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推进乳腺癌诊断:弹性成像纳入乳腺影像报告和数据系统(BIRADS)分类的影响。

Advancing Breast Cancer Diagnosis: The Impact of Elastography Integration Into Breast Imaging Reporting and Data System (BIRADS) Categorization.

作者信息

Asafu Adjaye Frimpong George, Aboagye Evans, Asante Emmanuel, Owusu-Afriyie Osei, Bonsu Ernest O, Mahama Fairuuj

机构信息

Radiology, Kwame Nkrumah University of Science and Technology, Kumasi, GHA.

Radiology, Spectra Health Imaging and Interventional Radiology, Kumasi, GHA.

出版信息

Cureus. 2024 Jul 26;16(7):e65449. doi: 10.7759/cureus.65449. eCollection 2024 Jul.

Abstract

OBJECTIVE

This study evaluates the impact of integrating elastography into the Breast Imaging Reporting and Data System (BIRADS) categorization on breast cancer diagnostics in an African population. It explores the association and agreement between traditional BIRADS and those modified by elastography, as well as between quantitative and qualitative elastography methods.

METHODS

A total of 200 participants who underwent breast imaging as part of their diagnostic evaluation for breast lesions were included in the study. Participant characteristics, including age distribution and indicators for breast cancer diagnoses, were analyzed. Brightness mode (B-mode) findings without elastography were assessed using the BIRADS classification. Elastography was integrated into the BIRADS categorization to evaluate its impact on breast cancer diagnostics. The association and agreement between BIRADS with and without elastography were analyzed.

RESULTS

Participants predominantly aged 40-49 showed significant staging differences with the integration of elastography. Traditional B-mode staging identified 29 (49%) of participants in BIRADS stage IV and 14 (23%) in stage V, whereas elastography adjusted these figures significantly, enhancing diagnostic refinement. There was a fair agreement between BIRADS with and without elastography (kappa = 0.322), while a substantial agreement was found between quantitative and qualitative elastography (kappa = 0.674).

CONCLUSION

The results of the study provide evidence that the integration of elastography into BIRADS categorization can significantly improve the accuracy of breast cancer diagnosis in African women. Elastography enhanced lesion characterization, supporting more personalized and precise clinical management. Continued research is needed to fully integrate elastography into routine diagnostic workflows and understand its broader clinical implications in Africa.

摘要

目的

本研究评估将弹性成像纳入乳腺影像报告和数据系统(BIRADS)分类对非洲人群乳腺癌诊断的影响。探讨传统BIRADS与经弹性成像修改后的BIRADS之间的关联和一致性,以及定量和定性弹性成像方法之间的关联和一致性。

方法

共有200名接受乳腺成像作为乳腺病变诊断评估一部分的参与者纳入本研究。分析参与者特征,包括年龄分布和乳腺癌诊断指标。使用BIRADS分类评估无弹性成像的亮度模式(B模式)检查结果。将弹性成像纳入BIRADS分类以评估其对乳腺癌诊断的影响。分析有和无弹性成像的BIRADS之间的关联和一致性。

结果

主要年龄在40 - 49岁的参与者在纳入弹性成像后显示出显著的分期差异。传统B模式分期将29名(49%)参与者判定为BIRADS IV期,14名(23%)为V期,而弹性成像显著调整了这些数字,提高了诊断的精确性。有和无弹性成像的BIRADS之间存在中等程度的一致性(kappa = 0.322),而定量和定性弹性成像之间存在高度一致性(kappa = 0.674)。

结论

研究结果提供了证据,表明将弹性成像纳入BIRADS分类可显著提高非洲女性乳腺癌诊断的准确性。弹性成像增强了病变特征描述,支持更个性化和精确的临床管理。需要持续研究以将弹性成像完全纳入常规诊断工作流程,并了解其在非洲更广泛的临床意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae62/11345039/1bfa8ac91106/cureus-0016-00000065449-i01.jpg

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