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Using data from the IPO-Porto hospital (Portugal) and the REAL-Oncology database (England), three algorithms are built from available RT information: RT duration, RT duration and type, and RT dose, then tested against reference datasets. Results show 91–100% overall accuracy for RT plus systemic anticancer therapy (SACT), and >99% for RT alone for algorithms #2 and #3, supporting intent classification when intent data are missing or incomplete.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\n[https://doi.org/10.1080/14796694.2024.2363133](https://doi.org/10.1080/14796694.2024.2363133)  \nMETHOD  \nAlgorithms to identify radiotherapy intent in unresected non-metastatic non-small-cell lung cancer: an I-O Optimise analysis  \nEleanor Ralphs* , a, Caroline Raultb, Alan Callejaa, Melinda J Daumontc, John R Penrodd, Matthew Thompsone, Sue Cheeseman‡, e and Marta Soares‡,f  \na Real World Solutions, IQVIA, The Point, 37 N Wharf Rd, London, W2 1AF, England; bData Gnosis, 26 Rue de Leon, 35000Rennes, France; cWorldwide Health Economics & Outcomes Research, Bristol Myers Squibb, Av. de Finlande 4, 1420 Braine-L’Alleud, Belgium; dWorldwide Health Economics & Outcomes Research, Bristol Myers Squibb, 3551 Lawrenceville Rd, Princeton, NJ 08540, USA; e REAL Oncology, Leeds Teaching Hospitals NHS Trust, Bexley Wing Cancer Centre, Beckett Street, Leeds, LS9 7TF, England; f Department of Medical Oncology, Portuguese Oncology Institute of Porto (IPO-Porto), Rua Dr. António Bernardino e Almeida, 4200–072Porto, Portugal  \nABSTRACT  \nThis 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.  \nPLAIN LANGUAGE SUMMARY  \nOne objective of many real-world studies is to evaluate which cancer treatments are given during routine visits to hospitals or cancer centers and assess how well the treatments work. This objective is easier to achieve when we know the reason for the cancer treatment (known as treatment intent), but doctors often do not record whether the treatment was given to actively treat the cancer (curative intent) or to slow down a cancer’s growth or control symptoms in people with incurable cancer (palliative intent). In this article, we describe the development and testing of algorithms to determine treatment intent in people with lung cancer given radiotherapy (the controlled application of radiation to cancer cells). These algorithms involve following a step-by-step process based on three key questions: for how long was the radiotherapy given? what type of radiotherapy was given? and what dose of radiotherapy was given? Answers were then tested true or false against reference answers provided by doctors who know a lot about radiotherapy. We found that all three algorithms were able to determine the correct treatment intent in more than nine out of ten people given radiotherapy with systemic anticancer therapy (e.g., chemotherapy) and two algorithms were able to determine the correct treatment intent in more than nine out often people given radiotherapy alone. These algorithms may be helpful in determining treatment intent in people given radiotherapy to treat lung cancer in real-world settings, and may help us learn more about real-world lung cancer treatment.  \nARTICLE HISTORY  \nReceived 24 August 2023 Accepted 30 May 2024  \nKEYWORDS  \nalgorithm; curative; I-OOptimise; methodology; non-metastatic;  \nnon-small-cell lung cancer; palliative; radiotherapy; treatment intent  \n1. Background  \nOver the past decade, European Society for Medical Oncology (ESMO) guidelines have consistently advocated surgery as the cornerstone of treatment forresectable non-metastatic (stage I–I","cbCaitZZrN1owgZk","https://ap.wps.com/l/cbCaitZZrN1owgZk","pdf",1184144,11,"English","# Background\n# Methods\n## Algorithm development\n## Reference dataset testing\n# Results\n## Accuracy for RT plus SACT\n## Accuracy for RT alone\n# Plain language summary\n# Article history\n# Keywords","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop and evaluate algorithms that identify radiotherapy treatment intent in real-world data for patients with unresected non-metastatic non-small-cell lung cancer.\"},{\"question\":\"What information do the three algorithms use?\",\"answer\":\"They use RT duration (#1), RT duration and type (#2), and RT dose (#3), based on available radiotherapy information in real-world datasets.\"},{\"question\":\"How accurate are the algorithms for patients receiving radiotherapy with systemic anticancer therapy?\",\"answer\":\"All three algorithms show good overall accuracy, reported as 91–100%, for patients receiving RT plus SACT.\"}]","Algorithms to identify radiotherapy intent in unresected non-metastatic non-small-cell lung cancer - an I-O Optimise analysis | PDF",1789982865,28]