Computer tomography guided lung biopsy using interactive breath-hold control: a randomized study

Ashraf, Haseem; Krag-Andersen, Shella; Naqibullah, Matiullah; Minddal, Valentina; Nørgaard, Annette; Naur, Therese Maria Henriette; Myschetzky, Peter Sand; Clementsen, Paul Frost

doi:10.21037/atm.2017.05.10

Cited by 11 publications

(12 citation statements)

References 17 publications

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“…Pneumothorax is a common and potentially life-threatening complication of puncturing the thorax. Emphysema has been widely recognized as an independent risk factor of pneumothorax after biopsy; however, other risk factors, such as lesion size, lesion depth, and patient age remain controversial (15,16,(21)(22)(23)(24)(25)(26). Although most researchers believe that the size and depth of the lesion as well as patient age are risk factors, there is no published evidence definitively supporting this.…”

Section: Introductionmentioning

confidence: 99%

Logistic regression analysis and a risk prediction model of pneumothorax after CT-guided needle biopsy

Zhao¹,

Wang²,

Wang³

et al. 2017

J. Thorac. Dis.

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Background: Pneumothorax is the most common complication of computed tomography (CT)-guided needle biopsy. The purpose of this study was to investigate independent risk factors of pneumothorax, other than emphysema, after CT-guided needle biopsy and to establish a risk prediction model. Methods: A total of 864 cases of CT-guided needle biopsy with an 18-gauge cutting needle were enrolled in this study. The relevant risk factors associated with pneumothorax included age, sex, emphysema, shortaxis size of the lesion, depth of the lesion, body position, and the number of pleural punctures. Several independent risk factors of pneumothorax were found, and a predictive model for pneumothorax was established using univariate and multivariate logistic regression analyses. Results: Pneumothorax occurred in 31.4% (271/864) of cases. Univariate analysis showed that significant risk factors of pneumothorax included age, emphysema, small lesion size, no contact between the lesion and the pleura, prone or lateral body position, and multiple punctures. Independent risk factors of pneumothorax in the multivariate logistic regression analysis included emphysema (P=0.000), no contact between the lesion and the pleura (P=0.000), prone or lateral body position (P=0.002), and the number of pleural punctures (P=0.000). The sensitivity, specificity, and accuracy of the predictive model for pneumothorax were 56.8%, 79.6%, and 72.5%, respectively. Conclusions: Pneumothorax is a common complication of CT-guided lung biopsy. Independent risk factors of pneumothorax include emphysema, no contact between the lesion and the pleura, and prone or lateral body position. The predictive model developed in this study was highly accurate in predicting the incidence of pneumothorax.

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Section: Introductionmentioning

confidence: 99%

Logistic regression analysis and a risk prediction model of pneumothorax after CT-guided needle biopsy

Zhao¹,

Wang²,

Wang³

et al. 2017

J. Thorac. Dis.

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“…18 Conversely, the level of breath holding at the end of expiration was not reproducible in another study. 17 We hypothesized that this is because patients tend to breathe deeply or perform Valsalva when asked to hold their breath, 4 which might induce significant movement of the nodule during full inspiration and at the end of exhalation. 19 Conversely, in patients with a large tumor or tumor in upper lobe, it was possible to conduct biopsies using free breathing without respiratory targeting.…”

Section: Discussionmentioning

confidence: 99%

Is Free Breathing Possible During Computed Tomography–Guided Percutaneous Transthoracic Lung Biopsy? The Clinical Experience in 585 Cases

Han

Lee

Choi

et al. 2022

J Comput Assist Tomogr

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The aim of the study was to retrospectively evaluate the safety and accuracy of computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) of lung lesions during quiet breathing. Methods:We investigated the diagnostic performance and complication rate of 585 procedures in 563 patients (357 men; mean age, 67.7 years), who underwent CT-guided PTNBs during quiet breathing, aided by a respiratory targeting technique from May 2017 to July 2019. Differences between the cases with and without respiratory targeting were analyzed. Logistic regression analyses were performed to examine the development of pneumothorax and hemoptysis.Results: Percutaneous transthoracic needle biopsy samples were successfully obtained in 574 of 585 procedures (98.1%). Final diagnoses included: 410 malignant cases, 119 benign cases, and 45 indeterminate cases. The sensitivity, specificity, and accuracy of diagnosis were 94.4%, 100%, and 95.7%, respectively. Use of respiratory targeting was associated with younger age ( P = 0.004), smaller lesion size ( P < 0.001), peripheral location ( P = 0.003), shorter distance from the diaphragm ( P < 0.001), lower lobe location ( P < 0.001), prone position ( P = 0.004), and visible motion artifact ( P < 0.001). Pneumothorax and hemoptysis rates were 22.9% and 7.9%, respectively. Upon multivariate analysis, emphysema ( P = 0.002) was the only independent risk factor for pneumothorax, whereas distance from the pleura greater than 2 cm ( P < 0.001), tissue sampling 3 times or more ( P = 0.003), and a less experienced operator ( P < 0.001) were risk factors for hemoptysis.Conclusions: Computed tomography-guided PTNB during quiet breathing with respiratory targeting yielded high diagnostic performance with a slightly higher rate of complications. Free-breathing PTNB can be applied in clinical practice, based on lesion location and risk factors for complications.

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“…All the methods need to manually set the learning rate or use a common method of artificially attenuating the learning rate to reduce the learning rate according to the number of training iteration steps. The optimization method is an adaptive optimization method that can adjust the learning rate according to each parameter [ 17 ]. First, for the number of iteration steps f and the weight item λ x , assume that there is a variable f t , x =∇ λ S ( λ x ), which is represented by f t for the convenience of presentation.…”

Section: Introduction To the Principles And Methods Of Wireless Senso...mentioning

confidence: 99%

“…e optimization method is an adaptive optimization method that can adjust the learning rate according to each parameter [17]. First, for the number of iteration steps f and the weight item λ x , assume that there is a variable f t,x � ∇ λ S(λ x ), which is represented by f t for the convenience of presentation.…”

Section: Adaptive Moment Estimation (Adam)mentioning

confidence: 99%

Sports Training Strategies and Interactive Control Methods Based on Neural Network Models

2022

Computational Intelligence and Neuroscience

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Sports training strategies should be combined with science and technology to design the most suitable coaching strategies for athletes. In the current 5G Internet of Everything, the collection of wireless sensors and the deep learning of neural networks provide a new direction for the formulation of sports training strategies, guiding sports strategies to be more effective and scientific. This article aims to study and formulate sports training strategies, through the empowerment of science and technology, to better guide scientific training. With the help of the collection and sorting of sensors, the neural network allows deep learning of data, realizes human-computer interaction, and allows machines to better serve humans. This paper proposes an interactive control strategy for sports training, which enhances the interactive control of humans and machines and improves the level of training through the deep fusion of data. The experimental results of this article show that the human-computer interaction exercise training can better guide the exercise training and improve the training efficiency by 20%.

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Computer tomography guided lung biopsy using interactive breath-hold control: a randomized study

Abstract: The use of IBC for CT-guided lung biopsy was not an advantage for unselected patients in our care, since it did not improve the biopsy accuracy and the risk of pneumothorax was increased.

Cited by 11 publications

References 17 publications

Logistic regression analysis and a risk prediction model of pneumothorax after CT-guided needle biopsy

Logistic regression analysis and a risk prediction model of pneumothorax after CT-guided needle biopsy

Is Free Breathing Possible During Computed Tomography–Guided Percutaneous Transthoracic Lung Biopsy? The Clinical Experience in 585 Cases

Sports Training Strategies and Interactive Control Methods Based on Neural Network Models

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