Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance
Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance
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DOI：
10.1016/j.acra.2019.12.009
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发表时间：
2020-11-01
影响因子：
4.8
通讯作者：
Grimm, Lars J.
中科院分区：
文献类型：
--
作者：
Selvakumaran, Vignesh;Hou, Rui;Grimm, Lars J.
基本原理和目标：本研究的目的是量化乳腺放射科医师在预测乳腺导管原位癌（DCIS）表现为钙化时的隐性浸润性疾病方面的表现，并确定与放射科医师表现相关的成像和组织病理学特征。材料和方法：2010年之间，乳腺X线检查发现钙化，在芯活检中最初诊断为DCIS，并接受了明确的手术切除2015年被认定。随机选择30例可疑钙化升级为浸润性导管癌的病例和120例在确定性手术时证实的DCIS病例。收集细胞核分级、雌激素和孕激素受体状态、患者年龄、钙化长轴长度和乳腺密度。10名对所有临床和病理学数据不知情的乳腺放射科医生独立审查了所有病例，并估计了DCIS在手术切除时升级为浸润性疾病的可能性。亚组分析的基础上进行核分级，长轴长度，乳腺密度和排除后的微创diseases.Results：读者的性能，以预测upstaging范围从一个受试者工作特征曲线下面积（AUC）为0.541-0.684，平均AUC为0.620（95%CI：0.489-0.751）。对于小于2 cm的病变，性能有所改善（AUC：0.676 vs 0.500; p = 0.002）。排除微创病例也提高了性能（AUC：0.651 vs 0.620; p = 0.005）。基于乳腺密度（p = 0.850）或核分级（p = 0.270）的性能没有差异。结论：放射科医师能够比偶然更好地预测浸润性疾病，特别是对于较小的DCIS病变（
Rationale and Objectives: The purpose of this study is to quantify breast radiologists' performance at predicting occult invasive disease when ductal carcinoma in situ (DCIS) presents as calcifications on mammography and to identify imaging and histopathological features that are associated with radiologists' performance.Materials and Methods: Mammographically detected calcifications that were initially diagnosed as DCIS on core biopsy and underwent definitive surgical excision between 2010 and 2015 were identified. Thirty cases of suspicious calcifications upstaged to invasive ductal carcinoma and 120 cases of DCIS confirmed at the time of definitive surgery were randomly selected. Nuclear grade, estrogen and progesterone receptor status, patient age, calcification long axis length, and breast density were collected. Ten breast radiologists who were blinded to all clinical and pathology data independently reviewed all cases and estimated the likelihood that the DCIS would be upstaged to invasive disease at surgical excision. Subgroup analysis was performed based on nuclear grade, long axis length, breast density and after exclusion of microinvasive disease.Results: Reader performance to predict upstaging ranged from an area under the receiver operating characteristic curve (AUC) of 0.541-0.684 with a mean AUC of 0.620 (95%CI: 0.489-0.751). Performances improved for lesions smaller than 2 cm (AUG: 0.676 vs 0.500; p = 0.002). The exclusion of microinvasive cases also improved performance (AUG: 0.651 vs 0.620; p = 0.005). There was no difference in performance based on breast density (p = 0.850) or nuclear grade (p = 0.270)Conclusion: Radiologists were able to predict invasive disease better than chance, particularly for smaller DCIS lesions (