Ilke Tunali

1.3k total citations
19 papers, 896 citations indexed

About

Ilke Tunali is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Oncology. According to data from OpenAlex, Ilke Tunali has authored 19 papers receiving a total of 896 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Pulmonary and Respiratory Medicine and 4 papers in Oncology. Recurrent topics in Ilke Tunali's work include Radiomics and Machine Learning in Medical Imaging (19 papers), Lung Cancer Diagnosis and Treatment (13 papers) and Medical Imaging Techniques and Applications (9 papers). Ilke Tunali is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), Lung Cancer Diagnosis and Treatment (13 papers) and Medical Imaging Techniques and Applications (9 papers). Ilke Tunali collaborates with scholars based in United States, Türkiye and China. Ilke Tunali's co-authors include Matthew B. Schabath, Robert J. Gillies, Jhanelle E. Gray, Wei Mu, Jin Qi, Jie Tian, Lei Jiang, Yu Shi, Albert Güveniş and Evangelia Katsoulakis and has published in prestigious journals such as Nature Communications, Cancer Research and Scientific Reports.

In The Last Decade

Ilke Tunali

19 papers receiving 889 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ilke Tunali United States 11 758 471 324 159 108 19 896
Mohammadhadi Khorrami United States 12 765 1.0× 502 1.1× 293 0.9× 180 1.1× 107 1.0× 28 878
Mehdi Alilou United States 13 774 1.0× 504 1.1× 238 0.7× 171 1.1× 138 1.3× 28 891
Qingyu Yuan China 15 669 0.9× 397 0.8× 242 0.7× 132 0.8× 106 1.0× 23 878
Pingzhen Guo United States 11 596 0.8× 569 1.2× 233 0.7× 146 0.9× 73 0.7× 13 931
David Brandão France 3 609 0.8× 329 0.7× 476 1.5× 123 0.8× 73 0.7× 4 934
Jiajun Deng China 18 536 0.7× 566 1.2× 192 0.6× 117 0.7× 132 1.2× 58 979
Ieva Kurilova Netherlands 10 398 0.5× 279 0.6× 322 1.0× 84 0.5× 58 0.5× 16 676
Xinming Zhao China 12 530 0.7× 297 0.6× 215 0.7× 127 0.8× 55 0.5× 45 710
Haitao Zhu China 13 755 1.0× 170 0.4× 520 1.6× 174 1.1× 119 1.1× 53 1.0k
Olya Stringfield United States 12 1.2k 1.6× 852 1.8× 221 0.7× 313 2.0× 197 1.8× 24 1.4k

Countries citing papers authored by Ilke Tunali

Since Specialization
Citations

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

Fields of papers citing papers by Ilke Tunali

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ilke Tunali

This figure shows the co-authorship network connecting the top 25 collaborators of Ilke Tunali. A scholar is included among the top collaborators of Ilke Tunali 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 Ilke Tunali. Ilke Tunali is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Lu, Hong, Wei Mu, Ilke Tunali, et al.. (2022). Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers. Cancer Biomarkers. 33(4). 489–501. 8 indexed citations
2.
Tunali, Ilke, Yan Tan, Jhanelle E. Gray, et al.. (2021). Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer. JNCI Cancer Spectrum. 5(4). 31 indexed citations
3.
Mu, Wei, Lei Jiang, Yu Shi, et al.. (2021). Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images. Journal for ImmunoTherapy of Cancer. 9(6). e002118–e002118. 148 indexed citations
4.
Tunali, Ilke, Robert J. Gillies, & Matthew B. Schabath. (2021). Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine. Cold Spring Harbor Perspectives in Medicine. 11(8). a039537–a039537. 80 indexed citations
5.
Tunali, Ilke, Olya Stringfield, Steven A. Eschrich, et al.. (2020). Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening. Scientific Reports. 10(1). 10528–10528. 60 indexed citations
6.
Mu, Wei, Lei Jiang, Yu Shi, et al.. (2020). Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nature Communications. 11(1). 5228–5228. 185 indexed citations
7.
Mu, Wei, Ilke Tunali, Jhanelle E. Gray, et al.. (2020). Abstract 868: Prediction of clinical benefit to checkpoint blockade in advanced NSCLC patients using radiomics of PET/CT images. Cancer Research. 80(16_Supplement). 868–868. 4 indexed citations
8.
Mu, Wei, Ilke Tunali, Jin Qi, Matthew B. Schabath, & Robert J. Gillies. (2020). Radiomics of 18F Fluorodeoxyglucose PET/CT Images Predicts Severe Immune-related Adverse Events in Patients with NSCLC. Radiology Artificial Intelligence. 2(1). e190063–e190063. 29 indexed citations
9.
Tunali, Ilke, Yan Tan, Jhanelle E. Gray, et al.. (2020). Abstract 5806: Hypoxia-related radiomics predict checkpoint blockade immunotherapy response of non-small cell lung cancer patients. Cancer Research. 80(16_Supplement). 5806–5806. 1 indexed citations
10.
Tunali, Ilke, Olya Stringfield, Steven A. Eschrich, et al.. (2019). OA02.08 Peritumoral and Intratumoral Radiomic Features Identify Aggressive Screen-Detected Early-Stage Lung Cancers. Journal of Thoracic Oncology. 14(11). S1130–S1130. 1 indexed citations
11.
Mu, Wei, Ilke Tunali, Jhanelle E. Gray, et al.. (2019). Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy. European Journal of Nuclear Medicine and Molecular Imaging. 47(5). 1168–1182. 132 indexed citations
12.
Tunali, Ilke, Jhanelle E. Gray, Jin Qi, et al.. (2019). Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report. Lung Cancer. 129. 75–79. 108 indexed citations
13.
Tunali, Ilke, Lawrence Hall, Sandy Napel, et al.. (2019). Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Medical Physics. 46(11). 5075–5085. 53 indexed citations
14.
Tunali, Ilke, Yan Tan, Jhanelle E. Gray, et al.. (2019). OA02.05 Clinical-Radiomic Models Predict Overall Survival Among Non-Small Cell Lung Cancer Patients Treated with Immunotherapy. Journal of Thoracic Oncology. 14(11). S1129–S1129. 3 indexed citations
15.
16.
Tunali, Ilke, Jhanelle E. Gray, Qi Jin, et al.. (2017). P1.01-041 Quantitative Imaging Features Predict Response of Immunotherapy in Non-Small Cell Lung Cancer Patients. Journal of Thoracic Oncology. 12(1). S474–S475. 4 indexed citations
17.
Tunali, Ilke, Olya Stringfield, Albert Güveniş, et al.. (2017). Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients. Oncotarget. 8(56). 96013–96026. 24 indexed citations
18.
Tunali, Ilke, Jhanelle E. Gray, Jin Qi, et al.. (2017). PUB063 Epidemiologic and Radiomic Analysis of Hyperprogressers of Lung Cancer Patients Treated with Immunotherapy. Journal of Thoracic Oncology. 12(11). S2386–S2386. 1 indexed citations
19.
Tunali, Ilke & Erdal Kılıç. (2013). Mass segmantation on mammograms using active contours. 1–4. 3 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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