Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue
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DOI：
10.2214/ajr.18.20901
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发表时间：
2019-08-01
影响因子：
5
通讯作者：
Fishman, Elliot K.
中科院分区：
文献类型：
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作者：
Chu, Linda C.;Park, Seyoun;Fishman, Elliot K.
OBJECTIVE.本研究的目的是确定放射组学特征在鉴别胰腺导管腺癌（PDAC）和正常胰腺CT病例中的应用。在这项回顾性病例对照研究中，190例PDAC患者2012年至2017年（97名男性，93名女性;平均年龄+/- SD，66 +/- 9岁）和190名健康潜在肾脏捐献者从放射学和病理学数据库中确定了2005年至2009年期间没有已知胰腺疾病的患者（96名男性，94名女性;平均年龄+/- SD，52 +/- 8岁）。胰腺的3D体积由四名训练有素的研究人员从术前CT扫描中手动分割，并由三名腹部放射科医生进行验证。提取了478个放射组学特征来表达胰腺的表型。由于计算特征的冗余性，选择了40个特征进行分析。数据集分为255个训练样本（125个正常对照样本和130个PDAC样本）和125个验证样本（65个正常对照样本和60个PDAC样本）。使用随机森林分类器对对照病例的PDAC与正常胰腺进行二元分类。计算准确度、灵敏度和特异性。平均肿瘤大小为4.1 ± 1.7（SD）cm。随机森林二元分类的总体准确率为99.2%（124/125），AUC为99.9%。所有PDAC病例（60/60）均得到正确分类。1例来自肾脏供体的病例被错误分类为PDAC（1/65）。敏感性为100%，特异性为98. 5%。从整个胰腺提取的放射组学特征可用于区分来自PDAC患者的CT病例和具有正常胰腺的健康对照受试者。
OBJECTIVE. The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas.MATERIALS AND METHODS. In this retrospective case-control study, 190 patients with PDAC (97 men, 93 women; mean age +/- SD, 66 +/- 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; mean age +/- SD, 52 +/- 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. The 3D volume of the pancreas was manually segmented from the preoperative CT scans by four trained researchers and verified by three abdominal radiologists. Four hundred seventy-eight radiomics features were extracted to express the phenotype of the pancreas. Forty features were selected for analysis because of redundancy of computed features. The dataset was divided into 255 training cases (125 normal control cases and 130 PDAC cases) and 125 validation cases (65 normal control cases and 60 PDAC cases). A random forest classifier was used for binary classification of PDAC versus normal pancreas of control cases. Accuracy, sensitivity, and specificity were calculated.RESULTS. Mean tumor size was 4.1 +/- 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.