Håvard Søiland

1.8k total citations
58 papers, 1.3k citations indexed

About

Håvard Søiland is a scholar working on Cancer Research, Oncology and Genetics. According to data from OpenAlex, Håvard Søiland has authored 58 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Cancer Research, 24 papers in Oncology and 17 papers in Genetics. Recurrent topics in Håvard Søiland's work include Breast Cancer Treatment Studies (21 papers), Estrogen and related hormone effects (16 papers) and Pharmacogenetics and Drug Metabolism (8 papers). Håvard Søiland is often cited by papers focused on Breast Cancer Treatment Studies (21 papers), Estrogen and related hormone effects (16 papers) and Pharmacogenetics and Drug Metabolism (8 papers). Håvard Søiland collaborates with scholars based in Norway, United States and Netherlands. Håvard Søiland's co-authors include Jan P. A. Baak, Emiel A. M. Janssen, Hartwig Kørner, Einar Gudlaugsson, Kjetil Søreide, Ivar Skaland, Emiel A. M. Janssen, Jan Terje Kvaløy, Tone Hoel Lende and Jan Kłos and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Annals of Internal Medicine.

In The Last Decade

Håvard Søiland

56 papers receiving 1.3k citations

Peers

Håvard Søiland
Anja Rudolph Germany
Gary S. Pittman United States
Jacqueline M. Lafky United States
Teresa Ramón y Cajal Spain
George Raptis United States
Atanas Ignatov Germany
Stacie C. Jeter United States
Mary Anne Rossing United States
Massimiliano D’Aiuto Italy
Yuri Yamaguchi Japan
Anja Rudolph Germany
Håvard Søiland
Citations per year, relative to Håvard Søiland Håvard Søiland (= 1×) peers Anja Rudolph

Countries citing papers authored by Håvard Søiland

Since Specialization
Citations

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

Fields of papers citing papers by Håvard Søiland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Håvard Søiland

This figure shows the co-authorship network connecting the top 25 collaborators of Håvard Søiland. A scholar is included among the top collaborators of Håvard Søiland 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 Håvard Søiland. Håvard Søiland 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.
Hoff, Geir, Tomm Bernklev, Charlotte Gibbs, et al.. (2024). Thyroidectomy for Euthyroid Patients with Hashimoto Disease and Persistent Symptoms: An Observational, Postrandomization Study. Journal of Thyroid Research. 2024. 1–7. 3 indexed citations
2.
Bofin, Anna M., et al.. (2024). How to Optimize Deimplementation of Sentinel Lymph Node Biopsy?. The Breast Journal. 2024. 1–9.
3.
Mittal, Karuna, Jaspreet Kaur, Shrikant Pawar, et al.. (2021). Hypoxia Drives Centrosome Amplification in Cancer Cells via HIF1α-dependent Induction of Polo-Like Kinase 4. Molecular Cancer Research. 20(4). 596–606. 13 indexed citations
4.
DeCensi, Andrea, Harriet Johansson, Matteo Puntoni, et al.. (2021). Association of CYP2D6 genotype and tamoxifen metabolites with breast cancer recurrence in a low-dose trial. npj Breast Cancer. 7(1). 34–34. 9 indexed citations
5.
Mittal, Karuna, Michael S. Toss, Jaspreet Kaur, et al.. (2020). A Quantitative Centrosomal Amplification Score Predicts Local Recurrence of Ductal Carcinoma In Situ. Clinical Cancer Research. 26(12). 2898–2907. 3 indexed citations
6.
Kvaløy, Jan Terje, Kirsten Lode, Ragna Lind, et al.. (2019). Drug monitoring of tamoxifen metabolites predicts vaginal dryness and verifies a low discontinuation rate from the Norwegian Prescription Database. Breast Cancer Research and Treatment. 177(1). 185–195. 18 indexed citations
7.
Lende, Tone Hoel, Marie Austdal, Ivar Skaland, et al.. (2019). Influence of pre-operative oral carbohydrate loading vs. standard fasting on tumor proliferation and clinical outcome in breast cancer patients ─ a randomized trial. BMC Cancer. 19(1). 1076–1076. 15 indexed citations
8.
Lende, Tone Hoel, Marie Austdal, Tone F. Bathen, et al.. (2019). Metabolic consequences of perioperative oral carbohydrates in breast cancer patients — an explorative study. BMC Cancer. 19(1). 1183–1183. 12 indexed citations
9.
Aas, Turid, et al.. (2019). Adherence to adjuvant endocrine therapy in postmenopausal breast cancer patients: A 5-year prospective study. The Breast. 44. 52–58. 30 indexed citations
10.
Aas, Turid, et al.. (2017). Diet in women with breast cancer compared to healthy controls – What is the difference?. European Journal of Oncology Nursing. 32. 20–24. 8 indexed citations
11.
Solbrække, Kari Nyheim, et al.. (2016). Our genes, our selves: hereditary breast cancer and biological citizenship in Norway. Medicine Health Care and Philosophy. 20(1). 89–103. 9 indexed citations
12.
Aas, Turid, Kirsten Lode, Jennifer Gjerde, et al.. (2014). Illness uncertainty in breast cancer patients: Validation of the 5-item short form of the Mishel Uncertainty in Illness Scale. European Journal of Oncology Nursing. 19(2). 113–119. 48 indexed citations
13.
Gudlaugsson, Einar, et al.. (2011). D2-40/p63 defined lymph vessel invasion has additional prognostic value in highly proliferating operable node negative breast cancer patients. Modern Pathology. 24(4). 502–511. 18 indexed citations
14.
Janssen, Emiel A. M., Einar Gudlaugsson, Kristín Jónsdóttir, et al.. (2010). Biologic profiling of lymph node negative breast cancers by means of microRNA expression. Modern Pathology. 23(12). 1567–1576. 56 indexed citations
15.
16.
Søiland, Håvard, Ivar Skaland, Bianca van Diermen, et al.. (2008). Androgen Receptor Determination in Breast Cancer. Applied immunohistochemistry & molecular morphology. 16(4). 362–370. 4 indexed citations
17.
Baak, Jan P. A., Einar Gudlaugsson, Ivar Skaland, et al.. (2008). Proliferation is the strongest prognosticator in node-negative breast cancer: significance, error sources, alternatives and comparison with molecular prognostic markers. Breast Cancer Research and Treatment. 115(2). 241–254. 61 indexed citations
18.
Janssen, Emiel A. M., Håvard Søiland, Ivar Skaland, et al.. (2007). Comparing the Prognostic Value of PTEN and Akt Expression with the Mitotic Activity Index in Adjuvant Chemotherapy‐Treated Node‐Negative Breast Cancer patients aged <55 years. Analytical Cellular Pathology. 29(1). 25–35. 16 indexed citations
19.
Skaland, Ivar, Emiel A. M. Janssen, Einar Gudlaugsson, et al.. (2007). Phosphohistone H3 expression has much stronger prognostic value than classical prognosticators in invasive lymph node-negative breast cancer patients less than 55 years of age. Modern Pathology. 20(12). 1307–1315. 91 indexed citations
20.
Søiland, Håvard, Kjetil Søreide, Emiel A. M. Janssen, et al.. (2007). Emerging Concepts of Apolipoprotein D with Possible Implications for Breast Cancer. Analytical Cellular Pathology. 29(3). 195–209. 34 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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