncertainties. Actual results and performance could differ materially from those projected in the forward-looking statements as a result of many factors. Biodesix has based these forward-looking statements largely on its current expectations and projections about future events and trends. These forward-looking statements are subject to a number of risks, uncertainties, and assumptions. Forward-looking statements may include information concerning the impact of the COVID-19 pandemic on Biodesix and its operations, its possible or assumed future results of operations, including descriptions of its revenues, profitability, outlook, and overall business strategy. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. The Company's ability to continue as a going concern could cause actual results to differ materially from those contemplated in this press release and additionally, other factors that could cause actual results to differ materially from those contemplated in this press release can be found in the Risk Factors section of Biodesix's most recent annual report on Form 10-K, filed March 14, 2022 or subsequent quarterly reports on Form 10-Q during 2022, if applicable. Biodesix undertakes no obligation to revise or publicly release the results of any revision to such forward-looking statements, except as required by law. Given these risks and uncertainties, readers are cautioned not to place undue reliance on such forward-looking statements. All forward-looking statements are qualified in their entirety by this cautionary statement.
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Finding Lung Cancer Before It's Too Late: Miamisburg Company Offers New Tool In Cancer Fight
When it comes to fighting lung cancer, early detection is one of the best weapons patients and medical teams can have.
A "visual intelligence" application from a Miamisburg company is providing that weapon, that company's chief executive believes.
ExploreDayton VA turns to local medical firm's technology to fight lung cancerSteve Worrell, chief executive of Riverain Technologies, said the process of detecting lung cancer can be exacting. Many lung nodules show up as a "bright spot" on a chest or CT (or "CAT") scan, he said. Many of those nodules are benign, appearing due to infection or reasons other than cancer.
But sometimes it can be challenging to tell right away. Multiple scans over time will often be needed to determine if a nodule is cancerous.
"First of all, do you have a lung nodule?" Worrell said in an interview at his company's offices. "Second of all, is that lung nodule, if you have two successve scans, is it growing?"
A growing nodule may, tracked over time, point to the possible beginnings of cancer, leading to the decision to do a biopsy of part of a patient's lung to be certain. It can be a laborious, worrisome process.
Radiologists can find themselves poring over "slices" of a scan or small, thin areas to ascertain whether a nodule — occupying perhaps a thousandth of a percent of the volume of an exam scan — is present.
Riverain's tool can help diagnosticians and radiologists detect problem nodules faster and more precisely, Worrell said.
"It is literally that proverbial needle in the haystack," the CEO said. "We help find it. Then we help characterize it. How big is it? What type of nodule is it?"
Once the tool characterizes the nodule, it then populates a detailed report, again saving time, he said.
"We're basically about the detection," he added.
The company says the tool is a "visual intelligence solution" that supresses "noise" — removing what can be the distracting appearance of vessels or machine noise on a chest CT image to reveal the vital data.
The Dayton VA Medical Center and other VA medical centers nationwide have turned to Riverrain for that tool, which the company calls "ClearRead CT."
Riverain said it has been selected to provide ClearRead CT to 22 VA hub locations and 87 spoke sites across the country, as part of the VA's Lung Precision Oncology Program (also called "LPOP.")
"They can basically take what we provide, and they can confirm it," Worrell said of medical teams working with CT scans. "It sort of streamlines the entire process. It makes them faster. But also, it's more accurate."
About two-thirds of the company's 32 employees work remotely. The talent that's in demand in AI and computer sciences expect that kind of work flexibility, Worrell said.
In late 2020, the company moved from 3020 S. Tech Blvd. To a renovated, 8,200-square-foot spact at 3130 S. Tech Blvd.
The new space provides the room the company's equipment needs.
"We knew we were sort of setting out to grow," Worrell said.
Nearly 8,000 veterans are diagnosed and treated by the VA for lung cancer each year, the department says. An estimated 900,000 are at risk for lung cancer due to age, smoking and environmental exposures during and after military service, the VA says.
"Veterans have a higher rate of lung cancer and a lower rate of survival than the general population," the VA says on its web site.
The LPOP program seeks to give VA clinicians the tools to address the problem.
A Dayton VA Medical Center spokeswoman declined to comment for this story, saying the VA cannot endorse any product.
The Dayton VA Medical Center serves more than 40,000 veterans each year and is one of the oldest VAs in the nation.
AI Is Capable Of Detecting Incidental Lung Cancer In Written Medical Reports
Recently published in JCO Global Oncology, a new article presents the development of artificial intelligence (AI) for the accurate detection of potentially cancerous nodules described in computed tomography (CT) reports, conducted outside of the cancer screening context.
The technology is a natural language processing (NLP) tool, trained to analyze chest CT reports. The study was conducted by the D'Or Institute for Research and Education (IDOR), in partnership with the Federal University of Health Sciences of Porto Alegre (UFCSPA), and the Universities of Florida and Stanford in the United States.
Natural language processing advancements in artificial intelligence have frequently made headlines in recent years, and while it may still be a topic of futuristic narratives, the truth is that the use of this technology is already well-established and surrounds us daily. If you've ever used online banking chatbots, enabled automatic video captions, or issued commands to virtual assistants on your devices, you may have wondered how machines can understand human communication so well.
Understanding and responding to human language is the primary goal of natural language processing (NLP), one of several fields within artificial intelligence. NLP is used for several functions in many well-known tools, including chatbots and digital assistants like Siri and Alexa. However, its potential is also being explored in the medical field.
With this in mind, the current study sought to invest in the technology to develop a tool capable of detecting the possibility of lung cancer through CT reports that had been performed for reasons other than cancer screening.
"When we perform a chest CT, in the context of lung cancer, there are two main indications. One is for cancer screening in a patient at risk, usually over 50 years old with a smoking history. The other context is a patient who has, for example, a suspicion of a pulmonary embolism and undergoes a CT scan to investigate that, and an incidental nodule appears in that exam."
"This is called an incidental finding. Our NLP tool operates in this latter situation because sometimes these patients may lose the opportunity to receive an early diagnosis. The incidental finding is easier to overlook because the doctor is focused on another hypothesis and may not suspect those details in the initial interpretation," explains Dr. Rosana Rodrigues, a radiologist researcher at IDOR and one of the study's authors.
Lung nodulesWhen analyzing chest images, the presence of lung nodules is a relatively common finding, and the majority of them are benign. However, serious risks can hide in 1 to 3% of these cases. Due to their high prevalence, lung nodules are often overlooked in emergency hospital exams.
A prime example of this occurred during the COVID-19 pandemic, where CT scans were frequently performed in clinics and hospitals to identify lung involvement in the disease. In this scenario, many of these nodules were described in medical reports but were not adequately investigated for their cancer potential. Identifying the issue in its early stages provides an opportunity to apply more effective therapies, with a greater chance of curing patients.
"We had the idea for the tool during the pandemic because we were performing 30 to 40 CT scans per day on patients suspected of COVID-19. The doctors who were requesting the CT had a complete focus on the disease because they needed to know if the patient required hospitalization. When we were writing the reports, in addition to the presence or absence of COVID-19 lung impairment, we also saw many lung nodules, some of which were suspicious for lung cancer," says Dr. Rodrigues.
"That raised a huge concern for us, because during the pandemic, no one would retrieve these reports, so this cancer diagnosis could be lost. That's when we started thinking about how we would recover all these suspicious exams. That's when we've planned the NLP tool," recalls the radiologist, who also works at 3 hospitals in Rio de Janeiro, including the public hospital of the Federal University of Rio de Janeiro (UFRJ).
The possibility of identifying lung cancer in patients who are not suspected of having the problem encouraged the article researchers to consider solutions for this diagnostic window. That's when they had the idea of developing an automated NLP tool capable of searching for suspicious nodules identified incidentally in chest CT medical reports.
Teaching the machineLike us, artificial intelligence isn't born knowing. To train the NLP tool developed for the study, the radiologists on the team conducted a retrospective analysis of over 21,500 chest CT reports performed at a research-affiliated hospital between 2020 and 2021. Of these thousands of exams, 484 presented incidentally detected lung nodules with potential carcinogenicity, whose descriptions were used to train the NLP tool in identifying these lesions.
After training, the NLP underwent internal validation involving the assessment of over 300 chest CT reports, with 157 of them containing incidentally detected nodules suspected of malignancy and 148 serving as control group to calculate the tool's accuracy potential.
The NLP was taught to understand the text written in the reports, without access to images. Researchers programmed it to report as suspicious any incidentally detected nodules not previously known in the patient's history, with a diameter greater than 4 mm and without clinical context associated with cancer, pneumonia, or small airway disease. The tool was also capable of categorizing higher-risk nodules, such as those larger than 8 mm or those with a solid component greater than 6 mm.
In the internal evaluation, the NLP tool achieved an accuracy of 98% in detecting the nodules of interest. The positive results led the researchers to conduct a second test in May 2022, this time analyzing over 900 chest CT reports, which were randomly selected from 57 different hospitals.
In this second test, the NLP demonstrated an even more impressive accuracy of 98.6%, which was further validated by a final check by radiologist doctors, establishing a gold standard for the tool's testing. These results reaffirmed the accuracy and competence of artificial intelligence for assistance in clinical applications.
Of the 484 incidental findings used for NPL training, 8 patients were diagnosed with lung cancer and were able to undergo early treatments. According to the study, 2 of these diagnoses could have been missed without the assistance of the NLP tool.
Considering that 70% of lung cancer cases are curable when treated in precocious stages, early detection of the disease significantly improves these chances for patients. The artificial intelligence was developed in a widely used programming language, Python, and its use is compatible with several institutions and hospitals where Portuguese is the official language. The tool may contribute significantly to the early recognition of lung cancer, especially in patients treated in emergency services and outside specialized oncology centers.
More information: Rodrigo Basilio et al, Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool, JCO Global Oncology (2023). DOI: 10.1200/GO.23.00191
Provided by D'Or Institute for Research and Education
Citation: AI is capable of detecting incidental lung cancer in written medical reports (2023, October 10) retrieved 15 October 2023 from https://medicalxpress.Com/news/2023-10-ai-capable-incidental-lung-cancer.Html
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