Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization

Peng, Gang; Cao, Xiaojing; Huang, Xiaoyu; Zhou, Xiang

doi:10.1016/j.ejro.2024.100551

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…The general workflow of radiomics and artificial intelligence is shown in Figure 1 . Radiomics aims to extract and analyze patient imaging data, uncovering medical imaging features such as shape, size, texture, and intensity, including perianeurysmal enhancement, that are difficult to discern with the naked eye but can reveal the microstructure of lesions ( 9 ). Radiomics data can be integrated with patients’ clinical data to identify biomarkers and correlations in imaging that indicate patient prognosis and lesion status.…”

Section: Overview Of Artificial Intelligence and Radiomicsmentioning

confidence: 99%

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

Wen,

Wang,

Zhong

et al. 2024

Front. Neurol.

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Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

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Section: Overview Of Artificial Intelligence and Radiomicsmentioning

confidence: 99%

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

Wen,

Wang,

Zhong

et al. 2024

Front. Neurol.

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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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Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis

Wu,

Lai,

et al. 2024

BMC Med Imaging

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Background The aim of this study was to conduct a systematic review and meta-analysis to comprehensively evaluate the performance and methodological quality of artificial intelligence (AI) in predicting recurrence after single first-line treatment for liver cancer. Methods A rigorous and systematic evaluation was conducted on the AI studies related to recurrence after single first-line treatment for liver cancer, retrieved from the PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The area under the curve (AUC), sensitivity (SENC), and specificity (SPEC) of each study were extracted for meta-analysis. Results Six percutaneous ablation (PA) studies, 16 surgical resection (SR) studies, and 5 transarterial chemoembolization (TACE) studies were included in the meta-analysis for predicting recurrence after hepatocellular carcinoma (HCC) treatment, respectively. Four SR studies and 2 PA studies were included in the meta-analysis for recurrence after intrahepatic cholangiocarcinoma (ICC) and colorectal cancer liver metastasis (CRLM) treatment. The pooled SENC, SEPC, and AUC of AI in predicting recurrence after primary HCC treatment via PA, SR, and TACE were 0.78, 0.90, and 0.92; 0.81, 0.77, and 0.86; and 0.73, 0.79, and 0.79, respectively. The values for ICC treated with SR and CRLM treated with PA were 0.85, 0.71, 0.86 and 0.69, 0.63,0.74, respectively. Conclusion This systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-024-01440-z.

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Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization

Cited by 3 publications

References 43 publications

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

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

Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis

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