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Dr. Yutaro Koide on the Classification of Patients with Painful Tumors to Predict Palliative RT Response

April 03, 2024

Video Transcript


Dr. Yutaro Koide: Hello, my name is Yutaro Koide Thank you for the opportunity to introduce our new article titled "Classification of Patients with Painful Tumors to Predict the Response to Palliative Radiotherapy." This study aimed to identify factors affecting pain response and classify patients who underwent palliative radiotherapy using our prospective cohort data. The primary endpoint of this study was the pain response rate within 12 weeks, and secondary endpoints included overall survival, time to response, time to progression and adverse events. Lesions with NRS 2 or higher enrolled. Intercranial or postoperative lesions were excluded. A total of 366 lesions from 261 patients met the criteria. Response assessments were prospectively scheduled at multiple time points within one year. The primary endpoint of the cumulative pain response rate was 219 lesions out of 366 overall lesions or 59.8%. We found that high-dose opioids and re-irradiation were detected as predictors of a lower response rate. Lesions were categorized into class 1, class 2, and class 3, with respective response rates of 75%, 61%, and 36%. These figures show the mean change from baseline NRS, time to response, and time to progression by class. Time to response was similar across classes, but time to progression was different: 24 weeks' progression rates were 11% in class 1, 27% in class 2, and 63% in class 3. Our classification system successfully estimates the pain response and response duration to palliative radiotherapy, suggesting the need for further treatment development for unfavorable prognosis populations such as class 3. In conclusion, this study demonstrates that the pain response to palliative radiotherapy significantly varies among patients. Personalized treatment strategies, tailored according to individual pain severity and history of irradiation, should be considered as stratification factors in future clinical trials. Thank you.



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