Radiomic feature-based prediction model of lung cancer recurrence in NSCLC patients

Soomin Lee; Julip Jung; Helen Hong; Bongseok Kim

doi:10.1117/12.2566451

1 June 2020 Radiomic feature-based prediction model of lung cancer recurrence in NSCLC patients

Soomin Lee, Julip Jung, Helen Hong, Bongseok Kim

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115150N (2020) https://doi.org/10.1117/12.2566451
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

This study investigates the potential of a radiomic feature-based prediction model of non-small cell lung cancer (NSCLC) recurrence within two years on chest CT images. First, tumor areas are defined as intra-tumoral areas that have been manually segmented by a radiologist and the largest tumor ROI are selected as the representative cross-section. Second, a total of 68 radiomic features including intensity, texture and shape features are extracted within the tumor area. Then, three features with weights that are clearly distinguished from other weights are defined as significant features using the Relief-F algorithm. Finally, to predict lung cancer recurrence within two years, random forests and SVM are trained for the classification of two groups representing recurrence and non-recurrence within two years. In the experimental results, since the accuracy, sensitivity, specificity, and AUC were 71.42, 80.95, 61.90, and 0.74 for random forest and were 66.66, 61.90, 71.42 and 0.65 for SVM, the prediction model constructed by the random forest shows better performance. Kaplan-meier curve that fitted with seperated patients shows the estimated probability by radiomicbased prediction model.

Citation Download Citation

Soomin Lee, Julip Jung, Helen Hong, and Bongseok Kim "Radiomic feature-based prediction model of lung cancer recurrence in NSCLC patients", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115150N (1 June 2020); https://doi.org/10.1117/12.2566451

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