New Classification of Small Pulmonary Nodules by Margin Characteristics on Highresolution CT

Furuya, Kenichi; Murayama, Sadayuki; Soeda, Hiroyasu; Murakami, Junji; Ichinose, Yukito; Yabuuchi, Hidetake; Katsuda, Yasaburo; Koga, Mitsuru; Masuda, Kouji

doi:10.3109/02841859909175574

Cited by 89 publications

(55 citation statements)

References 22 publications

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“…The higher LR of cancer in nodules with a spiculated margin compared to round or polygonal nodules in our study is consistent with the results of previous studies in SPN's [22][23][24], in which a spiculated contour occurs significantly more often in malignant SPNs. However, 95% CI intervals were wide, also limiting its use for SPN's in clinical practice.…”

Section: Discussionsupporting

confidence: 92%

“…However, other features in addition to size can be helpful in the discrimination between benign and malignant pulmonary nodules [11,22]. The higher LR of cancer in nodules with a spiculated margin compared to round or polygonal nodules in our study is consistent with the results of previous studies in SPN's [22][23][24], in which a spiculated contour occurs significantly more often in malignant SPNs.…”

Section: Discussionsupporting

confidence: 91%

“…Seemann reported that both benign and malignant SPN's could exhibit smooth edges [11,23], while others found round or polygonal margins more often in benign lesions [16,22,25]. The pathologic substrate for lobulation in malignant lesions is the presence of nodular excrescences of the tumor at its advancing edge or tumor growth in islands at the periphery of the nodule, indicating that the nodule has uneven growth rates [10,24].…”

Section: Discussionmentioning

confidence: 99%

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Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial

Klaveren

Bock

et al. 2008

European Journal of Radiology

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Purpose: To evaluate prospectively the value of size, shape, margin and density in discriminating between benign and malignant CT screen detected solid non-calcified pulmonary nodules. Material and methods: This study was institutional review board approved. For this study 405 participants of the NELSON lung cancer screening trial with 469 indeterminate or potentially malignant solid pulmonary nodules (>50 mm 3 ) were selected. The nodules were classified based on size, shape (round, polygonal, irregular) and margin (smooth, lobulated, spiculated). Mean nodule density and nodule volume were automatically generated by software. Analyses were performed by univariate and multivariate logistic regression. Results were presented as likelihood ratios (LR) with 95% confidence intervals (CI). Receiver operating characteristic analysis was performed for mean density as predictor for lung cancer. Results: Of the 469 nodules, 387 (83%) were between 50 and 500 mm 3 , 82 (17%) >500 mm 3 , 59 (13%) malignant, 410 (87%) benign. The median size of the nodules was 103 mm 3 (range 50-5486 mm 3 ). In multivariate analysis lobulated nodules had LR of 11 compared to smooth; spiculated nodules a LR of 7 compared to smooth; irregular nodules a LR of 6 compared to round and polygonal; volume a LR of 3. The mean nodule CT density did not predict the presence of lung cancer (AUC 0.37, 95% CI 0.32-0.43). Conclusion: In solid non-calcified nodules larger than 50 mm 3 , size and to a lesser extent a lobulated or spiculated margin and irregular shape increased the likelihood that a nodule was malignant. Nodule density had no discriminative power.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial

Klaveren

Bock

et al. 2008

European Journal of Radiology

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“…For example, in one study, 193 pulmonary nodules were subjectively graded as round, lobulated, densely spiculated, ragged, and halo. A high level of malignancy was found among the lobulated (82 %), spiculated (97 %), ragged (93 %), and halo nodules (100 %), while 66 % of the round nodules were benign [23]. Nakamura et al was one of the first to attempt to predict radiologists' perception of diagnostic characteristics, such as spiculation [24].…”

Section: Related Workmentioning

confidence: 99%

Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis

et al. 2015

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We analyze the importance of shape features for predicting spiculation ratings assigned by radiologists to lung nodules in computed tomography (CT) scans. Using the Lung Image Database Consortium (LIDC) data and classification models based on decision trees, we demonstrate that the importance of several shape features increases disproportionately relative to other image features with increasing size of the nodule. Our shaped-based classification results show an area under the receiver operating characteristic (ROC) curve of 0.65 when classifying spiculation for small nodules and an area of 0.91 for large nodules, resulting in a 26 % difference in classification performance using shape features. An analysis of the results illustrates that this change in performance is driven by features that measure boundary complexity, which perform well for large nodules but perform relatively poorly and do no better than other features for small nodules. For large nodules, the roughness of the segmented boundary maps well to the semantic concept of spiculation. For small nodules, measuring directly the complexity of hard segmentations does not yield good results for predicting spiculation due to limits imposed by spatial resolution and the uncertainty in boundary location. Therefore, a wider range of features, including shape, texture, and intensity features, are needed to predict spiculation ratings for small nodules. A further implication is that the efficacy of shape features for a particular classifier used to create computer-aided diagnosis systems depends on the distribution of nodule sizes in the training and testing sets, which may not be consistent across different research studies.

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“…For example, Furuya et al [319] analyzed the margin characteristics of 193 pulmonary nodules on high-resolution CT scans and subjectively classified them as one of several types, including round, lobulated, densely spiculated, ragged, and halo. They found a high level of malignancy among the lobulated (82%), spiculated (97%), ragged (93%), and halo nodules (100%), while 66% of the round nodules proved to be benign.…”

Section: Diagnosis Of Lung Nodules Based On Shape and Appearancementioning

confidence: 99%

Computational methods for the analysis of functional 4D-CT chest images.

Soliman¹

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DEDICATIONThis dissertation is dedicated to my father's soul.iii ACKNOWLEDGMENTS In the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me. Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients.v The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decisionmaking. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification.This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today's clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered...

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New Classification of Small Pulmonary Nodules by Margin Characteristics on Highresolution CT

Abstract: This HRCT classification method is useful for determining the nature of small pulmonary nodules and reflects the underlying pathological characteristics.

Cited by 89 publications

References 22 publications

Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial

Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial

Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis

Computational methods for the analysis of functional 4D-CT chest images.

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