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


  • 影响因子:5.83
  • DOI:10.1148/radiol.2019182465
  • 作者列表:"Sim Y","Chung MJ","Kotter E","Yune S","Kim M","Do S","Han K","Kim H","Yang S","Lee DJ","Choi BW
  • 发表时间:2020-01-01

:Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. 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. Results The average sensitivity of radiologists improved (from 65.1% [1375 of 2112; 95% confidence interval {CI}: 62.0%, 68.1%] to 70.3% [1484 of 2112; 95% CI: 67.2%, 73.1%], P < .001) and the number of false-positive findings per radiograph declined (from 0.2 [488 of 2400; 95% CI: 0.18, 0.22] to 0.18 [422 of 2400; 95% CI: 0.16, 0.2], P < .001) when the radiologists re-reviewed radiographs with the DCNN software. For the 12 radiologists in this study, 104 of 2400 radiographs were positively changed (from false-negative to true-positive or from false-positive to true-negative) using the DCNN, while 56 of 2400 radiographs were changed negatively. Conclusion Radiologists had better performance with deep convolutional network software for the detection of malignant pulmonary nodules on chest radiographs than without. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Jacobson in this issue.


: 背景需要多中心研究来验证使用深度卷积神经网络 (DCNN) 软件在胸片上检测恶性肺结节的额外益处。目的比较在多中心环境下,放射科医生在基于深度学习的 DCNN 软件辅助下在胸片上检测恶性肺结节与放射科医生或 DCNN 软件单独检测肺结节的性能。材料和方法 4 个医疗中心的研究者回顾性地确定了 600 例含肺癌的胸片和 200 例正常胸片。1 例肺癌的 x线片至少有一个经 CT 和病理证实的恶性结节。四个中心的 12 名放射科医生独立分析了胸片和标记的感兴趣区域。使用市售的基于深度学习的计算机辅助检测软件对 19 330 张 x光片进行单独培训、测试和验证,以发现可疑结节。然后放射科医生在 DCNN 软件的帮助下审查了图像。使用 logistic 回归和 Poisson 回归分析 DCNN 软件、单独放射科医生和使用 DCNN 软件的放射科医生的灵敏度和每张图像的假阳性结果数。结果放射科医生的平均敏感性提高 (从 65.1% [1375 of 2112; 95% 置信区间 {CI}: 62.0%,68.1%] 提高到 70.3% [1484 of 2112; 95% CI: 67.2%, 73.1%],P <.001) 和每张 x光片的假阳性结果数量下降 (从 0.2 [488 的 2400;95% CI: 0.18,0.22] 至 0.18 [422 中的 2400; 95% CI: 0.16,0.2],P <.001) 当放射科医生用 DCNN 软件重新审阅 x光片时。对于本研究中的 12 名放射科医生,使用 DCNN,104 张 x光片中的 2400 张呈阳性变化 (从假阴性变为真阳性或从假阳性变为真阴性),而 2400 张 x光片中有 56 张改变为阴性。结论放射科医生使用深度卷积网络软件在胸片上检测恶性肺结节的性能优于未使用。©本文提供 RSNA,2019 在线补充材料。另见雅各布森在本期的社论。



作者列表:["De Cunto G","Brancaleone V","Riemma MA","Cerqua I","Vellecco V","Spaziano G","Cavarra E","Bartalesi B","D'Agostino B","Lungarella G","Cirino G","Lucattelli M","Roviezzo F"]

METHODS:BACKGROUND AND PURPOSE:A critical role for sphingosine kinase/sphingosine-1-phosphate (S1P) pathway in the control of airway function has been demonstrated in respiratory diseases. Here, we address S1P contribution in a mouse model of mild chronic obstructive pulmonary disease (COPD). EXPERIMENTAL APPROACH:C57BL/6J mice have been exposed to room air or cigarette smoke up to 11 months and killed at different time points. Functional and molecular studies have been performed. KEY RESULTS:Cigarette smoke caused emphysematous changes throughout the lung parenchyma coupled to a progressive collagen deposition in both peribronchiolar and peribronchial areas. The high and low airways showed an increased reactivity to cholinergic stimulation and α-smooth muscle actin overexpression. Similarly, an increase in airway reactivity and lung resistances following S1P challenge occurred in smoking mice. A high expression of S1P, Sph-K2 , and S1P receptors (S1P2 and S1P3 ) has been detected in the lung of smoking mice. Sphingosine kinases inhibition reversed the increased cholinergic response in airways of smoking mice. CONCLUSIONS AND IMPLICATIONS:S1P signalling up-regulation follows the disease progression in smoking mice and is involved in the development of airway hyperresponsiveness. Our study defines a therapeutic potential for S1P inhibitors in management of airways hyperresponsiveness associated to emphysema in smokers with both asthma and COPD.

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作者列表:["Bernstein DM","Toth B","Rogers RA","Kling DE","Kunzendorf P","Phillips JI","Ernst H"]

METHODS::The interim results from this 90-day multi-dose, inhalation toxicology study with life-time post-exposure observation has shown an important fundamental difference in persistence and pathological response in the lung between brake dust derived from brake-pads manufactured with chrysotile, TiO2 or chrysotile alone in comparison to the amphiboles, crocidolite and amosite asbestos. In the brake dust exposure groups no significant pathological response was observed at any time. Slight macrophage accumulation of particles was noted. Wagner-scores, were from 1 to 2 (1 = air-control group) and were similar to the TiO2 group. Chrysotile being biodegradable, shows a weakening of its matrix and breaking into short fibers & particles that can be cleared by alveolar macrophages and continued dissolution. In the chrysotile exposure groups, particle laden macrophage accumulation was noted leading to a slight interstitial inflammatory response (Wagner-score 1-3). There was no peribronchiolar inflammation and occasional very slight interstitial fibrosis. The histopathology and the confocal analyses clearly differentiate the pathological response from amphibole asbestos, crocidolite and amosite, compared to that from the brake dust and chrysotile. Both crocidolite and amosite induced persistent inflammation, microgranulomas, and fibrosis (Wagner-scores 4), which persisted through the post exposure period. The confocal microscopy of the lung and snap-frozen chestwalls quantified the extensive inflammatory response and collagen development in the lung and on the visceral and parietal surfaces. The interim results reported here, provide a clear basis for differentiating the effects from brake dust exposure from those following amphibole asbestos exposure. The subsequent results through life-time post-exposure will follow.

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作者列表:["Zaragosi LE","Deprez M","Barbry P"]

METHODS::The respiratory tract is lined by a pseudo-stratified epithelium from the nose to terminal bronchioles. This first line of defense of the lung against external stress includes five main cell types: basal, suprabasal, club, goblet and multiciliated cells, as well as rare cells such as ionocytes, neuroendocrine and tuft/brush cells. At homeostasis, this epithelium self-renews at low rate but is able of fast regeneration upon damage. Airway epithelial cell lineages during regeneration have been investigated in the mouse by genetic labeling, mainly after injuring the epithelium with noxious agents. From these approaches, basal cells have been identified as progenitors of club, goblet and multiciliated cells, but also of ionocytes and neuroendocrine cells. Single-cell RNA sequencing, coupled to lineage inference algorithms, has independently allowed the establishment of comprehensive pictures of cell lineage relationships in both mouse and human. In line with genetic tracing experiments in mouse trachea, studies using single-cell RNA sequencing (RNAseq) have shown that basal cells first differentiate into club cells, which in turn mature into goblet cells or differentiate into multiciliated cells. In the human airway epithelium, single-cell RNAseq has identified novel intermediate populations such as deuterosomal cells, 'hybrid' mucous-multiciliated cells and progenitors of rare cells. Novel differentiation dynamics, such as a transition from goblet to multiciliated cells have also been discovered. The future of cell lineage relationships in the respiratory tract now resides in the combination of genetic labeling approaches with single-cell RNAseq to establish, in a definitive manner, the hallmarks of cellular lineages in normal and pathological situations.

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