Differentiation of gastric schwannomas from gastrointestinal stromal tumors by CT using machine learning

[1]  Zhuoli Zhang,et al.  Gastrointestinal stromal tumor risk classification: spectral CT quantitative parameters , 2019, Abdominal Radiology.

[2]  B. Liu,et al.  Spectral Computed Tomography Imaging of Gastric Schwannoma and Gastric Stromal Tumor , 2017, Journal of computer assisted tomography.

[3]  Yin Li,et al.  Differentiation between gastrointestinal schwannomas and gastrointestinal stromal tumors by computed tomography. , 2017, Oncology letters.

[4]  Christopher. Simons,et al.  Machine learning with Python , 2017 .

[5]  Meidong Xu,et al.  Endoscopic resection for gastric schwannoma with long-term outcomes , 2016, Surgical Endoscopy.

[6]  Huijun Hu,et al.  Predictive features of CT for risk stratifications in patients with primary gastrointestinal stromal tumour , 2016, European Radiology.

[7]  Apurva S. Shah,et al.  Gastric Schwannoma: A Benign Tumor Often Misdiagnosed as Gastrointestinal Stromal Tumor , 2015, Clinics and practice.

[8]  J. Ji,et al.  Gastric schwannoma: CT findings and clinicopathologic correlation , 2015, Abdominal Imaging.

[9]  B. Choi,et al.  Differentiation of large (≥ 5 cm) gastrointestinal stromal tumors from benign subepithelial tumors in the stomach: radiologists' performance using CT. , 2014, European journal of radiology.

[10]  M. Linch,et al.  Update on imatinib for gastrointestinal stromal tumors: duration of treatment , 2013, OncoTargets and therapy.

[11]  S. Atmatzidis,et al.  Gastric schwannoma: a case report and literature review. , 2012, Hippokratia.

[12]  Kyoung-Mee Kim,et al.  Small Submucosal Tumors of the Stomach: Differentiation of Gastric Schwannoma from Gastrointestinal Stromal Tumor with CT , 2012, Korean journal of radiology.

[13]  S. Diederich,et al.  Consensus report on the radiological management of patients with gastrointestinal stromal tumours (GIST): recommendations of the German GIST Imaging Working Group , 2012, Cancer imaging : the official publication of the International Cancer Imaging Society.

[14]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[15]  P. Kim,et al.  Gastric schwannomas: radiological features with endoscopic and pathological correlation. , 2008, Clinical radiology.

[16]  David S. Wishart,et al.  Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.

[17]  H. Joensuu Sunitinib for imatinib-resistant GIST , 2006, The Lancet.

[18]  J. Lasota,et al.  Gastrointestinal stromal tumors: pathology and prognosis at different sites. , 2006, Seminars in diagnostic pathology.

[19]  L. Sobin,et al.  Gastrointestinal schwannomas: CT features with clinicopathologic correlation. , 2005, AJR. American journal of roentgenology.

[20]  C. Tzen,et al.  Analysis of CD117-negative gastrointestinal stromal tumors. , 2005, World journal of gastroenterology.

[21]  B. Nilsson,et al.  Gastrointestinal stromal tumors: The incidence, prevalence, clinical course, and prognostication in the preimatinib mesylate era , 2005, Cancer.

[22]  L. Sobin,et al.  Gastrointestinal stromal tumors: radiologic features with pathologic correlation. , 2003, Radiographics : a review publication of the Radiological Society of North America, Inc.

[23]  C. Fisher,et al.  Malignant gastrointestinal stromal tumor: distribution, imaging features, and pattern of metastatic spread. , 2003, Radiology.

[24]  Markku Miettinen,et al.  Pathology and diagnostic criteria of gastrointestinal stromal tumors (GISTs): a review. , 2002, European journal of cancer.

[25]  J. Lasota,et al.  Gastrointestinal stromal tumors: recent advances in understanding of their biology. , 1999, Human pathology.

[26]  J. Vaillant,et al.  Benign schwannoma of the digestive tract: a clinicopathologic and immunohistochemical study of five cases, including a case of esophageal tumor. , 1999, The American journal of surgical pathology.

[27]  M. Miettinen,et al.  Gastric schwannoma—a clinicopathological analysis of six cases , 1995, Histopathology.