The Value of Whole-Tumor Histogram and Texture Analysis Using Intravoxel Incoherent Motion in Differentiating Pathologic Subtypes of Locally Advanced Gastric Cancer.
The Value of Whole-Tumor Histogram and Texture Analysis Using Intravoxel Incoherent Motion in Differentiating Pathologic Subtypes of Locally Advanced Gastric Cancer.
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使用体素内不相干运动的全肿瘤直方图和纹理分析在区分局部晚期胃癌病理亚型中的价值
DOI：
10.3389/fonc.2022.821586
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发表时间：
2022
影响因子：
4.7
通讯作者：
Peng WJ
中科院分区：
文献类型：
--
作者：
Li HH;Sun B;Tan C;Li R;Fu CX;Grimm R;Zhu H;Peng WJ
确定使用体素内不相干运动 (IVIM) 参数值进行全肿瘤直方图和纹理分析是否可以区分局部晚期胃癌的病理特征。回顾性纳入2017年4月至2018年12月期间在我机构接受手术的80例经组织学证实的局部进展期胃癌患者。如果患者病灶最小直径<5 mm且图像伪影严重，则被排除。 MR 扫描包括治疗前所有患者使用的 IVIM 序列（9 b 值、0、20、40、60、100、150,200、500 和 800 s/mm2）。通过在弥散加权成像（DWI）图像（b=800）的每个切片上手动绘制病灶轮廓来分割整个肿瘤。基于全肿瘤体积分析测量 IVIM 参数值和表观扩散系数 (ADC) 值的直方图和纹理指标。然后，使用单变量分析在高分化、中分化和低分化肿瘤之间以及不同 Lauren 分类、印戒细胞癌和其他低粘性癌之间比较所有 24 个提取的指标。使用多变量逻辑分析和多重共线性检验从单变量分析的显着变量中识别独立影响因素，以区分肿瘤分化和Lauren分类。进行ROC曲线分析以评估这些独立影响因素的诊断性能，以确定肿瘤分化和劳伦分类以及识别印戒细胞癌。两个观察者之间还就图像质量评估和参数度量测量进行了观察者间协议。对于诊断肿瘤分化，ADC中值、纯扩散系数中值（Dslowmedian）和纯扩散系数熵（Dslowentropy）显示出最大的AUC：分别为0.937、0.948和0.850，并且三个指标之间没有发现差异，P>0.05）。第 95 个百分位数灌注因子 (FP P95th) 是区分弥漫型 GC 与肠/混合型 GC 的最佳指标 (AUC=0.896)。用于区分印戒细胞癌和其他粘性较差的癌的 ROC 曲线显示，Dslowmedian 的 AUC 为 0.738。对于观察者间的可靠性，图像质量评估表现出极好的一致性（组间相关系数 [ICC]=0.85）；所有参数的指标测量均显示出良好到极好的一致性（ICC=0.65-0.89），但 Dfast 指标除外，其表现出中等一致性（ICC=0.41-0.60）。基于双指数模型的全肿瘤直方图和IVIM参数纹理分析为局部晚期胃癌患者术前区分病理肿瘤亚型提供了一种无创方法。从 IVIM 导出的度量 FP P95th 在确定 Lauren 分类方面比单指数模型表现更好。
To determine if whole-tumor histogram and texture analyses using intravoxel incoherent motion (IVIM) parameters values could differentiate the pathologic characteristics of locally advanced gastric cancer. Eighty patients with histologically confirmed locally advanced gastric cancer who received surgery in our institution were retrospectively enrolled into our study between April 2017 and December 2018. Patients were excluded if they had lesions with the smallest diameter < 5 mm and severe image artifacts. MR scanning included IVIM sequences (9 b values, 0, 20, 40, 60, 100, 150,200, 500, and 800 s/mm2) used in all patients before treatment. Whole tumors were segmented by manually drawing the lesion contours on each slice of the diffusion-weighted imaging (DWI) images (with b=800). Histogram and texture metrics for IVIM parameters values and apparent diffusion coefficient (ADC) values were measured based on whole-tumor volume analyses. Then, all 24 extracted metrics were compared between well, moderately, and poorly differentiated tumors, and between different Lauren classifications, signet-ring cell carcinomas, and other poorly cohesive carcinomas using univariate analyses. Multivariate logistic analyses and multicollinear tests were used to identify independent influencing factors from the significant variables of the univariate analyses to distinguish tumor differentiation and Lauren classifications. ROC curve analyses were performed to evaluate the diagnostic performance of these independent influencing factors for determining tumor differentiation and Lauren classifications and identifying signet-ring cell carcinomas. The interobserver agreement was also conducted between the two observers for image quality evaluations and parameter metric measurements. For diagnosing tumor differentiation, the ADCmedian, pure diffusion coefficient median (Dslowmedian), and pure diffusion coefficient entropy (Dslowentropy) showed the greatest AUCs: 0.937, 0.948, and 0.850, respectively, and no differences were found between the three metrics, P>0.05). The 95th percentile perfusion factor (FP P95th) was the best metric to distinguish diffuse-type GCs vs. intestinal/mixed (AUC=0.896). The ROC curve to distinguish signet-ring cell carcinomas from other poorly cohesive carcinomas showed that the Dslowmedian had AUC of 0.738. For interobserver reliability, image quality evaluations showed excellent agreement (interclass correlation coefficient [ICC]=0.85); metrics measurements of all parameters indicated good to excellent agreement (ICC=0.65-0.89), except for the Dfast metric, which showed moderate agreement (ICC=0.41-0.60). The whole-tumor histogram and texture analyses of the IVIM parameters based on the biexponential model provided a non-invasive method to discriminate pathologic tumor subtypes preoperatively in patients with locally advanced gastric cancer. The metric FP P95th derived from IVIM performed better in determining Lauren classifications than the mono-exponential model.
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影响因子：
3.9
作者：
Mocan L
通讯作者：
Mocan L
DOI：
10.1002/cac2.12197
发表时间：
2021-10
期刊：
Cancer communications (London, England)
影响因子：
--
作者：
Qiu H;Cao S;Xu R
通讯作者：
Xu R
影响因子：
3.5
作者：
Kim, H. S.;Suh, C. H.;Kim, S. J.
通讯作者：
Kim, S. J.
影响因子：
1.1
作者：
Alshehri, Abdulaziz;Alanezi, Hussain;Kim, Beom Su
通讯作者：
Kim, Beom Su
影响因子：
3.3
作者：
Giganti, Francesco;Marra, Paolo;De Cobelli, Francesco
通讯作者：
De Cobelli, Francesco