Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph

Dellios, Nikolaos; Teichgraeber, Ulf; Chelaru, Robert; Malich, Ansgar; Papageorgiou, Ismini

doi:10.4103/jcis.jcis_75_16

Cited by 14 publications

(12 citation statements)

References 14 publications

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“…Compared with previously reported conventional image processing-based computer-aided diagnoses, DLAD showed a markedly decreased rate of false-positive findings and provided high specificity while preserving sensitivity (1,12,(23)(24)(25)(26), resulting in better detection performance than thoracic radiologists. Whereas previous computer-aided diagnoses exhibited a range of grouped JAFROC FOM of the 18 physicians (JAFROC FOM, 0.885 vs 0.794, respectively; P = .002).…”

Section: Discussionmentioning

confidence: 70%

Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

Nam¹,

Park²,

Hwang³

et al. 2019

Radiology

434

328

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hest radiography, one of the most common diagnostic imaging tests in medicine, is used for screening, diagnostic work-ups, and monitoring of various thoracic diseases (1,2). One of its major objectives is detection of pulmonary nodules because pulmonary nodules are often the initial radiologic manifestation of lung cancers (1,2). However, to date, pulmonary nodule detection on chest radiographs has not been completely satisfactory, with a reported sensitivity ranging between 36%-84%, varying widely according to the tumor size and study population (2-6). Indeed, chest radiography has been shown to be prone to many reading errors with low interobserver and intraobserver agreements because of its limited spatial resolution, noise from overlapping anatomic structures, and the variable perceptual ability of radiologists. Recent work shows that 19%-26% of lung cancers visible on chest radiographs were in fact missed at their first readings (6,7). Of course, hindsight is always perfect when one knows where to look. For this reason, there has been increasing dependency on chest CT images over chest radiographs in pulmonary nodule detection. However, even low-dose CT scans require approximately 50-100 times higher radiation dose than single-view chest radiographic examinations (8,9)

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Section: Discussionmentioning

confidence: 70%

Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

Nam¹,

Park²,

Hwang³

et al. 2019

Radiology

434

328

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“…The CheXNeXt algorithm detected both consolidation and pleural effusion, the most common findings for primary tuberculosis, at the level of practicing radiologists. Similarly, CheXNeXt achieved radiologist-level accuracy for both pulmonary nodule and mass detection, a critical task for lung cancer diagnosis, with much higher specificity than previously reported computer-aided detection systems and comparable sensitivity [ 44 – 47 ]. Although chest radiography is not the primary method used to perform lung cancer screening, it is the most common thoracic imaging study in which incidental lung cancers (nodules or masses) are discovered.…”

Section: Discussionmentioning

confidence: 81%

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

et al. 2018

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BackgroundChest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.Methods and findingsWe developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt’s discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4–28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863–0.910), 0.911 (95% CI 0.866–0.947), and 0.985 (95% CI 0.974–0.991), respectively, whereas CheXNeXt’s AUCs were 0.831 (95% CI 0.790–0.870), 0.704 (95% CI 0.567–0.833), and 0.851 (95% CI 0.785–0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825–0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777–0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.ConclusionsIn this study, we developed and validated a deep learning algorithm that classified clinically ...

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“…The stand-alone DCNN software averaged 0.2 false-positive marks per image, which is considerably lower than those reported in most previous studies (0.9 to 3.9) (10,11,18,19,(22)(23)(24). A high number of false-positive marks per image (.1.0) has long been a limitation of CAD software use (25,26). Another DCNN-based algorithm (16) showed falsepositives rates ranging from 0.02 to 0.34 per image.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, our data included different phenotypes of nodules and patients. Most previous studies were conducted in a single institution or within a limited geographic region (26). Also, this study included both posteroanterior and anteroposterior radiographs, regardless of device manufacturer (Table E3 [online]).…”

Section: Discussionmentioning

confidence: 99%

Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

Sim¹,

Chung²,

Kotter³

et al. 2020

Radiology

189

141

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THORACIC IMAGINGC hest radiography is the most common radiologic examination, despite its inferiority to low-dose CT, for lung cancer screening (1). Some authors showed that up to 90% of "missed" lung cancer nodules can be found when the baseline chest radiograph is re-reviewed with the benefit of the follow-up examination showing the mass that has grown in size (2). Misdiagnoses of lung cancer can occur for many reasons. This oversight can be due to a lack of perception of the nodule, the decision to ignore a subtle density, and the satisfaction of search when another abnormality is identified (3-5). Lesion characteristics including size, density, and location make the detection of lung nodules more challenging on chest radiographs (6-8).To improve the efficacy of chest radiography for nodule detection, computer-aided detection (CAD) software has been developed and evaluated. In 2004, Kakeda et al (9) tested their CAD and reported that it was beneficial in analyzing radiographs with nodules but had an average falsepositive rate of 3.15 per image. de Hoop et al (10) showed Purpose: To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods: Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning-based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software. The sensitivity and number of false-positive findings per image of DCNN software, radiologists alone, and radiologists with the use of DCNN software were analyzed by using logistic regression and Poisson regression.

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Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph

Cited by 14 publications

References 14 publications

Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

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