Pietro Muto
- Radiology, Nuclear Medicine and Imaging top 10%
- Epidemiology
- Pulmonary and Respiratory Medicine
- Neurology top 10%
- Oncology
- Co-authors
- Secondo LastoriaMarco SalvatoreGiovannella PalmieriLuca BruneseAntonio RotondòP. VarrellaEmilia VergaraJohn Lister‐James
- Topics
- Radiomics and Machine Learning in Medical Imaging (4 papers)Medical Imaging Techniques and Applications (4 papers)Radiopharmaceutical Chemistry and Applications (4 papers)
In The Last Decade
Pietro Muto
14 papers receiving 521 citations
Peers
Comparison fields: 5 of 111
- Radiology, Nuclear Medicine and Imaging 173
- Epidemiology 135
- Pulmonary and Respiratory Medicine 131
- Neurology 112
- Oncology 77
Countries citing papers authored by Pietro Muto
This map shows the geographic impact of Pietro Muto'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 Pietro Muto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pietro Muto more than expected).
Fields of papers citing papers by Pietro Muto
This network shows the impact of papers produced by Pietro Muto. 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 Pietro Muto. The network helps show where Pietro Muto may publish in the future.
Co-authorship network of co-authors of Pietro Muto
This figure shows the co-authorship network connecting the top 25 collaborators of Pietro Muto. A scholar is included among the top collaborators of Pietro Muto 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 Pietro Muto. Pietro Muto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 188 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 35 | |
| 7 | 15 | |
| 8 | 10 | |
| 9 | Invited commentary: one step forward. | 2 |
| 10 | Use of radiolabeled peptides to image deep venous thrombosis and pulmonary embolism. | 1 |
| 11 | Functional imaging of thymic disorders. | 18 |
| 12 | Somatostatin receptor subtype specificity and in vivo binding of a novel tumor tracer, 99mTc-P829. | 95 |
| 13 | In vivo detection of malignant thymic masses by indium-111-DTPA-D-Phe1-octreotide scintigraphy. | 73 |
| 14 | 7 | |
| 15 | Technetium-99m-methylene diphosphonate scintimammography to image primary breast cancer. | 53 |
| 16 | Detecting deep venous thrombosis with technetium-99m-labeled synthetic peptide P280. | 32 |
About Pietro Muto
Pietro Muto is a scholar working on Radiology, Nuclear Medicine and Imaging, Internal Medicine and Health Information Management, having authored 16 papers that have together received 534 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), Medical Imaging Techniques and Applications (4 papers) and Radiopharmaceutical Chemistry and Applications (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (173 citations), Neurology (112 citations) and Internal Medicine (16 citations). Pietro Muto has collaborated with scholars based in Italy, Austria and France. Frequent co-authors include Secondo Lastoria, Marco Salvatore, Giovannella Palmieri, Luca Brunese, Antonio Rotondò, P. Varrella, Emilia Vergara, John Lister‐James, Massimo De Filippo and Raffaella Capasso. Their work appears in journals such as Cancer, Annals of the Rheumatic Diseases and BioMed Research International.
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.