Breast Cancer DiagnosisNCT06185855
A Simplified Approach to Predicting the Malignancy of Breast Lesions: Nomogram in Ultrasonography
An observational study of Breast Cancer Diagnosis and Breast Cancer, run by RenJi Hospital.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Breast Cancer Diagnosis, Breast Cancer
- What is being tested?
- Retrospective Ultrasonographic Data Analysis
- How many people
- 550 participants
- Where
- No sites listed yet
- Study dates
- 30 Dec 2023to 1 Mar 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet.
About this study
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.
Read the 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.
- Conditions
- Breast Cancer Diagnosis, Breast Cancer
- Being tested
- Retrospective Ultrasonographic Data Analysis
- Keywords
- Breast cancer diagnosis, Mammographic model, Breast disease, Clinical decision-making, Quantitative analysis
- Medical subject headings
- Breast Neoplasms, Breast Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Retrospective Ultrasonographic Data AnalysisOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Retrospective Ultrasonographic Data Analysis | |
| 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. Receives: Other: Retrospective Ultrasonographic Data Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Retrospective analysis of data collected from January 2020 to June 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Dec 2023
- Study start30 Dec 2023 (estimated)
- Primary completion1 Jan 2024 (estimated)
- Study completion1 Mar 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast Cancer DiagnosisStudies of the same condition, open and under way.
- More trials run by RenJi HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 550 participants (estimated)
Sponsor and investigators
- Lead sponsor
- RenJi Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lixin Jiang, RenJi Hospital
- Investigators
- Lixin Jiang, Principal investigator, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06185855
- Sponsor's protocol ID
- LY2023-210-B
- Status last verified
- Dec 2023
- First submitted
- 16 Dec 2023
- Last updated
- 29 Dec 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Simplified Approach to Predicting the Malignancy of Breast Lesions: Nomogram in Ultrasonography
- ClinicalTrials.gov ID
- NCT06185855
- Acronym / sponsor ID
- LY2023-210-B
- Conditions
- Breast Cancer Diagnosis, Breast Cancer
- Keywords
- Breast cancer diagnosis, Mammographic model, Breast disease, Clinical decision-making, Quantitative analysis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 550 participants (estimated)
- Lead sponsor
- RenJi HospitalUniversity, hospital or other
- Study start
- 30 Dec 2023 (estimated)
- Primary completion
- 1 Jan 2024 (estimated)
- Study completion
- 1 Mar 2024 (estimated)
- First posted
- 29 Dec 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Malignant | – | Other: Retrospective Ultrasonographic Data AnalysisMalignant 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. |
| Benign | – | Other: Retrospective Ultrasonographic Data AnalysisBenign 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Retrospective Ultrasonographic Data AnalysisThe 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. | Other | Benign, Malignant |
Primary outcome
Accuracy of the Ultrasonographic Nomogram in Predicting Breast Lesion Malignancy
Time frame: 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).
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Dec 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Lixin Jiang principal investigator
Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer Diagnosis and Breast Cancer, run by RenJi Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 29 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Lixin Jiang (+86-18930173496, jinger_28@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the Ultrasonographic Nomogram in Predicting Breast Lesion Malignancy (time frame: Retrospective analysis of data collected from January 2020 to June 2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Dec 2023.