Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images

TL;DR

This paper introduces a multi-task learning framework that enhances outcome prediction in 3D PET images by leveraging multiple related tasks, improving radiomics performance in oncology applications.

Contribution

The study presents a novel multi-task learning approach that uses auxiliary tasks as inductive biases to improve feature extraction and generalization in radiomic analysis.

Findings

Achieved 77% AUC for treatment response prediction in lung cancer.

Achieved 71% AUC for survival prediction in esophageal cancer.

Outperformed single-task learning methods in predictive accuracy.

Abstract

Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a field of study where images are used instead of invasive methods. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and can be physician subjective. Automated tools based on supervised deep learning have made great progress to assist physicians. However, they are data hungry, and annotated data remains a major issue in the medical field where only a small subset of annotated images is available. Methods: In this work, we propose a multi-task learning framework to predict patient's survival and response. We show that the encoder can leverage multiple tasks to extract meaningful and powerful features that improve radiomics performance. We show also that subsidiary tasks serve as an inductive bias so that the model can better generalize. Results: Our model was tested and validated for treatment response and survival in lung and esophageal cancers, with an area under the ROC curve of 77% and 71% respectively, outperforming single task learning methods. Conclusions: We show that, by using a multi-task learning approach, we can boost the performance of radiomic analysis by extracting rich information of intratumoral and peritumoral regions.