Using the data available from the journal website at the end of May 2019 for the first 92 articles, we obtained high Pearson linear correlation coefficients for the number of citations versus the number of online accesses (r = 0.824) or the Altmetric score (r = 0.745), as well as for the number of online accesses versus the Altmetric score (r = 0.823) (Excel® 2010, Microsoft, RedMond, WA, USA).
Interestingly, among the 100 published articles, the five most quoted articles concerned five different topics that outline the probable future of radiology. Of course, this ranking does not take into account the different exposition time of each article, as recently published papers had a lower probability to be cited. However, the weak inverse correlation of the number of citations with the chronologic order of publication (r = -0.228) shows that these articles were indeed the most attractive.
The top ranking is attributed to the article on augmented reality by Philip Pratt et al. [12], from the Department of Surgery and Cancer, Imperial College London, London, and other departments from the same institution, United Kingdom. The article accumulated 17 citations since January 31, 2018. The title well displays the content: Through the HoloLens™ looking glass: augmented reality for extremity reconstruction surgery using three-dimensional vascular models with perforating vessels. The authors presented six cases of accurate identification, dissection, and execution of vascular pedunculated flaps during reconstructive surgery of the low extremities using preoperative CT angiography to allow the surgeon to see through the patient’s skin and appreciate the underlying anatomy without making a single incision.
The second most quoted article (10 citations since December 22, 2017) regarded the future of CT: the spectral photon-counting era. Daniela Muenzel et al. [13], from the Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, and other institutions in Germany and France, reported a proof-of-concept in silico study on Simultaneous dual-contrast multi-phase liver imaging using spectral photon-counting computed tomography. The authors simulated the complementary distribution in the liver of two contrast agents intravenously injected one after another (a gadolinium- and an iodine-based contrast agent), distinguishing the arterial and portal venous pattern of haemangioma, hepatocellular carcinoma, cyst, and metastasis. Automatic lesion detection performed using a a multidimensional classification algorithm was presented.
The third most quoted paper is a review on the current hot topic of artificial intelligence (AI) in medical imaging [14], coming from my own group at the Department of Radiology, IRCCS Policlinico San Donato, and Università degli Studi di Milano, Italy (9 citations since October 24, 2018). A resident in radiology (Filippo Pesapane), a biomedical engineer (Marina Codari), and myself described the current scenario of research on AI in radiology (driven by the change from traditional machine learning to deep learning), with MRI and CT as the most involved techniques and neuroradiology as the most involved subspecialty. The main idea of the article is that radiologists, frontrunners of the digital era in medicine, can guide the current era of AI application to healthcare. They will not be replaced by AI because they hold the key of communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The suggestion was to exploit the higher efficiency provided by AI to perform more value-added tasks and to become more visible to patients.
The fourth most quoted paper was authored by Elizabeth J. Sutton et al. [15] from the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA and several other institutions from the same country. It is entitled Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes and has accumulated 8 citations since November 21, 2017. Using the MRI data from The Cancer Genome Atlas project of the National Cancer Institute from 91 breast cancer patients, the authors showed that breast tumour size, shape, and margin extracted by human readers can be replicated by the quantitative computer-extracted radiomics. In the authors’ opinion, as computer algorithms continue to be developed, radiology reports will include quantitative metrics resulting from validated computer algorithms.
Finally, Vito Chianca et al. [16] from the Department of Advanced Biomedical Sciences, Università Federico II, Napoli, and from the Departments of Radiology and Neuroradiology of different university hospitals, Milan, Italy, illustrated the potential of Diffusion tensor imaging (DTI) in the musculoskeletal and peripheral nerve systems. The article has had 8 citations since 30 September 2017. After explaining the concept of anisotropy and water diffusion, DTI, and tractography, the authors reviewed the application of DTI to a spectrum of tissues and clinical conditions: normal muscle tissue; muscle contraction and injury; muscular dystrophy; ligaments; peripheral neuropathies; brachial plexus; cubital and carpal tunnel syndromes; sciatic nerve and piriformis syndromes; and nerve tumours. They concluded that DTI and tractography are promising tools providing useful quantitative information about muscular tissue and peripheral nerves as an adjunct to morphological MRI sequences.
In my first editorial in 2017 [17], I discussed the potential role of European Radiology Experimental during the “changing times” we live in. Augmented reality, photon-counting CT, AI, radiomics, and advanced MRI are surely part of these changes. Our journal is an open window pointed towards the future.