Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	93993
Country	China

Category	Oncology
Manuscript Type	Retrospective Study
Article Title	Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study
Manuscript Source	Unsolicited Manuscript
All Author List	Zhi-Chun Zhao, Jia-Xuan Liu and Ling-Ling Sun
Funding Agency and Grant Number
Corresponding Author	Jia-Xuan Liu, Doctor, MD, Doctor, Department of Interventional Radiology, The First Affiliated Hospital of China Medical University, Heping District, Shenyang 110000, Liaoning Province, China. 3304352470@qq.com
Key Words	Rectal cancer; Perineural invasion; Radiomics; Deep learning; Machine learning
Core Tip	Four machine models (support vector machine, k-nearest neighbor, multilayer perceptron, and logistic regression) were used to predict the preoperative rectal cancer (RC) presence of perineural invasion (PNI) status, with good performance in both the arterial and venous phases. With an area under the curve of 0.964 in the training dataset and 0.955 in the test dataset, the stacking nomogram model to predict pretreatment PNI status had high predictive power and clinical utility, which can help diagnostic and treatment decision-making. Deep learning radiomics stacking models are rare in our RC PNI, which was also an innovation in our research.
Citation	Zhao ZC, Liu JX, Sun LL. Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study. Artif Intell Med Imaging 2024; In press

Received	2024-03-09 07:52
Peer-Review Started	2024-03-09 07:52
To Make the First Decision
Return for Revision	2024-08-15 19:25
Revised	2024-08-27 15:59
Second Decision	2024-09-05 02:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-09-05 09:23
Articles in Press	2024-09-05 09:23
Publication Fee Transferred	2024-09-09 02:49
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-09-18 03:10

ISSN	2644-3260 (online)
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