Weighted Concordance Index Loss-Based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy

Fang，Jiansheng1,2,3; Li，Anwei2; OuYang，Pu Yun4; Li，Jiajian2; Wang，Jingwen2; Liu，Hongbo2; Xie，Fang Yun4; Liu，Jiang3

10.1007/978-3-031-16449-1_19

2022

25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

0302-9743

1611-3349

978-3-031-16448-4

LECT NOTES ARTIF INT

13437 LNCS

191-201

SEP 18-22, 2022

null,Singapore,SINGAPORE

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Radiation encephalopathy (REP) is the most common complication for nasopharyngeal carcinoma (NPC) radiotherapy. It is highly desirable to assist clinicians in optimizing the NPC radiotherapy regimen to reduce radiotherapy-induced temporal lobe injury (RTLI) according to the probability of REP onset. To the best of our knowledge, it is the first exploration of predicting radiotherapy-induced REP by jointly exploiting image and non-image data in NPC radiotherapy regimen. We cast REP prediction as a survival analysis task and evaluate the predictive accuracy in terms of the concordance index (CI). We design a deep multimodal survival network (MSN) with two feature extractors to learn discriminative features from multimodal data. One feature extractor imposes feature selection on non-image data, and the other learns visual features from images. Because the priorly balanced CI (BCI) loss function directly maximizing the CI is sensitive to uneven sampling per batch. Hence, we propose a novel weighted CI (WCI) loss function to leverage all REP samples effectively by assigning their different weights with a dual average operation. We further introduce a temperature hyper-parameter for our WCI to sharpen the risk difference of sample pairs to help model convergence. We extensively evaluate our WCI on a private dataset to demonstrate its favourability against its counterparts. The experimental results also show multimodal data of NPC radiotherapy can bring more gains for REP risk prediction.

Concordance index Dose-volume histogram Loss function Multimodal learning Survival modeling

Corresponding

English

CPCI-S

Computer Science ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Interdisciplinary Applications ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000867568000019

2-s2.0-85139050555

Scopus

1.School of Computer Science and Technology,Harbin Institute of Technology,Harbin,China
2.CVTE Research,Guangzhou,China
3.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,China
4.Department of Radiation Oncology,Sun Yat-sen University Cancer Center,Guangzhou,China

Fang，Jiansheng,Li，Anwei,OuYang，Pu Yun,et al. Weighted Concordance Index Loss-Based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:191-201.