Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle

Yin, Jun; Wang, Junren; Jiang, Jingwen; Sun, Yajing; Chen, Xi; Qin, Ke

doi:10.3389/fpubh.2021.712827

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Patients pathologically diagnosed with BC have been prospectively registered in the Breast Cancer Information Management System at West China Hospital, Sichuan University since 2008 ( 15 – 18 ). Oncologists obtained the patients’ medical records, diagnostic pathology reports, and treatment data.…”

Section: Methodsmentioning

confidence: 99%

Comparison of dyslipidemia incidence in Chinese early-stage breast cancer patients following different endocrine therapies: A population-based cohort study

et al. 2022

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BackgroundThere is lack of large-scale real-world research evidence showing the impact of endocrine therapy on blood lipids in Chinese breast cancer patients, especially those with premenopausal breast cancer. Based on a large breast cancer cohort at West China Hospital, we aimed to compare the risk of dyslipidemia between premenopausal and postmenopausal women based on the endocrine therapy used.MethodsA total of 1,883 early-stage breast cancer (EBC) patients who received endocrine monotherapy [selective estrogen receptor modulator (SERM) and aromatase inhibitor (AI), with or without ovarian function suppression] with normal blood lipid levels at baseline were retrospectively included between October 2008 and April 2017. Dyslipidemia was defined as an abnormality in cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein, and total cholesterol (TC) levels. The risk accumulation function was used to calculate the incidence of dyslipidemia in order to assess the absolute risk, while the multivariate Cox regression model was used to calculate the relative risk of dyslipidemia between the groups.ResultsPatients with EBC were followed up for 60 months to monitor their blood lipid levels. The accumulated 5-year incidence of dyslipidemia in postmenopausal patients was higher than that in premenopausal patients (adjusted HR [95% confidence interval], 1.25 [1.01–1.56], 41.7% vs. 31.2%, p = 0.045). In premenopausal patients, the risk of abnormal TC was significantly higher in the OFS+AI group compared with that in the SERM group (adjusted HR [95% CI], 6.24 [3.19–12.20], p < 0.001, 5-year abnormal rates: 21.5% vs. 2.4%), and that of abnormal LDL-C level also increased (adjusted HR [95% CI], 10.54 [3.86–28.77], p < 0.001, 5-year abnormal rates: 11.1% vs. 0.9%). In postmenopausal patients, the risk of abnormal TC or LDL-C levels showed a similar trend in the AI and SERM groups.ConclusionsIn addition to postmenopausal patients, dyslipidemia is also common in premenopausal Chinese patients with EBC who received endocrine therapy. Irrespective of menopausal status, AI treatment increases the risk of TC/LDL-C dyslipidemia than SERM treatment.

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Section: Methodsmentioning

confidence: 99%

Comparison of dyslipidemia incidence in Chinese early-stage breast cancer patients following different endocrine therapies: A population-based cohort study

et al. 2022

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“…Therefore, existing breast cancer databases must be revised, and new ones must be designed so that they can accommodate changes to these criteria or otherwise new information. For example, Jin et al had been performing R&D on databases on clinical data related to breast cancer, with algorithms that are intended to cross-reference unstructured data with established information [59].…”

Section: Recommendationsmentioning

confidence: 99%

Review on Present-day Breast Cancer Detection Techniques

Chan

2024

IJORAS

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Breast cancer remains a prevalent health complication among the female population. Early and reliable detection in an individual is necessary for effective treatment. Thus, R&D into techniques for detection of breast cancer continues to the present. Non-invasive techniques include tactile examinations, electromagnetic scanning and checks for chemical markers. Invasive techniques include biopsies that extract tissue and liquid samples. These techniques have limitations and setbacks that are being addressed with supplementary or complementary techniques. Like the pre-existing techniques, these techniques also rely on comparison of data between control samples and afflicted patients to measure their reliability. Therefore, R&D efforts towards detection of breast cancer have resulted in incremental improvements on established methodologies.

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“…The continuing explosion in large datasets (some identified in the previous section) generated from images, genomic sequencing, and cancer staging is posing a significant challenge for developing models that can support diagnosis and prognosis (Jin et al, 2021) using digital twins. The proffered approach includes key steps that involve planning, implementation, monitoring, and evaluation (Lu et al, 2020) of the digital twin model development, where all key stakeholders, including clinicians, IT professionals, and data scientists, are involved.…”

Section: Digital Model Developmentmentioning

confidence: 99%

The role ofAIfor developing digital twins in healthcare: The case of cancer care

Kaul

Ossai

Forkan

et al. 2022

WIREs Data Min & Knowl

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Digital twins, succinctly described as the digital representation of a physical object, is a concept that has emerged relatively recently with increasing application in the manufacturing industry. This article proposes the application of this concept to the healthcare domain to provide enhanced clinical decision support and enable more patient‐centric, and simultaneously more precise and individualized care to ensue. Digital twins combined with advances in Artificial Intelligence (AI) have the potential to facilitate the integration and processing of vast amounts of heterogeneous data stemming from diversified sources. Hence, in healthcare this can provide enhanced diagnosis and treatment decision support. In applying digital twins in combination with AI to complex healthcare contexts to assist clinical decision making, it is also likely that a key current challenge in healthcare; namely, providing better quality care which is of high value and can lead to better clinical outcomes and a higher level of patient satisfaction, can ensue. In this focus article, we address this proposition by focusing on the case study of cancer care and present our conceptualization of a digital twin model combined with AI to address key, current limitations in endometrial cancer treatment. We highlight the role of AI techniques in developing digital twins for cancer care and simultaneously identify key barriers and facilitators of this process from both a healthcare and technology perspective. This article is categorized under: Application Areas > Health Care

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Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle

Cited by 7 publications

References 22 publications

Comparison of dyslipidemia incidence in Chinese early-stage breast cancer patients following different endocrine therapies: A population-based cohort study

Comparison of dyslipidemia incidence in Chinese early-stage breast cancer patients following different endocrine therapies: A population-based cohort study

Review on Present-day Breast Cancer Detection Techniques

The role ofAIfor developing digital twins in healthcare: The case of cancer care

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