Dataset Information

Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.

ABSTRACT: The aim of this study was to compare the performance of 2- (2D) and 3-dimensional (3D) quantitative computed tomography (CT) methods for classifying lung nodules as lung cancer, metastases, or benign.Using semiautomated software and computerized analysis, we analyzed more than 50 quantitative CT features of 96 solid nodules in 94 patients, in 2D from a single slice and in 3D from the entire nodule volume. Multivariable logistic regression was used to classify nodule types. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) using leave-one-out cross-validation.The AUC for distinguishing 53 primary lung cancers from 18 benign nodules and 25 metastases ranged from 0.79 to 0.83 and was not significantly different for 2D and 3D analyses (P = 0.29-0.78). Models distinguishing metastases from benign nodules were statistically significant only by 3D analysis (AUC = 0.84).Three-dimensional CT methods did not improve discrimination of lung cancer, but may help distinguish benign nodules from metastases.

SUBMITTER: Gierada DS

PROVIDER: S-EPMC4949123 | biostudies-literature | 2016 Jul-Aug

REPOSITORIES: biostudies-literature

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Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.

Gierada David S DS Politte David G DG Zheng Jie J Schechtman Kenneth B KB Whiting Bruce R BR Smith Kirk E KE Crabtree Traves T Kreisel Daniel D Krupnick Alexander S AS Patterson G Alexander GA Puri Varun V Meyers Bryan F BF

Journal of computer assisted tomography 20160701 4

<h4>Objective</h4>The aim of this study was to compare the performance of 2- (2D) and 3-dimensional (3D) quantitative computed tomography (CT) methods for classifying lung nodules as lung cancer, metastases, or benign.<h4>Methods</h4>Using semiautomated software and computerized analysis, we analyzed more than 50 quantitative CT features of 96 solid nodules in 94 patients, in 2D from a single slice and in 3D from the entire nodule volume. Multivariable logistic regression was used to classify no ...[more]

PMID: 27096403

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Project description:BackgroundComputed tomography (CT) signs of lung nodules play an important role in indicating lung nodules' malignancy, and accurate automatic classification of these signs can help doctors improve their diagnostic efficiency. However, few relevant studies targeting multilabel classification (MLC) of nodule signs have been conducted. Moreover, difficulty in obtaining labeled data also restricts this avenue of research to a large extent. To address these problems, a multilabel automatic classification system for nodule signs is proposed, which consists of a 3-dimensional (3D) convolutional neural network (CNN) and an efficient new semi-supervised learning (SSL) framework.MethodsTwo datasets were used in our experiments: Lung Nodule Analysis 16 (LUNA16), a public dataset for lung nodule classification, and a private dataset of nodule signs. The private dataset contains 641 nodules, 454 of which were annotated with 6 important signs by radiologists. Our classification system consists of 2 main parts: a 3D CNN model and an SSL method called uncertainty-aware self-training framework with consistency regularization (USC). In the system, supervised training is performed with labeled data, and simultaneously, an uncertainty-and-confidence-based strategy is used to select pseudo-labeled samples for unsupervised training, thus jointly realizing the optimization of the model.ResultsFor the MLC of nodule signs, our proposed 3D CNN achieved satisfactory results with a mean average precision (mAP) of 0.870 and a mean area under the curve (AUC) of 0.782. In semi-supervised experiments, compared with supervised learning, our proposed SSL method could increase the mAP by 7.6% (from 0.730 to 0.806) and the mean AUC by 8.1% (from 0.631 to 0.712); it thus efficiently utilized the unlabeled data and achieved superior performance improvement compared to the recently advanced methods.ConclusionsWe realized the optimal MLC of lung nodule signs with our proposed 3D CNN. Our proposed SSL method can also offer an efficient solution for the insufficiency of labeled data that may exist in the MLC tasks of 3D medical images.

Dataset Information

Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.

Publications

Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.

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