Algorithms to identify radiotherapy intent in unresected non-metastatic non-small-cell lung cancer: an I-O Optimise
Eleanor Ralphs1, Caroline Rault2, Alan Calleja1
1Real World Solutions, IQVIA, The Point, 37 N Wharf Rd, London, W2 1AF, England.
Abstract:
This study aimed to develop and evaluate the performance of algorithms for identifying radiotherapy (RT) treatment intent in real-world data from patients with non-metastatic non-small-cell lung cancer (NSCLC). Using data from IPO-Porto hospital (Portugal) and the REAL-Oncology database (England), three algorithms were developed based on available RT information (#1: RT duration, #2: RT duration and type, #3: RT dose) and tested versus reference datasets. Study results showed that all three algorithms had good overall accuracy (91-100%) for patients receiving RT plus systemic anticancer therapy (SACT) and algorithms #2 and #3 also had good accuracy (>99%) for patients receiving RT alone. These algorithms could help classify treatment intent in patients with NSCLC receiving RT with or without SACT in real-world settings where intent information is missing/incomplete.
