Ignite Creation Date:
2024-05-06 @ 8:14 PM
Last Modification Date:
2024-10-26 @ 3:23 PM
Study NCT ID:
NCT06309615
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-03-13
First Post:
2024-02-20
Brief Title:
Decision Impact Study of PreciseDx Breast
Sponsor:
Precise Dx Inc
Organization:
Precise Dx Inc
Study Overview
Official Title:
Prospective Clinical Utility Decision Support Study of an AI-enabled Digital Breast Cancer Test Precise Dx Breast PDxBRTM to Predict Early-stage Breast Cancer Recurrence Within 6 Years
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PDxBRUTILITY
Brief Summary:
The investigators developed a digital LDT to predict invasive breast cancer IBC recurrence within 6 years by combining histologic features extracted from an HE image of the patients IBC with clinical data including the patients age tumor size stage and number of positive lymph nodes The development of an artificial-intelligent AI-grade provides not only an objective quantitative advancement of classical breast cancer grading but also improves upon the accuracy and utility of clinical risk The investigators sought to understand how such a PreciseDx Breast would be used in clinical practice post-surgical resection for women with early-stage IBC
Detailed Description:
Female breast cancer BC has surpassed lung cancer as the most commonly diagnosed cancer worldwide which translates into 245 of all cancer diagnoses and 155 of all cancer death In the United States it is estimated that 290560 Americans will be diagnosed with breast cancer in 2022 and 43780 will die of disease Given these statistics the 2022 National Comprehensive Cancer Network NCCN American Society of Clinical Oncology ASCO and College of American Pathologists CAP clinical practice guidelines continue to stress the critical importance of the pathology assessment at diagnosis to establish extent of disease and features that reflect a biological potential for recurrence such as histologic grade and stage
Precise Dx Breast Assay PDxBR is an in vitro prognostic clinically approved test by the NYSDOH to predict breast cancer recurrence for patients diagnosed with early-stage IBC The test utilizes a digital scan of a representative HE-stained resection specimen from the patient Using advances in applied artificial intelligence AI outcome-based image analysis selected features of the invasive cancer are acquired and combined with clinical variables to produce a risk score predicting likelihood of having breast cancer recurrence within 6-years With the advent of computational methods the investigators investigated whether AI interrogation of whole slide images WSI could be used to improve on the characterization and accuracy of IBC histopathology The approach was based on the generation of quantitative discreet morphology features within a tissue section Morphology Feature Array MFA and the use of machine learning to create AI models that predict risk of recurrence in early-stage disease The investigators developed a test that improves risk stratification of IBC relative to the use of clinical features as well as re-classification of standard breast histologic grade into low- and high-risk groups using MFA-enabled AI models
Precise Dx Breast Assay PDxBR is an in vitro prognostic clinically approved test by the NYSDOH to predict breast cancer recurrence for patients diagnosed with early-stage IBC The test utilizes a digital scan of a representative HE-stained resection specimen from the patient Using advances in applied artificial intelligence AI outcome-based image analysis selected features of the invasive cancer are acquired and combined with clinical variables to produce a risk score predicting likelihood of having breast cancer recurrence within 6-years With the advent of computational methods the investigators investigated whether AI interrogation of whole slide images WSI could be used to improve on the characterization and accuracy of IBC histopathology The approach was based on the generation of quantitative discreet morphology features within a tissue section Morphology Feature Array MFA and the use of machine learning to create AI models that predict risk of recurrence in early-stage disease The investigators developed a test that improves risk stratification of IBC relative to the use of clinical features as well as re-classification of standard breast histologic grade into low- and high-risk groups using MFA-enabled AI models
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None