Decision Impact Study of PreciseDx Breast

NCT ID: NCT06309615

Last Updated: 2025-05-29

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 300 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-08-31
• Study Completion Date: 2029-05-30

Brief Summary

The investigator's developed a digital LDT to predict invasive breast cancer (IBC) recurrence within 6 years by combining histologic features extracted from an H&E 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 investigator's 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 24.5% of all cancer diagnoses and 15.5% of all cancer death. In the United States, it is estimated that 290,560 Americans will be diagnosed with breast cancer in 2022 and 43,780 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 H&E-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 investigator's 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 investigator's 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.

Conditions

Breast Cancer

Study Design

• Observational Model Type: CASE_CROSSOVER
• Study Time Perspective: PROSPECTIVE

Study Groups

Standard of Care

Patients with a diagnosis of early-stage invasive breast cancer, post-surgery, in the process of developing a treatment plan. After a period of 2-4 weeks, patient and provider will receive the PreciseDx breast test results with follow up questionnaires to assess change in care path.

Standard of Care

Intervention Type: OTHER

To use the patients age, tumor size, grade, and lymph node status and any genomic tests (i.e. OncotypeDx, MammaPrint etc to determine risk of recurrence,

Standard of Care plus PreciseDx Breast test

Patients with a diagnosis of early-stage invasive breast cancer, post-surgery, in the process of developing a treatment plan. In addition to standard of care the patient and their provider will also receive the results from the PreciseDx breast test. Questionnaires will be utilized to assess impact on decision making and planned care path.

Standard of Care

Intervention Type: OTHER

To use the patients age, tumor size, grade, and lymph node status and any genomic tests (i.e. OncotypeDx, MammaPrint etc to determine risk of recurrence,

Interventions

Standard of Care

To use the patients age, tumor size, grade, and lymph node status and any genomic tests (i.e. OncotypeDx, MammaPrint etc to determine risk of recurrence,

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Invasive breast cancer (ductal / mixed ductal-lobular)

Exclusion Criteria

• Prior history of invasive breast cancer
• Neoadjuvant therapy

• Minimum Eligible Age: 23 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Mount Sinai Hospital, New York

OTHER

Sponsor Role: collaborator

Precise Dx, Inc.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Gregory S Henderson, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

MOUNT SINAI HOSPITAL

Central Contacts

Kristian Cruz

Role: CONTACT

kcruz@precisedx.ai

6468189330

Michael J Donovan, PhD, MD

Role: CONTACT

mdonovan@precisedx.ai

6468189330

References

Fernandez G, Zeineh J, Prastawa M, Scott R, Madduri AS, Shtabsky A, Jaffer S, Feliz A, Veremis B, Mejias JC, Charytonowicz E, Gladoun N, Koll G, Cruz K, Malinowski D, Donovan MJ. Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer. Clin Breast Cancer. 2024 Feb;24(2):93-102.e6. doi: 10.1016/j.clbc.2023.10.008. Epub 2023 Nov 2.

Reference Type: BACKGROUND

PMID: 38114366 (View on PubMed)

Fernandez G, Prastawa M, Madduri AS, Scott R, Marami B, Shpalensky N, Cascetta K, Sawyer M, Chan M, Koll G, Shtabsky A, Feliz A, Hansen T, Veremis B, Cordon-Cardo C, Zeineh J, Donovan MJ. Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years. Breast Cancer Res. 2022 Dec 20;24(1):93. doi: 10.1186/s13058-022-01592-2.

Reference Type: RESULT

PMID: 36539895 (View on PubMed)

Other Identifiers

PDX-001_2

Identifier Type: -

Identifier Source: org_study_id