Decision Impact Study of PreciseDx Breast (PDxBRUTILITY)

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Breast Cancer
• Intervention / Treatment: Other: Standard of Care

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.

• Study Type: Observational
• Enrollment (Estimated): 300

Contacts and Locations

• Study Contact: Name: Kristian Cruz; Phone Number: 6468189330; Email: kcruz@precisedx.ai
• Study Contact Backup: Name: Michael J Donovan, PhD, MD; Phone Number: 6468189330; Email: mdonovan@precisedx.ai

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Accepting all patients newly diagnosed with early stage breast cancer and scheduled for a resection.

Description

Inclusion Criteria:

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

Exclusion Criteria:

• Prior history of invasive breast cancer
• Neoadjuvant therapy

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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.	Other: 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,
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.	Other: 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,

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Decision Impact Study of PreciseDx Breast on treating Oncologist	Proportion (target; 20%) of medical oncologists who utilized the PDxBR results in their management of patients with IBC including any of the following decisions / actions: i. overall confirmation or adjustment of original management plan, ii. order / defer genomic testing, iii. adjust type, dose, or regimen of endocrine therapy, iv. introduction of chemotherapy in addition to endocrine treatment, v. use radiotherapy etc.	6-12 months
Decision Impact Study of PreciseDx Breast on Diagnostic Pathologist	Proportion (target: 20%) of pathologists who utilized the PDxBR results in their routine diagnostic assessment of IBC including any of the following: i. supported and or changed their diagnostic histologic grade (based on the AI-grade provided by the PDxBR assay), ii. provided additional useful information in the histologic assessment of the IBC including the presence of lymphocytes, stromal content etc. iii. found the interactive smart phone accessible digital feature display tool helpful in their understanding and use of the test results in their assessment process.	6-12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Decision Impact on long term outcomes	Use of NPV, PPV, Sensitivity, Specificity, HR for predicting local-regional, distant metastasis or overall survival.	2-5 years

Collaborators and Investigators

• Sponsor: Precise Dx, Inc.
• Collaborators: Mount Sinai Hospital, New York
• Investigators: Principal Investigator: Gregory S Henderson, MD, PhD, Mount Sinai Hospital

Publications and helpful links

General Publications

• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): August 1, 2025
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): May 30, 2029

Study Registration Dates

• First Submitted: February 20, 2024
• First Submitted That Met QC Criteria: March 7, 2024
• First Posted (Actual): March 13, 2024

Study Record Updates

• Last Update Posted (Actual): May 29, 2025
• Last Update Submitted That Met QC Criteria: May 22, 2025
• Last Verified: May 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: PDX-001_2

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: Currently there is no plan to make IPD available to other researchers. As this is a multi-site study - depending on patient. numbers - we will collectively evaluate impact on decision making.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No