Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study

Song, Ping; Feng, Guoyan; Wang, Jianru; An, Ping; Duan, Peng; Hu, Yan; Ye, Yingjian; Li, Yang; Qin, Ping; Song, Ping

doi:10.1155/2022/8090529

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Based on CEUS, a hybrid model was constructed using the residual net 50 (ResNet-50) architecture and incorporating clinicopathologic features to predict early recurrence of HCC after hepatic resection, which was diagnostically more efficient than was the single-feature model. In another report, Wang et al ( 63 ) retrospectively analyzed the clinical imaging data of 159 patients with pathologically confirmed prostate cancer who underwent radical prostatectomy and divided the patients into a biochemical recurrence (BCR) group (n=59) and a control group (n=100). They then established radiomics modeling based on CEUS, magnetic resonance imaging (MRI), and CEUS-combined with MRI, respectively, with the combined model demonstrating the best performance.…”

Section: Discussionmentioning

confidence: 99%

Research on ultrasound-based radiomics: a bibliometric analysis

Yu,

Che,

et al. 2024

Quant Imaging Med Surg

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Background A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization. Methods A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms. Results Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer. Conclusions Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognostic evaluation of diseases. Addition...

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Section: Discussionmentioning

confidence: 99%

Research on ultrasound-based radiomics: a bibliometric analysis

Yu,

Che,

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

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Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study

Qin

et al. 2023

MBE

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<abstract> <sec><title>Objective</title>To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients. </sec> <sec><title>Method</title>We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set. </sec> <sec><title>Result</title>For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set. </sec> <sec><title>Conclusion</title>Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate. </sec> </abstract>

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Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study

Cited by 2 publications

References 23 publications

Research on ultrasound-based radiomics: a bibliometric analysis

Research on ultrasound-based radiomics: a bibliometric analysis

Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study

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