Lung cancer risk prediction model based on data of the SOS trial (single center non-randomized lung cancer early detection trial)

L. Bertolaccini, A. Viti, A. Terzi, L. Bertolaccini (Negrar Verona, Cuneo, Italy)

Source: International Congress 2014 – Risk assessment and tracheal reconstructions
Session: Risk assessment and tracheal reconstructions
Session type: Oral Presentation
Number: 1919
Disease area: Airway diseases

Congress or journal article abstractE-posterSlide presentation

Abstract

INTRODUCTION. SOS study [single center non-randomized Lung Cancer (LC) early detection trial with Digital Tomosynthesis (DT)] showed detection rate comparable to CT with low costs and radiation. Optimal target population and rounds time-interval are not defined. Aims: develop models to estimate LC probability, select lower risk patients to prolong rounds intervals.METHODS. LC incidence was calculated. Kaplan-Meier method represents LC cumulative incidence, log-rank compare LC incidence between categories. We compared LC frequency in first year with frequency predicted by Bach model (BM). Based on baseline DT, we developed predictive model to stratify individuals according to probability of LC diagnosis during subsequent rounds. Cox analysis was used. Discriminatory ability of various models fitted was assessed by Harrell's statistic.RESULTS. 1843 enrolled, LC founded in 23 (detection rate 1.25, resectability rate 73.9%). LC rate was high in COPD. Age, smoking duration, and cigarettes smoked were main LC risk determinants. BM estimated that 39 participants would develop symptomatic LC during first year, whereas 22 LC were detected in first screening round (standardized incidence ratio=1.30; 95%CI=0.97-2.41). Recalibrated BM predicted observed incidence (c2 test=6.2; p=0.63). Excluding LC diagnosed during baseline round, emphysema, nodule type, and nodule size strongly influenced LC diagnosis risk at subsequent round. 436 persons could avoid yearly recall DT and radiation exposure as none was diagnosed with LC.CONCLUSION. Models could help defining time interval to subsequent rounds in SOS participants. Further studies are necessary to validate models.


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Citations should be made in the following way:
L. Bertolaccini, A. Viti, A. Terzi, L. Bertolaccini (Negrar Verona, Cuneo, Italy). Lung cancer risk prediction model based on data of the SOS trial (single center non-randomized lung cancer early detection trial). Eur Respir J 2014; 44: Suppl. 58, 1919

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