Radiology Unit, "Dipartimento di Supporto ai Percorsi Oncologici Area Diagnostica, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale", Via Mariano Semmola, Naples, Italy.
Department of Electrical Engineering and Information Technologies, University "Federico II" of Naples, Via Claudio, Naples, Italy.
Biomed Res Int. 2018 May 30;2018:2610801. doi: 10.1155/2018/2610801. eCollection 2018.
Axillary lymph-node assessment is considered one of the most important prognostic factors concerning breast cancer survival.
We investigated the discriminative power of morphological and functional features in assessing the axillary lymph node.
We retrospectively analysed data from 52 consecutive patients who undergone DCE-MRI and were diagnosed with primary breast carcinoma: 94 lymph nodes were identified. Per each lymph node, we extracted morphological features: circularity, compactness, convexity, curvature, elongation, diameter, eccentricity, irregularity, radial length, entropy, rectangularity, roughness, smoothness, sphericity, spiculation, surface, and volume. Moreover, we extracted functional features: time to peak (TTP), maximum signal difference (MSD), wash-in intercept (WII), wash-out intercept (WOI), wash-in slope (WIS), wash-out slope (WOS), area under gadolinium curve (AUGC), area under wash-in (AUWI), and area under wash-out (AUWO). Selection of important features in predicting metastasis has been done by means of receiver operating characteristic (ROC) analysis. Performance of linear discriminant analysis was analysed.
All morphological features but circularity showed a significant difference between median values of metastatic lymph nodes group and nonmetastatic lymph nodes group. All dynamic parameters except for MSD and WOS showed a statistically significant difference between median values of metastatic lymph nodes group and nonmetastatic lymph nodes group. Best results for discrimination of metastatic and nonmetastatic lymph nodes were obtained by AUGC (accuracy 75.8%), WIS (accuracy 71.0%), WOS (accuracy 71.0%), and AUCWO (accuracy 72.6%) for dynamic features and by compactness (accuracy 82.3%), curvature (accuracy 71.0%), radial length (accuracy 71.0%), roughness (accuracy 74.2%), smoothness (accuracy 77.2%), and speculation (accuracy 72.6%) for morphological features. Linear combination of all morphological and/or of all dynamic features did not increase accuracy in metastatic lymph nodes discrimination.
Compactness as morphological feature and area under time-intensity curve as dynamic feature were the best parameters in identifying metastatic lymph nodes on breast MRI.
腋窝淋巴结评估被认为是与乳腺癌生存相关的最重要的预后因素之一。
我们研究了形态和功能特征在评估腋窝淋巴结中的鉴别能力。
我们回顾性分析了 52 例连续接受 DCE-MRI 检查并被诊断为原发性乳腺癌的患者的数据：共识别出 94 个淋巴结。对于每个淋巴结，我们提取形态特征：圆形度、紧致度、凸度、曲率、伸长率、直径、偏心率、不规则性、径向长度、熵、矩形度、粗糙度、平滑度、球形度、刺状度、表面和体积。此外，我们还提取了功能特征：达峰时间（TTP）、最大信号差（MSD）、灌洗截距（WII）、洗脱截距（WOI）、灌洗斜率（WIS）、洗脱斜率（WOS）、钆曲线下面积（AUGC）、灌洗下面积（AUWI）和洗脱下面积（AUWO）。通过受试者工作特征（ROC）分析选择预测转移的重要特征。分析了线性判别分析的性能。
除圆形度外，所有形态特征的中位数在转移性淋巴结组和非转移性淋巴结组之间均有显著差异。除 MSD 和 WOS 外，所有动态参数的中位数在转移性淋巴结组和非转移性淋巴结组之间均有统计学差异。用于区分转移性和非转移性淋巴结的最佳结果是通过 AUGC（准确性 75.8%）、WIS（准确性 71.0%）、WOS（准确性 71.0%）和 AUCWO（准确性 72.6%）获得的动态特征，以及通过紧凑度（准确性 82.3%）、曲率（准确性 71.0%）、径向长度（准确性 71.0%）、粗糙度（准确性 74.2%）、平滑度（准确性 77.2%）和推测（准确性 72.6%）获得的形态特征。所有形态和/或所有动态特征的线性组合并不能提高转移性淋巴结鉴别诊断的准确性。
形态学特征中的紧凑度和动力学特征中的时间-强度曲线下面积是在乳腺 MRI 上识别转移性淋巴结的最佳参数。
