An observational study in Breast Cancer Diagnosis and Breast Cancer, sponsored by RenJi Hospital. Not yet recruiting. Per ClinicalTrials.gov, last updated 2023-12-29.
Sponsored by RenJi Hospital · Observational
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.
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.
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This study's planned enrollment of 550 is above the median of 184 across 2,642 observational studies indexed under Breast Neoplasms.
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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.
Exclusion Criteria:
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.
Other: 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.
Other: Retrospective Ultrasonographic Data Analysis
The intervention involves a detailed retrospective analysis of ultrasonographic data from patients who underwent breast lesion surgery. The study focuses on developing a quantitative nomogram model, which integrates patient age and significant sonographic characteristics of breast lesions. The purpose is to differentiate breast lesions and assess their malignancy in a non-invasive, accurate manner. This analysis uses data collected from January 2020 to June 2023, including clinical and ultrasound examination records from patients who met the inclusion criteria. The intervention does not involve any direct patient interaction or new diagnostic procedures.
Accuracy of the Ultrasonographic Nomogram in Predicting Breast Lesion Malignancy
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).
Time frame: Retrospective analysis of data collected from January 2020 to June 2023
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Plan to share: No
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This study is not yet recruiting, as verified in Dec 2023. You cannot join it, but the record below documents what was studied.
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RenJi Hospital