A Simplified Approach to Predicting the Malignancy of Breast Lesions: Nomogram in Ultrasonography

Not yet recruiting

• Conditions: Breast Cancer; Breast Cancer Diagnosis
• Interventions: Other: Retrospective Ultrasonographic Data Analysis

• Registration Number: NCT06185855
• Lead Sponsor: RenJi Hospital

Brief Summary

This study aims to construct and validate a quantitative mammographic model based on breast ultrasound images, incorporating patient characteristics such as age and significant sonographic features. The model is intended for precise discrimination of breast lesions while assessing its diagnostic performance in clinical practice. Our goal is to provide a reliable adjunct tool to enhance the clinical decision-making of healthcare professionals and potentially improve early screening and accurate diagnosis of breast diseases.

Detailed Description

Data Collection: This study retrospectively collected clinical and ultrasound examination data from patients who underwent breast lesion surgery at our hospital from January 2020 to June 2023. Inclusion criteria included patients with complete clinical information and available ultrasound image data. Parameters extracted from this data included age, 2D ultrasound images, Doppler ultrasound images, and ultrasound diagnostic reports. Feature extraction from ultrasound images included 2D lesion information (maximum diameter, orientation, echogenicity, morphology, margins, calcification type, ductal changes), Doppler information (blood flow pattern, resistance index), and BI-RADS classification based on suspicious ultrasound findings by physicians.

Model Development: Firstly, we conducted multicollinearity analysis using Variance Inflation Factor (VIF) to select variables with VIF less than 5, aiming to reduce the impact of collinearity. We used post-operative pathological results of breast lesions as the gold standard for model development. In the R programming language, we utilized the caret package to randomly split the final samples into training and validation sets in a 7:3 ratio based on the outcome variable (benign or malignant breast lesions) while setting a random seed (set.seed) for result reproducibility. Subsequently, we performed univariate logistic regression analysis on binary variables in the training set, retaining variables with P \< 0.05, followed by multivariate logistic regression analysis to identify independent predictors of breast lesion malignancy.

Model Validation: To validate the model's performance, we constructed a nomogram based on the weight allocation of each independent predictor. Then, we comprehensively validated the model in the validation set, including calculating sensitivity, specificity, accuracy, and concordance. Receiver Operating Characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to determine the optimal threshold for quantitatively predicting the probability of breast cancer occurrence in patients. Additionally, we performed Decision Curve Analysis (DCA) to assess the net clinical benefit of the model at different patient decision thresholds. DCA helps determine the practical utility of the model in clinical decision-making and identifies the optimal threshold for predicting the probability of disease occurrence, aiding physicians in making better decisions. These validation metrics were used to evaluate the model's performance, accuracy, and potential application in real clinical practice.

Study Timeline

• Status Verified: 2023-12
• Study First Submitted: 2023-12-16
• Study First Submitted QC: 2023-12-16
• Last Update Submitted: 2023-12-16
• Study First Posted: 2023-12-29
• Last Update Posted: 2023-12-29
• Study Start: 2023-12-30
• Primary Completion: 2024-01-01
• Study Completion: 2024-03-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 550

Inclusion Criteria

• Patients who underwent breast lesion surgery at Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine during the specified period (January 2020 to June 2023).
• Patients who had a preoperative ultrasound examination of the breast lesion at the same hospital.
• Availability of complete clinical and ultrasonographic data for the patients.
• Histopathological confirmation of breast lesions post-surgery.

Exclusion Criteria

• Patients who received neoadjuvant therapy (chemotherapy, targeted therapy, immunotherapy, etc.) prior to surgery.
• Patients diagnosed with metastatic breast malignancy.
• Cases with poor quality or incomplete ultrasound images.
• Patients with a Breast Imaging Reporting and Data System (BI-RADS) category 1 diagnosis.
• Incomplete clinical records or missing critical data relevant to the study.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Benign	Retrospective Ultrasonographic Data Analysis	Benign Breast Lesion Control Group: This group would consist of patients with benign breast lesions, who also underwent breast lesion surgery and had preoperative ultrasound examinations.
Malignant	Retrospective Ultrasonographic Data Analysis	Malignant Breast Lesion Group: This group would include patients diagnosed with breast cancer who have undergone breast lesion surgery and had preoperative ultrasound examinations at the hospital.

Primary Outcome Measures

Name	Time	Method
Accuracy of the Ultrasonographic Nomogram in Predicting Breast Lesion Malignancy	Retrospective analysis of data collected from January 2020 to June 2023	The primary outcome measure is the accuracy of the developed nomogram in differentiating between malignant and benign breast lesions. This will be determined by comparing the nomogram's predictions against the actual histopathological findings from breast lesion surgeries. Accuracy will be quantified in terms of sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC).