过度诊断
医学
背景(考古学)
乳腺肿瘤
乳腺摄影术
一致性(知识库)
诊断准确性
乳腺癌
病理
医学物理学
放射科
癌症
内科学
计算机科学
古生物学
人工智能
生物
作者
Puay Hoon Tan,Ian O. Ellis,Kimberly H. Allison,Sunil Badve,Edi Brogi,Grace Callagy,Emmanuelle Charafe‐Jauffret,Chih‐Jung Chen,Yunn‐Yi Chen,Laura C. Collins,Gábor Cserni,Lounes Djerroudi,Maria Pia Foschini,Stephen B. Fox,Helenice Gobbi,Mihir Gudi,Oi Harada,Shabnam Jaffer,Janina Kulka,Hajime Kuroda
摘要
Phyllodes tumours (PTs) of the breast present diagnostic challenges due to their complex histological features and potential for malignant behaviour. The World Health Organisation (WHO) classification requires the presence of five adverse histological criteria to categorise PTs as malignant, aiming to avoid overdiagnosis and improve diagnostic consistency. However, emerging evidence suggests that these strict criteria may underdiagnose tumours with metastatic potential and histological features that would otherwise be considered malignant in soft tissue tumours, leading to significant implications for prognosis and treatment. Recent studies have highlighted cases where tumours classified as borderline PT by WHO criteria exhibited metastatic behaviour, emphasising the need to refine the diagnostic framework. Microscopic criteria used to classify PT also vary among reporting pathologists, resulting in suboptimal reproducibility. This review examines the histological parameters utilised in the classification of malignant PT, highlights existing evidence gaps and analyses international breast pathologist survey data to propose a pragmatic diagnostic approach. We recommend redefining malignant PTs to include cases meeting four of the five WHO criteria, supplemented by comprehensive sampling and clinical context. This approach balances the risk of underdiagnosis with the need for standardised, reproducible diagnostic practices. Future collaborative efforts should focus upon developing evidence‐based, biologically relevant classification systems and leveraging technological advancements to enhance diagnostic precision. These efforts aim to refine classification, improve prognostic accuracy and optimise patient management strategies.
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