1141P Prediction of cancer genomic instability using MALDI-imaging

Abstract

the starting weights were obtained from a pre-trained model on ImageNet. The model was then trained on our dataset using a supervised learning approach. To classify a WSI, the model was applied in a sliding window fashion with an input tile size of 224x224 and a stride of 128 on a magnification of x10. The maximum probability was then used as a WSI diagnosis. In this study, this established model was validated in 2171 TBLB specimen WSIs (439, 143, 73 and 1516 specimens of ADC, SCC, SCLC and non-neoplastic lesions, respectively). Results:The model achieved a Receiver Operator Curve Area Under the Curve (ROC-AUC) of 0.9458 (95% confidence interval [CI] 0.9316-0.9575) for non-neoplastic lesions with an accuracy, sensitivity and specificity of 0.8738, 0.8971 and 0.8661, respectively. The ROC-AUCs for ADC, SCC and SCLC were 0.8469 (95% CI 0.8170-0.8725), 0.9141 (95% CI 0.8825-0.9443) and 0.9412 (95% CI 0.905-0.9703), respectively.Conclusions: Our model achieved high ROC-AUCs for the differentiation of ADC, SCC, SCLC and non-neoplastic lesions. Further studies are warranted to refine the established model.Legal entity responsible for the study: The authors.