Multi-Institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume Using Planning CT and FDG-PET/CT

Autor: Ye, Xianghua, Guo, Dazhou, Tseng, Chen-Kan, Ge, Jia, Hung, Tsung-Min, Pai, Ping-Ching, Ren, Yanping, Zheng, Lu, Zhu, Xinli, Peng, Ling, Chen, Ying, Chen, Xiaohua, Chou, Chen-Yu, Chen, Danni, Yu, Jiaze, Chen, Yuzhen, Jiao, Feiran, Xin, Yi, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Lu, Le, Yan, Senxiang, Jin, Dakai, Ho, Tsung-Ying
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Frontiers in Oncology, Vol 11 (2022)
Frontiers in Oncology
ISSN: 2234-943X
DOI: 10.3389/fonc.2021.785788
Popis: Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To validate the clinical applicability of a deep learning (DL) multi-modality esophageal GTV contouring model, developed at 1 institution whereas tested at multiple ones. Methods and Materials: We collected 606 esophageal cancer patients from four institutions. 252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT. A two-streamed DL model for GTV segmentation was developed using pCT and PETCT scans of a 148 patient institution-1 subset. This built model had the flexibility of segmenting GTVs via only pCT or pCT+PETCT combined. For independent evaluation, the rest 104 institution-1 patients behaved as unseen internal testing, and 354 institutions 2-4 patients were used for external testing. We evaluated manual revision degrees by human experts to assess the contour-editing effort. The performance of the deep model was compared against 4 radiation oncologists in a multiuser study with 20 random external patients. Contouring accuracy and time were recorded for the pre-and post-DL assisted delineation process. Results: Our model achieved high segmentation accuracy in internal testing (mean Dice score: 0.81 using pCT and 0.83 using pCT+PET) and generalized well to external evaluation (mean DSC: 0.80). Expert assessment showed that the predicted contours of 88% patients need only minor or no revision. In multi-user evaluation, with the assistance of a deep model, inter-observer variation and required contouring time were reduced by 37.6% and 48.0%, respectively. Conclusions: Deep learning predicted GTV contours were in close agreement with the ground truth and could be adopted clinically with mostly minor or no changes.
36 pages, 10 figures
Databáze: OpenAIRE
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