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The advantage of DECT is tissue differentia­tion, identication, and quantication. For exam­ple, iodine, calcium, or barium can be separated from other tissues using one acquisition scan [36]. Tissue identication and quantication refer to assessment and the presence and amount of, for example, iodine in a specic anatomical region. The measurement of iodine content in tis­sue, for example, helps to differentiate between benign and aggressive CRCs [37]. As such DECT can also distinguish between stool and a polyp without compromising the sensitivity and speci­city of the examination [35, 38]. Additionally, the iodine density measurements can also assist in assessing treatment responses such as deter­mining the size of the tumour in response to treat­ment. These features make DECT useful in oncology imaging without an increase in the radiation dose to the patient [36] (see Chap. 26). Although CTC is regarded as a reliable screening tool [39] and as a gold standard procedure because of the high sensitivity, DECT has the advantage that bowel preparation and/or bowel distention of the patient is not required [35]. This option is therefore pleasant for a patient and pro­vides a positive patient experience [36].

Developments in the use of AI in radiation ther­apy were published recently in a review paper [49]. Santoro etal. [49] retrieved 71 papers of AI applications in radiation therapy planning. These included three papers on iterative optimi­sation (IO), 25 papers on machine learning (ML), and 43 papers on deep learning (DL). The ML technique is used to identify patient specic dose- volume constraints and custom design radiation therapy options and solutions. As these approaches may be time consuming when done manually, utilising IO procedures and auto- planning techniques can reduce the treatment planning time. The advantage of this approach is that there is a consistent production

Another application is for patient positioning and monitoring, thus to verify that the patient remains in the simulated treatment position. DL-based methods are used that enable accurate patient positioning. As such AI can be used for dynamic motion management models to improve tumour tracking. Irradiation can be interrupted if there is patient motion, or inadequate target posi­tioning. Such algorithms can track and accom­modate real-time breathing motion to track the tumour position in advance, adding to the accu­racy and custom delivery of the radiation therapy [49]. Santoro et al. [49] concluded that more research is required to explore the use of AI, such as auto-planning techniques and its application in the clinical environment. Also, there is a need to develop and implement reliable and trustworthy AI tools as well as looking into the ethical prin­ciples implementing AI in the radiation therapy environment. The WHO [50] underscores the importance of key ethical principles of AI in health.

prescribed medication.

• Diagnostic CTC, MRI, and PET/CT play a role in TNM staging of CRC.