Pulmonary Nodule: A Comprehensive Review and Update

Asija, Amit; Manickam, Rajapriya; Aronow, Wilbert S.; Chandy, Dipak

doi:10.3810/hp.2014.08.1125

Cited by 8 publications

(8 citation statements)

References 58 publications

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“…These reports included clinical data regarding sex, age, chief complaints, cigarette smoking status, previous medical histories and family histories (i.e., extra-thoracic disease history; chronic lung disease history, except cancer history; and cancer history, except lung cancer history), and histopathology. Information regarding the following chest radiological data was collected for all patients: nodule size (average of the maximum length and width) [ 8 , 9 ], edge characteristics (i.e., whether the edges featured spiculated protuberances, lobulation alone, spiculation alone, or lobulation and spiculation; and whether the edges were irregular or smooth) [ 6 ], density characteristics (i.e., whether the nodules were solid, purely ground-glass, or partly solid; whether the nodules featured thin cavitations or thickened cavitations; and whether the nodules displayed necrosis and calcification) (Figure 4 ) and location (i.e., whether the nodules were located in the upper, middle or lower lobe). We obtained approval from our institution to use patient medical records for this study, and patient confidentiality was maintained.…”

Section: Methodsmentioning

confidence: 99%

“…Fleishner and the American College of Chest Physicians (ACCP) guidelines recommend that nodules 8 mm or smaller undergo mandatory follow-up CT evaluations based on their diameters and associated risk factors. The guidelines also recommend that individuals with nodules larger than 8 mm undergo a diagnostic work-up consisting of more invasive diagnostic procedures [ 4 , 6 – 8 ]. Devising effective strategies for managing patients with SPNs depends critically on the pre-test probability of malignancy [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the cancer risk factors of solitary pulmonary nodules

Yang

Zhang

et al. 2017

Oncotarget

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There are no large samples or exact prediction models for assessing the cancer risk factors of solitary pulmonary nodules (SPNs) in the Chinese population. We retrospectively analyzed the clinical and imaging data of patients with SPNs who underwent computer tomography guided needle biopsy in our hospital from Jan 1st of 2011 to March 30th of 2016. These patients were divided into a development data set and a validation data set. These groups included 1078 and 344 patients, respectively. A prediction model was developed from the development data set and was validated with the validation data set using logistic regression. The predictors of cancer in our model included female gender, age, pack-years of smoking, a previous history of malignancy, nodule size, lobulated and spiculated edges, lobulation alone and spiculation alone. The Area Under the Curves, sensitivity and specificity of our model in the development and validation data sets were significantly higher than those of the Mayo model and VA model (p < 0.001). We established the largest sampling risk prediction model of SPNs in a Chinese cohort. This model is particularly applicable to SPNs > 8 mm in size. SPNs in female patients, as well as SPNs featuring a combination of lobulated and spiculated edges or lobulated edges alone, should be evaluated carefully due to the probability that they are malignant.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of the cancer risk factors of solitary pulmonary nodules

Yang

Zhang

et al. 2017

Oncotarget

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“…The appearance of a lung nodule is a small, rounded or irregular opacity, well or poorly defined, measuring up to 30 mm in diameter on the lung. 4,5 Lung nodules can have different shapes, densities, contours, or locations and may be attached to other tissues, such as neighboring blood vessel and pleural surface. Therefore, early detection and accurate identification of lung nodule plays an important role in treatment of lung cancer.…”

Section: Introductionmentioning

confidence: 99%

“…The early stage of lung cancer is characterized by lung nodule. The appearance of a lung nodule is a small, rounded or irregular opacity, well or poorly defined, measuring up to 30 mm in diameter on the lung 4,5 . Lung nodules can have different shapes, densities, contours, or locations and may be attached to other tissues, such as neighboring blood vessel and pleural surface.…”

Section: Introductionmentioning

confidence: 99%

Accurate classification of nodules and non‐nodules from computed tomography images based on radiomics and machine learning algorithms

Zhang

Liu

et al. 2021

Int J Imaging Syst Tech

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Accurate classification of lung nodules can lead to a more favorable diagnosis and treatment for lung cancer. In this study, an accurate classification of nodules and non‐nodules based on radiomics and machine learning algorithms has been presented. We validate our method on 4999 nodule candidates and use accuracy, recall, precision, f1‐score, and the area under receiver operating characteristic curve (AUC) as evaluation metrics. Experimental results manifest that for most classifiers, recursive feature elimination (RFE) has higher AUC values than chi‐square test and principal component analysis, selecting 15 features is better in AUC than 10 features and 20 features and the ratio between training data set and testing data set of 9:1 has the best predictive performance. When the feature selection method is RFE, the number of features is 15 and the ratio is 9:1, random forest has the highest AUC value (0.9536), accuracy (0.9580), recall (0.9893), precision (0.9392), and f1‐score (0.9636).

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“…Lung nodule is a rounded opacity whose maximum diameter is less than 3 cm [5]. A diversity of radiographic characteristics of lung nodule e.g., size, margin, nodular calcification, and nodular cavitation, can be used to differentiate malignancy levels of lung nodule [7], [8].…”

Section: Introductionmentioning

confidence: 99%

A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Wang

Cui

et al. 2020

IEEE Access

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Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average F1_Score of 78.14% and AUC value under PR curves of 0.8149. Moreover, our system reaches an average accuracy of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.

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Pulmonary Nodule: A Comprehensive Review and Update

Cited by 8 publications

References 58 publications

Assessment of the cancer risk factors of solitary pulmonary nodules

Assessment of the cancer risk factors of solitary pulmonary nodules

Accurate classification of nodules and non‐nodules from computed tomography images based on radiomics and machine learning algorithms

A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

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