Radiomics-based machine learning models to predict progression and biomarker status in non-small cell lung cancer (NSCLC) patients treated with immunotherapy (AACR 2023)
The radiomic features-based models lack accuracy in predicting clinical characteristics and outcomes. Further validation with larger cohorts is warranted.Statistics of radiomics-based models in predicting clinical characteristics and treatment outcomesDurable Disease Control(Yes/No)(n=64)TTF1 expression(Yes/No)(n=62)Histology(Adeno/Other)(n=71)NLR(>=5/<5)(n=71)PD-L1 expression(Yes/No)(n=52)Patient Number(%)33 (51.56%) / 31 (48.44%)37 (59.68%) / 25 (40.32%)48 (67.61%) / 23 (32.39%)28 (39.44%) / 43 (60.56%)35 (67.31%) / 17 (32.69%)Sensitivity (95% CI)0.63 (0.58, 0.72)0.62 (0.56, 0.74)0.69 (0.56, 0.82)0.55 (0.47, 0.61)0.57 (0.48, 0.65)Specificity (95% CI)0.46 (0.37, 0.52)0.68 (0.58, 0.76)0.22 (0.12, 0.34)0.60 (0.56, 0.68)0.36 (0.30, 0.45)Positive Predictive Value(95% CI)0.52 (0.49, 0.57)0.44(0.37, 0.60)0.62 (0.59, 0.64)0.69 (0.67, 0.74)0.72 (0.68, 0.77)Negative Predictive Value(95% CI)0.58 (0.54, 0.63)0.79 (0.74, 0.88)0.28 (0.22, 0.32)0.46 (0.39, 0.51)0.25 (0.21, 0.28)