AI reads tumor slides to forecast cancer spread

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: 1. Female patients aged 18 years or older. 2. Histologically confirmed primary invasive breast carcinoma. 3. Underwent curative surgical resection (mastectomy or breast-conserving surgery) between January 2015 and December 2025. 4. Before initiating the neoadjuvant therapy, there was a retention of the primary tumor specimen. 5. Availability of high-quality, digitizable Hematoxylin and Eosin (H\&E) stained whole-slide images (WSIs). 6. Availability of consecutive tissue sections from the same tumor block for multiplex immunohistochemistry (mIHC) staining (including markers such as Pan-CK, CD3, CD20). 7. 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. 8. A minimum follow-up of 5 years for patients with detailed information for distant metastasis events. Exclusion Criteria: 1. Pure ductal carcinoma in situ (DCIS) without an invasive component. 2. Special histological subtypes of invasive carcinoma (e.g., metaplastic carcinoma) with distinct biological behaviors. 3. No original lesion samples were retained before neoadjuvant therapy. 4. 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). 5. 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. 6. Low tumor cellularity (e.g., tumor area \< 10% in the scanned field of view). 7. Unavailable or unalignable consecutive tissue sections, preventing spatial registration of H\&E and mIHC images. 8. Lack of essential clinicopathological or follow-up data required for model training or validation.