Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation

Zhou, Chengwei; Zhao, Xiaodong; Zhao, Lili; Liu, Jiayuan; Chen, Zixuan; Fang, Shuai

doi:10.1155/2022/7326537

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Deep learning technologies, especially convolutional neural networks (CNNs), have become powerful tools for improving lung nodule detection and classification performance due to their ability to automatically learn complex and abstract features from large datasets. However, the performance of deep learning models largely depends on the availability of large amounts of annotated data, which is often difficult to obtain in medical imaging analysis [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Cheng,

Li,

et al. 2024

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Early detection and accurate classification of lung nodules are crucial for the treatment of lung cancer. With the widespread application of deep learning technologies in medical imaging analysis, significant progress has been made in the automatic detection and classification of lung nodules from computed tomography (CT) images. However, existing deep learning approaches often face challenges with limited annotated data and generalization across diverse datasets. To address these challenges, this study introduces two innovative methods: a domain-adaptive adversarial network for joint segmentation of lung CT images to enhance model generalization, and an improved deep propagation generation network (DPGN) for few-shot classification of lung CT images to reduce reliance on extensive annotated data. Through these methods, this research aims to improve the accuracy of lung nodule detection and classification, providing more reliable support for clinical diagnosis.

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Section: Introductionmentioning

confidence: 99%

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Cheng,

Li,

et al. 2024

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Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

Matsui,

Ueda,

Fujita

et al. 2024

Jpn J Radiol

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Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.

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Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

Wang,

Yang

et al. 2024

BMC Pulm Med

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Purpose To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images. Methods This is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility. Results The T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram. Conclusion A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-024-03360-8.

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Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation

Cited by 3 publications

References 19 publications

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

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