Évaluation d’un système de détection assisté par ordinateur des nodules parenchymateux pulmonaires avec verre dépoli au scanner multidétecteur

Hill, Catherine; Boulanger, X.; Brun, Anne; Leclercq, Delphine; Greniér, Philippe; Lucidarme, O.

doi:10.1016/s0221-0363(09)73590-5

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…Wormanns et al studied the traditional CADe system, and the sensitivity of radiologists and CADe were 90% and 24%, respectively, especially for nodules connected to the pleura, but higher sensitivity to nodules in the central area that were easily misdiagnosed as vascular structures by radiologists [ 26 , 27 ]. Some studies have also shown that the sensitivity of traditional CADe system to ground glass density nodules is low [ 28 ]. The traditional CADe system has shown good effectiveness in some aspects of pulmonary nodule detection, but the traditional CADe system only detects pulmonary nodules according to local characteristics from a statistical point of view, which cannot meet the needs of high sensitivity and low false positive in pulmonary nodule detection [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, with the application of big data and the improvement of computer computing ability, deep learning algorithm has been developed rapidly. Deep learning technology can effectively complete the tasks of image detection, recognition, and classification, so the deep learning technology in the field of imaging may help radiologists to complete a variety of diagnosis tasks [ 28 ]. The detection of pulmonary nodules using artificial intelligence (AI) algorithm is an important part of the medical field of AI [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

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The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study

Chen

Tian

Fan

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. To explore the value of artificial intelligence (AI) film reading system based on deep learning in the diagnosis of non-small-cell lung cancer (NSCLC) and the significance of curative effect monitoring. Methods. We retrospectively selected 104 suspected NSCLC cases from the self-built chest CT pulmonary nodule database in our hospital, and all of them were confirmed by pathological examination. The lung CT images of the selected patients were introduced into the AI reading system of pulmonary nodules, and the recording software automatically identified the nodules, and the results were compared with the results of the original image report. The nodules detected by the AI software and film readers were evaluated by two chest experts and recorded their size and characteristics. Comparison of calculation sensitivity, false positive rate evaluation of the NSCLC software, and physician’s efficiency of nodule detection whether there was a significant difference between the two groups. Results. The sensitivity, specificity, accuracy, positive predictive rate, and false positive rate of NSCLC diagnosed by radiologists were 72.94% (62/85), 92.06% (58/63), 81.08% (62+58/148), 92.53% (62/67), and 7.93% (5/63), respectively. The sensitivity, specificity, accuracy, positive prediction rate, and false positive rate of AI film reading system in the diagnosis of NSCLC were 94.12% (80/85), 77.77% (49/63), 87.161% ( 80 + 49 /148), 85.11% (80/94), and 22.22% (14/63), respectively. Compared with radiologists, the sensitivity and false positive rate of artificial intelligence film reading system in the diagnosis of NSCLC were higher ( P < 0.05 ). The sensitivity, specificity, accuracy, positive prediction rate, and negative prediction rate of artificial intelligence film reading system in evaluating the efficacy of patients with NSCLC were 87.50% (63/72), 69.23% (9/13), 84.70% ( 63 + 9 )/85, 94.02% (63/67), and 50% (9/18), respectively. Conclusion. The AI film reading system based on deep learning has higher sensitivity for the diagnosis of NSCLC than radiologists and can be used as an auxiliary detection tool for doctors to screen for NSCLC, but its false positive rate is relatively high. Attention should be paid to identification. Meanwhile, the AI film reading system based on deep learning also has a certain guiding significance for the diagnosis and treatment monitoring of NSCLC.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study

Chen

Tian

Fan

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Tan et al, 2011;Messay et al, 2010;Murphy et al, 2009;Sousa et al, 2009;Ye et al, 2009;Li et al, 2008;Retico et al, 2008;Bellotti et al, 2007;Dehmeshki et al, 2007;Enquobahrie et al, 2007;Marten and Engelke, 2007), only few studies have focused on detection of subsolid nodules (Jacobs et al, 2011;Tao et al, 2009;Ye et al, 2007;Zhou et al, 2006;Kim et al, 2005). In a study by Beigelman-Aubry et al (2009), it was shown that both a CAD system designed for solid nodules, and radiologists have difficulties in detecting subsolid nodules. Therefore, dedicated CAD algorithms for subsolid nodules are needed.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images

Jacobs

Rikxoort

Twellmann

et al. 2014

Medical Image Analysis

237

138

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“…CAD aims to automatically highlight cancer suspicious regions, leading to a reduction of search and interpretation errors, as well as a reduction of the variation between and within observers (Giger et al 2008). CAD research has been successfully pursued in other diagnostic areas such as mammography (Karssemeijer et al 2006, Singh et al 2008, CT chest (Ge et al 2005, Beigelman-Aubry et al 2009, Hogeweg et al 2010, CT colonography (Graser et al 2007, Summers et al 2010 as well as retinal imaging (Abràmoff et al 2008). CAD systems generally consist of multiple sequential stages.…”

Section: Introductionmentioning

confidence: 99%

Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

et al. 2012

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In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.

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Évaluation d’un système de détection assisté par ordinateur des nodules parenchymateux pulmonaires avec verre dépoli au scanner multidétecteur

Cited by 10 publications

References 29 publications

The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study

The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study

Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images

Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

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