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Rigshospitalet - en del af Københavns Universitetshospital
Udgivet

A competing risk model of first failure site after definitive (chemo) radiation therapy for locally advanced non-small cell lung cancer

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

DOI

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INTRODUCTION: The aim of the study was to build a model of first failure site and lesion specific failure probability after definitive chemo-radiotherapy for inoperable non-small cell lung cancer (NSCLC).

METHODS: We retrospectively analyzed 251 patients receiving definitive chemo/radiotherapy for NSCLC at a single institution between 2009-2015. All patients were FDG PET/CT scanned for radiotherapy planning. Clinical patient data and FDG PET standardized uptake values from primary tumor and nodal lesions were analyzed using multivariate cause-specific Cox regression. In patients experiencing loco-regional failure, multivariable logistic regression was applied to assess risk of each lesion being first site of failure. The two models were used in combination to predict lesion failure probability accounting for competing events.

RESULTS: Adenocarcinoma had a lower hazard ratio (HR) of loco-regional (LR) failure than squamous cell carcinoma, HR 0.45, 95% CI [0.26; 0.76], p =0.003. Distant failures were more common in the adenocarcinoma group, HR 2.21, 95% CI [1.41; 3.48], p<0.001. Multivariable logistic regression of individual lesions at the time of first failure showed primary tumors were more likely to fail than lymph nodes, OR 12.8, 95% CI [5.10; 32.17], p<0.001. Increasing SUVpeak was significantly associated with lesion failure, OR 1.26 per unit increase, 95% CI [1.12; 1.40], p<0.001. Electronic model: http://bit.ly/LungModelFDG.

CONCLUSIONS: We developed a failure-site specific competing risk model based on patient- and lesion-level characteristics. Failure patterns differed between adenocarcinoma and squamous cell carcinoma, illustrating the limitation of aggregating them into 'non-small-cell lung cancer'. Failure site specific models add complementary information to conventional prognostic models.

OriginalsprogEngelsk
TidsskriftJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
Vol/bind13
Udgave nummer4
Sider (fra-til)559-567
ISSN1556-0864
DOI
StatusUdgivet - 2018

ID: 52705770