Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and
- PDF / 1,073,578 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 45 Downloads / 163 Views
ORIGINAL ARTICLE
Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast‑enhanced computed tomography and apparent diffusion coefficient maps Kazuyoshi Ohki1 · Takao Igarashi1 · Hirokazu Ashida1 · Shinsuke Takenaga1 · Megumi Shiraishi1 · Yosuke Nozawa1 · Hiroya Ojiri1 Received: 10 June 2020 / Accepted: 21 August 2020 © Japan Radiological Society 2020
Abstract Purpose To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2–3) in patients with pancreatic neuroendocrine tumor (PNET). Materials and methods Thirty-three PNETs (22 G1 and 11 G2–3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs). Results G2–G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively. Conclusion Texture analysis may aid in differentiating between G1 and G2–3 PNET. Keywords CT · MR-imaging · Pancreas · Neuroendocrine neoplasm · Texture analysis
Introduction * Kazuyoshi Ohki ms99‑[email protected] Takao Igarashi igarashi‑[email protected] Hirokazu Ashida [email protected] Shinsuke Takenaga [email protected] Megumi Shiraishi [email protected] Yosuke Nozawa [email protected] Hiroya Ojiri [email protected] 1
Department of Radiology, The Jikei University School of Medicine, 3‑25‑8, Nishi‑Shimbashi, Minato‑ku, Tokyo, Japan
Pancreatic neuroendocrine neoplasm (PNEN) is rare and originates from ductal pluripotent stem cells [1, 2]. It has a prevalence of approximately 1 in 100,000 people and accounts for 1–2% of all pancreatic neoplasms [2]. According to the 2017 World Health Organization (WHO) classification system, PNEN includes well-differentiated PNEN, which is called pancreatic neuroendocrine tumor (PNET), and poorly differentiated PNEN, which is called pancreatic neuroendocrine carcinoma (PNEC). Based on its proliferative activity, PNET is classified into grade 1, 2, and 3 (G1, 2, and 3, respectively) according to their mitotic count and Ki-67 index determined by the MIB-1 monoclonal antibody [3, 4]. G3 tumors include G3 PNET and PNEC. According to the 8th edition of the American Joint Committee on Cancer staging manual, the TNM staging for these tumors has
Data Loading...
