Machine Learning-Based Computed Tomography Texture Analysis of Lytic Bone Lesions Needing Biopsy: A Preliminary Study

İlhan Nahit Mutlu; Burak Koçak; Ece Ateş Kuş; Melis Baykara Ulusan; Özgür Kılıçkesmez

doi:10.4274/imj.galenos.2021.87528

Machine Learning-Based Computed Tomography Texture Analysis of Lytic Bone Lesions Needing Biopsy: A Preliminary Study

Istanbul Medical Journal ◽

10.4274/imj.galenos.2021.87528 ◽

2021 ◽

Vol 22 (3) ◽

pp. 223-231

Author(s):

İlhan Nahit Mutlu ◽

Burak Koçak ◽

Ece Ateş Kuş ◽

Melis Baykara Ulusan ◽

Özgür Kılıçkesmez

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Texture Analysis ◽

Bone Lesions ◽

Lytic Bone Lesions ◽

Preliminary Study

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Bone Marrow Edema Pattern Identification in Patients With Lytic Bone Lesions Using Digital Subtraction Angiography–Like Bone Subtraction on Large-Area Detector Computed Tomography

Investigative Radiology ◽

10.1097/rli.0000000000000007 ◽

2014 ◽

Vol 49 (3) ◽

pp. 156-164 ◽

Cited By ~ 8

Author(s):

Pedro Augusto Gondim Teixeira ◽

Gabriela Hossu ◽

Sophie Lecocq ◽

Marco Razeto ◽

Matthias Louis ◽

...

Keyword(s):

Computed Tomography ◽

Bone Marrow Edema ◽

Bone Lesions ◽

Digital Subtraction ◽

Large Area ◽

Area Detector ◽

Lytic Bone Lesions ◽

Bone Marrow Edema Pattern ◽

Large Area Detector ◽

Marrow Edema

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Machine learning based texture analysis predicts postoperative pancreatic fistula in preoperative non-contrast enhanced computed tomography

HPB ◽

10.1016/j.hpb.2020.04.432 ◽

2020 ◽

Vol 22 ◽

pp. S384

Author(s):

P. Kambakamba ◽

M. Mannil ◽

P. Herrera ◽

M. Linecker ◽

P. Müller ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Pancreatic Fistula ◽

Texture Analysis ◽

Postoperative Pancreatic Fistula ◽

Contrast Enhanced ◽

Enhanced Computed Tomography ◽

Contrast Enhanced Computed Tomography

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

Cancer Management and Research ◽

10.2147/cmar.s218414 ◽

2019 ◽

Vol Volume 11 ◽

pp. 9253-9264 ◽

Cited By ~ 4

Author(s):

Wenli Qiu ◽

Na Duan ◽

Xiao Chen ◽

Shuai Ren ◽

Yifen Zhang ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Pancreatic Ductal Adenocarcinoma ◽

Texture Analysis ◽

Quantitative Computed Tomography ◽

Ductal Adenocarcinoma ◽

Histopathological Grade

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Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography

Investigative Radiology ◽

10.1097/rli.0000000000000448 ◽

2018 ◽

Vol 53 (6) ◽

pp. 338-343 ◽

Cited By ~ 36

Author(s):

Manoj Mannil ◽

Jochen von Spiczak ◽

Robert Manka ◽

Hatem Alkadhi

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Computed Tomography ◽

Texture Analysis ◽

Low Dose ◽

Low Dose Computed Tomography

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Computed tomography guided thin needle biopsy of small lytic bone lesions

Skeletal Radiology ◽

10.1007/bf00351355 ◽

1984 ◽

Vol 11 (4) ◽

pp. 289-291 ◽

Cited By ~ 13

Author(s):

Robert A. Gatenby ◽

Charles B. Mulhern ◽

Philip J. Moldofsky

Keyword(s):

Computed Tomography ◽

Needle Biopsy ◽

Bone Lesions ◽

Lytic Bone Lesions

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Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

European Radiology ◽

10.1007/s00330-018-5698-2 ◽

2018 ◽

Vol 29 (3) ◽

pp. 1153-1163 ◽

Cited By ~ 37

Author(s):

Ceyda Turan Bektas ◽

Burak Kocak ◽

Aytul Hande Yardimci ◽

Mehmet Hamza Turkcanoglu ◽

Ugur Yucetas ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Texture Analysis ◽

Renal Cell ◽

Clear Cell ◽

Quantitative Computed Tomography ◽

Nuclear Grade ◽

Cell Renal Cell Carcinoma

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Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation

European Journal of Radiology ◽

10.1016/j.ejrad.2018.08.014 ◽

2018 ◽

Vol 107 ◽

pp. 149-157 ◽

Cited By ~ 30

Author(s):

Burak Kocak ◽

Aytul Hande Yardimci ◽

Ceyda Turan Bektas ◽

Mehmet Hamza Turkcanoglu ◽

Cagri Erdim ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Texture Analysis ◽

Renal Cell ◽

External Validation ◽

Quantitative Computed Tomography

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Preliminary Study of Computed Tomography Texture Analysis in Pancreatic Cystadenoma

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2018.2509 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1573-1577

Author(s):

Zhengyu Hu ◽

Qijun Shen ◽

Liang Zhang ◽

Jianhua Yuan ◽

Zhongxiang Ding ◽

...

Keyword(s):

Computed Tomography ◽

Texture Analysis ◽

Pancreatic Cystadenoma ◽

Preliminary Study

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Machine Learning Classification of Low-grade and High-grade Chondrosarcomas Based on MRI-based Texture Analysis

10.1055/s-0039-1692575 ◽

2019 ◽

Author(s):

S. Gitto ◽

D. Albano ◽

V. Chianca ◽

R. Cuocolo ◽

L. Ugga ◽

...

Keyword(s):

Machine Learning ◽

Texture Analysis ◽

Low Grade ◽

High Grade ◽

Machine Learning Classification

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Faculty Opinions recommendation of Texture analysis differentiates persistent and transient T1 black holes at acute onset in multiple sclerosis: a preliminary study.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.8671962.9181063 ◽

2011 ◽

Author(s):

Nancy Richert ◽

Francesca Bagnato

Keyword(s):

Multiple Sclerosis ◽

Black Holes ◽

Texture Analysis ◽

Acute Onset ◽

Preliminary Study

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