# Personalized Pancreatic Tumor Growth Prediction via Group Learning

## TL;DR

This paper introduces a group learning-based deep neural network approach for personalized pancreatic tumor growth prediction, integrating population trends and individual clinical data to improve accuracy over existing models.

## Contribution

It presents a novel deep convolutional neural network that combines multimodal imaging, clinical factors, and group learning for personalized tumor growth prediction.

## Key findings

- Achieved a Dice coefficient of 86.8%, outperforming previous methods.
- Reduced RVD to 7.9%, indicating more accurate volume prediction.
- Demonstrated effectiveness on pancreatic tumor data set.

## Abstract

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method.