Department of Radiology, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.
Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom.
Br J Radiol. 2019 Nov;92(1103):20190177. doi: 10.1259/bjr.20190177. Epub 2019 Aug 9.
In the UK RCR 5-point breast imaging system (UKS), radiologists grade mammograms from 1 to 5 according to suspicion for malignancy, however unlike BI-RADS, no lexicon of descriptors is published. The aim of this study was to determine whether strict categorisation of microcalcifications (MCC) according to BI-RADS was a better predictor of malignancy than the UKS and whether these descriptors could be used within the UKS.
A retrospective review of 241 cases, with MCC on mammography, who underwent biopsy was performed. Morphology, distribution, extent, UKS score, BI-RADS category and pathology were recorded. The positive predictive value (PPV) of each classification system for malignancy was calculated.
28.6% were diagnosed with DCIS/IDC. The PPV for malignancy using the UKS was 18.9%, 69.4%, 100% for M3-5 respectively ( < 0.001) and using ΒI-RADS morphology was amorphous: 7.1%, coarse heterogeneous: 33.3%, fine pleomorphic: 48.1% and fine linear/fine linear branching: 85.2% ( < 0.001). The PPV based on distribution was grouped: 14.2%, regional: 32.3%, diffuse: 33.3% and linear/segmental: 77.8% ( < 0.001). Combining all cases of benign-appearing, amorphous and grouped coarse heterogenous and grouped fine pleomorphic MCC gave a PPV of 12.8%. Combining regional, linear or segmental coarse heterogenous and fine pleomorphic and all fine linear/branching MCC resulted in a PPV of 83.3% for malignancy.
Combining morphology and distribution of MCC is accurate in malignancy prediction. Use of BI-RADS descriptors could help standardise reporting within the UKS and an algorithm using these within the UKS is proposed. Better prediction would enable more appropriate counselling and help to identify discrepancies.
No guidance exists on scoring of suspicious MCC in the UK breast imaging system. Use of BI-RADS morphologic/distribution descriptors can aid malignancy prediction. Findings other than morphology of MCC are important in malignancy prediction. An algorithm for use by the UK radiologist when evaluating MCC is provided.
在英国 RCR 五分制乳腺成像系统（UKS）中，放射科医生根据恶性肿瘤的可疑程度将乳腺 X 线摄影图像从 1 分到 5 分进行分级，但是与 BI-RADS 不同的是，没有公布描述符词汇表。本研究的目的是确定根据 BI-RADS 对微钙化（MCC）进行严格分类是否比 UKS 更能预测恶性肿瘤，以及这些描述符是否可以在 UKS 中使用。
对 241 例接受活检的乳腺 X 线摄影有 MCC 的病例进行回顾性研究。记录形态、分布、范围、UKS 评分、BI-RADS 类别和病理学。计算每个分类系统对恶性肿瘤的阳性预测值（PPV）。
28.6%的病例被诊断为 DCIS/IDC。使用 UKS 诊断恶性肿瘤的 PPV 分别为 18.9%、69.4%、100%（<0.001），使用 BI-RADS 形态学为无定形：7.1%、粗不均匀：33.3%、细多形性：48.1%和细线性/细线性分支：85.2%（<0.001）。基于分布的 PPV 为分组：14.2%、区域性：32.3%、弥漫性：33.3%和线性/节段性：77.8%（<0.001）。将良性外观、无定形和分组性粗不均匀及分组性细多形性的所有 MCC 病例相结合，其 PPV 为 12.8%。将区域性、线性或节段性粗不均匀和细多形性以及所有细线性/分支性 MCC 相结合，恶性肿瘤的 PPV 为 83.3%。
MCC 的形态和分布相结合可准确预测恶性肿瘤。使用 BI-RADS 描述符可有助于在 UKS 中标准化报告，并且提出了一种在 UKS 中使用这些描述符的算法。更好的预测可以使更适当的咨询，并有助于识别差异。
在英国乳腺成像系统中，对可疑 MCC 评分没有指导。使用 BI-RADS 形态学/分布描述符可以帮助预测恶性肿瘤。MCC 的形态以外的发现对恶性肿瘤的预测很重要。提供了一种供英国放射科医生在评估 MCC 时使用的算法。
