ISSN:2167-7964
Journal of Radiology
Open Access
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Google Scholar citation report
Citations : 551
Journal of Radiology received 551 citations as per Google Scholar report
Journal of Radiology peer review process verified at publons
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Journal of Radiology : Citations & Metrics Report
Articles published in Journal of Radiology have been cited by esteemed scholars and scientists all around the world. Journal of Radiology has got h-index 10, which means every article in Journal of Radiology has got 10 average citations.
Following are the list of articles that have cited the articles published in Journal of Radiology.
| 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | |
|---|---|---|---|---|---|---|---|---|---|
Total published articles |
91 | 97 | 60 | 60 | 21 | 15 | 19 | 44 | 41 |
Research, Review articles and Editorials |
34 | 56 | 20 | 19 | 17 | 8 | 0 | 0 | 0 |
Research communications, Review communications, Editorial communications, Case reports and Commentary |
46 | 5 | 2 | 11 | 4 | 1 | 0 | 0 | 0 |
Conference proceedings |
0 | 0 | 5 | 0 | 0 | 0 | 42 | 88 | 35 |
Citations received as per Google Scholar, other indexing platforms and portals |
46 | 48 | 90 | 86 | 74 | 78 | 108 | 103 | 101 |
| Journal total citations count | 551 |
| Journal impact factor | 2.99 |
| Journal 5 years impact factor | 4.66 |
| Journal cite score | 4.52 |
| Journal h-index | 10 |
| Journal h-index since 2019 | 10 |
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Isaksson, L. J., Pepa, M., Zaffaroni, M., Marvaso, G., Alterio, D., Volpe, S., ... & Jereczek-Fossa, B. A. (2020). Machine learning-based models for prediction of toxicity outcomes in radiotherapy. Frontiers in oncology, 10, 790. |
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Toesca, D. A., Ibragimov, B., Koong, A. J., Xing, L., Koong, A. C., & Chang, D. T. (2018). Strategies for prediction and mitigation of radiation-induced liver toxicity. Journal of radiation research, 59(suppl_1), i40-i49. |
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Joyal, V. Research and Reviews: Journal of Pharmacy and Pharmaceutical Sciences Neoplastic Diseases: A Mini-Review. |
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Zhou, X., Long, L., Mo, Z., & Li, Y. (2021). OATP1B3 Expression in Hepatocellular Carcinoma Correlates with Intralesional Gd-EOB-DTPA Uptake and Signal Intensity on Gd-EOB-DTPA-Enhanced MRI. Cancer Management and Research, 13, 1169. |
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Joyal, V. Research and Reviews: Journal of Pharmacy and Pharmaceutical Sciences Neoplastic Diseases: A Mini-Review. |
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Yang, D., Du, J., Wang, J., & Sui, Y. (2021, May). Application of Intelligent Image Processing in Precision Diagnosis of Pulmonary Ground Glass Nodules. In 2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) (pp. 35-41). IEEE. |
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Bhusnurmath, R. A., & Hiremath, P. S. (2020). Anisotropic Diffusion-Based Color Texture Analysis for Industrial Application. In Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities (pp. 30-64). IGI Global. |
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Al-Shikha, S., & Kraman, S. A Review of Image Enhancement Techniques for Recognizing and Classifying Automatically the Medical Images. |
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Kirkwood, M. (2018). Ultrasound Characterization of Voluntary Muscle Contraction in Healthy Humans (Doctoral dissertation). |
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Moyya, P. D., Asaithambi, M., & Ramaniharan, A. K. (2021). Radiomics Based Analysis of Breast Tumors in DCE-MRI due to Neoadjuvant Treatment Therapy. In Advances in Automation, Signal Processing, Instrumentation, and Control (pp. 2197-2204). Springer, Singapore. |
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Usha, R., & Perumal, K. (2019). SVM classification of brain images from MRI scans using morphological transformation and GLCM texture features. International journal of computational systems engineering, 5(1), 18-23. |
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Charfi, S., & El Ansari, M. (2018). Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images. Multimedia Tools and Applications, 77(3), 4047-4064. |
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Szumowski, P., Szklarzewski, A., ?ukowski, ?., Abdelrazek, S., Mojsak, M., Por?bska, K., ... & My?liwiec, J. (2021). Pre-Processing Method for Contouring the Uptake Levels of [18F] FDG for Enhanced Specificity of PET Imaging of Solitary Hypermetabolic Pulmonary Nodules. Journal of Clinical Medicine, 10(7), 1430. |
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Liu, C., Meng, Q., Zeng, Q., Shen, Y., Li, B., Cen, R., ... & Wu, T. (2021). An exploratory study on the stable radiomics features of metastatic small pulmonary nodules in colorectal cancer patients. Frontiers in Oncology, 11, 2579. |
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Shao, S., Mao, N., Liu, W., Cui, J., Xue, X., Cheng, J., ... & Wang, B. (2020). Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors. Journal of X-ray Science and Technology, 28(4), 799-808. |
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Florez, E., Fatemi, A., Claudio, P. P., & Howard, C. M. (2018). Emergence of radiomics: novel methodology identifying imaging biomarkers of disease in diagnosis, response, and progression. SM journal of clinical and medical imaging, 4(1). |
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Xu, Y., Lu, L., E, L. N., Lian, W., Yang, H., Schwartz, L. H., ... & Zhao, B. (2019). Application of radiomics in predicting the malignancy of pulmonary nodules in different sizes. American Journal of Roentgenology, 213(6), 1213-1220. |
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Yin, P., Mao, N., Zhao, C., Wu, J., Chen, L., & Hong, N. (2019). A triple?classification radiomics model for the differentiation of primary chordoma, giant cell tumor, and metastatic tumor of sacrum based on T2?weighted and contrast?enhanced T1?weighted MRI. Journal of Magnetic Resonance Imaging, 49(3), 752-759. |
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Hu, T., Wang, S., Huang, L., Wang, J., Shi, D., Li, Y., ... & Peng, W. (2019). A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules. European radiology, 29(1), 439-449. |
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Thawani, R., McLane, M., Beig, N., Ghose, S., Prasanna, P., Velcheti, V., & Madabhushi, A. (2018). Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung cancer, 115, 34-41. |
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