Identifying Biomarkers to Select Patients with Borderline Resectable and Locally Advanced Pancreatic Ductal Adenocarcinoma (BRPC, LAPC) for Radiotherapy (RT)

https://openworks.mdanderson.org/sumexp22/1036/thumbnail.jp

Mathematical Modeling of CA19-9 Normalization in Pancreatic Cancer Patients

https://openworks.mdanderson.org/sumexp21/1077/thumbnail.jp

Predicting Tumor Related Liver Failure in Unresectable Intrahepatic Cholangiocarcinoma Patients through the Development of an Imaging Based Deep Learning Neural Network Model

https://openworks.mdanderson.org/sumexp21/1008/thumbnail.jp

The Role of Radiotherapy In the Management of Massive Intrahepatic Cholangiocarcinoma

https://openworks.mdanderson.org/sumexp21/1005/thumbnail.jp

Analysis and Prediction of Patient Survival After Radiotherapy For Liver Cancer Based On Volumetric Segmental Response and Clinically Relevant Factors

https://openworks.mdanderson.org/sumexp23/1121/thumbnail.jp

Identifying Differences in Spatial Transcriptomics Between Subtypes of Pancreatic Ductal Adenocarcinoma

https://openworks.mdanderson.org/sumexp22/1072/thumbnail.jp

Heterogeneous Image-based Classification Using Distributional Data Analysis

Diagnostic imaging has gained prominence as potential biomarkers for early detection and diagnosis in a diverse array of disorders including cancer. However, existing methods routinely face challenges arising from various factors such as image heterogeneity. We develop a novel imaging-based distributional data analysis (DDA) approach that incorporates the probability (quantile) distribution of the pixel-level features as covariates. The proposed approach uses a smoothed quantile distribution (via a suitable basis representation) as functional predictors in a scalar-on-functional quantile regression model. Some distinctive features of the proposed approach include the ability to: (i) account for heterogeneity within the image; (ii) incorporate granular information spanning the entire distribution; and (iii) tackle variability in image sizes for unregistered images in cancer applications. Our primary goal is risk prediction in Hepatocellular carcinoma that is achieved via predicting the change in tumor grades at post-diagnostic visits using pre-diagnostic enhancement pattern mapping (EPM) images of the liver. Along the way, the proposed DDA approach is also used for case versus control diagnosis and risk stratification objectives. Our analysis reveals that when coupled with global structural radiomics features derived from the corresponding T1-MRI scans, the proposed smoothed quantile distributions derived from EPM images showed considerable improvements in sensitivity and comparable specificity in contrast to classification based on routinely used summary measures that do not account for image heterogeneity. Given that there are limited predictive modeling approaches based on heterogeneous images in cancer, the proposed method is expected to provide considerable advantages in image-based early detection and risk prediction.Comment: 16, 2 figures, 3 table

Single-cell transcriptomics of intrahepatic cholangiocarcinoma (iCC) reveals novel tumor epithelial-stromal interactions

https://openworks.mdanderson.org/sumexp21/1093/thumbnail.jp