Di Segni Mattia, de Soccio Valeria, Cantisani Vito, Bonito Giacomo, Rubini Antonello, Di Segni Gabriele, Lamorte Sveva, Magri Valentina, De Vito Corrado, Migliara Giuseppe, Bartolotta Tommaso Vincenzo, Metere Alessio, Giacomelli Laura, de Felice Carlo, D'Ambrosio Ferdinando
U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy.
Arakne S.r.l., Via Edoardo d'Onofrio, 304, 00155, Rome, RM, Italy.
J Ultrasound. 2018 Jun;21(2):105-118. doi: 10.1007/s40477-018-0297-2. Epub 2018 Apr 21.
To assess the diagnostic performance and the potential as a teaching tool of S-detect in the assessment of focal breast lesions.
61 patients (age 21-84 years) with benign breast lesions in follow-up or candidate to pathological sampling or with suspicious lesions candidate to biopsy were enrolled. The study was based on a prospective and on a retrospective phase. In the prospective phase, after completion of baseline US by an experienced breast radiologist and S-detect assessment, 5 operators with different experience and dedication to breast radiology performed elastographic exams. In the retrospective phase, the 5 operators performed a retrospective assessment and categorized lesions with BI-RADS 2013 lexicon. Integration of S-detect to in-training operators evaluations was performed by giving priority to S-detect analysis in case of disagreement. 2 × 2 contingency tables and ROC analysis were used to assess the diagnostic performances; inter-rater agreement was measured with Cohen's k; Bonferroni's test was used to compare performances. A significance threshold of p = 0.05 was adopted.
All operators showed sensitivity > 90% and varying specificity (50-75%); S-detect showed sensitivity > 90 and 70.8% specificity, with inter-rater agreement ranging from moderate to good. Lower specificities were improved by the addition of S-detect. The addition of elastography did not lead to any improvement of the diagnostic performance.
S-detect is a feasible tool for the characterization of breast lesions; it has a potential as a teaching tool for the less experienced operators.
评估S-detect在乳腺局灶性病变评估中的诊断性能及其作为教学工具的潜力。
纳入61例年龄在21 - 84岁之间的患者,这些患者患有需随访的良性乳腺病变、或为病理采样候选者、或有可疑病变需活检。该研究基于前瞻性和回顾性两个阶段。在前瞻性阶段,由一位经验丰富的乳腺放射科医生完成基线超声检查和S-detect评估后,5名具有不同乳腺放射学经验和投入程度的操作人员进行弹性成像检查。在回顾性阶段,这5名操作人员进行回顾性评估,并根据2013版BI-RADS词典对病变进行分类。在意见不一致的情况下,优先进行S-detect分析,从而将S-detect整合到培训中的操作人员评估中。使用2×2列联表和ROC分析来评估诊断性能;使用Cohen's k来衡量评分者间的一致性;使用Bonferroni检验来比较性能。采用p = 0.05的显著性阈值。
所有操作人员的敏感性均> 90%,特异性各不相同(50 - 75%);S-detect的敏感性> 90%,特异性为70.8%,评分者间的一致性从中度到良好不等。通过添加S-detect,较低的特异性得到了改善。添加弹性成像并未导致诊断性能的任何提高。
S-detect是一种用于乳腺病变特征化的可行工具;它有潜力成为经验不足的操作人员的教学工具。