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Nipple-sparing mastectomy: external validation of a three-dimensional automated method to predict nipple occult tumour involvement on preoperative breast MRI.
European Radiology Experimental Pub Date : 2019-08-07 , DOI: 10.1186/s41747-019-0108-3 Marta D'Alonzo 1 , Laura Martincich 2 , Agnese Fenoglio 1 , Valentina Giannini 2, 3 , Lisa Cellini 4 , Viola Liberale 1 , Nicoletta Biglia 1
中文翻译:
保留乳头的乳房切除术:三维自动化方法的外部验证,以预测术前乳腺MRI上是否存在乳头隐匿性肿瘤。
更新日期:2019-08-07
European Radiology Experimental Pub Date : 2019-08-07 , DOI: 10.1186/s41747-019-0108-3 Marta D'Alonzo 1 , Laura Martincich 2 , Agnese Fenoglio 1 , Valentina Giannini 2, 3 , Lisa Cellini 4 , Viola Liberale 1 , Nicoletta Biglia 1
Affiliation
Background
Preoperative evaluation of nipple-areola complex (NAC) tumour involvement is crucial to select patients candidates for nipple-sparing mastectomy. Our aim was to validate a previously developed automated method able to compute the three-dimensional (3D) tumour-to-NAC distance (the most predictive parameter of nipple involvement), using magnetic resonance imaging (MRI) datasets acquired with a scanner and protocol different from those of the development phase.Methods
We performed a retrospective analysis of 77 patients submitted to total mastectomy and preoperatively studied with MRI. The new method consisted of automated segmentation of both NAC and tumour and subsequent computation of the 3D distance between them; standard manual two-dimensional segmentation was independently performed. Paraffin-embedded section examination of the removed NAC was performed to identify the neoplastic involvement. The ability of both methods to discriminate between patients with and without NAC involvement was compared using receiver operating characteristic (ROC) analysis.Results
The 3D tumour-to-NAC distance was correctly computed for 72/77 patients (93.5%); tumour and NAC segmentation method failed in two and three cases, respectively. The diagnostic performance of the 3D automated method at best cut-off values was consistently better than that of the 2D manual method (sensitivity 78.3%, specificity 71.4%, positive predictive value 87.5%, negative predictive value 56.3%, and AUC 0.77 versus 73.9%, 61.2%, 47.2%, 83.3%, and 0.72, respectively), even if the difference did not reach statistical significance (p = 0.431).Conclusions
The introduction of the 3D automated method in a clinical setting could improve the diagnostic performance in the preoperative assessment of NAC tumour involvement.中文翻译:
保留乳头的乳房切除术:三维自动化方法的外部验证,以预测术前乳腺MRI上是否存在乳头隐匿性肿瘤。