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  • Title: Predictive value of primary tumor volume change during concurrent chemoradiotherapy in patients with unresectable stage III non-small cell lung cancer.
    Author: In Lee H, Kyung Choi E, Ssan Kim S, Seob Shin Y, Won Park J, Yeol Song S.
    Journal: Radiother Oncol; 2024 Sep; 198():110383. PubMed ID: 38879129.
    Abstract:
    BACKGROUND AND PURPOSE: No established early biomarkers currently exist to predict responses during concurrent chemoradiotherapy (CCRT) in patients with unresectable non-small cell lung cancer (NSCLC). This study investigated the potential of gross tumor volume (GTV) and its changes during CCRT as predictors of survival outcomes. MATERIALS AND METHODS: We identified 227 patients with unresectable stage III NSCLC who underwent definitive CCRT followed by durvalumab between November 2018 and December 2022. GTV was defined as the volume of the primary tumor, assessed at two time points: before starting CCRT for initial planning (GTV1), and at the fourth week of CCRT for adaptive planning (GTV2). Both relative and absolute regressions between GTV1 and GTV2 were calculated. RESULTS: The median GTV1 volume was 90 mL (range, 5-840 mL), and the median GTV2 volume was 64 mL (range, 1-520 mL), resulting in median absolute and relative regressions of 18.6 mL and 25.0 %, respectively. Among the GTV parameters, relative GTV regression exhibited the strongest predictive value, with an area under the curve (AUC) of 0.804 for in-field progression and 0.711 for overall progression. The 1-year progression-free survival rates for the high (>30 %), intermediate (0-30 %), and low (≤0%) relative regression groups were 88.0 %, 62.6 %, and 14.3 %, respectively (p = 0.006 for high vs. intermediate; p < 0.001 for intermediate vs. low). Additionally, GTV2 volume demonstrated stronger associations with survival outcomes than GTV1 volume. CONCLUSION: Relative GTV regression was identified as a promising early predictor for patients with unresectable stage III NSCLC. Further development of a multi-parametric predictive model is warranted to guide patient-tailored therapeutic approaches.
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