Combining Images and T-Staging Information to Improve the Automatic Segmentation of Nasopharyngeal Carcinoma Tumors in MR Images

Abstract

The accurate and reproducible delineation of tumors from uninvolved tissue is essential for radiation oncology. However, the tumor margin may be challenging to identify from magnetic resonance (MR) images of nasopharyngeal carcinomas (NPCs). Additionally, clinical diagnoses such as T-staging may already provide some information on tumor invasion. To use this information and improve the performance of tumor segmentation, we propose a novel deep learning neural network architecture that can incorporate both T-staging and image information. Based on U-Net, our model adds a T-channel composed of T-staging information and uses the attention mechanism. Since the T-staging information is defined by the extent of tumor invasion, the T-channel using T-staging information can improve the segmentation accuracy at different stages. Additionally, the addition of an attention mechanism allows our model to retain the most valuable pixels of the image, thus further improving the delineation accuracy. In our experiments, the proposed network was trained and validated based on records from 251 clinical patients using 10-fold crossvalidation. The dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) were used to evaluate our network's results. The average DSC and ASSD and their standard deviation (SD) values are 0.841 ± 0.011 and 0.747 ± 0.199 mm. The unique T-channel effectively utilizes T-staging information to improve the results. With the combination of the T-channel module and the attention module, we significantly improved NPC tumor delineation performance.

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

“…66.7% (n=40) only used imaging data such as magnetic resonance imaging, computed tomography or endoscopic images. 15 , 16 , 18 , 19 , 21–24 , 26–28 , 30 , 32 , 34 , 37–39 , 41–43 , 45–56 , 58–63 , 67 , 69 There were also four studies that included clinicopathological data as well as images for training models, 25 , 31 , 36 , 40 while three other studies developed models using images, clinicopathological data, and plasma Epstein-Barr virus (EBV) DNA. 29 , 33 , 35 Furthermore, 4 studies used treatment plans, 64–66 , 68 while proteins and microRNA expressions data were each extracted by one study.…”

“… 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56 , 59–63 The specificity was only reported for prognosis (n=7) 12 , 14 , 28 , 34 , 39 , 40 , 43 and diagnosis (n=15). 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56 , 59–63 In addition, the DSC (n=20) 15 , 18 , 22 , 24 , 30–32 , 45–53 , 55 , 65 , 67 , 69 and ASSD (n=10) 18 , 22 , 24 , 31 , 32 , 45 , 46 , 48 , 51 , 69 were the primary metrics reported in studies on auto-contouring ( Figure 2B ).…”

“…Segmentation: ResNeXt U-net To use computed tomography for the segmentation of NPC 1. Segmentation performance - DSC: 0.6288±0.0812 –95HD: 6.07mm±2.53mm - F1-score: 0.6615 The developed DL algorithm had a significantly better performance than three existing DL models An NPC-seg algorithm was developed and won 9th place on the StructSeg 2019 Challenge leader-board N/A Cai et al (2021) 31 (China) NPC 251 (MRI images and clinicopathological data) Deep learning (Auto-contouring) 1. Segmentation: Attention U-net with T-channel module To use image and T-staging information to improve NPC tumor delineation accuracy 1.…”

“… ✓ ✓ ✓ Yes No Zhong et al (2020) 29 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Bai et al (2021) 30 ✓ ✓ ✓ ? ✓ ✓ ✓ Yes No Cai et al (2021) 31 ✓ ✓ ✘ ✓ ✓ ✓ ✓ Yes No Tang et al (2021) 32 ? ✓ ✓ ✓ ✓ ✓ ✓ Yes No Wen et al (2021) 57 ✓ ✓ ?…”

“…66.7% (n=40) only used imaging data such as magnetic resonance imaging, computed tomography or endoscopic images. 15,16,18,19,[21][22][23][24][26][27][28]30,32,34,[37][38][39][41][42][43][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62][63]67,69 There were also four studies that included clinicopathological data as well as images for training models, 25,31,36,40 while three other studies developed models using images, clinicopathological data, and plasma Epstein-Barr virus (EBV) DNA. 29,33,35 Furthermore, 4 studies used treatment plans, [64]…”