Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

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AA and CPG are co-inventors on patents assigned to Health Innovation Ventures (PCT/WO 2014031012 A1). AH, JT, and LD have shares in the company Convert Pharmaceuticals, and LD has a non-issue, non-licensed patent on LSRT (N2024889). JBS and AVP have served as scientific consultants to Convert Pharmaceuticals. AH, ST, and SD have been prior employees of Convert Pharmaceuticals. PL reports -within and outside the submitted work -grants or sponsored research agreements from Varian Medical, Oncoradiomics, ptTheragnostic/DNAmito, Convert Pharmaceuticals, and Health Innovation Ventures. He received an advisor/presenter fee and/or reimbursements of travel costs/external grant writing fee and/or kind manpower contribution from Oncoradiomics, BHV, Merck, Varian, Elekta, ptTheragnostic, and Convert Pharmaceuticals. PL has shares in the company Oncoradiomics SA, Convert Pharmamaceuticals, LivingMed Biotech, and Comunicare Solutions, and is co-inventor of two issues patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licensed to Oncoradiomics, one issue patent on mtDNA (PCT/EP2014/059089) licensed to ptTheragnostic/DNAmito, three non-patented inventions (software) licensed to ptTheragnostic/DNAmito and Oncoradiomics and Health Innovation Ventures, and three non-issues, non-licensed patents on Deep Learning-Radiomics and LSRT (N2024482, N2024889, N2024889).

Hypoxia—a common feature of the majority of solid tumors—is a negative prognostic factor, as it is associated with invasion, metastasis and therapy resistance. To date, a variety of methods are available for the assessment of tumor hypoxia, including the use of positron emission tomography (PET). A plethora of hypoxia PET tracers, each with its own strengths and limitations, has been developed and successfully validated, thereby providing useful prognostic or predictive information. The current review focusses on [18F]-HX4, a promising next-generation hypoxia PET tracer. After a brief history of its development, we discuss and compare its characteristics with other hypoxia PET tracers and provide an update on its progression into the clinic. Lastly, we address the potential applications of assessing tumor hypoxia using [18F]-HX4, with a focus on improving patient-tailored therapies.

The extracellular matrix protein fibronectin contains a domain that is rarely found in healthy adults and is almost exclusively expressed by newly formed blood vessels in tumours, particularly in solid tumours, different types of lymphoma and some leukaemias. This domain, called the extra domain B (ED-B), thus has broad therapeutic potential. The antibody L19 has been developed to specifically target ED-B and has shown therapeutic potential when combined with cytokines, such as IL-2. In this review article, we discuss the preclinical research and clinical trials that highlight the potential of ED-B targeting for the imaging and treatment of various types of cancer. ED-B-centred studies also highlight how proper patient stratification is of utmost importance for the successful implementation of novel antibody-based targeted therapies.