A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma

© 2023..

It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p < 0.001] vs. 0.3% [p = 0.302]; workload reduction, 79.3% vs. 76.2%). The workflow also yielded a triaging performance of 83.6%, with increases of 1.5% in sensitivity (p = 1.000) and 0.6%-1.3% (all p < 0.05) in specificity compared to three radiologists in the reader study. The semiautomated workflow shows its unique superiority in reducing radiologist's workload by eliminating negative scans while retaining the diagnostic performance of radiologists.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:26

Enthalten in:

iScience - 26(2023), 12 vom: 15. Dez., Seite 108347

Sprache:

Englisch

Beteiligte Personen:

Huang, Ying-Ying [VerfasserIn]
Deng, Yi-Shu [VerfasserIn]
Liu, Yang [VerfasserIn]
Qiang, Meng-Yun [VerfasserIn]
Qiu, Wen-Ze [VerfasserIn]
Xia, Wei-Xiong [VerfasserIn]
Jing, Bing-Zhong [VerfasserIn]
Feng, Chen-Yang [VerfasserIn]
Chen, Hao-Hua [VerfasserIn]
Cao, Xun [VerfasserIn]
Zhou, Jia-Yu [VerfasserIn]
Huang, Hao-Yang [VerfasserIn]
Zhan, Ze-Jiang [VerfasserIn]
Deng, Ying [VerfasserIn]
Tang, Lin-Quan [VerfasserIn]
Mai, Hai-Qiang [VerfasserIn]
Sun, Ying [VerfasserIn]
Xie, Chuan-Miao [VerfasserIn]
Guo, Xiang [VerfasserIn]
Ke, Liang-Ru [VerfasserIn]
Lv, Xing [VerfasserIn]
Li, Chao-Feng [VerfasserIn]

Links:

Volltext

Themen:

Applied computing
Health technology
Journal Article

Anmerkungen:

Date Revised 22.12.2023

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.1016/j.isci.2023.108347

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM366156845