방사선의학 웹진

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Abstract
The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT.

In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation.

The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map.

This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.

Affiliations

Yejin Kim 1, Ibrahim Chamseddine 2, Yeona Cho 3, Jin Sung Kim 4, Radhe Mohan 5, Nadya Shusharina 2, Harald Paganetti 2, Steven Lin 5, Hong In Yoon 6, Seungryong Cho 7, Clemens Grassberger 2
1Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
2Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
3Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea.
4Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Republic of Korea.
5Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
6Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University Health System, Yonsei University College of Medicine, Seoul, Republic of Korea. Electronic address: yhi0225@yuhs.ac.
7Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea. Electronic address: scho@kaist.ac.kr.