# Predicting the risk of relapsed or refractory in patients with diffuse large B-cell lymphoma via deep learning

## TL;DR

This study uses deep learning and clinical data to predict which patients with a type of lymphoma are at high risk of relapse or treatment resistance.

## Contribution

A novel fusion model combining histopathological images and clinical features for predicting relapsed or refractory risk in DLBCL.

## Key findings

- The fusion model achieved an average AUC of 0.71 in validation and 0.70 in testing.
- Combining image features and clinical data improved prediction performance.
- High-risk patients can benefit from intensified monitoring and adjusted treatment plans.

## Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL) in humans, and it is a highly heterogeneous malignancy with a 40% to 50% risk of relapsed or refractory (R/R), leading to a poor prognosis. So early prediction of R/R risk is of great significance for adjusting treatments and improving the prognosis of patients.

We collected clinical information and H&E images of 227 patients diagnosed with DLBCL in Xuzhou Medical University Affiliated Hospital from 2015 to 2018. Patients were then divided into R/R group and non-relapsed & non-refractory group based on clinical diagnosis, and the two groups were randomly assigned to the training set, validation set and test set in a ratio of 7:1:2. We developed a model to predict the R/R risk of patients based on clinical features utilizing the random forest algorithm. Additionally, a prediction model based on histopathological images was constructed using CLAM, a weakly supervised learning method after extracting image features with convolutional networks. To improve the prediction performance, we further integrated image features and clinical information for fusion modeling.

The average area under the ROC curve value of the fusion model was 0.71±0.07 in the validation dataset and 0.70±0.04 in the test dataset. This study proposed a novel method for predicting the R/R risk of DLBCL based on H&E images and clinical features.

For patients predicted to have high risk, follow-up monitoring can be intensified, and treatment plans can be adjusted promptly.