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The goal of this clinical trial is to compare the effects of modern remote health intervention regime with traditional in-person intervention strategies for high-risk breast cancer groups that with BI-RADS 3 or higher nodules.
The main questions it aims to answer are:
Participants will be divided into 2 groups, the Experimental group and the Control group. Participants in the Experimental group will be offered with modern remote interventions for 2 years, as describe below:
Participants in the Control group will be offered with traditional strategies provided in the 'Breast Cancer Screening Guideline for Chinese Women': Ultrasound follow-up review is recommended no less than 3 to 6 months later. If there is no change at 2-year follow-up, it can be downgraded to BI-RADS 2; if there is suspicious change in the lesion during follow-up, biopsy should be considered to clarify the nature of the pathology.
Breast cancer is one of the world's major public health problems. According to the report of the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO), breast cancer has replaced lung cancer as the world's number one cancer in 2020, posing a serious threat to women's lives and health. Breast nodules are benign lumps or masses that form in the breast tissue. Breast nodules may increase the risk of breast cancer to a certain extent, and enhancing early health management of breast nodules is an effective measure to diagnose and prevent breast cancer. This project aims to construct an intelligent breast cancer intelligent health management system using remote intervention technology. The system includes modules of intelligent breast health information management, breast disease risk assessment and breast stratified intervention, aiming to break through the bottleneck of breast cancer prevention and treatment.
The goal of this clinical trial is to compare the effects of modern remote health intervention regime with traditional in-person intervention strategies in high-risk breast cancer groups that with BI-RADS 3 or higher nodules.
The main questions it aims to answer are:
Research content:
The screened and qualified subjects were randomly divided into experimental group and control group, and different health management programs were adopted, the experimental group adopted the remote intervention health management mode, and the control group adopted the health management mode recommended by the conventional guidelines, to the end of the follow-up, and statistical analysis was performed to compare the effects of the two health management modes.
1) To analyze the effectiveness of the remote intervention health management model in slowing down the malignant progression of breast nodules relative to the traditional model, and to analyze the differences in the rates of nodal lesion progression, nodal malignancy, and nodal improvement as well as the time spent on health management.
(2) To analyze the effectiveness of the tele-intervention health management model relative to the traditional model in reducing the risk of progression and death of patients with potential breast cancer after detection of the intelligent health management model, and to analyze the differences in the risk of death of the patients, the risk of progression of the lesions and the survival time.
(3) To analyze the degree of change in knowledge, beliefs, and behaviors of breast nodule populations after different health management intervention modes of tele-intervention health management mode relative to the traditional mode.
Study Population:
Female medical examiners who participated in physical examinations and had TI-RADS category 3 or higher breast nodules on breast color Doppler ultrasound at the health management centers of five large tertiary hospitals nationwide during the trial period. The study was a clinical trial study with qualitative variables as the main efficacy outcome indicators, and the subjects were randomized into a test group, a control group, and an equally parallel 1:1 design for both groups. Each group required 1,000 cases, and taking into account a 20% dropout rate, 1,250 cases were recruited in each group, totaling 2,500 cases in both groups.
Statistical analysis The study was a clinical trial study with qualitative variables as the primary outcome indicators of efficacy, with an equally parallel 1:1 design for both groups. The researchers considered that the smart health management program (trial group) had to be at least 5% better than the conventional health management program (control group) to be clinically meaningful. An equal (1:1) superiority design scheme was used, setting α = 0.025 (unilateral), β = 0.20 (unilateral), Δ = 5%, and estimating the sample size n. Based on the design of this clinical pilot study, combined with the primary efficacy outcome metrics, the estimation yielded a need for 500 cases in each group, and considering a 20% shedding rate, 625 cases were recruited in each group, for a total of 1,250 cases in both groups. Unless otherwise stated, all statistical tests were performed using a two-sided test with a test level of α = 0.05 (one-sided test α = 0.025). The study was a clinical trial study with qualitative variables as the main efficacy outcome indicators, and the effect of the new regimen had to be at least 5% better than that of the control drug to be clinically meaningful, so it was set at α = 0.025 (one-sided) and β = 0.20 (one-sided).
Hypotheses for the test of superiority H0: the efficiency of the test group is less than or equal to the control group, π_T-π_C≤0 H1: The efficiency of the test group is greater than that of the control group, π_T-π_C>0 α = 0.025, the superiority cut-off value is 0. Calculate the difference between the effective rate of the experimental group and the control group, and calculate the 95% CI of the difference, if the lower limit of the 95% CI is greater than 0, the efficacy of the experimental group is considered to be better than that of the control group, and the expected goal of the study is achieved.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| the Experimental Group | Experimental |
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| the Control Group | Active Comparator | Ultrasound follow-up review is recommended no less than 3 to 6 months later. If there is no change at 2-year follow-up, it can be downgraded to BI-RADS 2; if there is suspicious change in the lesion during follow-up, biopsy should be considered to clarify the nature of the pathology. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| the Remote Intervention | Other | Remote health intervention refers to the use of big data, artificial intelligence and 5G technology to provide healthcare services and interventions to individuals at a distance. This approach allows healthcare providers to deliver medical care, monitor patients' health, offer consultations, provide education, and offer support without the need for in-person interaction. |
| Measure | Description | Time Frame |
|---|---|---|
| BI-RADS grade changes in breast nodules | During the follow up period, check the BI-RADS grade changes in both the experimental and the control group, to see the difference. BI-RADS stands for American Breast Imaging Reporting and Data System. It's a standardized system used by radiologists to categorize findings on mammograms. The BI-RADS system ranges from 0 to 6, with each category indicating a different level of suspicion for breast cancer: BI-RADS 0: Incomplete assessment, additional imaging needed. BI-RADS 1: Negative (normal) finding. BI-RADS 2: Benign (non-cancerous) finding. BI-RADS 3: Probably benign finding (less than 2% chance of being cancer). BI-RADS 4: Suspicious abnormality that may be cancerous (the likelihood of cancer increases with higher numbers within this category). BI-RADS 5: Highly suggestive of malignancy (more than 95% chance of being cancer). BI-RADS 6: Known biopsy-proven malignancy. | 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| The differences in the risk of developing into breast cancer | During the follow up period, check risk of breast cancer in both the experimental and the control group, to see the difference. The numbers and rates of developing into breast cancers of the experimental and the control group will be computed, and their statistically significance will be evaluated using student-t test or chi-square test. All the analysis will be performed with R software version 4.3.1. |
| Measure | Description | Time Frame |
|---|---|---|
| The knowledge, belief and behavioral changes | During the follow up period, check the knowledge, belief and behavioral changes in both the experimental and the control group, to see the difference. The 'knowledge' section consists of 22 questions, including basic knowledge, factors contributing to the disease, and self-examination awareness. The 'belief' section comprises 7 questions, including statements like "I can maintain good lifestyle habits" and "I can comfortably discuss breast issues." The breast cancer 'behavior' section comprises 4 questions, including statements like "If I notice any abnormalities in my breasts, I will seek medical attention" and "I have performed breast self-examinations before." All questions are multiple-choice, with each question scored as 1 point for 'aware/agree/done' and 0 points for 'unaware/disagree/not done.' The total KAP score is 33 points, where a higher score indicates a higher level of knowledge, belief, and behavior related to breast cancer. |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Wenqi Hu, MPH | Contact | 15098719908 | huwenqi_epi@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Guang Zhang, Dr | Qianfoshan Hospital | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26667886 | Background | Torre LA, Siegel RL, Ward EM, Jemal A. Global Cancer Incidence and Mortality Rates and Trends--An Update. Cancer Epidemiol Biomarkers Prev. 2016 Jan;25(1):16-27. doi: 10.1158/1055-9965.EPI-15-0578. Epub 2015 Dec 14. | |
| 26359465 | Background | DeSantis CE, Bray F, Ferlay J, Lortet-Tieulent J, Anderson BO, Jemal A. International Variation in Female Breast Cancer Incidence and Mortality Rates. Cancer Epidemiol Biomarkers Prev. 2015 Oct;24(10):1495-506. doi: 10.1158/1055-9965.EPI-15-0535. Epub 2015 Sep 10. |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| D006266 | Health Education |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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| the Traditional Intervention | Other | Ultrasound follow-up review is recommended. If there is suspicious change in the lesion during follow-up, biopsy should be considered to clarify the nature of the pathology. |
|
| 3 years |
| 3 years |
| 23322203 | Background | Taghipour S, Banjevic D, Miller AB, Montgomery N, Jardine AK, Harvey BJ. Parameter estimates for invasive breast cancer progression in the Canadian National Breast Screening Study. Br J Cancer. 2013 Feb 19;108(3):542-8. doi: 10.1038/bjc.2012.596. Epub 2013 Jan 15. |
| 34837254 | Background | Padilla A, Arponen O, Rinta-Kiikka I, Pertuz S. Image retrieval-based parenchymal analysis for breast cancer risk assessment. Med Phys. 2022 Feb;49(2):1055-1064. doi: 10.1002/mp.15378. Epub 2021 Dec 15. |
| 35168775 | Background | Green VL. Breast Cancer Risk Assessment and Management of the High-Risk Patient. Obstet Gynecol Clin North Am. 2022 Mar;49(1):87-116. doi: 10.1016/j.ogc.2021.11.009. |
| 36639189 | Background | Burka D, Gupta R, Moran AE, Cohn J, Choudhury SR, Cheadle T, Mullick R, Frieden TR. Keep it simple: designing a user-centred digital information system to support chronic disease management in low/middle-income countries. BMJ Health Care Inform. 2023 Jan;30(1):e100641. doi: 10.1136/bmjhci-2022-100641. |
| D017437 |
| Skin and Connective Tissue Diseases |
| D000099060 | Adherence Interventions |
| D055118 | Medication Adherence |
| D010349 | Patient Compliance |
| D010342 | Patient Acceptance of Health Care |
| D000074822 | Treatment Adherence and Compliance |
| D015438 | Health Behavior |
| D001519 | Behavior |