MRI-based Approaches for Multi-parametric Model to Early Predict Pathological Complete Response to Neoadjuvant Therapy in Breast Cancer (NeoMDSS)

Guangdong Provincial People's Hospital (Guangdong Provincial Academy of Medical Sciences)

Status: Completed

Conditions: Breast Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT04909554

20210504

Details and patient eligibility

About

The purpose of this clinical research is to evaluate the accuracy of a multi-parametric model based on magnetic resonance imaging (MRI) in predicting pathological complete response (pCR) after the first cycle of neoadjuvant therapy (NAT) given to patients with locally advanced breast cancer, thus allowing early chemotherapy regimen modification to increase number of patients achieving pCR or save patients from toxic effects of ineffective chemotherapy.

Full description

Breast cancer is the most prevalent cancer among women worldwide. NAT has been well established in managing breast cancer for patients with locally advanced cancer and early-stage operable breast cancers of specific molecular subtypes. Though pCR has been demonstrated to be associated with better survival, it can only be judged by pathological testing of surgically resected specimens. Thus, predicting pCR earlier during NAT is imperative and can timely switch to a new personalized treatment strategy and exempt from unnecessary chemotherapy toxicity for patients.

This is a multicenter, prospective cohort study of 301 patients undergoing MRI after the first cycle of neoadjuvant chemotherapy. This project plans to establish and validate a model for determining pCR during NAT in breast cancer based on clinical information, imaging and pathological information of patients in multiple centers, in order to provide important references for further early diagnosis and personalized treatment.

1. Collecting MRI images data, clinical and pathological information, treatment regimens, and curative effect information to build an MRI-based, multi-parametric model.
2. Evaluating the performance of model through internal and external validation cohort by using the receiver operating characteristic (ROC) curve, the area under the curve (AUC), discrimination and calibration measures.

Enrollment: 301 patients

Sex: Female

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

1. For training cohort:

   Inclusion Criteria:

   • Age ≥18 years;
   • Histologically confirmed invasive breast carcinoma;
   • Clinical stage II-III at presentation;
   • Complete basic information and image data;
   • Have MRI imaging data at baseline and after the first cycle of NAC;
   • Finish the standard NAC treatment and undergo surgery;

   Exclusion Criteria:

   • With chemotherapy contraindications;
   • Multifocal of multicentric lesions;
   • Poor quality of MRI images;

2. For validation cohort:

Inclusion Criteria:

• Age ≥18 years;
• Complete basic information and image data;
• Clinical stage II-III at presentation;
• Scheduled for neoadjuvant chemotherapy;
• Eastern Cooperative Oncology Group (ECOG) performance status of 0-1.
• Signed informed consent;

Exclusion Criteria:

• With chemotherapy contraindications;
• Metastatic breast cancer;
• Multifocal of multicentric lesions;

Trial design

301 participants in 3 patient groups

the training cohort

Description:

From January 2019 to December 2020, 140 patients from Guangdong Province People's Hospital with complete clinicopathological information and available images of MRI before treatment and after 1st-NAT were retrospectively recruited for the training cohort

the internal validation cohort

Description:

From June 2021 and December 2023, 120 patients from Guangdong Province People's Hospital were prospectively recruited for the internal validation cohort.

the external validation cohort

Description:

From June 2021 and December 2023, 41 patients from Shantou Central Hospital, The First Affiliated Hospital of Guangdong Pharmaceutical University and The First People's Hospital of Foshan were prospectively recruited for the external validation cohort.