Decision Impact Study of PreciseDx Breast

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Другое 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,

Первичные конечные точки

• Decision Impact Study of PreciseDx Breast on treating Oncologist [Срок оценки: 6-12 months]
• Decision Impact Study of PreciseDx Breast on Diagnostic Pathologist [Срок оценки: 6-12 months]

Вторичные конечные точки (1)

• Decision Impact on long term outcomes [Срок оценки: 2-5 years]

Критерии участия

Критерии включения

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

Критерии исключения

• Prior history of invasive breast cancer
• Neoadjuvant therapy

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Случай-кроссовер

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Публикации

• 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. PMID 38114366
• 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. PMID 36539895

Идентификаторы

NCT: NCT06309615 · PDX-001_2

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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