Kari S. Wagner‐Larsen

469 total citations
17 papers, 297 citations indexed

About

Kari S. Wagner‐Larsen is a scholar working on Obstetrics and Gynecology, Radiology, Nuclear Medicine and Imaging and Reproductive Medicine. According to data from OpenAlex, Kari S. Wagner‐Larsen has authored 17 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Obstetrics and Gynecology, 13 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Reproductive Medicine. Recurrent topics in Kari S. Wagner‐Larsen's work include Endometrial and Cervical Cancer Treatments (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and MRI in cancer diagnosis (7 papers). Kari S. Wagner‐Larsen is often cited by papers focused on Endometrial and Cervical Cancer Treatments (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and MRI in cancer diagnosis (7 papers). Kari S. Wagner‐Larsen collaborates with scholars based in Norway, United States and Netherlands. Kari S. Wagner‐Larsen's co-authors include Camilla Krakstad, Erlend Hodneland, Ingfrid S. Haldorsen, Kristine E. Fasmer, Øyvind Salvesen, Jone Trovik, Per Kristian Eide, Andrew Simmons, Christian M. Page and Valeria Vitelli and has published in prestigious journals such as Scientific Reports, British Journal of Cancer and Gynecologic Oncology.

In The Last Decade

Kari S. Wagner‐Larsen

16 papers receiving 293 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kari S. Wagner‐Larsen Norway 9 154 127 72 50 45 17 297
D Schmidt Germany 9 78 0.5× 99 0.8× 17 0.2× 13 0.3× 6 0.1× 36 321
Abdulaziz Al‐Sugair Saudi Arabia 10 113 0.7× 18 0.1× 29 0.4× 12 0.2× 31 0.7× 21 326
Shih‐Tien Hsu Taiwan 10 28 0.2× 44 0.3× 20 0.3× 3 0.1× 9 0.2× 26 258
Qiaoyue Tan China 10 217 1.4× 26 0.2× 9 0.1× 17 0.3× 9 0.2× 27 322
Francesca Bongioanni Italy 13 71 0.5× 122 1.0× 4 0.1× 18 0.4× 36 0.8× 25 417
Michael S. Kleinman United States 7 53 0.3× 162 1.3× 11 0.2× 3 0.1× 22 0.5× 7 318
Maria Elena Laino Italy 13 146 0.9× 15 0.1× 6 0.1× 25 0.5× 18 0.4× 28 344
Rahul Kapoor United States 10 189 1.2× 6 0.0× 103 1.4× 115 2.3× 59 1.3× 21 467
SO Schönberg Germany 6 280 1.8× 59 0.5× 5 0.1× 5 0.1× 5 0.1× 14 375

Countries citing papers authored by Kari S. Wagner‐Larsen

Since Specialization
Citations

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

Fields of papers citing papers by Kari S. Wagner‐Larsen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kari S. Wagner‐Larsen. 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 Kari S. Wagner‐Larsen. The network helps show where Kari S. Wagner‐Larsen may publish in the future.

Co-authorship network of co-authors of Kari S. Wagner‐Larsen

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

All Works

17 of 17 papers shown
1.
Wagner‐Larsen, Kari S., Erlend Hodneland, Kristine E. Fasmer, et al.. (2025). MRI delta radiomics during chemoradiotherapy for prognostication in locally advanced cervical cancer. BMC Cancer. 25(1). 122–122.
2.
Wagner‐Larsen, Kari S., Kristine E. Fasmer, Mari K. Halle, et al.. (2025). Tumor ADC value predicts outcome and yields refined prognostication in uterine cervical cancer. Cancer Imaging. 25(1). 23–23. 1 indexed citations
3.
Hodneland, Erlend, Erling Andersen, Kari S. Wagner‐Larsen, et al.. (2024). Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers. Scientific Reports. 14(1). 16826–16826. 5 indexed citations
4.
Halle, Mari K., Erlend Hodneland, Kari S. Wagner‐Larsen, et al.. (2024). Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer. Scientific Reports. 14(1). 11339–11339. 2 indexed citations
5.
Halle, Mari K., Kari S. Wagner‐Larsen, Kathrine Woie, et al.. (2023). Clinicopathological and radiological stratification within FIGO 2018 stages improves risk-prediction in cervical cancer. Gynecologic Oncology. 181. 110–117. 1 indexed citations
6.
Wagner‐Larsen, Kari S., Erlend Hodneland, Kristine E. Fasmer, et al.. (2023). MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer. Cancer Medicine. 12(20). 20251–20265. 7 indexed citations
7.
Halle, Mari K., Kristine E. Fasmer, Kari S. Wagner‐Larsen, et al.. (2023). Visceral fat percentage for prediction of outcome in uterine cervical cancer. Gynecologic Oncology. 176. 62–68. 6 indexed citations
8.
Fasmer, Kristine E., Kari S. Wagner‐Larsen, Øyvind Salvesen, et al.. (2022). Preoperative pelvic MRI and 2-[18F]FDG PET/CT for lymph node staging and prognostication in endometrial cancer—time to revisit current imaging guidelines?. European Radiology. 33(1). 221–232. 8 indexed citations
9.
Wagner‐Larsen, Kari S., Øyvind Salvesen, Mari K. Halle, et al.. (2022). Interobserver agreement and prognostic impact for MRI–based 2018 FIGO staging parameters in uterine cervical cancer. European Radiology. 32(9). 6444–6455. 8 indexed citations
10.
Wagner‐Larsen, Kari S., Jone Trovik, Mari K. Halle, et al.. (2022). What MRI-based tumor size measurement is best for predicting long-term survival in uterine cervical cancer?. Insights into Imaging. 13(1). 105–105. 10 indexed citations
11.
Hodneland, Erlend, Kari S. Wagner‐Larsen, Erling Andersen, et al.. (2022). Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer. Cancers. 14(10). 2372–2372. 15 indexed citations
12.
Høivik, Erling A., Erlend Hodneland, Kari S. Wagner‐Larsen, et al.. (2021). A radiogenomics application for prognostic profiling of endometrial cancer. Communications Biology. 4(1). 1363–1363. 26 indexed citations
13.
Halle, Mari K., Kathrine Woie, Kari S. Wagner‐Larsen, et al.. (2021). A 10-gene prognostic signature points to LIMCH1 and HLA-DQB1 as important players in aggressive cervical cancer disease. British Journal of Cancer. 124(10). 1690–1698. 22 indexed citations
14.
Hodneland, Erlend, Kari S. Wagner‐Larsen, Kristine E. Fasmer, et al.. (2021). Automated segmentation of endometrial cancer on MR images using deep learning. Scientific Reports. 11(1). 41 indexed citations
15.
Wagner‐Larsen, Kari S., Erlend Hodneland, Camilla Krakstad, et al.. (2020). RadEx: Integrated Visual Exploration of Multiparametric Studies for Radiomic Tumor Profiling. Computer Graphics Forum. 39(7). 611–622. 3 indexed citations
16.
Fasmer, Kristine E., Erlend Hodneland, Kari S. Wagner‐Larsen, et al.. (2020). Whole‐Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer. Journal of Magnetic Resonance Imaging. 53(3). 928–937. 50 indexed citations
17.
Brix, Maiken K., Eric Westman, Andrew Simmons, et al.. (2017). The Evans’ Index revisited: New cut-off levels for use in radiological assessment of ventricular enlargement in the elderly. European Journal of Radiology. 95. 28–32. 92 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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