Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Abstract

Objective: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. Methods and procedures: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. Results: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. Conclusion: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. Clinical impact: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery

“…Several metabolic alterations follow the progression of pancreatic cancer. 35,62,125 Thus, circulating elements that may be derived from precursor lesions or pancreatic cancers are of interest as genomic and proteomic biomarkers, 8,[126][127][128] as well as circulating cancer cells (CTCs) and exosomes, 58 and cell free DNA (cfDNA). [129][130][131][132][133][134][135][136] Such biomarkers have been used to demonstrate the ability to detect several cancer types, 137 with ability to diagnose at an early stage for when resection is possible.…”

Pancreatic Cancer Detection Through Hyperparameter Tuning and Ensemble Methods

“…To identify PC at the localized stage, D Agarwal et al [19] proposed highly sensitive nano biosensors for protease/arginine detection. The hard hierarchical decision structure (HDS) and soft hierarchical decision structure (SDS) beat traditional multi-class classification methods, achieving an accuracy score of 92%.…”