Prediction of Axillary Lymph Node Metastasis Status in Breast Cancer Based on PET/CT Radiomics

NCT05826197 | Unknown

• 1 other identifier: 2023-YBSF-480
• Study Type: observational
• Target: 100
• Locations: 0 countries
• Sites: N/A

Brief Summary

Patients with suspected breast cancer undergoing PET/CT at our hospital. The PET/CT center's chief physician and senior attending physician reviewed the films together and disagreement, if any, was resolved by consensus. The lesion was visually identified. A 3D region of interest(ROI) of the lesion was automatically outlined using the 40% threshold method, and PET metabolic parameters were measured . Breast lesions with radionuclide concentrations greater than those in normal breast tissue are considered to be breast cancer lesions, while lymph nodes with radionuclide concentrations greater than those in muscle tissue are considered to be metastatic lymph nodes. Image segmentation: Image segmentation was performed using ITK-SNAP software (4) (version 3.6.0, http://www.itksnap.org/), Brush Style: circular, Brush Size: 10, Brush Options: 3D. The entire tumor volume was outlined on the PET image as ROI for segmentation. An open source Python package (PyRadiomics version 3.0.1(5)) was used to extract the radiomics features from the ROI. Univariate and multivariate binary logistic regressions were used to construct model for predicting lymph node metastasis in breast cancer.

Conditions

Breast Neoplasms

Outcome Measures

Primary Outcomes (1)

• Radiomics score

  Higher radiomics scores indicate better model prediction performance

  1 day During the inspection

Study Arms (2)

Axillary lymph node metastasis

Other: Radiomics

No axillary lymph node metastasis

Interventions

Radiomics OTHER

PET Radiomics

Axillary lymph node metastasis

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: female
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with breast cancer undergoing PET/CT at our hospital.

You may qualify if:

• \) 18F-FDG PET/CT for breast occupancy; 2) adult female patients with pathologically confirmed breast cancer (age ≥18 years); 3) no history of surgery, radiotherapy, or chemotherapy before 18F-FDG PET/CT; and 4) interval between 18F-FDG PET/CT and puncture/surgery ≤2 weeks.

You may not qualify if:

• \) multifocal, bilateral, or occult breast cancer; 2) incomplete clinical or pathological data; 3) poor PET/CT image quality, when metabolic tumor volume(MTV) cannot be automatically segmented; and 4) concomitant malignant tumors.

Sponsors & Collaborators

• First Affiliated Hospital Xi'an Jiaotong University: lead

Related Publications (2)

• Li Y, Han D, Shen C. Prediction of the axillary lymph-node metastatic burden of breast cancer by 18F-FDG PET/CT-based radiomics. BMC Cancer. 2024 Jun 7;24(1):704. doi: 10.1186/s12885-024-12476-3.

  PMID: 38849770 DERIVED

• Li Y, Han D, Shen C, Duan X. Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study. BMC Cancer. 2023 Oct 24;23(1):1028. doi: 10.1186/s12885-023-11498-7.

  PMID: 37875818 DERIVED

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

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

Central Study Contacts

Yan Li

CONTACT

0086-15829364429; 29396779@qq.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 12, 2023
• First Posted: April 24, 2023
• Study Start: May 10, 2023
• Primary Completion: May 1, 2024
• Study Completion: December 1, 2024
• Last Updated: May 9, 2023

Record last verified: 2023-04