Matteo Interlenghi

1.6k total citations · 1 hit paper
29 papers, 986 citations indexed

About

Matteo Interlenghi is a scholar working on Radiology, Nuclear Medicine and Imaging, Obstetrics and Gynecology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Matteo Interlenghi has authored 29 papers receiving a total of 986 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Obstetrics and Gynecology and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Matteo Interlenghi's work include Radiomics and Machine Learning in Medical Imaging (20 papers), Endometrial and Cervical Cancer Treatments (6 papers) and Medical Imaging Techniques and Applications (5 papers). Matteo Interlenghi is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (20 papers), Endometrial and Cervical Cancer Treatments (6 papers) and Medical Imaging Techniques and Applications (5 papers). Matteo Interlenghi collaborates with scholars based in Italy, United States and United Kingdom. Matteo Interlenghi's co-authors include Isabella Castiglioni, Christian Salvatore, Francesca Gallivanone, Francesco Sardanelli, Andrea Cozzi, Natascha Claudia D’Amico, Marina Codari, Giovanni Di Leo, Leonardo Rundo and Valentina Chiappa and has published in prestigious journals such as Journal of Neurotrauma, Gynecologic Oncology and European Journal of Nuclear Medicine and Molecular Imaging.

In The Last Decade

Matteo Interlenghi

26 papers receiving 969 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.

AI applications to medical images: From machine learning to deep learning

2021 412 citations Isabella Castiglioni, Leonardo Rundo et al. Physica Medica profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Matteo Interlenghi Italy	15	577	280	171	149	118	29	986
Theresa Thai United States	12	519 0.9×	331 1.2×	126 0.7×	164 1.1×	47 0.4×	32	1.0k
Jaron Chong Canada	20	649 1.1×	209 0.7×	136 0.8×	176 1.2×	293 2.5×	52	1.2k
Avishek Chatterjee Netherlands	17	815 1.4×	340 1.2×	283 1.7×	247 1.7×	139 1.2×	38	1.2k
Siri Willems Belgium	11	342 0.6×	158 0.6×	97 0.6×	107 0.7×	75 0.6×	14	616
Yang Song China	19	820 1.4×	193 0.7×	373 2.2×	190 1.3×	59 0.5×	106	1.3k
Masahiro Yakami Japan	14	469 0.8×	197 0.7×	239 1.4×	153 1.0×	35 0.3×	32	756
Fajin Dong China	15	335 0.6×	174 0.6×	117 0.7×	192 1.3×	61 0.5×	99	1.0k
M. Steinborn Germany	21	309 0.5×	187 0.7×	166 1.0×	81 0.5×	68 0.6×	69	1.4k
Yuchen Qiu United States	18	1.1k 1.9×	859 3.1×	271 1.6×	238 1.6×	105 0.9×	62	1.8k
Magda Marcon Switzerland	20	882 1.5×	524 1.9×	277 1.6×	249 1.7×	111 0.9×	63	1.5k

Countries citing papers authored by Matteo Interlenghi

Since Specialization

Citations

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

Fields of papers citing papers by Matteo Interlenghi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matteo Interlenghi

This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Interlenghi. A scholar is included among the top collaborators of Matteo Interlenghi 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 Matteo Interlenghi. Matteo Interlenghi 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

Gitto, Salvatore, Alessio Annovazzi, Matteo Interlenghi, et al.. (2024). X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones. EBioMedicine. 101. 105018–105018. 9 indexed citations

Crimì, Filippo, Christian Salvatore, Matteo Interlenghi, et al.. (2024). A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer. Life. 14(12). 1530–1530. 1 indexed citations

Interlenghi, Matteo, Giancarlo Sborgia, Alessandro Venturi, et al.. (2023). A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography. Diagnostics. 13(18). 2965–2965. 4 indexed citations

Gargano, Marco, et al.. (2023). Giovanna Garzoni Miniaturist at the Savoy Court: Imaging and Materials Investigations to Discover the Painting Technique. Applied Sciences. 13(5). 2790–2790.

Interlenghi, Matteo, Christian Salvatore, Veronica Magni, et al.. (2022). A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses. Diagnostics. 12(1). 187–187. 11 indexed citations

Magni, Veronica, Matteo Interlenghi, Andrea Cozzi, et al.. (2022). Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus. Radiology Artificial Intelligence. 4(2). e210199–e210199. 26 indexed citations

Alì, Marco, Natascha Claudia D’Amico, Matteo Interlenghi, et al.. (2021). A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study. Applied Sciences. 11(6). 2503–2503. 6 indexed citations

Salvatore, Christian, Matteo Interlenghi, Caterina Beatrice Monti, et al.. (2021). Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. Diagnostics. 11(3). 530–530. 14 indexed citations

Kirienko, Margarita, Martina Sollini, Emanuele Voulaz, et al.. (2021). Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. European Journal of Nuclear Medicine and Molecular Imaging. 48(11). 3643–3655. 73 indexed citations

10.

Castiglioni, Isabella, Leonardo Rundo, Marina Codari, et al.. (2021). AI applications to medical images: From machine learning to deep learning. Physica Medica. 83. 9–24. 412 indexed citations breakdown →

11.

Chiappa, Valentina, Matteo Interlenghi, Giorgio Bogani, et al.. (2021). A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125. European Radiology Experimental. 5(1). 28–28. 21 indexed citations

12.

Chiappa, Valentina, Matteo Interlenghi, Christian Salvatore, et al.. (2021). Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors. Gynecologic Oncology. 161(3). 838–844. 32 indexed citations

13.

Caccia, Michele, et al.. (2021). Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters. Journal of Visualized Experiments. 1 indexed citations

14.

Castiglioni, Isabella, Davide Ippolito, Matteo Interlenghi, et al.. (2021). Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy. European Radiology Experimental. 5(1). 7–7. 66 indexed citations

15.

Chiappa, Valentina, Giorgio Bogani, Matteo Interlenghi, et al.. (2020). The Adoption of Radiomics and machine learning improves the diagnostic processes of women with Ovarian MAsses (the AROMA pilot study). Journal of Ultrasound. 24(4). 429–437. 43 indexed citations

16.

Nanni, Loris, Matteo Interlenghi, Sheryl Brahnam, et al.. (2020). Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease. Frontiers in Neurology. 11. 576194–576194. 53 indexed citations

17.

Castiglioni, Isabella, Francesca Gallivanone, Paolo Soda, et al.. (2019). AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. European Journal of Nuclear Medicine and Molecular Imaging. 46(13). 2673–2699. 28 indexed citations

18.

Gargano, Marco, Anna Gallì, Letizia Bonizzoni, et al.. (2018). The Giotto's workshop in the XXI century: looking inside the “God the Father with Angels” gable. Journal of Cultural Heritage. 36. 255–263. 18 indexed citations

19.

Gallivanone, Francesca, et al.. (2016). An anthropomorphic phantom for advanced image processing of realistic¹⁸F-FDG PET-CT oncological studies. 1–7. 1 indexed citations

20.

Gallivanone, Francesca, et al.. (2016). A fully automatic, threshold-based segmentation method for the estimation of the Metabolic Tumor Volume from PET images: validation on 3D printed anthropomorphic oncological lesions. Journal of Instrumentation. 11(1). C01022–C01022. 19 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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