This study aims to develop an AI-based predictive tool to help clinicians more accurately determine whether breast cancer patients can benefit from HER-2-targeted antibody-drug conjugate (T-DXd) therapy before treatment. While HER-2-targeted ADC drugs have significantly improved outcomes for patients with HER-2 positive and low-expression advanced breast cancer, there are notable individual differences in efficacy. Currently, there is a lack of precise clinical methods to predict response, which means some patients might receive ineffective treatment and face unnecessary drug side effects and financial burden.
This study is a retrospective multicenter observational study, planning to collect pathological images (including HE staining and HER-2, ER, PR, Ki-67 immunohistochemical staining), proteomics data, and clinical efficacy information from HER-2 positive and low-expression advanced breast cancer patients who have received T-DXd treatment.
The research will be carried out in five phases:
1. Build a clinical database for ADC drug therapy, integrating basic patient information, treatment plans, efficacy data, and pathology specimen information from multiple centers. 2. Use LC-MS/MS proteomics technology to screen for key protein markers related to T-DXd efficacy and use bioinformatics analysis to identify predictive protein indicators. 3. Extract IHC staining features from pathological images and evaluate their correlation with efficacy alongside clinical data. 4. Integrate proteomics, pathology, and clinical big data, using AI technologies such as foundational pathology models (like TITAN), biomedical large language models (like BioBERT), and protein large language models (like ESM2-15B). Apply a multiple instance learning strategy to build a multimodal efficacy prediction model, and evaluate the model's performance on the training set using 5-fold cross-validation. 5. Establish an internal validation cohort (200 cases) and a multicenter external validation cohort (300 cases). Considering that the external validation group may lack proteomics data, the multimodal model will be fine-tuned and distilled into a simplified predictive model based on standard IHC features (HER-2, ER, PR, Ki-67, plus key protein markers identified from proteomics) and clinical text information, then its performance will be verified in the external cohort.
Ultimately, this research will create an AI tool to support clinical decision-making, promoting personalized treatment for HER-2 positive and low-expression breast cancer and the clinical adoption of AI in healthcare.
Female, 18 years or older;
Advanced breast cancer confirmed by pathology (AJCC 8th edition, stage IV);
HER2 status known;
Received at least 2 cycles of Pyrotinib monotherapy;
Baseline IHC or HE slides of poor quality (e.g., faded, folded, or tissue loss >10%);
Previous treatment with other HER2-ADC drugs;
History of other malignancies (except non-melanoma skin cancer or cases with no recurrence for over 5 years);
Participation in other interventional clinical trials at the same time (past trials already completed are fine);
Retrospective cohort (modeling and internal validation): We collected data from breast cancer patients at Zhejiang Cancer Hospital who received Youherde treatment from January 2023 to June 2026, to build a dataset for developing an efficacy prediction model. We integrated the following multimodal information: 1. HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the most recent biopsy of recurrent or metastatic lesions before starting Youherde treatment; 2. For patients with post-surgery recurrence/metastasis and available surgical specimens, HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the primary tumor; 3. Corresponding clinical efficacy data (PFS, ORR, etc.) and proteomics data. Prospective cohort (multicenter external validation): Working together with multiple centers, we will prospectively include breast cancer patients who are planned to receive second-line or higher treatm
Cohort
Other
Not listed
Area Under the Receiver Operating Characteristic Curve (AUC) of the Predictive Model
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