S. Broggi
About
S. Broggi is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine.
According to data from OpenAlex, S. Broggi has authored 113 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Radiation, 62 papers in Radiology, Nuclear Medicine and Imaging and 52 papers in Pulmonary and Respiratory Medicine. Recurrent topics in S. Broggi's work include Advanced Radiotherapy Techniques (73 papers), Radiomics and Machine Learning in Medical Imaging (28 papers) and Medical Imaging Techniques and Applications (21 papers). S. Broggi is often cited by papers focused on Advanced Radiotherapy Techniques (73 papers), Radiomics and Machine Learning in Medical Imaging (28 papers) and Medical Imaging Techniques and Applications (21 papers). S. Broggi collaborates with scholars based in Italy, United States and Belgium. S. Broggi's co-authors include C. Fiorino, R. Calandrino, N. Di Muzio, Giovanni Mauro Cattaneo, Ferruccio Fazio, C. Cozzarini, I. Dell’Oca, Filippo Alongi, L. Perna and A. Pierelli and has published in prestigious journals such as Blood, International Journal of Radiation Oncology*Biology*Physics and Physics in Medicine and Biology.
In The Last Decade
S. Broggi
105 papers receiving 2.2k citations
Peers
S. Broggi
Giovanni Mauro Cattaneo Italy
T. Lacornerie France
P. Voet Netherlands
R. Calandrino Italy
Minsong Cao United States
C. Rowbottom United Kingdom
Xavier Geets Belgium
Angela van Baardwijk Netherlands
Francesco Ricchetti Italy
Cristina Garibaldi Italy
Citations per year, relative to S. Broggi S. Broggi (= 1×)
peers
Giovanni Mauro Cattaneo
Countries citing papers authored by S. Broggi
Since
Specialization
Citations
This map shows the geographic impact of S. Broggi'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 S. Broggi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Broggi more than expected).
Fields of papers citing papers by S. Broggi
Since
Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences
This network shows the impact of papers produced by S. Broggi. 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 S. Broggi. The network helps show where S. Broggi may publish in the future.
Co-authorship network of co-authors of S. Broggi
This figure shows the co-authorship network connecting the top 25 collaborators of S. Broggi. A scholar is included among the top collaborators of S. Broggi 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 S. Broggi. S. Broggi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
1.
Boldrini, Luca, Giuditta Chiloiro, Davide Cusumano, et al.. (2024). Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study. La radiologia medica. 129(4). 615–622.
2.
Castriconi, Roberta, Andrei Fodor, Chiara Lucrezia Deantoni, et al.. (2023). Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol. Physica Medica. 110. 102606–102606.
3.
Castriconi, Roberta, S. Broggi, Javier Pascau, et al.. (2023). Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization. Physics and Imaging in Radiation Oncology. 28. 100488–100488.
4.
Mori, Martina, Diego Palumbo, S. Broggi, et al.. (2022). Atlas-based lung segmentation combined with automatic densitometry characterization in COVID-19 patients: Training, validation and first application in a longitudinal study. Physica Medica. 100. 142–152.
5.
Castriconi, Roberta, P. Mangili, S. Broggi, et al.. (2022). Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy. Physics and Imaging in Radiation Oncology. 23. 54–59.
6.
Mori, Martina, Diego Palumbo, Rebecca De Lorenzo, et al.. (2021). Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry. Physica Medica. 85. 63–71.
7.
Mori, Martina, Paolo Passoni, Elena Incerti, et al.. (2020). Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer. Radiotherapy and Oncology. 153. 258–264.
8.
Incerti, Elena, Emilia Giovanna Vanoli, S. Broggi, et al.. (2019). Early variation of 18-fluorine-labelled fluorodeoxyglucose PET-derived parameters after chemoradiotherapy as predictors of survival in locally advanced pancreatic carcinoma patients. Nuclear Medicine Communications. 40(10). 1072–1080.
9.
Castriconi, Roberta, C. Fiorino, S. Broggi, et al.. (2018). Comprehensive Intra-Institution stepping validation of knowledge-based models for automatic plan optimization. Physica Medica. 57. 231–237.
10.
Muzio, N.G. Di, Andrei Fodor, B. Noris Chiorda, et al.. (2016). Moderate Hypofractionation with Simultaneous Integrated Boost in Prostate Cancer: Long-term Results of a Phase I–II Study. Clinical Oncology. 28(8). 490–500.
11.
Belli, Maria Luisa, C. Fiorino, S. Broggi, et al.. (2015). Early volume variation of positive lymph nodes assessed by in-room MVCT images predicts risk of loco-regional relapses in head and neck cancer patients treated with IMRT. Acta Oncologica.
12.
Belli, Maria Luisa, C. Fiorino, S. Broggi, et al.. (2015). Early volume variation of positive lymph nodes assessed by in-room mega voltage CT images predicts risk of loco-regional relapses in head and neck cancer patients treated with intensity-modulated radiotherapy. Acta Oncologica. 54(9). 1490–1495.
13.
Belli, Maria Luisa, Elisa Scalco, Giuseppe Sanguineti, et al.. (2014). Early changes of parotid density and volume predict modifications at the end of therapy and intensity of acute xerostomia. Strahlentherapie und Onkologie. 190(11). 1001–1007.
14.
Passoni, Paolo, C. Fiorino, N. Slim, et al.. (2013). Feasibility of an Adaptive Strategy in Preoperative Radiochemotherapy for Rectal Cancer With Image-Guided Tomotherapy: Boosting the Dose to the Shrinking Tumor. International Journal of Radiation Oncology*Biology*Physics. 87(1). 67–72.
15.
Fiorino, C., Giovanna Rizzo, Elisa Scalco, et al.. (2012). Density variation of parotid glands during IMRT for head–neck cancer: Correlation with treatment and anatomical parameters. Radiotherapy and Oncology. 104(2). 224–229.
16.
Fiorino, C., Paolo Passoni, S. Broggi, et al.. (2012). Characterisation of rectal motion during neo-adjuvant radiochemotherapy for rectal cancer with image-guided tomotherapy: Implications for adaptive dose escalation strategies. Acta Oncologica. 51(3). 318–324.
17.
Broggi, S., et al.. (2011). Dosimetric accuracy of tomotherapy dose calculation in thorax lesions. Radiation Oncology. 6(1). 14–14.
18.
Cozzarini, C., C. Fiorino, L. Da Pozzo, et al.. (2010). Clinical Factors Predicting Late Severe Urinary Toxicity After Postoperative Radiotherapy for Prostate Carcinoma: A Single-Institute Analysis of 742 Patients. International Journal of Radiation Oncology*Biology*Physics. 82(1). 191–199.
19.
Fiorino, C., Filippo Alongi, L. Perna, et al.. (2009). Dose–Volume Relationships for Acute Bowel Toxicity in Patients Treated With Pelvic Nodal Irradiation for Prostate Cancer. International Journal of Radiation Oncology*Biology*Physics. 75(1). 29–35.
20.
Perna, L., C. Fiorino, C. Cozzarini, et al.. (2009). Sparing the penile bulb in the radical irradiation of clinically localised prostate carcinoma: A comparison between MRI and CT prostatic apex definition in 3DCRT, Linac-IMRT and Helical Tomotherapy. Radiotherapy and Oncology. 93(1). 57–63.
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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