Hwan-ho Cho

1.1k total citations
29 papers, 819 citations indexed

About

Hwan-ho Cho is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Hwan-ho Cho has authored 29 papers receiving a total of 819 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Pulmonary and Respiratory Medicine and 6 papers in Biomedical Engineering. Recurrent topics in Hwan-ho Cho's work include Radiomics and Machine Learning in Medical Imaging (24 papers), MRI in cancer diagnosis (8 papers) and Advanced X-ray and CT Imaging (6 papers). Hwan-ho Cho is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (24 papers), MRI in cancer diagnosis (8 papers) and Advanced X-ray and CT Imaging (6 papers). Hwan-ho Cho collaborates with scholars based in South Korea, United States and Italy. Hwan-ho Cho's co-authors include Hyunjin Park, Seung-Hak Lee, Jong‐Hoon Kim, Eun Sook Ko, Ko Woon Park, Jeong Eon Lee, Ji Soo Choi, Ho Yun Lee, Boo‐Kyung Han and Yaeji Lim and has published in prestigious journals such as Scientific Reports, Clinical Cancer Research and Medicine.

In The Last Decade

Hwan-ho Cho

28 papers receiving 814 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Hwan-ho Cho South Korea 15 683 219 174 163 122 29 819
Qiuchang Sun China 11 649 1.0× 198 0.9× 258 1.5× 177 1.1× 115 0.9× 15 755
Jay Patel United States 15 447 0.7× 179 0.8× 205 1.2× 94 0.6× 115 0.9× 31 725
Shiteng Suo China 19 923 1.4× 198 0.9× 72 0.4× 75 0.5× 111 0.9× 65 1.1k
Archya Dasgupta India 15 306 0.4× 160 0.7× 259 1.5× 89 0.5× 94 0.8× 75 615
Chaoyue Chen China 17 370 0.5× 93 0.4× 258 1.5× 50 0.3× 70 0.6× 56 815
Mehrdad Oveisi Iran 14 691 1.0× 241 1.1× 69 0.4× 140 0.9× 281 2.3× 41 873
Sohi Bae South Korea 11 681 1.0× 184 0.8× 489 2.8× 77 0.5× 109 0.9× 20 895
Luke Macyszyn United States 16 410 0.6× 83 0.4× 274 1.6× 94 0.6× 193 1.6× 44 975
Shuaitong Zhang China 12 487 0.7× 185 0.8× 279 1.6× 49 0.3× 60 0.5× 24 617

Countries citing papers authored by Hwan-ho Cho

Since Specialization
Citations

This map shows the geographic impact of Hwan-ho Cho's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hwan-ho Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hwan-ho Cho more than expected).

Fields of papers citing papers by Hwan-ho Cho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hwan-ho Cho. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hwan-ho Cho. The network helps show where Hwan-ho Cho may publish in the future.

Co-authorship network of co-authors of Hwan-ho Cho

This figure shows the co-authorship network connecting the top 25 collaborators of Hwan-ho Cho. A scholar is included among the top collaborators of Hwan-ho Cho based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hwan-ho Cho. Hwan-ho Cho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Maria, Lucio De, Hwan-ho Cho, Karoline Skogen, et al.. (2024). The Current Diagnostic Performance of MRI-Based Radiomics for Glioma Grading: A Meta-Analysis. Journal of Integrative Neuroscience. 23(5). 100–100. 4 indexed citations
2.
Kim, Sung-Soo, et al.. (2024). Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors. Frontiers in Veterinary Science. 11. 1450304–1450304. 1 indexed citations
3.
Cho, Hwan-ho, Hyung Jun Kim, Jong‐Won Chung, et al.. (2024). Global intracranial arterial tortuosity is associated with intracranial atherosclerotic burden. Scientific Reports. 14(1). 11318–11318.
4.
Cho, Hwan-ho, Jong‐Hoon Kim, Ji Eun Lee, et al.. (2024). Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels. Heliyon. 10(11). e32375–e32375. 1 indexed citations
5.
Cho, Hwan-ho, Jieun Lee, Yoon‐Chul Kim, et al.. (2023). Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks. Scientific Reports. 13(1). 3255–3255. 7 indexed citations
6.
Cho, Hwan-ho, et al.. (2022). Robust multimodal fusion network using adversarial learning for brain tumor grading. Computer Methods and Programs in Biomedicine. 226. 107165–107165. 7 indexed citations
7.
8.
Cho, Hwan-ho, Sang Yu Nam, Jeong Eon Lee, et al.. (2022). Measurement of Perfusion Heterogeneity within Tumor Habitats on Magnetic Resonance Imaging and Its Association with Prognosis in Breast Cancer Patients. Cancers. 14(8). 1858–1858. 36 indexed citations
9.
Cho, Hwan-ho, et al.. (2021). Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Communications Biology. 4(1). 1286–1286. 25 indexed citations
10.
Cho, Hwan-ho, Joon Young Choi, Teri J. Franks, et al.. (2021). Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients. European Journal of Radiology Open. 8. 100351–100351. 8 indexed citations
11.
Cho, Hwan-ho, Geewon Lee, Ho Yun Lee, & Hyunjin Park. (2020). Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma. European Radiology. 30(5). 2984–2994. 26 indexed citations
12.
Kwon, Mi-ri, Jung Hee Shin, Hyunjin Park, et al.. (2020). Radiomics Study of Thyroid Ultrasound for Predicting BRAF Mutation in Papillary Thyroid Carcinoma: Preliminary Results. American Journal of Neuroradiology. 41(4). 700–705. 40 indexed citations
13.
Cho, Hwan-ho, Minsu Park, Hyunjin Park, et al.. (2020). The Tumor–Fat Interface Volume of Breast Cancer on Pretreatment MRI Is Associated with a Pathologic Response to Neoadjuvant Chemotherapy. Biology. 9(11). 391–391. 3 indexed citations
14.
Choi, Seung Won, Hwan-ho Cho, Harim Koo, et al.. (2020). Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic Significance. Cancers. 12(7). 1707–1707. 30 indexed citations
15.
Lee, Seung-Hak, Hwan-ho Cho, Ho Yun Lee, & Hyunjin Park. (2019). Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer. Cancer Imaging. 19(1). 54–54. 38 indexed citations
16.
Park, Hyunjin, Yaeji Lim, Eun Sook Ko, et al.. (2018). Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer. Clinical Cancer Research. 24(19). 4705–4714. 189 indexed citations
17.
Cho, Hwan-ho, Seung-Hak Lee, Jong‐Hoon Kim, & Hyunjin Park. (2018). Classification of the glioma grading using radiomics analysis. PeerJ. 6. e5982–e5982. 138 indexed citations
18.
Kim, Yi Kyung, Hwan-ho Cho, Sung Tae Kim, et al.. (2018). Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology. 60(12). 1297–1305. 56 indexed citations
19.
Cho, Hwan-ho & Hyunjin Park. (2017). Classification of low-grade and high-grade glioma using multi-modal image radiomics features. PubMed. 2017. 3081–3084. 76 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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