Manoj Mannil

2.0k total citations
69 papers, 1.4k citations indexed

About

Manoj Mannil is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Biomedical Engineering. According to data from OpenAlex, Manoj Mannil has authored 69 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Radiology, Nuclear Medicine and Imaging, 18 papers in Genetics and 17 papers in Biomedical Engineering. Recurrent topics in Manoj Mannil's work include Radiomics and Machine Learning in Medical Imaging (25 papers), Glioma Diagnosis and Treatment (18 papers) and Cardiac Imaging and Diagnostics (12 papers). Manoj Mannil is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (25 papers), Glioma Diagnosis and Treatment (18 papers) and Cardiac Imaging and Diagnostics (12 papers). Manoj Mannil collaborates with scholars based in Switzerland, Germany and Netherlands. Manoj Mannil's co-authors include Hatem Alkadhi, Robert Manka, Jochen von Spiczak, David Maintz, Bettina Baeßler, Roman Guggenberger, Sabrina Oebel, Thomas Flohr, Bernhard Schmidt and Tilman Hickethier and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Manoj Mannil

64 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manoj Mannil Switzerland 21 947 605 215 186 156 69 1.4k
Masafumi Kidoh Japan 25 1.8k 1.9× 1.2k 1.9× 320 1.5× 224 1.2× 202 1.3× 180 2.3k
Jialiang Ren China 22 1.2k 1.2× 302 0.5× 462 2.1× 205 1.1× 47 0.3× 113 1.5k
Kai Roman Laukamp Germany 22 924 1.0× 718 1.2× 275 1.3× 76 0.4× 54 0.3× 78 1.4k
Shaofeng Duan China 28 1.9k 2.0× 449 0.7× 855 4.0× 314 1.7× 103 0.7× 141 2.3k
David Zopfs Germany 18 669 0.7× 544 0.9× 151 0.7× 75 0.4× 21 0.1× 77 1.1k
Ida Häggström United States 9 1.2k 1.2× 353 0.6× 320 1.5× 157 0.8× 25 0.2× 20 1.4k
Bino Varghese United States 22 873 0.9× 345 0.6× 533 2.5× 179 1.0× 21 0.1× 76 1.3k
Ahmed E. Othman Germany 20 959 1.0× 320 0.5× 233 1.1× 99 0.5× 31 0.2× 93 1.3k
Emine Şebnem Durmaz Türkiye 15 672 0.7× 182 0.3× 349 1.6× 102 0.5× 25 0.2× 26 915
Hwiyoung Kim South Korea 18 466 0.5× 215 0.4× 143 0.7× 67 0.4× 31 0.2× 42 902

Countries citing papers authored by Manoj Mannil

Since Specialization
Citations

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

Fields of papers citing papers by Manoj Mannil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manoj Mannil

This figure shows the co-authorship network connecting the top 25 collaborators of Manoj Mannil. A scholar is included among the top collaborators of Manoj Mannil 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 Manoj Mannil. Manoj Mannil 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.
Esmaeili, Morteza, Manoj Mannil, Frederick J. A. Meijer, et al.. (2024). Distinguishing glioblastoma progression from treatment-related changes using DTI directionality growth analysis. Neuroradiology. 66(12). 2143–2151.
2.
Krähling, Hermann, Benjamin Brokinkel, Dorothee Cäcilia Spille, et al.. (2024). Analysis of the Predictability of Postoperative Meningioma Resection Status Based on Clinical Features. Cancers. 16(22). 3751–3751.
3.
Sartoretti, Elisabeth, Manoj Mannil, Stefan Seidel, et al.. (2024). MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis. Journal of Clinical Medicine. 13(8). 2344–2344. 4 indexed citations
4.
Bauer, Jochen, et al.. (2024). 2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 196(12). 1228–1235. 2 indexed citations
5.
6.
Iuso, Arcangela, Janine Reunert, Marianne Grüneberg, et al.. (2024). Expanding the genetic and clinical spectrum of SLC25A42‐associated disorders and testing of pantothenic acid to improve CoA level in vitro. JIMD Reports. 65(6). 417–425.
7.
8.
Heindel, Walter, et al.. (2023). Use Test of Automated Machine Learning in Cancer Diagnostics. Diagnostics. 13(14). 2315–2315. 9 indexed citations
9.
Spille, Dorothee Cäcilia, Eva C. Bunk, Christian Thomas, et al.. (2023). Protoporphyrin IX (PpIX) Fluorescence during Meningioma Surgery: Correlations with Histological Findings and Expression of Heme Pathway Molecules. Cancers. 15(1). 304–304. 7 indexed citations
10.
Heindel, Walter, et al.. (2023). Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine Learning. Diagnostics. 13(13). 2216–2216. 10 indexed citations
11.
Krähling, Hermann, Benjamin Brokinkel, Dylan Henssen, et al.. (2022). Assessing preoperative risk of STR in skull meningiomas using MR radiomics and machine learning. Scientific Reports. 12(1). 14043–14043. 15 indexed citations
12.
Henssen, Dylan, Elisabeth Sartoretti, Thomas Sartoretti, et al.. (2022). Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent. Heliyon. 8(8). e10023–e10023. 8 indexed citations
13.
Wanderer, Stefan, Lucia Schwyzer, Jatta Berberat, et al.. (2022). Predicting Meningioma Resection Status: Use of Deep Learning. Academic Radiology. 30(7). 1232–1237. 5 indexed citations
14.
15.
Kobe, Adrian, Gilbert Puippe, Thomas Sartoretti, et al.. (2021). Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study. European Journal of Radiology Open. 8. 100375–100375. 17 indexed citations
16.
Huber, Florian A., Moritz C. Wurnig, Manoj Mannil, et al.. (2021). Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis. European Journal of Radiology. 140. 109755–109755. 15 indexed citations
17.
Sartoretti, Elisabeth, Sabine Sartoretti‐Schefer, David Czell, et al.. (2021). Single shot zonal oblique multislice SE-EPI diffusion-weighted imaging with low to ultra-high b-values for the differentiation of benign and malignant vertebral spinal fractures. European Journal of Radiology Open. 8. 100377–100377. 6 indexed citations
18.
Spiczak, Jochen von, et al.. (2019). Radiomics for Distinguishing Myocardial Infarction from Myocarditis at Late Gadolinium Enhancement at MRI: Comparison with Subjective Visual Analysis. Radiology Cardiothoracic Imaging. 1(5). e180026–e180026. 34 indexed citations
19.
Mannil, Manoj, Jakob M. Burgstaller, Ulrike Held, Mazda Farshad, & Roman Guggenberger. (2018). Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS). Zurich Open Repository and Archive (University of Zurich). 1 indexed citations
20.
Mannil, Manoj, Tilman Hickethier, Jochen von Spiczak, et al.. (2017). Photon-Counting CT. Investigative Radiology. 53(3). 143–149. 106 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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