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

NCT06856616 | Active Not Recruiting

• 1 other identifier: MNiu
• Study Type: observational
• Target: 6,000
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (1)

• 5-10 year survival rate

  5-10 year survival rate of female breast cancer patients treated with neoadjuvant chemotherapy

  2008-2019

Study Arms (3)

Harbin Medical University Cancer Hospital

Quanzhou First Hospital Affiliated to Fujian Medical University

Xiamen Maternity and Child Healthcare Hospital

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

From 2008 to 2019 (with follow-up ending on December 31, 2024), women with invasive breast cancer who received NAC treatment at various hospitals

You may qualify if:

• Women with invasive breast cancer who received neoadjuvant chemotherapy (NAC) treatment in various hospitals from 2008 to 2019 (follow-up endpoint December 31, 2024)
• Women with primary breast cancer (Stage II-III) confirmed by pre-NAC needle biopsy, along with recorded clinical, pathological, and prognostic information
• With MR images before the first cycle of NAC and before surgery
• With pathological HE staining images, including biopsy pathology and postoperative major pathology

You may not qualify if:

• Any form of treatment was received before NAC, including endocrine therapy, radiotherapy, and chemotherapy
• Disease metastasis occurred during NAC
• Breast cancer patients with secondary malignancies from other cancers
• Patients did not complete surgery and were lost to follow-up

Sponsors & Collaborators

• The Third Affiliated Hospital of Harbin Medical University: lead

Study Sites (1)

Ming Niu

Harbin, Longjiang Hei, 150000, China

Location

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: February 26, 2025
• First Posted: March 4, 2025
• Study Start: May 13, 2025
• Primary Completion: November 1, 2025
• Study Completion (Estimated): June 1, 2026
• Last Updated: May 31, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)