A hybrid deep learning model for forecasting lymphocyte depletion during radiation therapy. Issue 5 (16th March 2022)

• Record Type: Journal Article
• Authors: Ebrahimi, Saba; Lim, Gino; Hobbs, Brian P.; Lin, Steven H.; Mohan, Radhe; Cao, Wenhua
• Is Part Of: Medical physics. Volume 49:Issue 5(2022)
• Page Start: 3507
• Page End: 3522
• Publication Date: 2022-03-16
• Subjects: deep learning -- LSTM -- lymphopenia -- radiation therapy treatment planning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15584
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Recent studies have shown that severe depletion of the absolute lymphocyte count (ALC) induced by radiation therapy (RT) has been associated with poor overall survival of patients with many solid tumors. In this paper, we aimed to predict radiation‐induced lymphocyte depletion in esophageal cancer patients during the course of RT based on patient characteristics and dosimetric features. Methods: We proposed a hybrid deep learning model in a stacked structure to predict a trend toward ALC depletion based on the clinical information before or at the early stages of RT treatment. The proposed model consisted of four channels, one channel based on long short‐term memory (LSTM) network and three channels based on neural networks, to process four categories of features followed by a dense layer to integrate the outputs of four channels and predict the weekly ALC values. Moreover, a discriminative kernel was developed to extract temporal features and assign different weights to each part of the input sequence that enabled the model to focus on the most relevant parts. The proposed model was trained and tested on a dataset of 860 esophageal cancer patients who received concurrent chemoradiotherapy. Results: The performance of the proposed model was evaluated based on several important prediction metrics and compared to other commonly used prediction models. The results showed that the proposed model outperformed off‐the‐shelf prediction methods with at least a 30% reduction in the mean squared error (MSE) of weekly ALC predictions based on pretreatment data. Moreover, using an extended model based on augmented first‐week treatment, data reduced the MSE of predictions by 70% compared to the model based on the pretreatment data. Conclusions: In conclusion, our model performed well in predicting radiation‐induced lymphocyte depletion for RT treatment planning. The ability to predict ALC will enable physicians to evaluate individual RT treatment plans for lymphopenia risk and to identify patients at high risk who would benefit from modified treatment approaches.