Chiharu Sako

2.0k total citations
27 papers, 251 citations indexed

About

Chiharu Sako is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Chiharu Sako has authored 27 papers receiving a total of 251 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Genetics and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Chiharu Sako's work include Radiomics and Machine Learning in Medical Imaging (20 papers), Glioma Diagnosis and Treatment (9 papers) and MRI in cancer diagnosis (5 papers). Chiharu Sako is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (20 papers), Glioma Diagnosis and Treatment (9 papers) and MRI in cancer diagnosis (5 papers). Chiharu Sako collaborates with scholars based in United States, France and Japan. Chiharu Sako's co-authors include Christos Davatzikos, Spyridon Bakas, Hamed Akbari, Sung Min Ha, Güray Erus, Michel Bilello, Anahita Fathi Kazerooni, Russell T. Shinohara, Sarthak Pati and Siddhesh Thakur and has published in prestigious journals such as Journal of Clinical Oncology, Journal of Neuroscience and Scientific Reports.

In The Last Decade

Chiharu Sako

23 papers receiving 248 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chiharu Sako United States 10 141 75 46 43 38 27 251
Roelant S. Eijgelaar Netherlands 7 190 1.3× 131 1.7× 32 0.7× 71 1.7× 25 0.7× 13 286
Zhengda Yu China 6 199 1.4× 128 1.7× 27 0.6× 67 1.6× 45 1.2× 8 317
Niels Verburg Netherlands 11 226 1.6× 215 2.9× 67 1.5× 42 1.0× 32 0.8× 19 484
Lianwang Li China 9 183 1.3× 158 2.1× 22 0.5× 36 0.8× 43 1.1× 23 322
Domenique M. J. Müller Netherlands 6 105 0.7× 75 1.0× 17 0.4× 39 0.9× 48 1.3× 10 217
Francesco Sanvito United States 8 152 1.1× 74 1.0× 21 0.5× 32 0.7× 13 0.3× 32 235
Zezhong Ye United States 9 161 1.1× 28 0.4× 25 0.5× 31 0.7× 11 0.3× 21 224
Matthew J. Barkovich United States 9 123 0.9× 39 0.5× 26 0.6× 48 1.1× 28 0.7× 20 304
Paul Eichinger Germany 9 201 1.4× 97 1.3× 48 1.0× 30 0.7× 16 0.4× 12 314
Markand Patel United Kingdom 10 158 1.1× 104 1.4× 23 0.5× 34 0.8× 23 0.6× 21 400

Countries citing papers authored by Chiharu Sako

Since Specialization
Citations

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

Fields of papers citing papers by Chiharu Sako

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chiharu Sako

This figure shows the co-authorship network connecting the top 25 collaborators of Chiharu Sako. A scholar is included among the top collaborators of Chiharu Sako 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 Chiharu Sako. Chiharu Sako 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.
Guo, Jun, Anahita Fathi Kazerooni, Erik Toorens, et al.. (2024). Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach. Scientific Reports. 14(1). 4922–4922. 10 indexed citations
2.
Sako, Chiharu, Chong Duan, Kevin Maresca, et al.. (2024). Real-world and clinical trial validation of a deep learning radiomic biomarker for PD-(L)1 immune checkpoint inhibitor response in stage IV NSCLC.. Journal of Clinical Oncology. 42(16_suppl). 102–102.
3.
Sako, Chiharu, Chong Duan, Kevin Maresca, et al.. (2024). Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non–Small Cell Lung Cancer. JCO Clinical Cancer Informatics. 8(8). e2400133–e2400133. 5 indexed citations
4.
Simon, George R., Chiharu Sako, Ryan Beasley, et al.. (2023). AI-based radiomic biomarkers to predict PD-(L)1 immune checkpoint inhibitor response within PD-L1 high/low/negative expression categories in stage IV NSCLC.. Journal of Clinical Oncology. 41(16_suppl). 1517–1517. 1 indexed citations
5.
Khanna, Omaditya, Anahita Fathi Kazerooni, Aria Mahtabfar, et al.. (2023). Radiomic signatures of meningiomas using the Ki-67 proliferation index as a prognostic marker of clinical outcomes. Neurosurgical FOCUS. 54(6). E17–E17. 14 indexed citations
6.
Sako, Chiharu, Dwight H. Owen, Arya Amini, et al.. (2023). Multi-center real-world data curation and assessment of tumor growth rate and overall survival in advanced NSCLC treated with PD-(L)1 immune checkpoint inhibitor therapy.. Journal of Clinical Oncology. 41(16_suppl). 9124–9124. 1 indexed citations
7.
Thakur, Siddhesh, Sarthak Pati, Deepthi Karkada, et al.. (2022). Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. Lecture notes in computer science. 12962. 151–167. 5 indexed citations
8.
Pati, Sarthak, Ujjwal Baid, Brandon Edwards, et al.. (2022). The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research. Physics in Medicine and Biology. 67(20). 204002–204002. 17 indexed citations
9.
Sayah, Anousheh, Krithika Bhuvaneshwar, Anas Belouali, et al.. (2022). Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features. Scientific Data. 9(1). 338–338. 9 indexed citations
10.
Kazerooni, Anahita Fathi, Erik Toorens, Hamed Akbari, et al.. (2022). NIMG-22. AN AI-BASED COORDINATE SYSTEM ELUCIDATES RADIOGENOMIC HETEROGENEITY OF GLIOBLASTOMA VIA DEEP LEARNING AND MANIFOLD EMBEDDINGS. Neuro-Oncology. 24(Supplement_7). vii166–vii166.
11.
Guo, Jun, Anahita Fathi Kazerooni, Hamed Akbari, et al.. (2021). NIMG-58. CANONICAL CORRELATION ANALYSIS IN GLIOBLASTOMA REVEALS ASSOCIATIONS BETWEEN EXPRESSION OF RADIOMIC SIGNATURES AND GENOMICS. Neuro-Oncology. 23(Supplement_6). vi142–vi142. 1 indexed citations
12.
Saxena, Sanjay, Anahita Fathi Kazerooni, Erik Toorens, et al.. (2021). NIMG-73. CAPTURING GLIOBLASTOMA HETEROGENEITY USING IMAGING AND DEEP LEARNING: APPLICATION TO MGMT PROMOTER METHYLATION. Neuro-Oncology. 23(Supplement_6). vi146–vi146. 3 indexed citations
13.
Akbari, Hamed, Anahita Fathi Kazerooni, Jeffrey B. Ware, et al.. (2021). Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging. Scientific Reports. 11(1). 15011–15011. 16 indexed citations
14.
15.
Pati, Sarthak, Ruchika Verma, Hamed Akbari, et al.. (2020). Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset. Medical Physics. 47(12). 6039–6052. 28 indexed citations
16.
Truelove‐Hill, Monica, Güray Erus, Vishnu Bashyam, et al.. (2020). A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents. Journal of Neuroscience. 40(6). 1265–1275. 35 indexed citations
17.
Bakas, Spyridon, Gaurav Shukla, Hamed Akbari, et al.. (2020). Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols. PubMed. 11315. 3 indexed citations
18.
Thakur, Siddhesh, Jimit Doshi, Sarthak Pati, et al.. (2020). Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning. Lecture notes in computer science. 11992. 57–68. 19 indexed citations
19.
Takahashi, Hiroyuki, Takeshi Fujiwara, Chiharu Sako, et al.. (2010). The transparent microstrip gas counter. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 623(1). 123–125. 1 indexed citations
20.
Itoh, Takeshi, Chiharu Sako, M. Kokubun, et al.. (2005). Evaluation of radiation tolerance of FETs used for Astro-E2 hard X-ray detector (HXD-II). Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 541(1-2). 241–247. 1 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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