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.
The goal of establishing prompt localization of the malignant spread or recurrence of a tumor has found a powerful solution in the definition of follow-up protocols, which include the indication for CT scans on an annual or semiannual basis. In the case of long-surviving patients, however, this approach will lead to a considerable integrated dose level over a period of several years after recovery from the illness. Pathologies treated primarily by surgery and/or chemotherapy have been considered, not taking into account cancers treated with adjuvant or radical radiotherapy. Given that the most likely protocols for these cancers often call for total body scans, an estimation of the consequent effective and organ doses can be performed with acceptable accuracy. The data acquired from five centers have been collected and the related effective and organ doses calculated by means of IMPACT software. Use of the effective dose concept, however, has lately become the subject of criticism, and the recently proposed Effective Risk Model has therefore also been applied. The evaluated absolute additional risk of second tumor induction ranges between 0.1% and 10%, depending primarily on age and pathology. These results depict this additional risk as an issue of significant importance for clinical practice. A revision of follow-up and scan parameter protocols, as well as the introduction of new algorithms for dose reduction, could significantly improve the risk-benefit ratio for all the pathologies studied.
BackgroundTo analyse limits and capabilities in dose calculation of collapsed-cone-convolution (CCC) algorithm implemented in helical tomotherapy (HT) treatment planning system for thorax lesions.MethodsThe agreement between measured and calculated dose was verified both in homogeneous (Cheese Phantom) and in a custom-made inhomogeneous phantom. The inhomogeneous phantom was employed to mimic a patient's thorax region with lung density encountered in extreme cases and acrylic inserts of various dimensions and positions inside the lung cavity. For both phantoms, different lung treatment plans (single or multiple metastases and targets in the mediastinum) using HT technique were simulated and verified. Point and planar dose measurements, both with radiographic extended-dose-range (EDR2) and radiochromic external-beam-therapy (EBT2) films, were performed. Absolute point dose measurements, dose profile comparisons and quantitative analysis of gamma function distributions were analyzed.ResultsAn excellent agreement between measured and calculated dose distributions was found in homogeneous media, both for point and planar dose measurements. Absolute dose deviations <3% were found for all considered measurement points, both inside the PTV and in critical structures. Very good results were also found for planar dose distribution comparisons, where at least 96% of all points satisfied the gamma acceptance criteria (3%-3 mm), both for EDR2 and for EBT2 films. Acceptable results were also reported for the inhomogeneous phantom. Similar point dose deviations were found with slightly worse agreement for the planar dose distribution comparison: 96% of all points passed the gamma analysis test with acceptable levels of 4%-4 mm and 5%-4 mm, for EDR2 and EBT2 films respectively. Lower accuracy was observed in high dose/low density regions, where CCC seems to overestimate the measured dose around 4-5%.ConclusionsVery acceptable accuracy was found for complex lung treatment plans calculated with CCC algorithm implemented in the tomotherapy TPS even in the heterogeneous phantom with very low lung-density.
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