Similar Lesion Suggestion Engine


Purpose

Provide a type of 'reverse image' search for a highlighted lesion to provide the radiologist with similar appearing lesions and their pathologic outcomes.

Tag(s)


Panel

Breast Imaging

Define-AI ID

19060007

Originator

Hari Trivedi

Lead

Hari Trivedi

Panel Chair

Beth Burnside

Panel Reviewers

Breast Panel

License

Creative Commons 4.0

Status

Public Comment

Organization

Emory University

Clinical Implementation


Value Proposition

Many rural and international radiology practices suffer from a lack of breast-trained radiologists. Patients undergoing breast cancer screening at these sites may be under- or over-diagnosed dependent upon the skill level of the interpreting radiologists. Because over 90% of studies are negative on screening mammography and at least 75% of studies are negative on diagnostic imaging, non-breast radiologists may not have the breadth of experience required to identify nuanced differences that separate malignant from benign lesions. A tool that enables radiologists to specify a lesion of interest and receive a list of similar-appearing lesions with biopsy-proven pathologic outcomes would increase confidence in management decisions and curb unnecessary recall imaging and biopsies.

Narrative(s)

A 45 year-old woman arrives for annual screening mammography in a rural area. The radiologist reviewing the case notices calcifications in the left breast but has not encountered this specific appearance before and is leaning towards bringing back the patient for recall imaging. The radiologist highlights the area of concern on the mammogram and a DL algorithm provides a list of similar appearing lesions which the radiologist is able to review. All of the similar appearing lesions were benign on pathology and the radiologist therefore determines that this lesion is of no concern. The patient returns to annual screening mammography and an unnecessary recall study is avoided. 

Workflow Description

A radiologist is viewing a screening mammogram in their standard PACS software. A potential abnormality is identified and the radiologist draws a bounding box around the lesion. The bounding box can be of any size or shape specified by the radiologist. This image is then processed by either a local or cloud DL algorithm and an internal database is reviewed for similar lesions. A list of similar appearing lesions is then displayed in a table format with a ‘similarity score’ and final pathologic diagnosis for each lesion. If no similar lesions are found, the algorithm will return no results. The radiologist will use this information to help guide their decision and can close the panel to continue with their regular workflow.

Considerations for Dataset Development


Sex: Female

Age: > 18 years old

Pathologic diagnosis: Varied

Views: Full-field diagnostic views such as CC, MLO, LM, XCCM, XCCL, cleavage view, etc.
Clinical Note: Spot and magnification views should be identified separately as these have higher resolution, smaller fields of coverage, and higher incidences of malignancy which would bias the algorithm.

Bounding box dimensions over lesion of interest: Varied

Technical Specifications


Inputs

DICOM Study

Procedure

Diagnostic Mammography

Views

CC, MLO, LM, XCCM, XCCL, Cleavage view, etc.

Data Type

DICOM

Modality

MAMMO

Body Region

Chest

Anatomic Focus

Breast

Pharmaceutical

N/A

Scenario

N/A


Secondary Inputs

Bounding Box Around a Lesion

Definition

Clinician selected bounding box around a lesion of interest

Data Type

DICOM

Value Set


Units

N/A



Primary Outputs

Similar Lesions in the Database

RadElement ID


Definition

Set of all images from a database that look similar to the lesion selected in a bounding box

Data Type

Categorical

Value Set

Images from the database

Units

N/A


Similarity Score

RadElement ID


Definition

Similarity score between the lesions selected

Data Type

Numeric

Value Set


Units

N/A


Pathologic Diagnosis For Similar Lesions

RadElement ID


Definition

From the set of similar lesions in the database, list the pathologic diagnosis for each identified lesion

Data Type

Categorical

Value Set


Units

N/A


Secondary Outputs


Patient Age

RadElement ID


Definition

From the set of similar lesions in the database, present the age of patient

Data Type

Numeric

Value Set


Units

N/A


Breast Cancer History

RadElement ID


Definition

From the set of similar lesions in the database, present breast cancer history of the patient

Data Type

Categorical

Value Set

1- Patient with a history of breast cancer

2- Patient with no history of breast cancer

3- Unknown

Units

N/A

Genetic Risk Factors for Breast Cancer

RadElement ID


Definition

From the set of similar lesions in the database, present genetic risk factors for breast cancer

Data Type

Categorical

Value Set

genetic factors known to be involved in breast cancer risk

Units

N/A