LAUSR

The automated analysis of large-scale clinical datasets poses new compelling challenges for data-driven and model-based computational methods in medical imaging. As a matter of fact, the amount of heterogeneous biomedical data is considerably increasing due to the advances in medical imaging acquisition modalities and high-throughput technologies for multi-omics. In addition, electronic health records can be properly integrated to support personalized screening and diagnosis. In such a context, artificial intelligence (AI) is revolutionizing cancer image analysis by relying on sophisticated machine learning and computational intelligence techniques. Therefore, cutting-edge AI methods can enable the shift from organization-centric to patient-centric models, leading to effective multi-institutional health care services in terms of both clinical outcomes and costs. Computerized oncologic image analysis is encouraging the transition from largely qualitative image interpretation to quantitative assessment through automated methods aiming at early detection as well as lesion characterization. Nevertheless, several challenges and opportunities exist: • Reproducible and reliable segmentation methods are required to cope with time-consuming and error-prone manual delineation in laborious human decision-making tasks. As a matter of fact, accurate operator-independent segmentation procedures can improve the robustness of radiomics analyses. • Accurate computer-assisted diagnosis, associated with proper data curation, can reduce the risk of overlooking the diagnosis in a clinical environment. • Prognostic/predictive biomarker discovery by capturing the underlying tumor heterogeneity. • Quantification and monitoring of intra-/intertumoral heterogeneity during the course of the disease, by a proper integration of imaging, clinical, and molecular data on a patient-by-patient level • From a health-economics perspective, increasing costs of highly specific oncologic treatments (including immunotherapies) require accurate patient selection strategies, as well as better biomarkers of treatment response. Currently, many state-of-the-art methods based on deep learning have been achieving outstanding performance. The key to their success is the strong learning ability of fully supervised ML models and the availability of large-scale labeled datasets that include precise annotations. Unfortunately, in biomedical research, collecting such accurate annotations is an expensive process due to the need for domain experts’ knowledge. The generation of large mineable imaging datasets might mitigate this challenge by overcoming data paucity and heterogeneity issues. However, along with the availability of samples, data quality and diversity should be considered by collecting and preparing harmonized datasets. Therefore, the generalization abilities in multi-institutional studies can be improved by exploiting transfer learning and domain adaptation techniques. Finally, the explainability and safety of clinical decision support systems should be guaranteed, by avoiding “black-box” AI models exhibiting unpredictable behaviors. In conclusion, precision oncology should be directed towards the development of integrated radiogenomics paradigms that provide robust computational tools for investigating cancer biology as well as its implications for predicting treatment response. This solution allows for large-scale data collection (from multiple institutions) and continuous learning, by dealing with cyber-security and privacy issues. At present, the ultimate challenge is bridging the gap between AI and clinical practice, by firstly performing well-validated clinical research studies. This step is vital for the final translation and deployment of these advanced AI approaches in precision oncology. Citation Format: Evis Sala. Clinical challenges in oncologic imaging: AI support from image analysis to integrated diagnostics [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr IA04.