To perform a systematic review on the research on the application of artificial intelligence (AI) to imaging published in Italy and identify its fields of application, methods and results. Materials and Methods: A Pubmed search was conducted using terms Artificial Intelligence, Machine Learning, Deep learning, imaging, and Italy as affiliation, excluding reviews and papers outside time interval 2015-2020. In a second phase, participants of the working group AI4MP on Artificial Intelligence of the Italian Association of Physics in Medicine (AIFM) searched for papers on AI in imaging. Results: The Pubmed search produced 794 results. 168 studies were selected, of which 122 were from Pubmed search and 46 from the working group. The most used imaging modality was MRI (44%) followed by CT(12%) ad radiography/mammography (11%). The most common clinical indication were neurological diseases (29%) and diagnosis of cancer (25%). Classification was the most common task for AI (57%) followed by segmentation (16%). 65% of studies used machine learning and 35% used deep learning. We observed a rapid increase of research in Italy on artificial intelligence in the last 5 years, peaking at 155% from 2018 to 2019.

Purpose Interest in the field of radiomics is rapidly growing because of its potential to characterize tumor phenotype and provide predictive and prognostic information. Nevertheless, the reproducibility and robustness of radiomics studies are hampered by the lack of standardization in feature definition and calculation. In the context of the image biomarker standardization initiative (IBSI), we investigated the grade of compliance of the image biomarker explorer (IBEX), a free open‐source radiomic software, and we developed and validated standardized‐IBEX (S‐IBEX), an adaptation of IBEX to IBSI. Methods Image biomarker explorer source code was checked against IBSI standard. Both the feature implementation and the overall image preprocessing chain were evaluated. Sections were re‐implemented wherever differences emerged: in particular, contour‐to‐binary‐mask conversion, image sub‐portion extraction, re‐segmentation, gray‐level discretization and interpolation were aligned to IBSI. All reported IBSI features were implemented in S‐IBEX. On a patient phantom, S‐IBEX was validated by benchmarking five different preprocessing configurations proposed by IBSI. Results Most IBEX feature definitions are IBSI compliant; however, IBEX preprocessing introduces non‐negligible nonconformities, resulting in feature values not aligned with the corresponding IBSI benchmarks. On the contrary, S‐IBEX features are in agreement with the standard regardless of preprocessing configurations: the percentage of features equal to their benchmark values ranges from 98.1% to 99.5%, with overall maximum percentage error below 1%. Moreover, the impact of noncompliant preprocessing steps has been assessed: in these cases, the percentage of features equal to the standard drops below 35%. Conclusions The use of standardized software for radiomic feature extraction is essential to ensure the reproducibility of results across different institutions, easing at the same time their external validation. This work presents and validates S‐IBEX, a free IBSI‐compliant software, developed upon IBEX, for feature extraction that is both easy to use and quantitatively accurate.

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InTroDucTIon 18 F-FDG PET/CT (18 F-fludeoxyglucose PET/CT) has become a standard procedure in many types of neoplasms in children. 1-11 The combination of anatomical and metabolic imaging modalities provides accurate diagnostic information useful in initial staging, therapy monitoring, and follow-up of different pediatric diseases. Nevertheless, the introduction of integrated PET/MR scanners in clinical practice has raised interest in its use and benefit in pediatric patients. Published studies have confirmed its non-inferiority to PET/CT in many oncological applications. 12-15 The main reasons in support of PET/MR use in children are the potential of MRI to complement PET metabolic information and the significant reduction in radiation exposure.