How can AI-based auto-contouring improve the life of patients who need palliative radiation therapy?

Cancer is one of the leading causes of morbidity and death, worldwide. Approximately 18 million people are diagnosed with cancer each year and the number is expected to increase. Pain is experienced by two thirds of patients having advanced, metastatic or terminal disease (1). Other symptoms caused by compression on the nervous system components, blood vessels, airways, digestive tract, urinary system, or by direct organ invasion, can significantly deteriorate the quality of life or even become life-threatening. Radiation therapy offers an effective, well-tolerated and affordable solution to treat local symptoms of advanced cancer. AI-based auto-contouring solutions decrease the necessary time for treatment preparation and support a more complex approach, when needed. 

Symptoms improvement as a result of palliative radiation therapy 

It was previously estimated that palliative radiation therapy is given at one point during their treatment to 40% of patients with advanced cancer and that 40% of radiation therapy treatments are given with palliative intent (2). It is likely that variations can be found in time, and in different locations, but the role of Radiation Oncology in this field will probably stay an important one in the future, too.

An improvement in the quality of life was reported by 62% and by 44% of the patients with head and neck cancers  included in two phase II trials using palliative irradiation schedules. Despite the fact that many studies evaluating outcomes of palliative radiation therapy did not explicitly assess improvements in the quality of life, it is reasonable to believe that symptoms’ improvement had a positive effect (3,4). 

Table 1 (at the bottom of the page) includes the most frequent indications for palliative radiotherapy and reported outcomes. Data source – Spencer K et al, 2018  (5). 

Figure 1. A visual summary based on Table 1 data, including the most promising results for pain reduction, according to clinical situations in which palliative radiotherapy is recommended (5).

A paradigm shift

In most of the situations, palliative treatments are considered as emergencies, so any time saving matters. On the other hand, when prioritizing a short time to delivery, fewer and simpler volumes might be delineated. However, when delivering high doses per fraction, which is often the case for palliative irradiation, the accuracy of delineation is even more important. 

Fortunately, quality AI-predicted contours can be provided to treating physicians in very few minutes, supporting the fast preparation of an adapted treatment plan (6). This complex approach becomes important especially nowadays, when the distinction between curative and palliative irradiation becomes less clear. New and more efficient systemic treatments improve patients’ outcomes and re-irradiation might be needed either on the same target, or in a closely situated one (7). 

Time race

Traditionally, palliative radiation therapy is successfully used for symptoms that cannot be controlled using standard doses of medication or by surgical intervention. This means that the patients already went through a very difficult period when other solutions were inefficient to control their symptoms. Delaying treatment in such cases has a higher negative impact compared with other cases, when patients are asymptomatic. 

An interesting study published in 2021 retrospectively evaluated patterns of practice in two USA institutions from 2012 to 2018. The authors found that the median time from consultation request to RT was 1 to 4 days for spinal cord compression, 2 to 7 days for pain and 1 to 6 days for symptomatic brain metastases. Duration from consultation to initiation of RT was ranging from 2 to 7 days and decreased to 0-3 days after a nurse practitioner was involved in the process (8). Apart from the fact that any delay of treating highly symptomatic patients extends their hard times, in the case of spinal cord compression it increases the risk of permanent loss of function.

Another solution to decrease the total time spent in the cancer center from first appointment to treatment delivery completion was tested by a Canadian group, who evaluated the feasibility of using a recent diagnostic CT instead of a simulation CT for palliative treatments (9). 

New and better ways of winning this time race are provided by wisely using artificial intelligence for contouring and probably also for planning, in the near future.

Auto-contouring solutions’ role in the radiation therapy workflow

An ASTRO Consensus Paper that was published in 2018 provided recommendations for standardizing normal tissue contouring and already mentioned some of the positive results of auto-contouring technology, but the body of evidence significantly increased since then (10). 

Contouring of the target structures and the organs at risk is probably the most time-consuming stage of the treatment preparation process. The AI-based solutions implementation was constantly proven to save time and decrease inter-observer variability. Especially if  the predicted structures are following the guidelines published by international professional groups, it’s likely that clinicians’ mission gets much simplified. Previous studies shown that and the needed adjustments for the automatic contours are minimal or not needed at all (6, 11, 12).

Helping hands

Palliative care is defined by WHO as “an approach that improves the quality of life of patients and their families facing the problems associated with life-threatening illness…” (13).  So, by definition, palliative radiotherapy is meant to improve patients’ quality of life, being part . Unfortunately, sometimes it is given too late or not at all, even if needed. AI-based solutions can be used to improve process efficiency and indirectly, radiation therapy accessibility and quality.

MVision AI is committed to providing solutions for higher efficiency in radiation therapy workflows and has already contributed to faster treatment preparation for more than a hundred thousand patients, internationally. Contour + is providing clinicians a comprehensive set of more than 270 guideline-based structures to design a personalized plan. Cancer patients, especially those who experience pain or other disturbing symptoms, should safely benefit from the Radiation Therapy “fast track”.

Written by
Monica-Emilia Chirilă
Radiation Oncologist

References

  1. WHO Guidelines for the pharmacological and radiotherapeutic management of cancer pain in adults and adolescents. https://www.who.int/publications/i/item/9789241550390
  2. Lam TC, Tseng Y. Defining the radiation oncologist’s role in palliative care and radiotherapy. Ann Palliat Med. 2019;8(3):246-263. doi:10.21037/apm.2018.10.02
  3. Porceddu SV, Rosser B, Burmeister BH, et al. Hypofractionated radiotherapy for the palliation of advanced head and neck cancer in patients unsuitable for curative treatment–“Hypo Trial”. Radiother Oncol. 2007;85(3):456-462. doi:10.1016/j.radonc.2007.10.020
  4. Corry J, Peters LJ, Costa ID, et al. The ‘QUAD SHOT’–a phase II study of palliative radiotherapy for incurable head and neck cancer. Radiother Oncol. 2005;77(2):137-142. doi:10.1016/j.radonc.2005.10.008
  5. Spencer K, Parrish R, Barton R, Henry A. Palliative radiotherapy. BMJ. 2018;360:k821. Published 2018 Mar 23. doi:10.1136/bmj.k821
  6. Strolin S, Santoro M, Paolani G, et al. How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images. Front Oncol. 2023;13:1089807. Published 2023 Mar 2. doi:10.3389/fonc.2023.1089807
  7. Rembielak A, Dennis K. The Evolving Practice of Palliative Radiotherapy. Clin Oncol (R Coll Radiol). 2020;32(11):685-687. doi:10.1016/j.clon.2020.08.001
  8. Wu SY, Yee E, Chan JW, Chapman CH, Boreta L, Braunstein SE. Timing of Urgent Inpatient Palliative Radiation Therapy. Adv Radiat Oncol. 2021;6(3):100670. Published 2021 Feb 11. doi:10.1016/j.adro.2021.100670
  9. M. O’Neil, J.M. Laba, T. Nguyen, M.I. Lock, C.D. Goodman, E. Huynh, J. Snir, V. Munro, J.A. Alce, L. Schrijver, S. Lemay, T. Macdonald, A. Warner, D.A. Palma, Diagnostic CT-Enabled Radiation Therapy (DART): Results of a Randomized Trial for Palliative Radiation Therapy, International Journal of Radiation Oncology*Biology*Physics, Volume 117, Issue 4, 2023, https://doi.org/10.1016/j.ijrobp.2023.08.038.
  10. Wright JL, Yom SS, Awan MJ, et al. Standardizing Normal Tissue Contouring for Radiation Therapy Treatment Planning: An ASTRO Consensus Paper. Pract Radiat Oncol. 2019;9(2):65-72. doi:10.1016/j.prro.2018.12.003
  11. Turcas A, Leucuta D, Balan C, et al. Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution. Phys Imaging Radiat Oncol. 2023;27:100454. Published 2023 Jun 6. doi:10.1016/j.phro.2023.100454
  12. S Warren, N Richmond, A Wowk, M Wilkinson, K Wright, AI segmentation as a quality improvement tool in radiotherapy planning for breast cancer, IPEM-Translation, Volumes 6–8, 2023, doi.org/10.1016/j.ipemt.2023.100020.
  13. Palliative care. https://www.who.int/news-room/fact-sheets/detail/palliative-care

Table 1 . The most frequent indications* for palliative radiotherapy and reported outcomes. Data source – Spencer K et al, 2018 (5)

*Note: Symptomatic brain metastases is an important indication for palliative radiotherapy, but its effect on improving symptoms is mentioned in the studies included in the original table using a different type of assessment, so it could not be mentioned above in a comparable way. Please access the original article (5) and its references for more details

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