Kaustav Bera

Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma

Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naïve MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS (pu2009=u20090.003).

Feature Driven Local Cell Graph (FeDeG): Predicting Overall Survival in Early Stage Lung Cancer

The local spatial arrangement of nuclei in histopathology image has been shown to have prognostic value in the context of different cancers. In order to capture the nuclear architectural information locally, local cell cluster graph based measurements have been proposed. However, conventional ways of cell graph construction only utilize nuclear spatial proximity, and do not differentiate different cell types while constructing a cell graph. In this paper, we present feature driven local cell graph (FeDeG), a new approach to constructing local cell graphs by simultaneously considering spatial proximity and attributes of the individual nuclei (e.g. shape, size, texture). In addition, we designed a new set of quantitative graph derived metrics to be extracted from FeDeGs, in turn capturing the interplay between different local cell clusters. We evaluated the efficacy of FeDeG features in a digitized H&E stained tissue micro-array (TMA) images cohort consists of 434 early stage non-small cell lung cancer for predicting short-term ( 5 years) survival. Across a 100 runs of 10-fold cross-validation, a linear discriminant classifier in conjunction with the 15 most predictive FeDeG features identified via the Wilcoxon Rank Sum Test (WRST) yielded an average of AUC = 0.68. By comparison, four state-of-the-art pathomic and a deep learning based classifier had a corresponding AUC of 0.56, 0.54, 0.61, 0.62, and 0.55 respectively.

RaPtomics: Integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer

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.

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.