■乳腺癌(BC)是女性癌症相关死亡率的主要原因之一。为了临床管理,以帮助患者存活更长时间,花费更少的时间在治疗上,早期和精确的癌症识别和鉴别乳腺病变是至关重要的。探讨动态对比增强磁共振成像(DCEMRI)影像组学特征(RF)对浸润性导管癌(IDC)与浸润性小叶癌(ILC)鉴别诊断的准确性。
■这是一项回顾性研究。Dukes乳腺癌MRI数据集的30例IDC和28例患者的ILC癌症影像档案(TCIA),包括在内。RF类别,如基于形状,灰度相关矩阵(GLDM),灰度共生矩阵(GLCM),第一顺序,灰度级游程长度矩阵(GLRLM),灰度大小区域矩阵(GLSZM),使用3D切片器从DCE-MRI序列中提取NGTDM(相邻灰度差矩阵)。使用GoogleColab应用最大相关性和最小冗余(mRMR)来识别前十五个相关放射学特征。进行Mann-WhitneyU检验以鉴定用于区分IDC和ILC的显著RF。进行接收器工作特性(ROC)曲线分析以确定RF在区分IDC和ILC方面的准确性。
■我们研究中使用的十个基于DCEMRI的RF在IDC和ILC之间显示出显着差异(p<0.001)。我们注意到DCERF,如灰度游程长度矩阵(GLRLM)灰度方差(灵敏度(SN)97.21%,特异性(SP)96.2%,曲线下面积(AUC)0.998),灰度共生矩阵(GLCM)差异平均值(SN95.72%,SP96.34%,AUC0.983),GLCM四分位数间距(SN95.24%,SP97.31%,AUC0.968),具有最强的区分IDC和ILC的能力。
■来自DCE序列的基于MRI的RF可用于临床环境,以区分乳腺的恶性病变,例如IDC和ILC,不需要侵入性程序。
UNASSIGNED: Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC).
UNASSIGNED: This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC.
UNASSIGNED: Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC.
UNASSIGNED: MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.