Pancreatic Ductal Adenocarcinoma: Machine Learning–Based Quantit

Wenli Qiu,¹ Na Duan,¹ Xiao Chen,¹ Shuai Ren,¹ Yifen Zhang,² Zhongqiu Wang,¹ Rong Chen³

¹Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; ²Department of Pathology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; ³Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

Correspondence: Zhongqiu Wang
Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong Road, Nanjing 210029, Jiangsu Province, People’s Republic of China
Tel +86 25 8661 8472
Fax +86 25 8661 8139
Email zhq200105@sina.com

Purpose: To assess the performance of combining computed tomography (CT) texture analysis with machine learning for discriminating different histopathological grades of pancreatic ductal adenocarcinoma (PDAC).
Methods: 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.
Results: 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 (P>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.
Conclusion: 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.

Keywords: computed tomography, texture analysis, machine learning, pancreatic ductal adenocarcinoma, histopathological grade