Recently immune-checkpoint inhibitors have demonstrated promising clinical efficacy in patients with advanced non-small cell lung cancer (NSCLC). However, the response rates to immune checkpoint blockade drugs remain modest (45% in the front line setting and 20% in the second line setting). Consequently, there is an unmet need to develop accurate, validated biomarkers to predict which NSCLC patients will benefit from immunotherapy. While there has been recent interest in evaluating the role of texture and shape patterns of the nodule on CT scans to predict response to checkpoint inhibitors for NSCLC, our group has shown that nodule vessel morphology might also play a role in determining tumor aggressiveness and behavior. In this work we present a new approach using quantitative vessel tortuosity (QVT) radiomics, to predict response to checkpoint inhibitors and overall survival for patients with NSCLC treated with Nivolumab (a PD1 inhibitor) on a retrospective data set of 111 patients (D1) including 56 responders and 45 non-responders. Patients who did not receive Nivolumab after 2 cycles due to a lack of response or progression as per Response Evaluation Criteria in Solid Tumors (RECIST) were classified as non-responders, patients who had radiological response or stable disease as per RECIST were classified as responders. On D1, in conjunction with a linear discriminant analysis (LDA) classifier the QVT features were able to predict response to immunotherapy with an AUC of 0.73_0.04. Kaplan Meier analysis showed significant difference of overall survival between patients with low risk and high risk defined by the radiomics classifier (p-value = 0.004, HR= 2.29, 95% CI= 1.35 - 3.87).
Non-small cell lung cancer (NSCLC) is the leading cause of cancer related deaths worldwide. The treatment of choice for early stage NSCLC is surgical resection followed by adjuvant chemotherapy for high risk patients. Currently, the decision to offer chemotherapy is primarily dependent on several clinical and visual radiographic factors as there is a lack of a biomarker which can accurately stratify and predict disease risk in these patients. Computer extracted image features from CT scans (radiomic) and (pathomic) from H&E tissue slides have already shown promising results in predicting recurrence free survival (RFS) in lung cancer patients. This paper presents new radiology-pathology fusion approach (RaPtomics) to combine radiomic and pathomic features for predicting recurrence in early stage NSCLC. Radiomic textural features (Gabor, Haralick, Law, Laplace and CoLlAGe) from within and outside lung nodules on CT scans and intranuclear pathology features (Shape, Cell Cluster Graph and Global Graph Features) were extracted from digitized whole slide H&E tissue images on an initial discovery set of 50 patients. The top most predictive radiomic and pathomic features were then combined and in conjunction with machine learning algorithms were used to predict classifier. The performance of the RaPtomic classifier was evaluated on a training set from the Cleveland Clinic (n=50) and independently validated on images from the publicly available cancer genome atlas (TCGA) dataset (n=43). The RaPtomic prognostic model using Linear Discriminant Analysis (LDA) classifier, in conjunction with two radiomic and two pathomic shape features, significantly predicted 5-year recurrence free survival (RFS) (AUC 0.78; p<0.005) as compared to radiomic (AUC 0.74; p<0.01) and pathomic (AUC 0.67; p<0.05) features alone.
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