International Intelligent Informatics Solution Laboratory LLC — Department of Health and Human Services SBIR Phase I: 101

Description

DESCRIPTION provided by applicant Primarily due to the lack of physical symptoms in the early stage lung cancer remains the leading cause of cancer deaths in the United States and worldwide Although high resolution computed tomography CT has been proved to be a sensitive non invasive modality for visualizing small lung nodules which could be the early manifestation of lung cancer a considerable number of false positive detections are often resulted Consequently additional procedures such as invasive biopsy follow up scans are frequently needed to verify the nature of the indeterminate nodules The negative effects associated with these over diagnosis procedures such as biopsy complications exposure to additional radiation patient anxiety and economic cost significantly limits the efficacy of CT screening for early diagnosis of lung cancer In this project we propose to develop a computer model to quantitatively assess the nature of indeterminate nodules using a longitudinal dataset Unlike available investigations or lung cancer risk models we will comprehensively quantify a wide variety of properties features of a nodule in an unprecedented detailed manner as well as their variations over time and synergize them with patient demographic information e g age gender smoke history using machine learning techniques Not only the image features of lung nodules but also their spatial relationship with respect to important lung landmarks as well as other smoke related lung abnormalities e g emphysema will be incorporated into this model The output of this project namely a novel computer tool could aid clinicians to more accurately and efficiently assess the nature of indeterminate nodules ultimately leading to the reduction of unnecessary harm and costs to patients and the healthcare system All these will significantly improve the efficacy of CT for early lung cancer screening by maintaining its high sensitivity while reducing false positive findings In terms of commercial potential the developed tool could be easily integrated with the available image information systems at medical institutions by following the widely adopted Digital Imaging and Communications in Medicine DICOM standard PUBLIC HEALTH RELEVANCE This project aims to develop a novel computer tool for aiding in accurate assessment of indeterminate nodules Its novelty lies in the incorporation of a large variety of image features and patient demographic information using machine learning techniques and a longitudinal dataset The availability of this tool would significantly improve th efficacy of CT by reducing false positive findings while maintaining its high sensitivity