Department of Radiology, Tepecik Training and Research Hospital, Turkey.
Department of Pathology, Tepecik Training and Research Hospital, Turkey.
Neuroradiol J. 2022 Jun;35(3):370-377. doi: 10.1177/19714009211049082. Epub 2021 Oct 5.
A fast, reliable and non-invasive method is required in differentiating brain metastases (BMs) originating from lung cancer (LC) and breast cancer (BC). The aims of this study were to assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating BMs originated from LC and BC, and then to investigate further the association of ADC histogram parameters with Ki-67 index in BMs.
A total of 55 patients (LC, = 40; BC, = 15) with BMs histopathologically confirmed were enrolled in the study. The LC group was divided into small-cell lung cancer (SCLC; = 15) and non-small-cell lung cancer (NSCLC; = 25) groups. ADC histogram parameters (ADC, ADC, ADC, ADC, ADC, ADC, ADC and ADC, skewness, kurtosis and entropy) were derived from ADC maps. Mann-Whitney -test, independent samples -test, receiver operating characteristic (ROC) analysis and Spearman correlation analysis were used for statistical assessment.
ADC histogram parameters did not show significant differences between LC and BC groups ( > 0.05). Subgroup analysis showed that various ADC histogram parameters were found to be statistically lower in the SCLC group compared to the NSCLC and BC groups ( < 0.05). ROC analysis showed that ADC and ADC for differentiating SCLC BMs from NSCLC, and ADC for differentiating SCLC BMs from BC achieved optimal diagnostic performances. Various histogram parameters were found to be significantly correlated with Ki-67 ( < 0.05).
Histogram analysis of ADC maps may reflect tumoural proliferation potential in BMs and can be useful in differentiating SCLC BMs from NSCLC and BC BMs.
需要一种快速、可靠和非侵入性的方法来区分肺癌(LC)和乳腺癌(BC)起源的脑转移瘤(BMs)。本研究旨在评估表观扩散系数(ADC)图直方图分析在区分 LC 和 BC 起源的 BMs 中的作用,并进一步研究 ADC 直方图参数与 BMs 中 Ki-67 指数的相关性。
共纳入 55 例经组织病理学证实为 BMs 的患者(LC 组=40 例;BC 组=15 例)。LC 组分为小细胞肺癌(SCLC;=15 例)和非小细胞肺癌(NSCLC;=25 例)组。从 ADC 图中提取 ADC 直方图参数(ADC、ADC、ADC、ADC、ADC、ADC、ADC 和 ADC、偏度、峰度和熵)。采用 Mann-Whitney -检验、独立样本 -检验、受试者工作特征(ROC)分析和 Spearman 相关分析进行统计学评估。
LC 和 BC 组之间的 ADC 直方图参数无显著差异(>0.05)。亚组分析显示,与 NSCLC 和 BC 组相比,SCLC 组的各种 ADC 直方图参数均显著降低(<0.05)。ROC 分析显示,ADC 和 ADC 可用于区分 SCLC BMs 与 NSCLC,ADC 可用于区分 SCLC BMs 与 BC,具有最佳的诊断性能。各种直方图参数与 Ki-67 均呈显著相关(<0.05)。
ADC 图直方图分析可反映 BMs 中的肿瘤增殖潜能,有助于区分 SCLC BMs 与 NSCLC 和 BC BMs。