Clinical trial NCT06185855: A Simplified Approach to Predicting the Malignancy of Breast Lesions: Nomogram in Ultrasonography
Official title: A Simplified Approach to Predicting the Malignancy of Breast Lesions: Nomogram in Ultrasonography
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Breast Cancer Diagnosis
Breast Cancer
Keywords: Breast cancer diagnosis
Mammographic model
Breast disease
Clinical decision-making
Quantitative analysis
Lead sponsor: RenJi Hospital
Sponsor class: OTHER
First posted: 2023-12-29
Enrollment: 550
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population consists of patients who underwent breast lesion surgery at Renji Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, between January 2020 and June 2023. This population is diverse in terms of age and includes individuals diagnosed with various types of breast lesions, ranging from benign to malignant. All participants had undergone preoperative ultrasound examinations, which are critical for the retrospective analysis in this study.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Accuracy of the Ultrasonographic Nomogram in Predicting Breast Lesion Malignancy
timeFrame: Retrospective analysis of data collected from January 2020 to June 2023
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital
name: Lixin Jiang
role: PRINCIPAL_INVESTIGATOR
References: