Literature DB >> 26271125

CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer.

Michael Brun Andersen1, Stefan Walbom Harders2, Balaji Ganeshan3, Jesper Thygesen2, Hans Henrik Torp Madsen2, Finn Rasmussen2.   

Abstract

BACKGROUND: In patients with non-small-cell lung carcinoma NSCLC the lymph node staging in the mediastinum is important due to impact on management and prognosis. Computed tomography texture analysis (CTTA) is a postprocessing technique that can evaluate the heterogeneity of marked regions in images.
PURPOSE: To evaluate if CTTA can differentiate between malignant and benign lymph nodes in a cohort of patients with suspected lung cancer.
MATERIAL AND METHODS: With tissue sampling as reference standard, 46 lymph nodes from 29 patients were analyzed using CTTA. For each lymph node, CTTA was performed using a research software "TexRAD" by drawing a region of interest (ROI) on all available axial contrast-enhanced computed tomography (CT) slices covering the entire volume of the lymph node. Lymph node CTTA comprised image filtration-histogram analysis undertakes two stages: the first step comprised an application of a Laplacian of Gaussian filter to highlight fine to coarse textures within the ROI, followed by a quantification of textures via histogram analysis using mean gray-level intensity from the entire volume of the lymph nodes.
RESULTS: CTTA demonstrated a statistically significant difference between the malignant and the benign lymph nodes (P = 0.001), and by binary logistic regression we obtained a sensitivity of 53% and specificity of 97% in the test population. The area under the receiver operating curve was 83.4% and reproducibility was excellent.
CONCLUSION: CTTA may be helpful in differentiating between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer, with a low intra-observer variance. © The Foundation Acta Radiologica 2015.

Entities:  

Keywords:  Computed tomography (CT); computer applications – detection/diagnosis; lymphatic; mediastinum; thorax

Mesh:

Substances:

Year:  2015        PMID: 26271125     DOI: 10.1177/0284185115598808

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  33 in total

1.  Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.

Authors:  Raymond H Mak; Hugo J W L Aerts; Thibaud P Coroller; Vishesh Agrawal; Elizabeth Huynh; Vivek Narayan; Stephanie W Lee
Journal:  J Thorac Oncol       Date:  2016-11-27       Impact factor: 15.609

2.  Application of CT texture analysis in predicting histopathological characteristics of gastric cancers.

Authors:  Shunli Liu; Song Liu; Changfeng Ji; Huanhuan Zheng; Xia Pan; Yujuan Zhang; Wenxian Guan; Ling Chen; Yue Guan; Weifeng Li; Jian He; Yun Ge; Zhengyang Zhou
Journal:  Eur Radiol       Date:  2017-06-22       Impact factor: 5.315

3.  Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography.

Authors:  Hayato Tomita; Tsuneo Yamashiro; Joichi Heianna; Toshiyuki Nakasone; Yusuke Kimura; Hidefumi Mimura; Sadayuki Murayama
Journal:  Eur Radiol       Date:  2021-03-31       Impact factor: 5.315

Review 4.  Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization.

Authors:  Michael MacManus; Sarah Everitt; Tanja Schimek-Jasch; X Allen Li; Ursula Nestle; Feng-Ming Spring Kong
Journal:  Transl Lung Cancer Res       Date:  2017-12

Review 5.  Clinical applications of textural analysis in non-small cell lung cancer.

Authors:  Iain Phillips; Mazhar Ajaz; Veni Ezhil; Vineet Prakash; Sheaka Alobaidli; Sarah J McQuaid; Christopher South; James Scuffham; Andrew Nisbet; Philip Evans
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

6.  Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.

Authors:  Bettina Baessler; Tim Nestler; Daniel Pinto Dos Santos; Pia Paffenholz; Vikram Zeuch; David Pfister; David Maintz; Axel Heidenreich
Journal:  Eur Radiol       Date:  2019-12-11       Impact factor: 5.315

7.  A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram.

Authors:  Xinguan Yang; Xiaohuan Pan; Hui Liu; Dashan Gao; Jianxing He; Wenhua Liang; Yubao Guan
Journal:  J Thorac Dis       Date:  2018-04       Impact factor: 2.895

8.  Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients.

Authors:  Allan F F Alves; Sérgio A Souza; Raul L Ruiz; Tarcísio A Reis; Agláia M G Ximenes; Erica N Hasimoto; Rodrigo P S Lima; José Ricardo A Miranda; Diana R Pina
Journal:  Phys Eng Sci Med       Date:  2021-03-17

9.  Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule.

Authors:  Lan He; Yanqi Huang; Zelan Ma; Cuishan Liang; Changhong Liang; Zaiyi Liu
Journal:  Sci Rep       Date:  2016-10-10       Impact factor: 4.379

10.  Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer.

Authors:  Han Na Lee; Jung Im Kim; So Youn Shin; Dae Hyun Kim; Chanwoo Kim; Il Ki Hong
Journal:  Br J Radiol       Date:  2020-04-15       Impact factor: 3.629

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