A Simplified Approach to Predicting the Malignancy of Breast Lesions: Nomogram in Ultrasonography
NCT ID: NCT06185855
Last Updated: 2023-12-29
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
550 participants
OBSERVATIONAL
2023-12-30
2024-03-01
Brief Summary
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Detailed Description
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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.
Conditions
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Keywords
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Malignant
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.
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.
Benign
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.
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* 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 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.
ALL
No
Sponsors
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RenJi Hospital
OTHER
Responsible Party
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Lixin Jiang
Chief Physician
Principal Investigators
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Lixin Jiang
Role: PRINCIPAL_INVESTIGATOR
Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital
Central Contacts
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Other Identifiers
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LY2023-210-B
Identifier Type: -
Identifier Source: org_study_id