Industry insight

Patient-reported outcomes in radiotherapy and other key takeaways from ESTRO and SASRO 2021

With ASTRO 2021 around the corner, let’s take a moment to look at the main insights from the European radiation oncology events of the year, ESTRO and SASRO. Both congresses took place on-site for the first time since 2019 and the Kaiku Health Medical Science team was there to reconnect with colleagues and discuss key trends of the hour.

PROs in Radiotherapy

“Radiation Oncology. Optimal Health for All, Together”. In line with the vision statement of the European Society for Radiotherapy for 2030, the annual ESTRO congress emphasised the ambition of societies to further reinforce radiation oncology as a core partner in multidisciplinary cancer care and to guarantee accessible, high-value radiation therapy for all cancer patients who need it. In other words, to optimise radiotherapy and make it accessible for more patients.

Patient-reported outcomes (PROs) bear the potential to play a significant role in making the vision reality. This was present throughout the congress from official research discussions to general conversations. Dr Juha Kononen from Docrates Cancer Center presented a study about the use of machine learning in predicting patient-reported symptoms during breast and prostate cancer radiotherapy [1]. According to the research, the severity of patient-reported symptoms can be well predicted for most symptoms for breast and prostate cancer patients receiving radiotherapy by using only ePRO data and basic information such as age as inputs.

Another interesting session on the topic was held by Senior Medical Physicist Arjen van der Schaaf, PhD, from the University Medical Center Groningen in the Netherlands. He presented a study about harnessing PROs as such in radiotherapy treatment planning; the implementation of direct quality of life guided treatment plan optimisation [2]. The research group had used data related to quality of life from PROs to build an algorithm for radiotherapy planning – something that physicians have traditionally done according to research evidence and subjective interpretations based on clinical experience. The goal was to see if and how algorithms trained with patient-reported outcomes could be used to optimise a treatment plan resulting in maximized safety and quality of life.

The plan was drafted for the treatment of head and neck tumours, a region that requires highlighted attention to precision. The results showed no significant difference between a human-drafted, experience-based treatment plan and one by artificial intelligence. Except in one parameter: the experience-based plan was more liberal in terms of contouring organs at risk. The plan by AI allowed more radiation to certain regions such as salivary glands.

Since the algorithms were trained with patient-reported data, the result could indicate that the patients had experienced that the side effects from radiation to salivary glands had not negatively affected their quality of life as much as is usually expected. This information sheds light on the potential of PROs for optimising radiotherapy by personalising the treatment according to true patient experience. 

Yet, the treatment decisions, including final treatment plans, should be managed and accepted by medical professionals. With personalised digital health interventions, the experiences of both clinicians and patients are taken into consideration in aiming to maximize treatment efficacy without compromising the long-term quality of life of patients.

AI meets Compassion

Celebrating its 25th anniversary, SASRO 2021 focused on the combination of AI and compassion. The theme brought up different ways technology innovations are shaping radiotherapy and they are combined with the human touch. With the right tools, we have the potential to bring more personalization to radiotherapy and cancer care in general.

The congress had several presentations that illustrated the theme. Justus Wolff from Syte Institute brought up the economic impact of AI in healthcare. He discussed the financial effects that technological innovations can have on patients, clinics and societies. Sonja Stieb from Cantonal Hospital Aarau talked about the development of a prediction model for radiation-associated late taste impairment in oropharyngeal cancer patients. Dr Christian von Briel from Hirslanden presented research featuring Kaiku Health that shows how patient-reported outcomes help to predict the severity of symptoms with high accuracy in breast cancer patients treated with radiotherapy. 

Cutting-edge technologies and innovations are nothing new in radiotherapy. One could claim that it is the very core of the field: Professor Lars Leksell for example invented radiosurgery already 40 years ago. AI, machine learning algorithms and solutions built with them are the innovations of today. And, as the congresses highlighted, they are here to stay. They allow us to shift from efficacy-focused radiation oncology to effectiveness and value-based care. By harnessing PROs in the development of machine learning algorithms, we can take another step forward towards a more patient-focused approach. This is groundbreaking. We can bring together the human touch, the patients’ voices, and intelligent technologies to enable better radiotherapy treatments for more patients.




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