Temporal Subtraction Method for Lung Nodule Detection on Successive Thoracic CT Soft-Copy Images

Aoki, Takatoshi; Murakami, Seiichi; Kim, Hyoungseop; Fujii, Masami; Takahashi, Hiroyuki; H, Oki; Hayashida, Yoshiko; Katsuragawa, Shigehiko; Shiraishi, Junji; Korogi, Yukunori

doi:10.1148/radiol.13130460

Cited by 27 publications

(17 citation statements)

References 22 publications

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“…A recent feasibility study showed significant improvement in the detection of actionable nodules when comparing temporal subtraction to standard viewing (54). Further experience will be required to ascertain the generalizability and utility of this technique.…”

Section: Image Based Methods For Performance Improvementmentioning

confidence: 99%

Lung Nodule and Cancer Detection in Computed Tomography Screening

Rubin

2015

Journal of Thoracic Imaging

105

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Fundamental to the diagnosis of lung cancer in CT scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners have advanced, higher levels of spatial resolution reveal tinier lung abnormalities. While not all detected lung nodules should be reported, radiologists strive to detect all nodules that might have relevance to cancer diagnosis. Although medium to large lung nodules are detected consistently, inter-reader agreement and reader sensitivity for lung nodule detection diminish substantially as nodule size falls below 8–10 mm. The difficulty in establishing an absolute reference standard presents a challenge to the reliability of studies performed to evaluate lung nodule detection. In the interest of improving detection performance, investigators are using eye tracking to analyze the effectiveness with which radiologists search CT scans relative to their ability to recognize nodules within their search path to determine if strategies might exist to improve performance across readers. Beyond the viewing of transverse CT reconstructions, image processing techniques such as thin-slab maximum intensity projections are used to substantially improve reader performance. Finally, the development of computer-aided detection has continued to evolve with the expectation that one day it will serve routinely as a tireless partner to the radiologist to enhance detection performance without significant prolongation of the interpretive process. This review provides an introduction to the current understanding of these varied issues as we enter the era of widespread lung cancer screening.

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Section: Image Based Methods For Performance Improvementmentioning

confidence: 99%

Lung Nodule and Cancer Detection in Computed Tomography Screening

Rubin

2015

Journal of Thoracic Imaging

105

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“…Often these changes are subtle and temporal subtraction has been proposed as a technique to increase radiologist sensitivity in detecting these changes. 7,61 This involves registration of two scans from the same patient and subtraction of matched voxels from the earlier time point. Work in this area is still in the preliminary stages and prone to misregistration artefacts.…”

Section: Follow-upmentioning

confidence: 99%

“…2 Machine-based analysis of thoracic imaging dates as far back as the 1960s when Lodwick and colleagues coded imaging features from chest radiographs in patients with lung cancer to create a prognostic model predicting 1 and 5 year survival. 3 In the intervening years, computer-aided detection/diagnosis (CAD) underpinned by machine-learning techniques 4 became a leading research subject in radiology 5 with many applications such as detection and distinction of benign and malignant pulmonary nodules, 6 temporal subtraction for assessment of interval changes between scans, 7 quantifying emphysema, 8 and detection of interstitial lung disease. 9 The primary focus for the CAD developers had been to develop algorithms that improve reporter accuracy and provide computer output that acts as a "second opinion" to the reporting radiologists; however, despite a number of studies showing that CAD helped radiologists to improve their diagnostic accuracy, the technology failed to achieve clinical acceptance.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications

Kadir²,

2020

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Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli. Despite extensive literature on the use of convolutional neural networks in thoracic imaging over the last few decades, we are yet to see these systems in use in clinical practice. The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques.

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“…Temporal subtraction of chest radiograph is a CAD technique that initial digital image subtract from follow-up image on workstation, and it has already been commercialized [13, 14]. In addition, a temporal subtraction method of three-dimensional CT (3D-CT) has been reported recently [15–17]. Using temporal subtraction chest 3D-CT, both osteolytic and osteoblastic lesions derived from bone metastases of lung cancer can be detected.…”

Section: Introductionmentioning

confidence: 99%

Thoracic Temporal Subtraction Three Dimensional Computed Tomography (3D-CT): Screening for Vertebral Metastases of Primary Lung Cancers

Iwano¹,

Ito²,

Umakoshi³

et al. 2017

PLoS ONE

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PurposeWe developed an original, computer-aided diagnosis (CAD) software that subtracts the initial thoracic vertebral three-dimensional computed tomography (3D-CT) image from the follow-up 3D-CT image. The aim of this study was to investigate the efficacy of this CAD software during screening for vertebral metastases on follow-up CT images of primary lung cancer patients.Materials and MethodsThe interpretation experiment included 30 sets of follow-up CT scans in primary lung cancer patients and was performed by two readers (readers A and B), who each had 2.5 years’ experience reading CT images. In 395 vertebrae from C6 to L3, 46 vertebral metastases were identified as follows: osteolytic metastases (n = 17), osteoblastic metastases (n = 14), combined osteolytic and osteoblastic metastases (n = 6), and pathological fractures (n = 9). Thirty-six lesions were in the anterior component (vertebral body), and 10 lesions were in the posterior component (vertebral arch, transverse process, and spinous process). The area under the curve (AUC) by receiver operating characteristic (ROC) curve analysis and the sensitivity and specificity for detecting vertebral metastases were compared with and without CAD for each observer.ResultsReader A detected 47 abnormalities on CT images without CAD, and 33 of them were true-positive metastatic lesions. Using CAD, reader A detected 57 abnormalities, and 38 were true positives. The sensitivity increased from 0.717 to 0.826, and on ROC curve analysis, AUC with CAD was significantly higher than that without CAD (0.849 vs. 0.902, p = 0.021). Reader B detected 40 abnormalities on CT images without CAD, and 36 of them were true-positive metastatic lesions. Using CAD, reader B detected 44 abnormalities, and 39 were true positives. The sensitivity increased from 0.783 to 0.848, and AUC with CAD was nonsignificantly higher than that without CAD (0.889 vs. 0.910, p = 0.341). Both readers detected more osteolytic and osteoblastic metastases with CAD than without CAD.ConclusionOur temporal 3D-CT subtraction CAD software easily detected vertebral metastases on the follow-up CT images of lung cancer patients regardless of the osteolytic or osteoblastic nature of the lesions.

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Temporal Subtraction Method for Lung Nodule Detection on Successive Thoracic CT Soft-Copy Images

Abstract: The novel CT TS method would increase observer performance for lung nodule detection without considerably extending the reading time.

Cited by 27 publications

References 22 publications

Lung Nodule and Cancer Detection in Computed Tomography Screening

Lung Nodule and Cancer Detection in Computed Tomography Screening

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications

Thoracic Temporal Subtraction Three Dimensional Computed Tomography (3D-CT): Screening for Vertebral Metastases of Primary Lung Cancers

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