Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Sep 28, 2024; 5(1): 93993
Published online Sep 28, 2024. doi: 10.35711/aimi.v5.i1.93993
Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study
Zhi-Chun Zhao, Jia-Xuan Liu, Ling-Ling Sun
Zhi-Chun Zhao, Jia-Xuan Liu, Department of Interventional Radiology, The First Affiliated Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
Ling-Ling Sun, Department of Radiology, The fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning Province, China
Author contributions: Zhao ZC and Liu JX contributed to data curation and analysis; Liu JX contributed to methodology, conceptualization, writing (original draft), and validation; Sun LL contributed to supervision and resources; all authors contributed to the study conception and design; all authors have read and approved the final manuscript.
Institutional review board statement: This retrospective study was approved by the medical ethics committee of our hospital (Ethical review number: EC-2022-KS-035).
Informed consent statement: The need for informed consent from all individual participants included in study was waived.
Conflict-of-interest statement: The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of this article.
Data sharing statement: Dataset available from the corresponding author at 3304352470@qq.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jia-Xuan Liu, MD, Doctor, Department of Interventional Radiology, The First Affiliated Hospital of China Medical University, Heping District, Shenyang 110000, Liaoning Province, China. 3304352470@qq.com
Received: March 9, 2024
Revised: August 27, 2024
Accepted: September 5, 2024
Published online: September 28, 2024
Processing time: 200 Days and 23.4 Hours
Abstract
BACKGROUND

The presence of perineural invasion (PNI) in patients with rectal cancer (RC) is associated with significantly poorer outcomes. However, traditional diagnostic modalities have many limitations.

AIM

To develop a deep learning radiomics stacking nomogram model to predict preoperative PNI status in patients with RC.

METHODS

We recruited 303 RC patients and separated them into the training (n = 242) and test (n = 61) datasets on an 8: 2 scale. A substantial number of deep learning and hand-crafted radiomics features of primary tumors were extracted from the arterial and venous phases of computed tomography (CT) images. Four machine learning models were used to predict PNI status in RC patients: support vector machine, k-nearest neighbor, logistic regression, and multilayer perceptron. The stacking nomogram was created by combining optimal machine learning models for the arterial and venous phases with predicting clinical variables.

RESULTS

With an area under the curve (AUC) of 0.964 [95% confidence interval (CI): 0.944-0.983] in the training dataset and an AUC of 0.955 (95%CI: 0.900-0.999) in the test dataset, the stacking nomogram demonstrated strong performance in predicting PNI status. In the training dataset, the AUC of the stacking nomogram was greater than that of the arterial support vector machine (ASVM), venous SVM, and CT-T stage models (P < 0.05). Although the AUC of the stacking nomogram was greater than that of the ASVM in the test dataset, the difference was not particularly noticeable (P = 0.05137).

CONCLUSION

The developed deep learning radiomics stacking nomogram was effective in predicting preoperative PNI status in RC patients.

Keywords: 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.