In this editorial, we discussed the current research status of artificial intelligence (AI) in Oncology, reviewing the basics of machine learning (ML) and deep learning (DL) techniques and their emerging applications on clinical and imaging cancer workflow. The growing amounts of available “big data” coupled to the increasing computational power have enabled the development of computer-based systems capable to perform advanced tasks in many areas of clinical care, especially in medical imaging. ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions. DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks; convolutional neural networks are the most common DL models used in medical image analysis. AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications. In detail, we explored the emerging applications of AI in oncological risk assessment, lesion detection, characterization, staging, and therapy response. Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice. Nevertheless, AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.

In this new era of health-related technology and medical advances, artificial intelligence (AI) has put down roots making it possible to teach computers to do an intelligence human task, thus emerging as a problem-solving tool in data analysis and improving many aspects of clinical care[1,2].

Machine learning (ML) is a subset of AI that develops computer algorithms to make predictions or decision tasks without prior explicit programmed rules. ML algorithms use iterative static methods learning from “training” data to progressively improve the model performance over time. Based on the type of learning, ML is generally divided in (1) supervised learning, which uses labelled training data to map the expected outputs; (2) unsupervised learning, which deploys unlabelled data to learn new patterns; and (3) reinforcement learning, considered as a subfield of ML using reinforcement tools in a dynamic setting[3] (Figure 1). The supervised method is the most used ML technique in medical imaging applications and, relying on the relationship between input features and expected outcomes, the ML algorithms are grouped into three broad categories: Linear, Nonlinear and Ensemble, as described in Table 1. Furthermore, based on the data features exploited by the algorithms, ML can be applied to handcrafted features as predefined features in the data set, or to non-handcrafted features, involving raw data as part of the learning process[4]. Deep learning (DL), is a subgroup of ML techniques using non-handcrafted features and it is composed of artificial neural networks (ANN) modelled as neuron multi-layered networks allowing to automatically extract features without prior labelling and perform high-level tasks[5]. The most common ANN used in medical image analysis is based on convolutional architecture [convolutional neural networks (CNN)], consisting of hidden multi-layers that compute and filter high dimensional data to obtain the correct outputs, such as detection and characterization of tumoral lesions on imaging examinations[6].

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Figure 1 Diagram showing different categories of artificial intelligence.

AI-based approaches have been investigated in many fields of oncology, from imaging to histopathological and molecular diagnosis. Indeed, encouraging results have been obtained in cancer imaging, especially in screening environments. Among the different available imaging modalities, computed tomography (CT) and magnetic resonance imaging (MRI) are the most widely employed due to their prominent role in oncologic patients for staging, treatment monitoring and follow-up. Moreover, the introduction of advanced imaging techniques such as perfusion CT, MRI and MRI-diffusion-weighted imaging could provide the addition of functional over morphological data to further characterize tumor phenotype and behavior. Of note, radiology and oncology share the need for precision diagnosis and prediction models, by using cross-valuable multiple parameters from medical images and clinical data. The current applications of AI in cancer imaging include the optimization of the clinical-radiological workflow (patient screening, image acquisition) and also more specific image-based tasks (cancer detection, characterization, and treatment monitoring).

In the next sections we introduce the possible applications of AI in oncology imaging (Table 2).

Tumor segmentation, characterization and staging

Segmentation represents one of the most challenging tasks of oncological image analysis and AI algorithms have allowed the development of systems that can enable automatic tumor segmentation. Recently, DL networks, such as CNN, have been applied in segmentation tasks gaining accurate results regarding radiotherapy treatment planning, volume measurements and, monitoring disease progression[15,16]. Indeed, increasing evidence in recent literature has highlighted the high performance of DL models in performing fully automated whole-breast segmentation to obtain reliable and robust methods for quantitative imaging analysis[17,18].

High-performance levels of AI algorithms in handling multi-dimensional data have allowed extracting and analyzing radiomic biomarkers reflecting image tumor heterogeneity thus empowering precision diagnosis and staging in cancer imaging[19] (Figure 2). For instance, with our research group, we used a combined model of MRI radiomic features and ML analysis to differentiate typical and atypical adenomas from non-adenoma adrenal lesions, which showed a better performance than the radiologist assessment[20]. Further, our group assessed the usefulness of an ML-based radiomic approach applied to MR imaging to differentiate high- from low-grade clear cell renal cell carcinoma achieving accuracy greater than 90%[21]. Reliable results and robust evidence have been providing in lung cancer diagnosis as showed in a recent work of Beig et al[22], which proposed a radiomic-based ML algorithm using non-contrast lung CT to distinguish non-small cell lung cancer adenocarcinomas from benign granuloma, resulting with outperforming results of AI system in comparison to the radiologists’ evaluation (accuracy = 75% vs 61%).

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Figure 2 A schematic diagram of a radiomic and machine learning workflow pipeline applied to contrast-enhanced computed tomography images.

Staging represents a crucial point of the oncological workflow to delineate the most appropriate treatment in a personalized and precision way. In this view, recent pilot studies have been carried out on the staging of primary tumor size, lymph nodes involvement, and distant metastasis[23]. For example, our group investigated the clinical feasibility of a combined approach of radiomics and ML-based on MR images for the identification of deep myometrial invasion in endometrial cancer in a clinical context; indeed, the integration of the developed ML algorithm improved radiologist accuracy from 82% to 100%[24].

AI techniques still have to face some issues to be incorporated in clinical practice. Of note, large datasets containing annotated images are needed for training of DL algorithms, but standardized imaging workflow lacks[15]. Complex AI functions are not easily interpreted by healthcare providers, and this “black box” nature could affect the acceptance of AI programs, also from the ethical and legal points of view[27]. Moreover, variability across multi-center and multi-vendor should be addressed with future studies sharing more reliable and robust validation[28].

Despite these drawbacks, AI incorporation into cancer imaging has been boosting the shift of oncology towards a precision diagnostics and personalized cancer treatment. Indeed, as previously discussed, recent literature evidence pointed out the emerging role of AI in supporting all cancer imaging pathways from screening programs to diagnostic and prognostic tasks, offering new methods to increase radiologists’ performance in order to improve oncological care environment. Furthermore, the integration of AI into molecular pathological epidemiology can aid in developing pathologic signatures to stratify patients’ risk and predict biological tumor behavior by using clinical, radiological and pathological data[29]. In the future, collaborations between different expertise figures, such as oncologists, epidemiologists, radiologists, pathologists and data scientists, should be encouraged to achieve harmonized and integrated development of AI systems in cancer research.

We have provided an overview of AI integration in cancer imaging, focusing on the basics of this disruptive technology and on the significant results obtained in improving clinical and radiological workup for oncological patients. Although, more evidence is still demanding the outlook of AI in cancer imaging remains bright.