Olya Stringfield

1.8k total citations · 1 hit paper
24 papers, 1.4k citations indexed

About

Olya Stringfield is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Radiation. According to data from OpenAlex, Olya Stringfield has authored 24 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Pulmonary and Respiratory Medicine and 5 papers in Radiation. Recurrent topics in Olya Stringfield's work include Radiomics and Machine Learning in Medical Imaging (18 papers), Lung Cancer Diagnosis and Treatment (12 papers) and MRI in cancer diagnosis (5 papers). Olya Stringfield is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (18 papers), Lung Cancer Diagnosis and Treatment (12 papers) and MRI in cancer diagnosis (5 papers). Olya Stringfield collaborates with scholars based in United States, China and Türkiye. Olya Stringfield's co-authors include Robert J. Gillies, Yoganand Balagurunathan, Zhaoxiang Ye, Ying Liu, Alberto López García, Qian Li, Chintan Parmar, Hugo J.W.L. Aerts, Emmanuel Rios Velazquez and Jongphil Kim and has published in prestigious journals such as Cancer Research, Scientific Reports and Clinical Cancer Research.

In The Last Decade

Olya Stringfield

24 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

2017 304 citations Emmanuel Rios Velazquez, Chintan Parmar et al. Cancer Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Olya Stringfield United States	12	1.2k	852	313	221	197	24	1.4k
Sarah Boughdad Switzerland	11	1.1k 0.9×	421 0.5×	266 0.8×	294 1.3×	119 0.6×	32	1.3k
Nathaniel Braman United States	9	1.5k 1.2×	496 0.6×	290 0.9×	282 1.3×	349 1.8×	22	1.6k
Marta Bogowicz Switzerland	20	1.1k 0.9×	475 0.6×	314 1.0×	277 1.3×	209 1.1×	45	1.3k
Ilke Tunali United States	11	758 0.6×	471 0.6×	159 0.5×	324 1.5×	108 0.5×	19	896
Mehdi Alilou United States	13	774 0.6×	504 0.6×	171 0.5×	238 1.1×	138 0.7×	28	891
Mohammadhadi Khorrami United States	12	765 0.6×	502 0.6×	180 0.6×	293 1.3×	107 0.5×	28	878
Cristiana Fanciullo Italy	6	726 0.6×	251 0.3×	244 0.8×	131 0.6×	140 0.7×	10	867
Elaine Johanna Limkin France	12	1.4k 1.1×	578 0.7×	324 1.0×	616 2.8×	202 1.0×	26	1.8k
François Lucia France	18	962 0.8×	478 0.6×	250 0.8×	211 1.0×	132 0.7×	75	1.2k
Pingzhen Guo United States	11	596 0.5×	569 0.7×	146 0.5×	233 1.1×	73 0.4×	13	931

Countries citing papers authored by Olya Stringfield

Since Specialization

Citations

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

Fields of papers citing papers by Olya Stringfield

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Olya Stringfield

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

All Works

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20 of 20 papers shown

Kütük, Tuğçe, Mahmoud A. Abdalah, Olya Stringfield, et al.. (2024). Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery. Physics and Imaging in Radiation Oncology. 31. 100602–100602. 3 indexed citations

Mayfield, John D., Dana Ataya, Mahmoud A. Abdalah, et al.. (2024). Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI. Radiology Artificial Intelligence. 6(5). e230348–e230348. 1 indexed citations

Lee, Kyubum, Mahmoud A. Abdalah, Theresa A. Boyle, et al.. (2023). Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI). npj Precision Oncology. 7(1). 68–68. 4 indexed citations

Liveringhouse, Casey, Matthew N. Mills, Kamran A. Ahmed, et al.. (2023). Phase 2 Study of Preoperative SABR for Early-Stage Breast Cancer: Introduction of a Novel Form of Accelerated Partial Breast Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics. 116(3). 611–616. 3 indexed citations

Weinfurtner, R. Jared, Mahmoud A. Abdalah, Olya Stringfield, et al.. (2022). Quantitative Changes in Intratumoral Habitats on MRI Correlate With Pathologic Response in Early-stage ER/PR+ HER2− Breast Cancer Treated With Preoperative Stereotactic Ablative Body Radiotherapy. Journal of Breast Imaging. 4(3). 273–284. 6 indexed citations

Hawkins, Samuel, Olya Stringfield, Jasreman Dhillon, et al.. (2021). Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI. Scientific Reports. 11(1). 3785–3785. 10 indexed citations

Weinfurtner, R. Jared, Natarajan Raghunand, Olya Stringfield, et al.. (2021). MRI Response to Pre-operative Stereotactic Ablative Body Radiotherapy (SABR) in Early Stage ER/PR+ HER2- Breast Cancer correlates with Surgical Pathology Tumor Bed Cellularity. Clinical Breast Cancer. 22(2). e214–e223. 9 indexed citations

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

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

10.

Stringfield, Olya, John A. Arrington, Sandra K. Johnston, et al.. (2019). Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme. Tomography. 5(1). 135–144. 30 indexed citations

11.

Liu, Ying, Jongphil Kim, Yoganand Balagurunathan, et al.. (2018). Prediction of pathological nodal involvement by CT‐based Radiomic features of the primary tumor in patients with clinically node‐negative peripheral lung adenocarcinomas. Medical Physics. 45(6). 2518–2526. 30 indexed citations

12.

Velazquez, Emmanuel Rios, Chintan Parmar, Ying Liu, et al.. (2017). Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. Cancer Research. 77(14). 3922–3930. 304 indexed citations breakdown →

13.

Li, Qian, Jongphil Kim, Yoganand Balagurunathan, et al.. (2017). Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall‐cell lung cancer patients treated with stereotactic body radiotherapy. Medical Physics. 44(8). 4341–4349. 54 indexed citations

14.

Großmann, Patrick, Olya Stringfield, Nehmé El-Hachem, et al.. (2017). Defining the biological basis of radiomic phenotypes in lung cancer. eLife. 6. 260 indexed citations

15.

Liu, Ying, Jongphil Kim, Yoganand Balagurunathan, et al.. (2016). Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas. Clinical Lung Cancer. 17(5). 441–448.e6. 241 indexed citations

16.

Schabath, Matthew B., Ying Liu, Hua Wang, et al.. (2016). Diagnostic and predictive quantitative-imaging features in lung cancer screening. Journal of Thoracic Oncology. 11(2). S41–S42. 1 indexed citations

17.

Schabath, Matthew B., Yoganand Balagurunathan, Olya Stringfield, et al.. (2016). Radiomics of lung cancer. Journal of Thoracic Oncology. 11(2). S5–S6. 4 indexed citations

18.

Hawkins, Samuel, Hua Wang, Ying Liu, et al.. (2016). Predicting Malignant Nodules from Screening CT Scans. Journal of Thoracic Oncology. 11(12). 2120–2128. 220 indexed citations

19.

Wang, Hua, Matthew B. Schabath, Ying Liu, et al.. (2015). Association Between Computed Tomographic Features and Kirsten Rat Sarcoma Viral Oncogene Mutations in Patients With Stage I Lung Adenocarcinoma and Their Prognostic Value. Clinical Lung Cancer. 17(4). 271–278. 18 indexed citations

20.

Schabath, Matthew B., Ying Liu, Anders Berglund, et al.. (2015). Semiquantitative Computed Tomography Characteristics for Lung Adenocarcinoma and Their Association With Lung Cancer Survival. Clinical Lung Cancer. 16(6). e141–e163. 44 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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