An investigative expansion of a competing risk model for first failure site in locally advanced non-small cell lung cancer. (3rd October 2019) - Lacoppidan, Thomas; Vogelius, Ivan R.; Pøhl, Mette; Strange, Malene; Persson, Gitte F.; Nygård, Lotte
Abstract: Introduction: We hypothesized that gross tumor volume (GTV) of primary tumor (GTVT ) and nodal volumes (GTVN ) were predictors of first failure site in non-small cell lung cancer (NSCLC). We aimed at also comparing the prognostic model's complexity to its ability to generate absolute risk predictions with emphasis on variables available at the time of diagnosis. Materials and methods: Three hundred and forty-two patients treated with definitive chemoradiotherapy (CRT) for adenocarcinoma (AC) or squamous cell carcinoma (SCC) in 2009–2017 were analyzed. Clinical data, standardized uptake values on FDG-PET/CT, GTVT and GTVN were analyzed using multivariate competing risk models. Results: One hundred and thirty-seven patients had SCC. As first site of failure 49 had locoregional failure (LRF), 40 had distant metastasis (DM) and 24 died with no evidence of disease (NED). In 205 patients with AC, 34 had LRF, 118 had DM as first failure site and 17 died with NED. Performance status predicted LRF ( p = .02) and UICC stage risk of DM ( p = .05 for stage 3, p < .001 for stage 4). Adding histopathology changed predictions with much reduced risk of LRF in AC compared to SCC (HR = 0.5, 95% CI: (0.3–0.75), p = .001). Conversely, AC had a higher rate of DM than SCC (HR = 2.1, 95% CI: (1.5–3.0], p < .001). Addition of FDG metrics and tumor/nodal volume data predicted DM risk ( p = .001), but with smaller impact on absolute risk compared to histopathology. Separation of GTV in nodal and tumor lesions did not improve risk predictions. Conclusions: We quantified the effect of adding volumetric and quantitative imaging to competing risk models of first failure site, but did not find tumor volume components to be important. Histopathology remains the simplest and most important factor in prognosticating failure patterns in NSCLC.