Sensitivity and specificity of computed tomography hypodense sign when differentiating pulmonary inflammatory and malignant mass-like lesions

Fu, B.; Lv, Zhuo-Ma; Lv, Fajin; Li, Wang-jia; Lin, Rui-Yu; Chu, Zhi‐gang

doi:10.21037/qims-21-851

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…These networks are widely used in the diagnosis of pulmonary diseases. Various diagnostic models have been developed that use CT in combination with deep learning networks, including for the diagnosis of COVID-19 (12,13). Xie et al (14) proposed a multiview knowledge-based collaborative deep model to separate malignant from benign nodules using limited chest CT data.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Alimu¹,

Chen²,

Han³

et al. 2023

Quant Imaging Med Surg

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Background: Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs. Methods: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance. Results: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05). Conclusions: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Alimu¹,

Chen²,

Han³

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…With a high capability to image the internal structure of the human body in a noninvasive manner, computed tomography (CT) is critical in detecting lesions, tumors, and metastasis ( 1 ). However, the high level of accumulated radiation exposure from a CT examination and the risk of radiation-induced cancer and genetic or other diseases is of a significant concern to patients and operators.…”

Section: Introductionmentioning

confidence: 99%

Structure-preserving low-dose computed tomography image denoising using a deep residual adaptive global context attention network

Zhang,

Hao,

Lin

et al. 2023

Quant Imaging Med Surg

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Background Low-dose computed tomography (LDCT) scans can effectively reduce the radiation damage to patients, but this is highly detrimental to CT image quality. Deep convolutional neural networks (CNNs) have shown their potential in improving LDCT image quality. However, the conventional CNN-based approaches rely fundamentally on the convolution operations, which are ineffective for modeling the correlations among nonlocal similar structures and the regionally distinct statistical properties in CT images. This modeling deficiency hampers the denoising performance for CT images derived in this manner. Methods In this paper, we propose an adaptive global context (AGC) modeling scheme to describe the nonlocal correlations and the regionally distinct statistics in CT images with negligible computation load. We further propose an AGC-based long-short residual encoder-decoder (AGC-LSRED) network for efficient LDCT image noise artifact-suppression tasks. Specifically, stacks of residual AGC attention blocks (RAGCBs) with long and short skip connections are constructed in the AGC-LSRED network, which allows valuable structural and positional information to be bypassed through these identity-based skip connections and thus eases the training of the deep denoising network. For training the AGC-LSRED network, we propose a compound loss that combines the L 1 loss, adversarial loss, and self-supervised multi-scale perceptual loss. Results Quantitative and qualitative experimental studies were performed to verify and validate the effectiveness of the proposed method. The simulation experiments demonstrated the proposed method exhibits the best result in terms of noise suppression [root-mean-square error (RMSE) =9.02; peak signal-to-noise ratio (PSNR) =33.17] and fine structure preservation [structural similarity index (SSIM) =0.925] compared with other competitive CNN-based methods. The experiments on real data illustrated that the proposed method has advantages over other methods in terms of radiologists’ subjective assessment scores (averaged scores =4.34). Conclusions With the use of the AGC modeling scheme to characterize the structural information in CT images and of residual AGC-attention blocks with long and short skip connections to ease the network training, the proposed AGC-LSRED method achieves satisfactory results in preserving fine anatomical structures and suppressing noise in LDCT images.

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Sensitivity and specificity of computed tomography hypodense sign when differentiating pulmonary inflammatory and malignant mass-like lesions

Cited by 2 publications

References 40 publications

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Structure-preserving low-dose computed tomography image denoising using a deep residual adaptive global context attention network

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