Alan E. Nahum

7.2k total citations · 1 hit paper
157 papers, 5.5k citations indexed

About

Alan E. Nahum is a scholar working on Radiation, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Alan E. Nahum has authored 157 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 126 papers in Radiation, 88 papers in Pulmonary and Respiratory Medicine and 79 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Alan E. Nahum's work include Advanced Radiotherapy Techniques (119 papers), Radiation Therapy and Dosimetry (65 papers) and Radiation Dose and Imaging (27 papers). Alan E. Nahum is often cited by papers focused on Advanced Radiotherapy Techniques (119 papers), Radiation Therapy and Dosimetry (65 papers) and Radiation Dose and Imaging (27 papers). Alan E. Nahum collaborates with scholars based in United Kingdom, Italy and United States. Alan E. Nahum's co-authors include S Webb, John D. Fenwick, B. Sánchez‐Nieto, Alison J. D. Scott, David P. Dearnaley, Diana Tait, Vincent Khoo, Alan Horwich, John Yarnold and Paul Mobit and has published in prestigious journals such as The Lancet, SHILAP Revista de lepidopterología and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Alan E. Nahum

155 papers receiving 5.3k citations

Hit Papers

Comparison of radiation s... 1999 2026 2008 2017 1999 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Alan E. Nahum 4.5k 3.8k 2.7k 800 303 157 5.5k
Carlos De Wagter 3.9k 0.9× 3.1k 0.8× 3.2k 1.2× 1.2k 1.6× 147 0.5× 143 5.4k
Ali S. Meigooni 3.9k 0.9× 3.1k 0.8× 2.3k 0.9× 1.1k 1.4× 200 0.7× 120 4.9k
W. F. Hanson 4.8k 1.1× 3.4k 0.9× 2.9k 1.1× 1.3k 1.6× 145 0.5× 79 5.8k
Tatsuaki Kanai 5.1k 1.1× 5.9k 1.6× 1.9k 0.7× 354 0.4× 223 0.7× 178 6.9k
Indra J. Das 6.1k 1.4× 4.2k 1.1× 3.9k 1.5× 1.6k 2.0× 160 0.5× 295 7.2k
Larry A. DeWerd 4.7k 1.1× 3.3k 0.9× 3.1k 1.1× 1.5k 1.8× 288 1.0× 214 5.5k
Slobodan Dević 3.1k 0.7× 2.5k 0.7× 1.8k 0.7× 647 0.8× 201 0.7× 154 4.3k
Sam Beddar 6.0k 1.3× 5.0k 1.3× 2.5k 0.9× 664 0.8× 290 1.0× 248 7.3k
Jan Seuntjens 7.1k 1.6× 6.0k 1.6× 4.7k 1.8× 1.8k 2.2× 456 1.5× 307 9.1k
M. Saiful Huq 5.4k 1.2× 3.9k 1.0× 3.2k 1.2× 1.3k 1.6× 93 0.3× 104 5.9k

Countries citing papers authored by Alan E. Nahum

Since Specialization
Citations

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

Fields of papers citing papers by Alan E. Nahum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alan E. Nahum. 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 Alan E. Nahum. The network helps show where Alan E. Nahum may publish in the future.

Co-authorship network of co-authors of Alan E. Nahum

This figure shows the co-authorship network connecting the top 25 collaborators of Alan E. Nahum. A scholar is included among the top collaborators of Alan E. Nahum 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 Alan E. Nahum. Alan E. Nahum 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.
Stavrev, Pavel, Nadejda Stavreva, Ruggero Ruggieri, Alan E. Nahum, & D. Pressyanov. (2022). Analysis of tumour dose–response data from animal experiments via two TCP models accounting for tumor hypoxia and resensitization. Physical and Engineering Sciences in Medicine. 45(4). 1093–1102. 1 indexed citations
2.
Stavrev, Pavel, et al.. (2021). Analysis of a cohort of prostate patients treated with HDR mono-brachytherapy. Physical and Engineering Sciences in Medicine. 44(2). 487–495. 1 indexed citations
3.
Stavreva, Nadejda, et al.. (2019). Modelling the effect of spread in radiosensitivity parameters and repopulation rate on the probability of tumour control. Physica Medica. 63. 79–86. 4 indexed citations
4.
Tommasino, Francesco, Alan E. Nahum, & Laura Cella. (2017). Increasing the power of tumour control and normal tissue complication probability modelling in radiotherapy: recent trends and current issues. Translational Cancer Research. 6. 7 indexed citations
5.
Ruggieri, Ruggero, Pavel Stavrev, S. Naccarato, et al.. (2017). Optimal dose and fraction number in SBRT of lung tumours: A radiobiological analysis. Physica Medica. 44. 188–195. 24 indexed citations
6.
Stavrev, Pavel, Nadejda Stavreva, Ruggero Ruggieri, & Alan E. Nahum. (2015). On differences in radiosensitivity estimation: TCP experiments versus survival curves. A theoretical study. Physics in Medicine and Biology. 60(15). N293–N299. 9 indexed citations
7.
Borasi, Giovanni, et al.. (2014). Experimental evidence for the use of ultrasound to increase tumor-cell radiosensitivity. Translational Cancer Research. 3(5). 512–520. 2 indexed citations
8.
Li, X. Allen, M. Alber, Joseph O. Deasy, et al.. (2012). The use and QA of biologically related models for treatment planning: Short report of the TG-166 of the therapy physics committee of the AAPM. Medical Physics. 39(3). 1386–1409. 193 indexed citations
9.
Nahum, Alan E., et al.. (2012). Radiobiologically guided optimisation of the prescription dose and fractionation scheme in radiotherapy using BioSuite. British Journal of Radiology. 85(1017). 1279–1286. 49 indexed citations
10.
Baker, Colin, et al.. (2010). Mechanistic simulation of normal-tissue damage in radiotherapy-–implications for dose–volume analyses. Physics in Medicine and Biology. 55(8). 2121–2136. 26 indexed citations
11.
12.
Iori, Mauro, E. Cagni, Marta Paiusco, Peter R. T. Munro, & Alan E. Nahum. (2009). Dosimetric verification of IMAT delivery with a conventional EPID system and a commercial portal dose image prediction tool. Medical Physics. 37(1). 377–390. 36 indexed citations
14.
Seco, Joao, et al.. (2003). Incorporation of a combinatorial geometry package and improved scoring capabilities in the EGSnrc Monte Carlo Code system. Medical Physics. 30(6). 1076–1085. 3 indexed citations
15.
Tait, Diana, et al.. (1999). Comparison of proton therapy and conformal X-ray therapy in non-small cell lung cancer (NSCLC).. British Journal of Radiology. 72(863). 1078–1084. 30 indexed citations
16.
Bedford, J., Peter J. Childs, Alan E. Nahum, et al.. (1998). A comparison of conventional and conformal radiotherapy of the oesophagus: work in progress.. British Journal of Radiology. 71(850). 1076–1082. 14 indexed citations
17.
Mobit, Paul, Philip Mayles, & Alan E. Nahum. (1996). The quality dependence of LiF TLD in megavoltage photon beams: Monte Carlo simulation and experiments. Physics in Medicine and Biology. 41(3). 387–398. 67 indexed citations
18.
Nahum, Alan E., et al.. (1995). Monte Carlo calculated stem effect corrections for NE2561 and NE2571 chambers in medium-energy X-ray beams. Physics in Medicine and Biology. 40(1). 63–72. 26 indexed citations
19.
Webb, S & Alan E. Nahum. (1993). A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density. Physics in Medicine and Biology. 38(6). 653–666. 403 indexed citations
20.
Tait, Diana, et al.. (1988). Benefits expected from simple conformal radiotherapy in the treatment of pelvic tumours. Radiotherapy and Oncology. 13(1). 23–30. 20 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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