Automatic lung nodule classification with radiomics approach

Ma, Jingchen; Wang, Qian; Ren, Yi; Hu, Haibo; Zhao, Jun

doi:10.1117/12.2220768

Cited by 40 publications

(29 citation statements)

References 13 publications

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“…Hawkins et al proposed a random forest classifier using 23 stable (high reproducibility – concordance correlation coefficients ≥0.95 in test–retest) radiomic features . Ma et al proposed a random forest classifier using 583 radiomic features . Its performance on the same LIDC dataset as used in the present study was comparable to the proposed SVM‐LASSO model.…”

Section: Discussionmentioning

confidence: 61%

“…30 Ma et al proposed a random forest classifier using 583 radiomic features. 31 Its performance on the same LIDC dataset as used in the present study was comparable to the proposed SVM-LASSO model. However, the number of features used was more than eight times of the number of patients, which may cause a model overfitting problem.…”

Section: C Comparison With Recently Reported Radiomics Modelsmentioning

confidence: 56%

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Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer

et al. 2018

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Section: Discussionmentioning

confidence: 61%

Section: C Comparison With Recently Reported Radiomics Modelsmentioning

confidence: 56%

Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer

et al. 2018

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“…The authors assessed 127 indeterminate pulmonary nodules and identified 583 features of nodule intensity, shape and heterogeneity. By analyzing these features they achieved 82.7% accuracy in classification of malignant primary lung nodules and benign nodules (17).…”

Section: Texture Based Analysesmentioning

confidence: 99%

Radiomics of pulmonary nodules and lung cancer

Wilson

Devaraj

2017

Transl. Lung Cancer Res.

162

104

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The large number of indeterminate pulmonary nodules encountered incidentally or during CT-based lung screening provides considerable diagnostic and management challenges. Conventional nodule evaluation relies on visually identifiable discriminators such as size and speculation. These visible nodule features are however small in number and subject to considerable interpretation variability. With the development of novel targeted therapies for lung cancer the diagnosis and characterization of early stage lung tumours has never been more important. Radiomics is a developing field aimed at deriving automated quantitative imaging features from medical images that can predict nodule and tumour behavior non-invasively. In contrast to conventional visual image features radiomics can extract substantially greater numbers of nodule features with much better reproducibility. This paper summarizes the basic process of radiomics and outlines why radiomic feature analysis may be particularly well suited to the evaluation of lung nodules. We review the current evidence for its clinical application with regards to pulmonary nodule management, considering promising applications such as predicting malignancy, histological subtyping, gene expression and post-treatment prognosis. Radiomics has the potential to transform the management of pulmonary nodules offering early diagnosis and personalized medicine using a method that is in cost-effective and non-invasive.

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“…A diagnostic model was then executed with the random forest method. Satisfactorily, the sensitivity, specificity and accuracy of this radiomics classifier toward distinguishing malignant primary lung nodules from benign ones achieved 80.0%, 85.5% and 82.7%, respectively; however, the sensitivity of the traditional experienced radiologists’ annotations was only 56.9% with the same specificity [ 34 ]. Another study has described quantitative analyses of low-dose CT lung cancer screening images from the well-known National Lung Screening Trial (NLST) at baseline to evaluate whether radiomics could predict the subsequent emergence of cancer.…”

Section: Clinical Application Of Radiomics In the Precision Diagnosismentioning

confidence: 99%

Development and clinical application of radiomics in lung cancer

et al. 2017

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Since the discovery of X-rays at the end of the 19th century, medical imageology has progressed for 100 years, and medical imaging has become an important auxiliary tool for clinical diagnosis. With the launch of the human genome project (HGP) and the development of various high-throughput detection techniques, disease exploration in the post-genome era has extended beyond investigations of structural changes to in-depth analyses of molecular abnormalities in tissues, organs and cells, on the basis of gene expression and epigenetics. These techniques have given rise to genomics, proteomics, metabolomics and other systems biology subspecialties, including radiogenomics. Radiogenomics is an important revolution in the traditional visually identifiable imaging technology and constitutes a new branch, radiomics. Radiomics is aimed at extracting quantitative imaging features automatically and developing models to predict lesion phenotypes in a non-invasive manner. Here, we summarize the advent and development of radiomics, the basic process and challenges in clinical practice, with a focus on applications in pulmonary nodule evaluations, including diagnostics, pathological and molecular classifications, treatment response assessments and prognostic predictions, especially in radiotherapy.

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Automatic lung nodule classification with radiomics approach

Cited by 40 publications

References 13 publications

Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer

Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer

Radiomics of pulmonary nodules and lung cancer

Development and clinical application of radiomics in lung cancer

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