Sophie Dellas

1.5k total citations
18 papers, 803 citations indexed

About

Sophie Dellas is a scholar working on Pathology and Forensic Medicine, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Sophie Dellas has authored 18 papers receiving a total of 803 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Pathology and Forensic Medicine, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Artificial Intelligence. Recurrent topics in Sophie Dellas's work include AI in cancer detection (5 papers), Multiple Sclerosis Research Studies (4 papers) and Breast Cancer Treatment Studies (4 papers). Sophie Dellas is often cited by papers focused on AI in cancer detection (5 papers), Multiple Sclerosis Research Studies (4 papers) and Breast Cancer Treatment Studies (4 papers). Sophie Dellas collaborates with scholars based in Switzerland, Chile and Germany. Sophie Dellas's co-authors include H. Brunnschweiler, Klaus L. Leenders, Jeannette Lechner‐Scott, Ludwig Kappos, R. P. Maguire, Ulrich Roelcke, Jack Missimer, Andrea M. Plohmann, A. J. Steck and Steffen Huber and has published in prestigious journals such as Journal of Clinical Oncology, Neurology and Cancer Research.

In The Last Decade

Sophie Dellas

15 papers receiving 770 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sophie Dellas Switzerland	10	491	175	174	142	118	18	803
Svetlana Egorova United States	15	322 0.7×	149 0.9×	179 1.0×	169 1.2×	74 0.6×	24	963
Ruediger P. Laubender Germany	21	129 0.3×	82 0.5×	123 0.7×	107 0.8×	247 2.1×	54	947
Jonas Graf Germany	17	294 0.6×	81 0.5×	209 1.2×	28 0.2×	84 0.7×	41	775
Laureen D. Hachem Canada	16	328 0.7×	63 0.4×	112 0.6×	57 0.4×	85 0.7×	35	964
D. Ryan Ormond United States	21	115 0.2×	151 0.9×	241 1.4×	174 1.2×	209 1.8×	78	1.3k
Takashi Nihashi Japan	22	329 0.7×	77 0.4×	320 1.8×	510 3.6×	221 1.9×	62	1.4k
Christian Brogna Italy	16	109 0.2×	96 0.5×	142 0.8×	414 2.9×	194 1.6×	42	1.1k
Timo Uphaus Germany	18	308 0.6×	78 0.4×	218 1.3×	92 0.6×	62 0.5×	53	952
W.P.T.M. Mali Netherlands	18	102 0.2×	121 0.7×	92 0.5×	283 2.0×	342 2.9×	34	966
Michaela K. Bode Finland	19	81 0.2×	106 0.6×	283 1.6×	94 0.7×	96 0.8×	45	863

Countries citing papers authored by Sophie Dellas

Since Specialization

Citations

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

Fields of papers citing papers by Sophie Dellas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sophie Dellas

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

All Works

Sort: Min cites: Since: Top N: Style:

18 of 18 papers shown

1.

Gachechiladze, Mariam, Sara Nizzero, Sabine Schädelin, et al.. (2024). Abstract 6394: Tissue nanomechanical signature predicts response to neoadjuvant chemotherapy in patients with breast cancer. Cancer Research. 84(6_Supplement). 6394–6394.

2.

Weikert, Thomas, et al.. (2023). Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks. European Radiology. 33(7). 4589–4596. 11 indexed citations

3.

Muenst, Simone, Philip Went, Michel Bihl, et al.. (2020). Tall cell carcinoma of the breast with reversed polarity (TCCRP) with mutations in the IDH2 and PIK3CA genes: a case report. Molecular Biology Reports. 47(6). 4917–4921. 18 indexed citations

4.

Oertle, Philipp, Roderick Y. H. Lim, Sophie Dellas, et al.. (2019). Abstract 3140: A prospective, double-blinded clinical study using atomic force microscopy for fast diagnosis and subtyping of low and high-risk breast cancers. Cancer Research. 79(13_Supplement). 3140–3140. 2 indexed citations

5.

Montagna, Giacomo, Charlotte K.Y. Ng, Tatjana Vlajnic, et al.. (2018). Fibroepithelial Breast Lesion: When Sequencing Can Help to Make a Clinical Decision. A Case Report. Clinical Breast Cancer. 19(1). e1–e6. 4 indexed citations

6.

Dellas, Sophie, et al.. (2018). Does the menstrual cycle affect the multimodal ultrasound tomography?. Acta Radiologica. 60(7). 846–851.

7.

Rageth, Christoph, Elizabeth O’Flynn, Katja Pinker, et al.. (2018). Second International Consensus Conference on lesions of uncertain malignant potential in the breast (B3 lesions). Breast Cancer Research and Treatment. 174(2). 279–296. 155 indexed citations

8.

Dellas, Sophie, et al.. (2017). Multimodal ultrasound tomography for breast imaging: a prospective study of clinical feasibility. European Radiology Experimental. 1(1). 27–27. 9 indexed citations

9.

Oertle, Philipp, et al.. (2017). Nanomechanical profiling of human breast tumors as prognostic marker for breast cancer.. Journal of Clinical Oncology. 35(15_suppl). 11618–11618. 1 indexed citations

10.

Schlaeger, Regina, Christian Schindler, Leticia Grize, et al.. (2014). Combined visual and motor evoked potentials predict multiple sclerosis disability after 20 years. Multiple Sclerosis Journal. 20(10). 1348–1354. 31 indexed citations

11.

Dellas, Sophie, et al.. (2013). Von der Diagnostik zur Therapie. 16(1). 26–35.

12.

Schlaeger, Regina, Marcus D’Souza, Christian Schindler, et al.. (2011). Prediction of long-term disability in multiple sclerosis. Multiple Sclerosis Journal. 18(1). 31–38. 46 indexed citations

13.

Weber, William P., Rosanna Zanetti, Igor Langer, et al.. (2005). Mammotome: Less Invasive than ABBI with Similar Accuracy for Early Breast Cancer Detection. World Journal of Surgery. 29(4). 495–499. 13 indexed citations

14.

Dellas, Sophie, Carlos Buitrago, Jakob Roth, et al.. (2003). Unenhanced helical computed tomography vs intravenous urography in patients with acute flank pain: accuracy and economic impact in a randomized prospective trial. European Radiology. 13(11). 2513–2520. 95 indexed citations

15.

Roelcke, Ulrich, Ludwig Kappos, Jeannette Lechner‐Scott, et al.. (1997). Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue:A F-18-fluorodeoxyglucose positron emission tomography study. University of Groningen research database (University of Groningen / Centre for Information Technology). 18 indexed citations

16.

Roser, Werner, G Hagberg, Irina Mader, et al.. (1997). Assignment of glial brain tumors in humans byin vivo 1H-magnetic resonance spectroscopy and multidimensional metabolic classification. Magnetic Resonance Materials in Physics Biology and Medicine. 5(3). 179–183. 12 indexed citations

17.

Roelcke, Ulrich, Ludwig Kappos, Jeannette Lechner‐Scott, et al.. (1997). Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue. Neurology. 48(6). 1566–1571. 385 indexed citations

18.

Dellas, Athanassios, et al.. (1995). Urodynamic and radiologic results after surgical treatment of female stress urinary incontinence. International Urogynecology Journal. 6(3). 153–157. 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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