Rema Wasan

522 total citations
11 papers, 249 citations indexed

About

Rema Wasan is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine and Pathology and Forensic Medicine. According to data from OpenAlex, Rema Wasan has authored 11 papers receiving a total of 249 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Pulmonary and Respiratory Medicine and 5 papers in Pathology and Forensic Medicine. Recurrent topics in Rema Wasan's work include AI in cancer detection (9 papers), Digital Radiography and Breast Imaging (7 papers) and Breast Lesions and Carcinomas (5 papers). Rema Wasan is often cited by papers focused on AI in cancer detection (9 papers), Digital Radiography and Breast Imaging (7 papers) and Breast Lesions and Carcinomas (5 papers). Rema Wasan collaborates with scholars based in United Kingdom. Rema Wasan's co-authors include David Evans, Michael Michell, Abdel Douiri, Patricia Whelehan, R. Wilson, MJ Michell, Elizabeth O’Flynn, Neelanjana Dutt, Keshthra Satchithananda and Asif Iqbal and has published in prestigious journals such as International Journal of Cancer, Breast Cancer Research and Clinical Radiology.

In The Last Decade

Rema Wasan

10 papers receiving 236 citations

Peers

Rema Wasan

PRM

AI

RNMI

PFM

CR

Lorenza Meneghetti Italy

David Spak United States

Vivianne Freitas Canada

Giulia Bicchierai Italy

Veronica Magni Italy

Susanne Luftner-Nagel Germany

Hildegard K. Toth United States

Rita Vacondio Italy

Yeong Yi An South Korea

Giovanna Romanucci Italy

Lorenza Meneghetti Italy

Rema Wasan

97 ×0.6

PRM

127 ×0.9

AI

178 ×1.4

RNMI

71 ×0.9

PFM

75 ×1.1

CR

Citations per year, relative to Rema Wasan Rema Wasan (= 1×) peers Lorenza Meneghetti

Countries citing papers authored by Rema Wasan

Since Specialization

Citations

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

Fields of papers citing papers by Rema Wasan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rema Wasan

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

All Works

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11 of 11 papers shown

1.

Michell, Michael, Jonathan P. Myles, Fiona J. Gilbert, et al.. (2024). Diagnostic performance of tomosynthesis plus synthetic mammography versus full‐field digital mammography with or without tomosynthesis in breast cancer screening: A systematic review and meta‐analysis. International Journal of Cancer. 156(5). 969–979.

2.

Wasan, Rema, et al.. (2019). Can digital breast tomosynthesis accurately predict whether circumscribed masses are benign or malignant in a screening population?. Clinical Radiology. 74(4). 327.e1–327.e5. 4 indexed citations

3.

Fang, Cheng, et al.. (2019). An audit of mammographic screen detected lesions of uncertain malignant potential (B3) diagnosed on initial image guided needle biopsy: how has our practice changed over 10 years?. Clinical Radiology. 74(8). 653.e19–653.e25. 12 indexed citations

4.

Iqbal, Asif, et al.. (2014). The accuracy of digital breast tomosynthesis compared with coned compression magnification mammography in the assessment of abnormalities found on mammography. Clinical Radiology. 69(11). 1112–1116. 24 indexed citations

5.

Wasan, Rema, et al.. (2014). Digital breast tomosynthesis improves the accuracy of the diagnosis of circumscribed lesions because of increase of margin visibility. Breast Cancer Research. 16(S1). 2 indexed citations

6.

Michell, Michael, Rema Wasan, David Evans, et al.. (2012). A comparison of the accuracy of film-screen mammography, full-field digital mammography, and digital breast tomosynthesis. Clinical Radiology. 67(10). 976–981. 132 indexed citations

7.

Wall, P. D., et al.. (2011). Can digital breast tomosynthesis offer an alternative to MRI in preoperative imaging of lobular carcinoma?. Breast Cancer Research. 13(S1). 4 indexed citations

8.

O’Flynn, Elizabeth, et al.. (2008). Prediction of the presence of invasive disease from the measurement of extent of malignant microcalcification on mammography and ductal carcinoma in situ grade at core biopsy. Clinical Radiology. 64(2). 178–183. 38 indexed citations

9.

Michell, MJ, et al.. (2008). Vacuum-assisted breast biopsy: comparison of three systems. Breast Cancer Research. 10(S3). 2 indexed citations

10.

Michell, MJ, et al.. (2006). The impact of image guided needle biopsy on the outcome of mammographically detected indeterminate microcalcification. The Breast. 15(5). 635–639. 9 indexed citations

11.

Satchithananda, Keshthra, et al.. (2005). An Audit of Pain/Discomfort Experienced during Image-Guided Breast Biopsy Procedures. The Breast Journal. 11(6). 398–402. 22 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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