Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer—being one of the most extensively malignancy studied by hybrid medical imaging—has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
Highlights
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence (Chartrand et al 2017)
Nuclear medicine has been, at least in the past, doubtless more variable and less standardized than radiology, resulting in a lower intra- and inter-method consistency
The mutual information and rough set with particle swarm optimization (MIRSPSO) performance was far higher than that of conventional statistic methods achieving the highest accuracy in survival prediction (AUC = 0.978 ± 0.06)
Summary
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence (Chartrand et al 2017). Automated image analysis tools are mainly based on deep learning algorithms These approaches aim to autonomously (i.e., without explicit human programming) identify relevant features to predict a certain outcome starting from images (Kirienko et al in Press). The majority of the studies focused on hybrid PET/CT aimed to predict EGFR status by radiogenomic signatures using machine learning approaches (Koyasu et al 2019; Li et al 2019) Koyasu et al compared random forest and gradient tree boosting classifiers used single type or multiple types of imaging features. Performance status, and number of positive lymph nodes on PET were identified as prognostic factors by machine learning approaches Both models had similar performance when considering patients with complete data set, as expected Bayesian network which has a natural ability to reason under uncertainty, resulted more accurate than SVM in handling missing data (AUCs = 0.70–0.77 and 0.68–071, respectively) (Jayasurya et al 2010). AI-based approaches alone or combined with clinical parameters have been tested to predict EGFR mutational status with good performance (AUCs 0.75–0.84) (Li et al 2018; Wang et al 2019b; Xiong et al 2018; Zhao et al 2019), outperforming visual analysis and radiomics (AUCs 0.81 versus 0.64– 0.74) (Li et al 2018; Wang et al 2019b)
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