Prediction of risk of lung cancer in populations and in pulmonary nodules: Significant progress to drive changes in paradigms

Baldwin, David R

doi:10.1016/j.lungcan.2015.05.004

Cited by 27 publications

(10 citation statements)

References 26 publications

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“…Clinical experience has shown that if lung cancer can be diagnosed at an early stage, the chance of survival will be greatly increased [4]. The use of diagnostic methods based on computed tomography (CT) images is an important strategy for early diagnosis of lung cancer and improvement of patient survival [5].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Cao

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, robust nodule detection has been a challenging task. In this study, we propose a two-stage convolutional neural network (TSCNN) architecture for lung nodule detection. The CNN architecture in the first stage is based on the improved UNet segmentation network to establish an initial detection of lung nodules. Simultaneously, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a novel sampling strategy, and use the offline hard mining idea for training and prediction according to the proposed cascaded prediction method. The CNN architecture in the second stage is based on the proposed dual pooling structure, which is built into three 3D CNN classification networks for false positive reduction.Since the network training requires a significant amount of training data, we adopt a data augmentation method based on random mask. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. The proposed method has been experimentally verified on the LUNA dataset.Experimental results show that the proposed TSCNN architecture can obtain competitive detection performance. Keywords: lung nodule detection; UNet; 3D CNN; ensemble learning;computer-aided diagnosis methods in the following aspects: 1) using the improved UNet segmentation model for lung nodule detection; 2) for the segmentation model training, we propose a new sampling strategy and an offline hard mining training approach; 3) we propose a cascade prediction method different from the traditional prediction method; 4) build three 3D CNN classification networks based on the dual pooling method; 5) design a data augmentation method based on random mask. MethodsThe lung nodule detection framework proposed in this paper is divided into two stages. The first stage: the detection of candidate nodules, which is based on the UNet architecture to achieve the detection of candidate nodules by segmenting suspicious nodules. The second stage: the reduction of false positive nodules, which is based on the 3DCNN architecture to eliminate false positive nodules through the integration of multiple models. The overall architecture of the proposed lung nodule detection method is shown in Fig. 2.

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Section: Introductionmentioning

confidence: 99%

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Cao

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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“…Estimating the risk of lung cancer is important in three common clinical scenarios: the management of pulmonary nodules, the selection of people for lung cancer screening, and in the early identification of symptomatic disease. 38 The models in the current study were to predict nodule risk of malignancy based on a complete representative hospital series, thus to provide evidence for pulmonary nodule management in routine clinical practice. Hence, there is a need to understand the potential population in which the models can be used and its' preferred to perform validation studies in the population to ensure that models reliably predict the chance of malignancy.…”

Section: Discussionmentioning

confidence: 99%

<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

Zhang¹,

Tian²,

Chen³

et al. 2020

CMAR

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Objective: Malignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size. Materials and Methods: This retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models. Results: The study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78-0.88) and 0.70 (0.61-0.80) for SSNs ≤15 mm, 0.84 (0.74-0.93) and 0.72 (0.57-0.87) for SSNs between 15 and 30 mm, 0.82 (0.77-0.87) and 0.71 (0.61-0.80) for solid nodules ≤15 mm, 0.82 (0.79-0.85) and 0.81 (0.76-0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size. Conclusion: We developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.

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“…Minor calcifications were found in a small proportion of lung cancers, and the calcified area of the malignant nodules generally did not exceed 10% of the lesion (22). Fat was not found in any lung cancer or pulmonary sarcoma, only in benign lesions (hamartoma and lipoid pneumonia) (23). …”

Section: Discussionmentioning

confidence: 99%

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

Shi

Wang

2016

Experimental and Therapeutic Medicine

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The aim of the present study was to analyze the value of applying dual-source 64-layer spiral computed tomography (CT) in the differential diagnosis of solitary pulmonary nodules (SPNs). Mediastinal windows from 45 cases were selected to study SPNs (maximum diameter, ≤3 cm), and the pathological nature of lesions was determined by clinical and pathological diagnosis. Conventional 64-layer spiral CT scanning, local enhancement and 3D recombination technologies were used to determine the occurrence rate, lesion diameter, degree of enhancement, lobular sign, spicule sign, pleural indentation sign, vessel convergence sign and bronchus sign. The final diagnoses indicated 34 cases of malignant SPNs (75.6%) and 11 benign cases (24.4%). When the nodule diameter in the malignant group was compared with that of the benign group, the difference was not statistically significant (P>0.05). Nodules in the malignant group showed inhomogeneous enhancement while nodules in the benign group showed homogeneous enhancement. The enhanced CT values in the malignant group were higher than those in the benign group, and the difference was statistically significant (P<0.05). The proportion of nodules with lobular sign in the malignant group was significantly higher than that in the benign group (P<0.05). The proportion of nodules with calcification, vessel convergence sign and bronchus sign in the malignant group were significantly higher than those in the benign group, and the differences were statistically significant (P<0.05). A comparison of vacuole sign, pleural indentation sign, spiculate protuberance and fat occurrence between the two groups yielded no statistically significant differences (P>0.05). The sensitivity of CT enhancement was 85.6%, specificity was 79.6%, positive predicated value was 92.3%, and the negative predicted value was 85.2%. In conclusion, SPNs diagnosed by CT enhancement manifested with enhancement degree, lobular sign, calcification, vessel convergence sign and bronchus sign with high diagnostic accuracy.

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Prediction of risk of lung cancer in populations and in pulmonary nodules: Significant progress to drive changes in paradigms

Cited by 27 publications

References 26 publications

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

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