Algorithms to identify radiotherapy intent in unresected non-metastatic non-small-cell lung cancer: an I-O Optimise analysis.

Ralphs, E.; Rault, C.; Calleja, A.; Daumont, MJ.; Penrod, JR.; Thompson, M.; Cheeseman, S.; Soares, M.

Algorithms to identify radiotherapy intent in unresected non-metastatic non-small-cell lung cancer: an I-O Optimise analysis.

All Authors

Ralphs, E.

Rault, C.

Calleja, A.

Daumont, MJ.

Penrod, JR.

Thompson, M.

Cheeseman, S.

Soares, M.

LTHT Author

Thompson, Matthew
Cheeseman, Sue

LTHT Department

Research & Innovation
Leeds Cancer Centre

Publication Date

2024

Item Type

Journal Article

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.