Abstract: Modern radiotherapy (RT) is being enriched by big digital data and intensive technology. Multimodality image registration, intelligence-guided planning, real-time tracking, image-guided RT (IGRT), and automatic follow-up surveys are the products of the digital era. Enormous digital data are created in the process of treatment, including benefits and risks. Generally, decision making in RT tries to balance these two aspects, which is based on the archival and retrieving of data from various platforms. However, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow. These errors can lead to imbalance between benefits and risks. In addition, the exact mechanism and dose–response relationship for radiation-induced malignancy are not well understood. The cancer risk in modern RT workflow continues to be a problem. Therefore, in this review, we develop risk assessments based on our current knowledge of IGRT and provide strategies for cancer risk reduction. Artificial intelligence (AI) such as machine learning is also discussed because big data are transforming RT via AI.

Keywords: cancer risk, radiotherapy, workflow, big data 


With the growth of cancer survivors, there was more than 15.5 million on January 1, 2016, in USA and is projected to reach more than 20 million by January 1, 2026.1 Electronic health records for them are skyrocketing. The US Cancer Moonshot Initiative recommends large-scale genetic analysis of tumors and clinical trial networks to better harness the information gleaned from cancer patients and to optimize care, pushing forward the personalized medicine.2

In radiotherapy (RT), cancer data are firmly in the realm of big data mainly due to ubiquitous images constituting one-third of total global storage demand. Medical imaging is crucial to RT. Its application in RT, referred to as image-guided RT (IGRT), encompasses tumor diagnosis, staging, prognosis, treatment planning, radiation targeting, and follow-up care.3 Enormous digital data are created in the workflow, so IGRT can be redefined as information-guided RT. Large setup errors, ranges of organ motion, and changes in tumor position, size, and shape are most likely to be detected during the course of RT with frequent imaging, which has been becoming a requirement to attain the best tumor coverage improving local control and the most healthy tissue sparing, thereby improving quality of life.4,5

For decades, cancer patients have benefited from advances in IGRT. Survival times are likely to be longer for them, and the number of cancer survivors is increasing. Late sequelae of RT are becoming the next concern. Second malignant neoplasms (SMNs) are among the most serious and life-threatening sequelae for the growing number of cancer survivors, especially for younger patients who have a longer life expectancy. Numerous epidemiological cohort studies have demonstrated radiation-related risks of thyroid and breast cancers, leukemia, and other neoplasms.6,7

However, the exact mechanism and dose–response relationship for radiation-induced malignancy are not well understood. The cancer risk associated with RT is still an area of controversy in clinical radiation oncology with impact on treatment decision making and on patient management. Many debates on cancer risks have existed for a decade or more.8–10 In addition, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow and process.11These errors may further exacerbate the risk. In any case, research on cancer risk has substantially expanded our knowledge of clinical radiation oncology.

In this review, we highlight the latest research on IGRT workflow and discuss cancer risks in four key areas: screening and diagnosis, contouring and planning, targeting and delivery, and follow-up care and re-irradiation (Figure 1). Ethics approval for this study was obtained from Ethics Committee of Chongqing Cancer Institute. All methods were performed according to the relevant guidelines and regulations. These insights will help clinicians better understand the technology and the IGRT process in general and have an effect on personal trade-off between the risks and benefits of treatment options, improving safe RT delivery and patient treatment outcomes. In addition, these insights have the potential to decrease health care costs with a more rational use of medical technology. To transform these data into knowledge, data-driven machine-learning approaches such as automatic diagnosis, automated RT planning, and medical image retrieval are being utilized in the modern RT, so they are also discussed in this review.