Proceedings ArticleDOI
Automatic lung nodule classification with radiomics approach
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TLDR
A novel CAD method to distinguish the benign and malignant lung cancer from CT images directly is proposed, which can not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy collecting process.Abstract:
Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while
some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant
nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months
later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we
propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can
not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy
collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step
for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random
Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our
proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA)
which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project.
Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign
nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide
improvement in decision-support with much lower cost.read more
Citations
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Journal ArticleDOI
Radiomics of pulmonary nodules and lung cancer.
Ryan Wilson,Anand Devaraj +1 more
TL;DR: The basic process of radiomics is summarized and why radiomic feature analysis may be particularly well suited to the evaluation of lung nodules, considering promising applications such as predicting malignancy, histological subtyping, gene expression and post-treatment prognosis.
Journal ArticleDOI
Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.
Wookjin Choi,Jung Hun Oh,Sadegh Riyahi,Chia-Ju Liu,Feng Jiang,Wengen Chen,Charles White,Andreas Rimner,James Mechalakos,Joseph O. Deasy,Wei Lu +10 more
TL;DR: An SVM-LASSO model to predict malignancy of PNs with two CT radiomic features was developed and demonstrated that the model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS.
Proceedings ArticleDOI
Lung Nodule Classification via Deep Transfer Learning in CT Lung Images
Raul Victor Medeiros da Nóbrega,Solon Alves Peixoto,Suane Pires P. da Silva,Pedro Pedrosa Rebouças Filho +3 more
TL;DR: Deep transfer learning proved to be a relevant strategy to extract representative imaging biomarkers for lung nodule malignancy classification in chest CT images and was the best combination of deep extractor and classifier.
Proceedings ArticleDOI
TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process
TL;DR: In this article, an end-to-end trainable multi-view deep convolutional neural network (CNN) was proposed for nodule characterization. And the network was used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score.
Journal ArticleDOI
Development and clinical application of radiomics in lung cancer
TL;DR: The advent and development of radiomics, the basic process and challenges in clinical practice, are summarized, 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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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
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Benefits and Harms of CT Screening for Lung Cancer: A Systematic Review
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TL;DR: Low-dose computed tomography screening may benefit individuals at an increased risk for lung cancer, but uncertainty exists about the potential harms of screening and the generalizability of results.
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