Lung Cancer Screening
As lung cancer screening recommendations expand, so does the need for decision support tools. contextflow SEARCH Lung CT simplifies lung cancer screening via:
Nodule detection & quantification
- Gain quick insights from within your native viewer
- Explore results more thoroughly from within our UI
- Objectively determine changes over time
Malignancy score calculation
- Reduce false positives
- Flag at-risk patients sooner
- Read clinical study information here.
Detect co-morbidities in the lung parenchyma
- SEARCH analyzes 19 image patterns in lung CTs
- The most holistic patient view on the market
ILD diagnosis and treatment decisions
Interstitial lung diseases are complex to diagnose. But you knew that already. SEARCH Lung CT supports ILD treatment:
From within your native viewer
- Disease patterns appear color-coded in your native viewer
With comprehensive pattern detection & quantification
- We support 19 different disease patterns, more than any other tool
- Changes in disease pattern amount and distribution are visible via heatmap visualizations
- Objectively determine changes over time
We support the following standards
- British Thoracic Society guidelines for the investigation and management of pulmonary nodules
- European Union Position Statement on Lung Cancer Screening
- Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017
- International Early Lung Cancer Action Program: Screening Protocol, 2021
- Lung-RADS Version 1.1 2019
- Response Evaluation Criteria in Solid Tumors (RECIST)
Testimonials
We are very interested in using tools based on artificial intelligence like contextflow SEARCH to support the decision in the diagnostic process based on the image.
Lluís Donoso Bach
President of the International Society of Radiology
At Dubrava University Hospital, we take pride in providing the best care possible to our patients. There are many AI radiology solutions, but we agreed to the proof of concept with contextflow because their solution provides real value, particularly for new residents.
Boris Brkljacic
President of the European Society of Radiology
We’re very interested in using AI to improve the hospital experience for both doctors and patients; contextflow’s use of deep learning, particularly for lung diseases, is exactly the type of technology we want to evaluate. I very much look forward to the results.
Christian Herold
Head of Radiology at Vienna General Hospital
I really like the transparency of contextflow SEARCH as opposed to other black box AI solutions. It’s designed to support my workflow while leaving the final decision up to me.
Elmar Kotter
Vice Chair and Head of Imaging Informatics at the Department of Radiology at Freiburg University Medical Center
After using and advising several radiology AI software companies, I can say that what contextflow offers is actually the next generation of AI products to support the radiologist, not replace them. Their general approach means they recognize all relevant findings, not just one.
Anand Patel
MD, Chief of Interventional Radiology, Providence Little Company of Mary Medical Centers
contextflow SEARCH Lung CT is one of the applications that certainly fits radiology’s current needs and can simplify the analysis of complex lung pathology. With the right insights and technology, we can succeed in introducing AI in a very attractive way to radiology departments on a global scale.
Erik Ranschaert
Former President of the European Society of Medical Imaging Informatics (EuSoMII), Radiologist at St. Nikolaus Hospital in Eupen
I have been following contextflow’s progress practically since the company’s founding, and their traction in the area of lung CT is impressive. Being able to shape clinical decision support tools that myself and colleagues can benefit from in clinical practice is a big motivator. We’re literally shaping the future.
Jacob Visser
Chief Medical Information Officer & Head of Imaging IT and Value-Based Imaging at Erasmus MC