Use of computer-aided detection and computer-aided diagnosis to improve computed tomography lung scans for the purpose of preventing lung cancer.
Lung cancer remains the number one killer in all cancer related eaths in the United States. Virtual biopsy on detected lung nodules has been a challenging due to the high prevalence of lung cancer and the difficulty of differentiating nodules. Computer assisted detection of the nodules is also challenging because the large volume of data that must be read by radiologists. This invention provides a way to differentiate nodules while also addressing the issues with computer assisted detection.
Dr. Jerome Liang, professor of Radiology at Stony Brook University, has been using Computer-aided detection (CADe) and computer-aided diagnosis (CADx) to increase the efficiency of Computed Tomography (CT) lung scanning. CADe can be used to improve the Radiologists' efficiency in image interpretation. CADe will ultimately advance CT scanning toward a fully screening modality for detection of nodule presence, differentiation of nodule types, and optimal management of nodule treatment or management. Implementing these two techniques for CT lung scanning will greatly increase its efficiency for the purpose of preventing lung cancer.
Will advance CT lung scanning toward a fully screening modality with capability to perform not only the automated detection of the presence of nodule and the differentiation of nodule types for nodule management for adequate, cost effective treatment
Improve the efficiency of CT lung scanning for the purpose of preventing lung cancer
Prototype developed and available for testing.
We seek to develop and commercialize by an exclusive or non-exclusive license agreement and/or sponsored research with a company active in the area.
Available for License
Virtual lung nodule piopesy, Computer-aided detection and diagnosis, Differentiation of malignancy from benignancy, Texture features
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