A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists

Kawagishi, Masami; Chen, Bin; Furukawa, Daisuke; Sekiguchi, Hiroyuki; Sakai, Koji; Kubo, Takeshi; Yakami, Masahiro; Fujimoto, Koji; Sakamoto, Ryo; Emoto, Yutaka; Aoyama, Gakuto; Iizuka, Yoshio; Nakagomi, Keita; Yamamoto, Hiroyuki; Togashi, Kaori

doi:10.1007/s11548-017-1554-0

Cited by 12 publications

(11 citation statements)

References 22 publications

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“…First, we ignored all nodule-specific features, such as nodule size and type. The results of a previous study [ 4 ] show that CADx using radiological findings provided better results; given this, utilizing radiological findings may improve DCNN-based CADx. We hope that our study could serve as a basis for further exploration of CADx based on lung nodule characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…The previous study [ 4 ] evaluated the performance of CADx without DCNN using the data for 1000 lung nodules obtained from our database. The study produced classification accuracies of 57.7% and 61.3% based on the conventional method and their proposed method (feature vectors calculated based on radiological findings), respectively.…”

Section: Discussionmentioning

confidence: 99%

“…This retrospective study was approved by the ethical committee of Kyoto University Hospital, which waived need for informed consent. We used a database which were built for previous research of CADx [ 4 , 22 ]. Because the previous studies focused on CADx without DCNN, the purpose of the current study is different from those of the previous studies.…”

Section: Methodsmentioning

confidence: 99%

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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

et al. 2018

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We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

et al. 2018

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“…The database includes CT images and clinical information of 1,240 patients presenting with at least one lung nodule. The previous studies have focused on the computer-aided diagnosis system [14],[15], which directly uses characteristics related to lung nodules; thus, the purpose of the current study stands different from that of the previous studies.…”

Section: Methodsmentioning

confidence: 96%

“…This retrospective study was approved by the institutional review board of Kyoto University Hospital (number: R1054); the requirement of acquiring informed consent was waived. We used a database of lung nodules, which was previously generated for computer-aided diagnosis [14],[15]. The database includes CT images and clinical information of 1,240 patients presenting with at least one lung nodule.…”

Section: Methodsmentioning

confidence: 99%

Estimation of lung cancer risk using homology-based emphysema quantification in patients with lung nodules

2019

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The purpose of this study was to assess whether homology-based emphysema quantification (HEQ) is significantly associated with lung cancer risk. This retrospective study was approved by our institutional review board. We included 576 patients with lung nodules (317 men and 259 women; age, 66.8 ± 12.3 years), who were selected from a database previously generated for computer-aided diagnosis. Of these, 283 were diagnosed with lung cancer, whereas the remaining 293 showed benign lung nodules. HEQ was performed and percentage of low-attenuation lung area (LAA%) was calculated on the basis of computed tomography scans. Statistical models were constructed to estimate lung cancer risk using logistic regression; sex, age, smoking history (Brinkman index), LAA%, and HEQ were considered independent variables. The following three models were evaluated: the base model (sex, age, and smoking history); the LAA% model (the base model + LAA%); and the HEQ model (the base model + HEQ). Model performance was assessed using receiver operating characteristic analysis and the associated area under the curve (AUC). Differences in AUCs among the models were evaluated using Delong’s test. AUCs of the base, LAA%, and HEQ models were 0.585, 0.593, and 0.622, respectively. HEQ coefficient was statistically significant in the HEQ model (P = 0.00487), but LAA% coefficient was not significant in the LAA% model (P = 0.199). Delong’s test revealed significant difference in AUCs between the LAA% and HEQ models (P = 0.0455). In conclusion, after adjusting for age, sex, and smoking history (Brinkman index), HEQ was significantly associated with lung cancer risk.

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DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

Alsadoon

Al‐Naymat

Osman

et al. 2023

Multimed Tools Appl

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A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists

Abstract: The results suggest that CFT and AFD are similar information and CFT represent only a portion of AFD. Particularly, CFT did not contain shape information in AFD. In order to decrease an interaction of radiologists, a development of a method which overcomes these problems is necessary.

Cited by 12 publications

References 22 publications

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

Estimation of lung cancer risk using homology-based emphysema quantification in patients with lung nodules

DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

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