多参数 MRI 全肿瘤直方图分析鉴别肺癌脑转移组织学亚型：与 Ki-67 增殖指数的关系
Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index.
机构信息
Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, Gansu, 730030, People's Republic of China.
Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.
出版信息
Neurosurg Rev. 2023 Sep 2;46(1):218. doi: 10.1007/s10143-023-02129-7.
This study aims to investigate the predictive value of preoperative whole-tumor histogram analysis of multi-parametric MRI for histological subtypes in patients with lung cancer brain metastases (BMs) and explore the correlation between histogram parameters and Ki-67 proliferation index. The preoperative MRI data of 95 lung cancer BM lesions obtained from 73 patients (42 men and 31 women) were retrospectively analyzed. Multi-parametric MRI histogram was used to distinguish small-cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC), and adenocarcinoma (AC) from squamous cell carcinoma (SCC), respectively. The T1-weighted contrast-enhanced (T1C) and apparent diffusion coefficient (ADC) histogram parameters of the volumes of interest (VOIs) in all BMs lesions were extracted using FireVoxel software. The following histogram parameters were obtained: maximum, minimum, mean, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, entropy, and 1st-99th percentiles. Then investigated their relationship with the Ki-67 proliferation index. The skewness-T1C, kurtosis-T1C, minimum-ADC, mean-ADC, CV-ADC and 1st - 90th ADC percentiles were significantly different between the SCLC and NSCLC groups (all p < 0.05). When the 10th-ADC percentile was 668, the sensitivity, specificity, and accuracy (90.80%, 76.70% and 86.32%, respectively) for distinguishing SCLC from NSCLC reached their maximum values, with an AUC of 0.895 (0.824 - 0.966). Mean-T1C, CV-T1C, skewness-T1C, 1st - 50th T1C percentiles, maximum-ADC, SD-ADC, variance-ADC and 75th - 99th ADC percentiles were significantly different between the AC and SCC groups (all p < 0.05). When the CV-T1C percentiles was 3.13, the sensitivity, specificity and accuracy (75.00%, 75.60% and 75.38%, respectively) for distinguishing AC and SCC reached their maximum values, with an AUC of 0.829 (0.728-0.929). The 5th-ADC and 10th-ADC percentiles were strongly correlated with the Ki-67 proliferation index in BMs. Multi-parametric MRI histogram parameters can be used to identify the histological subtypes of lung cancer BMs and predict the Ki-67 proliferation index.
本研究旨在探讨多参数 MRI 术前全肿瘤直方图分析对肺癌脑转移瘤（BM）组织学亚型的预测价值，并探讨直方图参数与 Ki-67 增殖指数的相关性。回顾性分析了 73 例（42 例男性，31 例女性）肺癌 BM 病变患者的术前 MRI 数据。多参数 MRI 直方图分别用于区分小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC），以及腺癌（AC）和鳞状细胞癌（SCC）。使用 FireVoxel 软件提取所有 BM 病变的感兴趣容积（VOI）的 T1 加权对比增强（T1C）和表观扩散系数（ADC）直方图参数。获得以下直方图参数：最大值、最小值、平均值、标准差（SD）、方差、变异系数（CV）、偏度、峰度、熵和 1-99 百分位数。然后研究它们与 Ki-67 增殖指数的关系。SCLC 和 NSCLC 组之间的斜度-T1C、峰度-T1C、最小 ADC、平均 ADC、CV-ADC 和 1-90 ADC 百分位数差异均有统计学意义（均 P<0.05）。当第 10 ADC 百分位数为 668 时，SCLC 与 NSCLC 鉴别诊断的敏感性、特异性和准确性（90.80%、76.70%和 86.32%）达到最大值，AUC 为 0.895（0.824-0.966）。T1C 的均值、CV-T1C、斜度-T1C、第 1-50 T1C 百分位数、最大 ADC、SD-ADC、方差-ADC 和第 75-99 ADC 百分位数在 AC 和 SCC 组之间差异均有统计学意义（均 P<0.05）。当 CV-T1C 百分位数为 3.13 时，AC 和 SCC 鉴别诊断的敏感性、特异性和准确性（75.00%、75.60%和 75.38%）达到最大值，AUC 为 0.829（0.728-0.929）。第 5 ADC 和第 10 ADC 百分位数与 BM 中的 Ki-67 增殖指数呈强相关。多参数 MRI 直方图参数可用于识别肺癌 BM 的组织学亚型，并预测 Ki-67 增殖指数。
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