Classifying High-Risk Lesions - FEA

Value Proposition

Microcalcifications identified on mammography are a common manifestation of high-risk lesions at percutaneous biopsy, which routinely undergo surgical excision to exclude associated malignancy. Although high-risk lesions (such as FEA) are considered nonobligate precursor lesions with variable upgrade rates to malignancy, majority of surgical excisions are ultimately benign, therefore potentially unnecessary. There is variability in radiology-pathology correlation by the radiologist, and in histologic classification by the pathologist. AI can help improve accuracy in predicting the likelihood of malignancy in high risk lesions (specifically, FEA in this case), by incorporating quantitative imaging features of originally biopsied microcalcifications at diagnostic imaging, and text features of pathology report and/or histopathologic slide imaging features, as well as patient risk factors and characteristics. This could serve as a more consistent and reproducible form of multi-disciplinary evaluation, to potentially decrease the rate of unnecessary surgical excisions.

Narrative(s)

A 45-year old female found to have a new group of microcalcifications in the right breast at screening is recalled and undergoes diagnostic imaging with magnification views in CC and ML projections. The calcifications are recommended for stereotactic biopsy, which yields FEA without other abnormality. At the time of radiology-pathology correlation, the AI algorithm provides the radiologist with an automated continuous numerical risk score for upgrade to malignancy based on radiologic lesion morphology (microcalcifications on CC and ML magnification views) and pathology result (text features in pathology report, and/or actual histopathologic slide imaging features), as well as patient characteristics (age, family history, breast density, prior cancer, mutations) to help guide the best clinical recommendation.

Workflow Description

Magnification mammography images obtained at diagnostic work-up are sent from PACS to the AI engine. Pathology reports and/or digitized histopathologic slide images are fed to the AI engine. Patient characteristics from EMR are also incorporated. Radiologic images are analyzed in the context of pathology report key words (text features); or, radiologic images and histopathologic slide images are analyzed in conjunction; also factoring in patient characteristics, to render a numerical malignancy risk score. A message is sent to PACS from the engine with this information which will be used by the interpreting radiologist to make a final assessment and appropriate recommendation of further surgical excision versus imaging follow up.

Inputs

DICOM Study

Secondary Inputs

Pathology Report

Digitized Slide Image Data

Primary Outputs

Detection of Suspicious Microcalcifications

Develop robust algorithm to not only be able to provide a numerical malignancy risk score of immediate surgical upgrade rate, but provide an individualized long-term risk of breast cancer in a given patient.  

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