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Predicting Long-Term Clinical Outcomes in Chinese Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

NCT06856616 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 6000

Last updated 2025-05-31

No results posted yet for this study

Summary

At present, the majority of studies on neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC) use pathological complete response (pCR) as a surrogate marker for patient prognosis, with significant improvements in pCR indicating better long-term survival. However, there is still a lack of non-invasive tools for accurately predicting the prognosis and pCR of BC patients undergoing NAC. Recent research has introduced emerging artificial intelligence machine learning (ML) and deep learning (DL) algorithms such as Bayesian methods, K-nearest neighbors (KNN), decision trees, support vector machines (SVM), XGBoost, ResNet, convolutional neural networks, and Transformer models, which have brought new avenues of exploration for cancer researchers.

The integration of AI with imaging, pathology, genomics, and other multi-omics has non-invasively improved preoperative diagnosis of breast cancer and, when combined with clinical factors, can assess postoperative survival. Moreover, current research data is limited, and reliable predictive models require extensive data for training. Therefore, establishing a multi-center database is essential.

Conditions

  • Breast Neoplasms

Sponsors & Collaborators

  • The Third Affiliated Hospital of Harbin Medical University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-05-13
Primary Completion
2025-11-01
Completion
2026-06-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06856616 on ClinicalTrials.gov