Feng Hou

916 total citations
50 papers, 566 citations indexed

About

Feng Hou is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Feng Hou has authored 50 papers receiving a total of 566 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Pulmonary and Respiratory Medicine, 20 papers in Radiology, Nuclear Medicine and Imaging and 15 papers in Oncology. Recurrent topics in Feng Hou's work include Radiomics and Machine Learning in Medical Imaging (19 papers), Sarcoma Diagnosis and Treatment (16 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Feng Hou is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), Sarcoma Diagnosis and Treatment (16 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Feng Hou collaborates with scholars based in China and United States. Feng Hou's co-authors include Dapeng Hao, Hexiang Wang, Jihua Liu, Hexiang Wang, Chencui Huang, Shunli Liu, Cheng Dong, Pei Nie, Tengbo Yu and Peng Gao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Cancer.

In The Last Decade

Feng Hou

45 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Feng Hou China 15 268 257 120 103 99 50 566
Xiao Song China 15 156 0.6× 296 1.2× 138 1.1× 163 1.6× 95 1.0× 40 581
Tania Kaprealian United States 18 203 0.8× 370 1.4× 107 0.9× 135 1.3× 56 0.6× 71 944
Chiara Trentin Italy 14 149 0.6× 211 0.8× 148 1.2× 158 1.5× 74 0.7× 44 615
Keisuke Hanioka Japan 16 119 0.4× 296 1.2× 186 1.6× 172 1.7× 147 1.5× 58 728
Futoshi Sano Japan 17 153 0.6× 484 1.9× 120 1.0× 145 1.4× 240 2.4× 48 693
J.C. Sabourin France 10 118 0.4× 169 0.7× 137 1.1× 94 0.9× 126 1.3× 16 546
B. Adam United States 9 104 0.4× 261 1.0× 140 1.2× 124 1.2× 120 1.2× 13 519
Jae‐Soo Koh South Korea 17 201 0.8× 530 2.1× 173 1.4× 124 1.2× 127 1.3× 39 809
Toshitake Yakushiji Japan 10 277 1.0× 285 1.1× 50 0.4× 107 1.0× 47 0.5× 18 666
Salvatore Lorenzo Renne Italy 13 84 0.3× 360 1.4× 205 1.7× 85 0.8× 120 1.2× 46 693

Countries citing papers authored by Feng Hou

Since Specialization
Citations

This map shows the geographic impact of Feng Hou'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 Feng Hou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feng Hou more than expected).

Fields of papers citing papers by Feng Hou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Feng Hou. 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 Feng Hou. The network helps show where Feng Hou may publish in the future.

Co-authorship network of co-authors of Feng Hou

This figure shows the co-authorship network connecting the top 25 collaborators of Feng Hou. A scholar is included among the top collaborators of Feng Hou 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 Feng Hou. Feng Hou 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.
Shan, Yuping, et al.. (2025). Cervical metastasis of breast cancer: a case report and review of the literature. Discover Oncology. 16(1). 633–633.
2.
3.
Zhou, Hu, Liang Hong, Hongbin Zhao, et al.. (2023). Preoperative contrast-enhanced CT-based radiomics signature for predicting hypoxia-inducible factor 1α expression in retroperitoneal sarcoma. Clinical Radiology. 78(8). e543–e551. 2 indexed citations
4.
Liu, Ping, et al.. (2023). A deep-learning model using enhanced chest CT images to predict PD-L1 expression in non-small-cell lung cancer patients. Clinical Radiology. 78(10). e689–e697. 3 indexed citations
5.
Yu, Boyang, Na Li, Rui Sun, et al.. (2023). CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study. Cancer Imaging. 23(1). 89–89. 13 indexed citations
6.
Hou, Feng, Duan‐Bo Shi, Xiangyu Guo, et al.. (2023). HRCT1, negatively regulated by miR-124-3p, promotes tumor metastasis and the growth of gastric cancer by activating the ERBB2-MAPK pathway. Gastric Cancer. 26(2). 250–263. 7 indexed citations
7.
8.
Hou, Feng, et al.. (2022). Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study. Frontiers in Oncology. 12. 897676–897676. 19 indexed citations
9.
Gao, Yan, Xianqi Feng, Shanshan Liu, et al.. (2021). Acute myeloid leukemia with T lymphoblastic lymphoma: a case report and literature review. Journal of International Medical Research. 49(5). 3619077066–3619077066.
10.
Hao, Dapeng, Jie Li, Jihua Liu, et al.. (2021). Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study. Journal of Magnetic Resonance Imaging. 53(6). 1683–1696. 42 indexed citations
11.
Liu, Shunli, Chencui Huang, Jingxu Xu, et al.. (2021). Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. European Radiology. 32(2). 793–805. 39 indexed citations
12.
13.
Hua, Hui, Yuanxiang Gao, Feng Hou, et al.. (2020). Quantitative Analysis of Enhanced Computed Tomography in Differentiating Cystitis Glandularis and Bladder Cancer. BioMed Research International. 2020(1). 4930621–4930621. 2 indexed citations
14.
Sun, Xiao, Yunpeng Xuan, Yandong Zhao, et al.. (2020). Relevance and prognostic ability of Twist, Slug and tumor spread through air spaces in lung adenocarcinoma. Cancer Medicine. 9(6). 1986–1998. 16 indexed citations
15.
Wang, Hexiang, Jian Zhang, Shan Bao, et al.. (2020). Preoperative MRI‐Based Radiomic Machine‐Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft‐Tissue Lesions: A Two‐Center Study. Journal of Magnetic Resonance Imaging. 52(3). 873–882. 55 indexed citations
16.
17.
Shi, Duan‐Bo, et al.. (2019). GAGE7B promotes tumor metastasis and growth via activating the p38δ/pMAPKAPK2/pHSP27 pathway in gastric cancer. Journal of Experimental & Clinical Cancer Research. 38(1). 124–124. 17 indexed citations
18.
Wang, Hexiang, Pei Nie, Cheng Dong, et al.. (2018). Computed Tomography Imaging Findings of Pulmonary Chondroma. BioMed Research International. 2018. 1–7. 4 indexed citations
19.
Wang, Hexiang, et al.. (2018). MRI Findings of Early Myositis Ossificans without Calcification or Ossification. BioMed Research International. 2018. 1–6. 10 indexed citations
20.
Cui, Jiufa, et al.. (2017). Magnetic Resonance Features and Characteristic Vascular Pattern of Alveolar Soft-Part Sarcoma. Oncology Research and Treatment. 40(10). 580–585. 14 indexed citations

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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