Mario Silva

6.3k total citations · 3 hit papers
178 papers, 3.7k citations indexed

About

Mario Silva is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Mario Silva has authored 178 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 105 papers in Pulmonary and Respiratory Medicine, 82 papers in Radiology, Nuclear Medicine and Imaging and 27 papers in Oncology. Recurrent topics in Mario Silva's work include Lung Cancer Diagnosis and Treatment (71 papers), Radiomics and Machine Learning in Medical Imaging (61 papers) and Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (28 papers). Mario Silva is often cited by papers focused on Lung Cancer Diagnosis and Treatment (71 papers), Radiomics and Machine Learning in Medical Imaging (61 papers) and Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (28 papers). Mario Silva collaborates with scholars based in Italy, Netherlands and United States. Mario Silva's co-authors include Nicola Sverzellati, Ugo Pastorino, Gianluca Milanese, Alfonso Marchianò, Stefano Sestini, Gabriella Sozzi, Lucio Calandriello, Simon Walsh, Federica Sabia and Mattia Boeri and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Mario Silva

162 papers receiving 3.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy

2019 359 citations Ugo Pastorino, Mario Silva et al. profile →
Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study

2018 256 citations Mario Silva, Nicola Sverzellati et al. profile →
COVID-19 patients and the radiology department – advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI)

2020 253 citations Marie‐Pierre Revel, Anagha P. Parkar et al. European Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mario Silva Italy	31	2.0k	1.5k	637	483	281	178	3.7k
K.W. Chiu Hong Kong	23	775 0.4×	1.3k 0.9×	715 1.1×	481 1.0×	1.0k 3.6×	170	3.7k
Michel M. van den Heuvel Netherlands	37	2.2k 1.1×	389 0.3×	1.9k 2.9×	282 0.6×	588 2.1×	196	5.0k
Chih‐Yen Tu Taiwan	30	1.4k 0.7×	345 0.2×	487 0.8×	208 0.4×	401 1.4×	164	2.8k
Shaolin Li China	27	768 0.4×	3.0k 2.0×	870 1.4×	2.5k 5.1×	412 1.5×	110	6.0k
Koen J. Hartemink Netherlands	25	1.1k 0.6×	475 0.3×	786 1.2×	93 0.2×	478 1.7×	98	2.6k
Hong Zhang China	27	724 0.4×	609 0.4×	591 0.9×	163 0.3×	301 1.1×	214	2.9k
Daniel M. Libby United States	29	3.7k 1.9×	2.1k 1.4×	771 1.2×	178 0.4×	410 1.5×	48	5.3k
Choon‐Taek Lee South Korea	33	2.0k 1.0×	392 0.3×	839 1.3×	395 0.8×	373 1.3×	138	3.5k
Yoko Ito Japan	29	1.5k 0.8×	715 0.5×	121 0.2×	304 0.6×	703 2.5×	105	3.9k
Giulia Benedetti Italy	23	305 0.2×	640 0.4×	285 0.4×	294 0.6×	501 1.8×	67	3.0k

Countries citing papers authored by Mario Silva

Since Specialization

Citations

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

Fields of papers citing papers by Mario Silva

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mario Silva

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

All Works

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20 of 20 papers shown

Silva, Mario, Rui Pinto, Sandra Carvalho, et al.. (2025). Neuronal Count, Brain Injury, and Sustained Cognitive Function in 5×FAD Alzheimer’s Disease Mice Fed DHA-Enriched Diets. Biomolecules. 15(8). 1164–1164.

Revel, Marie‐Pierre, Jürgen Biederer, Arjun Nair, et al.. (2025). ESR Essentials: lung cancer screening with low-dose CT—practice recommendations by the European Society of Thoracic Imaging. European Radiology. 36(3). 2064–2073.

Heuvelmans, Marjolein A., et al.. (2024). MA02.07 AI as Primary Reader in 4-IN-THE-LUNG-RUN Lung Cancer Screening Trial: Impact on Negative Misclassification and Referral Rate. Journal of Thoracic Oncology. 19(10). S58–S58. 1 indexed citations

Gratama, Jan W., Mario Silva, Geertruida H. de Bock, et al.. (2024). MA02.05 Inter-Reader Agreement of AI Versus Human Readers in Baseline LDCT Lung Nodule Classification; A UKLS Trial Dataset Sub-Study. Journal of Thoracic Oncology. 19(10). S58–S58.

Yang, Junlin, Nicha C. Dvornek, Aurélie Pahud de Mortanges, et al.. (2024). Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week. Computerized Medical Imaging and Graphics. 118. 102442–102442.

Jiang, Beibei, Daiwei Han, Carlijn M. van der Aalst, et al.. (2024). Lung cancer volume doubling time by computed tomography: A systematic review and meta-analysis. European Journal of Cancer. 212. 114339–114339. 4 indexed citations

Han, Daiwei, Carlijn van der Aalst, Alexander Schmitz, et al.. (2024). Cardiovascular Risk Stratification In Lung Cancer Screening Participants: Initial Findings From The European 4-IN-THE-LUNG-RUN Trial. Journal of cardiovascular computed tomography. 18(4). S56–S56.

Gratama, Jan W., et al.. (2023). MA19.08 AI Can Greatly Reduce Radiologist Workload if Used as a First Read Filter to Rule-Out Negative Cases in Lung Cancer Screening. Journal of Thoracic Oncology. 18(11). S172–S173.

Milanese, Gianluca, Roberta Eufrasia Ledda, Federica Sabia, et al.. (2023). Ultra-low dose computed tomography protocols using spectral shaping for lung cancer screening: Comparison with low-dose for volumetric LungRADS classification. European Journal of Radiology. 161. 110760–110760. 10 indexed citations

10.

Ventura, Luigi, Letizia Gnetti, Gianluca Milanese, et al.. (2023). Relationship Between the Diffusing Capacity of the Lung for Carbon Monoxide (DLCO) and Lung Adenocarcinoma Patterns: New Possible Insights. Archivos de Bronconeumología. 59(7). 418–426.

11.

Qian, Liqiang, Yinjie Zhou, Xiaoke Chen, et al.. (2022). A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules. Translational Lung Cancer Research. 11(6). 1132–1144. 6 indexed citations

12.

Mazzaschi, Giulia, Gianluca Milanese, Denise Madeddu, et al.. (2021). Validation of a radiomic approach to decipher NSCLC immune microenvironment in surgically resected patients. Tumori Journal. 108(1). 86–92. 6 indexed citations

13.

Sambataro, Gianluca, Domenico Sambataro, Martina Orlandi, et al.. (2021). Feasibility, face, and content validity of quantitative computed tomography in interstitial lung disease related to connective tissue diseases. Journal of Basic and Clinical Physiology and Pharmacology. 33(4). 493–497. 1 indexed citations

14.

Perrone, Fabiana, Maurizio Balbi, Veronica Alfieri, et al.. (2021). Review on radiological evolution of COVID-19 pneumonia using computed tomography. World Journal of Radiology. 13(9). 294–306. 2 indexed citations

15.

Mazzaschi, Giulia, Gianluca Milanese, Paolo Pagano, et al.. (2020). Dataset on the identification of a prognostic radio-immune signature in surgically resected Non Small Cell Lung Cancer. SHILAP Revista de lepidopterología. 31. 105781–105781. 5 indexed citations

16.

Sestini, Stefano, Mattia Boeri, Alfonso Marchianò, et al.. (2017). [Lung cancer screening in high-risk subjects: early detection with LDCT and risk stratification using miRNA-based blood test].. PubMed. 40(1 Suppl 1). 42–50. 6 indexed citations

17.

Silva, Mario, Ugo Pastorino, & Nicola Sverzellati. (2017). Lung cancer screening with low-dose CT in Europe: strength and weakness of diverse independent screening trials. Clinical Radiology. 72(5). 389–400. 42 indexed citations

18.

Paz, Cristian, José Becerra, Mario Silva, et al.. (2016). (-)-8-Oxohobartine a New İndole Alkaloid from Aristotelia chilensis (Mol.) Stuntz. SHILAP Revista de lepidopterología. 8 indexed citations

19.

Landini, Nicholas, et al.. (2016). Computed tomography - histology correlations of unusual lung tumors.. PubMed. 108(3). 110–119. 1 indexed citations

20.

Pastorino, Ugo, Roberto Boffi, Alfonso Marchianò, et al.. (2016). Stopping Smoking Reduces Mortality in Low-Dose Computed Tomography Screening Participants. Journal of Thoracic Oncology. 11(5). 693–699. 35 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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