Literature DB >> 25811833

Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer.

Hyungjin Kim1, Chang Min Park, Jin Mo Goo, Joachim E Wildberger, Hans-Ulrich Kauczor.   

Abstract

Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction.

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Year:  2015        PMID: 25811833     DOI: 10.1097/RLI.0000000000000152

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  17 in total

1.  Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

Authors:  Matthew C Hancock; Jerry F Magnan
Journal:  J Med Imaging (Bellingham)       Date:  2016-12-08

Review 2.  Pulmonary imaging after stereotactic radiotherapy-does RECIST still apply?

Authors:  Sarah A Mattonen; Aaron D Ward; David A Palma
Journal:  Br J Radiol       Date:  2016-06-20       Impact factor: 3.039

3.  Implication of total tumor size on the prognosis of patients with clinical stage IA lung adenocarcinomas appearing as part-solid nodules: Does only the solid portion size matter?

Authors:  Hyungjin Kim; Jin Mo Goo; Young Joo Suh; Chang Min Park; Young Tae Kim
Journal:  Eur Radiol       Date:  2018-08-21       Impact factor: 5.315

Review 4.  Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.

Authors:  Mario Silva; Gianluca Milanese; Valeria Seletti; Alarico Ariani; Nicola Sverzellati
Journal:  Br J Radiol       Date:  2018-01-12       Impact factor: 3.039

5.  Measuring Computed Tomography Scanner Variability of Radiomics Features.

Authors:  Dennis Mackin; Xenia Fave; Lifei Zhang; David Fried; Jinzhong Yang; Brian Taylor; Edgardo Rodriguez-Rivera; Cristina Dodge; Aaron Kyle Jones; Laurence Court
Journal:  Invest Radiol       Date:  2015-11       Impact factor: 6.016

6.  Two-stage multitask U-Net construction for pulmonary nodule segmentation and malignancy risk prediction.

Authors:  Yangfan Ni; Zhe Xie; Dezhong Zheng; Yuanyuan Yang; Weidong Wang
Journal:  Quant Imaging Med Surg       Date:  2022-01

7.  Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.

Authors:  Yan Xu; Lin Lu; Shawn H Sun; Lin-Ning E; Wei Lian; Hao Yang; Lawrence H Schwartz; Zheng-Han Yang; Binsheng Zhao
Journal:  Eur Radiol       Date:  2021-09-21       Impact factor: 7.034

8.  Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules.

Authors:  Johanna M Uthoff; Sarah L Mott; Jared Larson; Christine M Neslund-Dudas; Ann G Schwartz; Jessica C Sieren
Journal:  Chronic Obstr Pulm Dis       Date:  2022-04-29

9.  Radial endobronchial ultrasound-assisted transbronchial needle aspiration for pulmonary peripheral lesions in the segmental bronchi adjacent to the central airway.

Authors:  Nan Song; Li Yang; Hao Wang; Lei Jiang; Lishu Zhao; Sara Colella; Nikhil Jagan; Francisco A Almeida; Liang Wu; Ye Gu; Yayi He
Journal:  Transl Lung Cancer Res       Date:  2021-06

10.  Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules.

Authors:  Kai Ye; Min Chen; Qiao Zhu; Yuliu Lu; Huishu Yuan
Journal:  Quant Imaging Med Surg       Date:  2021-06
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