Tagliafico Alberto Stefano, Calabrese Massimo, Brunetti Nicole, Garlaschi Alessandro, Tosto Simona, Rescinito Giuseppe, Zoppoli Gabriele, Piana Michele, Campi Cristina
Dipartimento di Radiodiagnostica, IRCCS-Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, Italy.
Dipartimento di Scienze della Salute (DISSAL), Università di Genova, Via L.B. Alberti 2, 16132 Genova, Italy.
Diagnostics (Basel). 2023 Mar 7;13(6):1007. doi: 10.3390/diagnostics13061007.
Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant values (<0.05): (1) 'AverageIntensity', (2) 'Autocorrelation', (3) 'Contrast', (4) 'Compactness', (5) 'StandardDeviation', (6) 'MeanAbsoluteDeviation' and (7) 'InterquartileRange'. AUC values of 0.86 (95% C.I. 0.73-0.94) for the training set and 0.73 (95% C.I. 0.54-0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28-0.57) for the training set and 0.4 (0.23-0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.
放射组学和人工智能在乳腺磁共振成像(MRI)中的应用越来越广泛。然而,利用放射组学评估适合磁共振引导下真空辅助乳腺活检(MR-VABB)的病变的优势尚不清楚。本研究纳入了计划进行MR-VABB的患者,即仅在MRI上可见病变且二次超声检查为阴性的受试者。选择多期动态对比增强MRI(DCE-MRI)序列的首次采集进行图像分割和放射组学分析。共纳入80例平均年龄为55.8岁±11.8(标准差)的患者。然后将数据集分为训练集(50例患者)和验证集(30例患者)。30例癌症组织学检查阳性的患者中有20例在训练集,其余10例组织学检查阳性的患者纳入测试集。对训练集进行逻辑回归分析得到7个具有显著p值(<0.05)的特征:(1)“平均强度”,(2)“自相关”,(3)“对比度”,(4)“紧密度”,(5)“标准差”,(6)“平均绝对偏差”和(7)“四分位间距”。放射组学模型在训练集的AUC值为0.86(95%置信区间0.73-0.94),在测试集的AUC值为0.73(95%置信区间0.54-0.87)。对计划进行MR-VABB的相同病变进行放射学评估,训练集的AUC值为0.42(95%置信区间0.28-0.57),测试集的AUC值为0.4(0.23-0.59)。在本研究中,应用于DCE-MRI图像的放射组学逻辑回归模型提高了对计划进行MR-VABB的女性MRI可疑发现的标准放射学评估的诊断准确性。在大型多中心试验中证实这一性能将意味着,在需要MR-VABB的可疑乳腺病变中,使用放射组学评估计划进行MR-VABB的患者有可能减少活检次数,这对患者和医疗资源具有明显优势。