Ultrasound Radiomics for Predicting Breast Cancer and Axi... | NCT05768451

Studies
Ultrasound Radiomics for Predicting Breast Cancer and Axillary Lymph Node Metastasis

NCT05768451

Sponsor

The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Status

Unknown status

Last Update Posted

Mar 14, 2023Actual

Enrollment

200Estimated

Phase

Not provided

Conditions

Breast Cancer
Axillary Lymph Node Metastasis

Interventions

Ultrasonic image analysis

Countries

Not provided

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Mar 2023

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Not yet recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Apr 1, 2023Estimated

Primary Completion Date

Dec 31, 2024Estimated

Completion Date

Aug 31, 2025Estimated

First Submitted Date

Dec 5, 2022

First Submission Date that Met QC Criteria

Mar 13, 2023

First Posted Date

Mar 14, 2023Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Mar 13, 2023

Last Update Posted Date

Mar 14, 2023Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

The project is proposed based on multimodal ultrasonic imaging omics building used for accurate prediction of the breast cancer and axillary lymph node metastasis load artificial intelligence forecasting model, this method can dig the hidden features of ultrasonic image is not visible to the naked eye, make up the subjectivity in the process of clinical doctors in diagnosis and treatment, provide accurate, objective basis for clinical decision making.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

200Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
benign			Other: Ultrasonic image analysis
malignant			Other: Ultrasonic image analysis

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Ultrasonic image analysis	Other	Preoperative conventional ultrasound (US), elastic ultrasound (UE) and contrast-enhanced ultrasound (CEUS) images were analyzed. Histopathological results were used as the gold standard. The cases were randomly divided into training set and test set with a ratio of 7:3. The US image, UE image and CEUS image of the maximum long-axis section of each lesion were selected, and the region of interest (ROI) of the lesion was manually delineated.

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Accuracy of differential diagnosis between benign and malignant	ROC curve, sensitivity, specificity, accuracy, decision curve	2022.12.15-2023.12.30

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

patients who have not received breast puncture biopsy or neoadjuvant chemotherapy, etc. prior to ultrasonography;
patients who are not allergic to the ultrasound contrast agent (SonoVue) and have signed an informed consent form for ultrasonography
patients with breast tumors who have undergone surgical treatment at our hospital and have complete case histories.

Exclusion Criteria:

patients with incomplete case data;
cases with large breast masses that do not completely show the region of interest (ROI);
patients who have undergone breast aspiration biopsy or other treatments such as neoadjuvant chemotherapy or endocrine therapy before ultrasound examination.

Accepts Healthy Volunteers

Sex

Female

Sex/Gender Based

Yes

Sex/Gender Description

Female patients with breast tumors

Minimum Age

18 Years

Maximum Age

90 Years

Standard Ages

AdultOlder Adult

Study Population

Patients with breast tumors seen in Nanjing Gulou Hospital in September 2018 and later and confirmed by histological pathology were selected, and all patients had complete preoperative US, UE and CEUS examination data.

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
baojie wen, doctor	Contact	02583106666	53500	359408031@qq.com

Overall Officials

Not provided

Locations

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D001943	Breast Neoplasms

Ancestor Terms

ID	Term
D009371	Neoplasms by Site
D009369	Neoplasms
D001941	Breast Diseases
D012871	Skin Diseases

Browse Leaves

Not provided

Browse Branches

Not provided