Pancreatic Ductal Adenocarcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis For Prediction Of Histopathological Grade

• Published in: Cancer management and research 2019-01, Vol.11, p.9253-9264
• Main Authors: Qiu, Wenli, Duan, Na, Chen, Xiao, Ren, Shuai, Zhang, Yifen, Wang, Zhongqiu, Chen, Rong
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Artificial intelligence; Cancer therapies; CAT scans; Chemotherapy; computed tomography; Diagnostic imaging; histopathological grade; Hospitals; Kidney cancer; Machine learning; Medical prognosis; Medical research; Medicine; Original Research; Pancreatic cancer; pancreatic ductal adenocarcinoma; Performance evaluation; Prognosis; Surgery; texture analysis; Tomography; Tumors; Women
• ISSN: 1179-1322; 1179-1322
• DOI: 10.2147/CMAR.S218414

To assess the performance of combining computed tomography (CT) texture analysis with machine learning for discriminating different histopathological grades of pancreatic ductal adenocarcinoma (PDAC). From July 2012 to August 2017, this retrospective study comprised 56 patients with confirmed histopathological PDAC (32 men, 24 women, mean age 64.04±7.82 years) who had undergone preoperative contrast-enhanced CT imaging within 1 month before surgery. Two radiologists blinded to the histopathological outcome independently segmented lesions for quantitative texture analysis. Histogram features, co-occurrence, and run-length texture were calculated. A support-vector machine was constructed to predict the pathological grade of PDAC based on preoperative texture features. Pathological analysis confirmed 37 low-grade PDAC (five well-differentiated/grade I and 32 moderately differentiated/grade II) and 19 high-grade PDAC (19 poorly differentiated/grade III) tumors. There were no significant differences in clinical or biological characteristics between patients with high-grade and low-grade tumors ( >0.05). There were significant differences between low-grade PDAC and high-grade PDAC on nine histogram features, seven run-length features, and two co-occurrence features. Cluster shade was the most important predictor (sensitivity 0.315). Using these texture features, the support-vector machine achieved 86% accuracy, 78% sensitivity, 95% and specificity. Machine learning-based CT texture analysis accurately predicted histopathological differentiation grade of PDAC based on preoperative texture features, leading to maximization patient survival and achievement of personalized precision treatment.