A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps

Обзор

The goal of this observational study is to develop and validate an artificial intelligence (AI) model for predicting the risk of distant metastasis in patients with primary breast cancer. The main question it aims to answer is: Can a multimodal AI model, trained on routinely available histopathological images, accurately predict the long-term risk of breast cancer metastasis? Researchers will analyze existing hematoxylin and eosin (H\&E) and immunohistochemistry (IHC) stained tissue slides from patients who underwent surgery between 2015 and 2025. Clinical data will be used to train the AI model and evaluate its performance in predicting metastasis.

Вмешательства

• Другое Diagnostic Test: AI-Based Spatial Pathomic Analysis
  This is an observational study with no therapeutic or procedural interventions. The "intervention" refers to the analytical method applied to existing data. Archived tissue samples (H\&E and IHC stained slides) will be digitally scanned and analyzed by a multimodal artificial intelligence (AI) model to develop a risk prediction tool for distant metastasis. Patients' clinical data will be collected for model training and validation. No direct interaction with patients occurs, and no treatment dec

Первичные конечные точки

• Predictive accuracy for distant metastasis risk assessed by Time-dependent Area Under the Receiver Operating Characteristic Curve (Time-dependent AUC) [Срок оценки: From the date of initial surgery up to 5 years post-operatively, with the occurrence of distant metastasis defined as the event of interest.]

Вторичные конечные точки (3)

• Sensitivity and Specificity [Срок оценки: Assessed at the 5-year post-operative time point.]
• Concordance Index (C-index) [Срок оценки: From the time of the initial surgical treatment until distant metastasis occurs or until the end of the follow-up (the longest duration can be up to 10 years).]
• Model calibration assessed by calibration curve [Срок оценки: From the time of the initial surgical treatment until distant metastasis occurs or until the end of the follow-up (the longest duration can be up to 10 years).]

Критерии участия

Критерии включения

• Female patients aged 18 years or older.
• Histologically confirmed primary invasive breast carcinoma.
• Underwent curative surgical resection (mastectomy or breast-conserving surgery) between January 2015 and December 2025.
• Before initiating the neoadjuvant therapy, there was a retention of the primary tumor specimen.
• Availability of high-quality, digitizable Hematoxylin and Eosin (H\&E) stained whole-slide images (WSIs).
• Availability of consecutive tissue sections from the same tumor block for multiplex immunohistochemistry (mIHC) staining (including markers such as Pan-CK, CD3, CD20).
• Complete clinicopathological data and follow-up information must be available, including but not limited to: TNM stage, histological grade, molecular subtype (ER, PR, HER2 status), adjuvant treatment records, and clearly documented distant metastasis-free survival (DMFS) data.
• A minimum follow-up of 5 years for patients with detailed information for distant metastasis events.

Критерии исключения

• Pure ductal carcinoma in situ (DCIS) without an invasive component.
• Special histological subtypes of invasive carcinoma (e.g., metaplastic carcinoma) with distinct biological behaviors.
• No original lesion samples were retained before neoadjuvant therapy.
• Presence of contralateral breast cancer or a history of any other prior malignancy (except for cured non-melanoma skin cancer or carcinoma in situ of the cervix).
• H\&E or IHC slides with significant technical artifacts (e.g., fading, folds, heavy knife marks, tissue tearing, uneven staining) that preclude reliable image analysis.
• Low tumor cellularity (e.g., tumor area < 10% in the scanned field of view).
• Unavailable or unalignable consecutive tissue sections, preventing spatial registration of H\&E and mIHC images.
• Lack of essential clinicopathological or follow-up data required for model training or validation.

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

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Случай-контроль

Центры проведения

Китай · 4 центра

• Jilin Cancer Hospital — Changchun
• Cancer Institute and Hospital, Tianjin Medical University, China — Тяньцзинь
• 2nd Affiliated Hospital, School of Medicine, Zhejiang University, China — Ханчжоу
• The Fourth Affiliated Hospital of Zhejiang University School of Medicine — Ханчжоу

Идентификаторы

NCT: NCT07244094 · 2025-1104

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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