Retrospective Study
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 6, 2019; 7(13): 1611-1622
Published online Jul 6, 2019. doi: 10.12998/wjcc.v7.i13.1611
Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers
Rui-Quan Zhou, Hong-Chen Ji, Qu Liu, Chun-Yu Zhu, Rong Liu
Rui-Quan Zhou, Rong Liu, School of Medicine, Nankai University, Tianjin 300071, China
Hong-Chen Ji, Qu Liu, Chun-Yu Zhu, Rong Liu, The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, China
Author contributions: Zhou RQ, Ji HC and Liu Q contributed equally to this study; Zhou RQ, Ji HC and Liu R contributed to study conception and design; Ji HC, Liu Q and Zhu CY contributed to data acquisition, analysis, and interpretation; Zhou RQ contributed to writing of the article; Liu Q and Liu R contributed to the editing and final approval of the article.
Supported by “Miaopu” Innovation Foundation of the Chinese PLA General Hospital, No. 17KMM07.
Institutional review board statement: The present study was approved by the Ethics Committee of the PLA General Hospital, China and adhered to the tenets of the Declaration of Helsinki.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: None.
Data sharing statement: No additional data are available.
Open-Access: This 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 Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Rong Liu, MD, PhD, Professor, School of Medicine, Nankai University; The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing 100853, China. liurong301@126.com
Telephone: +86-10-66937591 Fax: +86-10-66937591
Received: March 28, 2019
Peer-review started: March 28, 2019
First decision: May 15, 2019
Revised: May 15, 2019
Accepted: May 16, 2019
Article in press: May 17, 2019
Published online: July 6, 2019
Processing time: 101 Days and 0.2 Hours
ARTICLE HIGHLIGHTS
Research background

The incidence of pancreatic neuroendocrine tumors (PNETs) has increased rapidly, and establishment of a prediction system for the tumor grade of PNETs defined by World Health Organization is beneficial for the prognosis and treatment of PNETs. However, determining of the tumor grade by surgery or biopsy means a lot trauma; therefore, a simple and feasible method to non-invasively predict PNET grade would be very meaningful.

Research motivation

Machine learning (ML) algorithms have shown potential in improving the prediction accuracy using comprehensive data. We used four classical ML models in this article and we found that ML could be a potential and feasible method to predict the grade of PNETs by using routine clinical data. ML could be effectively utilized in solving some medical classification problems.

Research objectives

To provide a ML approach to predict PNET tumor grade using clinical data, and ML is effective in classifying PNET grade by using the routine data obtained from the results of biochemical and tumor markers. This approach may be a promising method to non-invasively predict PNET grade and has the potential to be widely used in clinical settings.

Research methods

The biochemical outcomes and tumor markers of 91 patients with histologically confirmed PNETs were collected, and a novel method of minimum P for the Chi-square test (MPCST) was used to divide the continuous variables into binary variables. Four classical supervised ML models, including logistic regression, support vector machine, linear discriminant analysis (LDA) and multi-layer perceptron (MLP) were trained by clinical data. The models were labeled with the pathological tumor grade of each patient. The performance of the different models was then evaluated. Finally, the weight of the different parameters in each of the models were calculated.

Research results

All four models showed a potential performance in this classification task. Among them, LDA showed the best performance in predicting PNET grade, and MLP had the highest recall rate for grade 3 (G3) patients. These results proved that the models trained by the clinical data would provide a feasible approach to predict the pathological tumor grade of PNETs. However, there are still a few limitations in this study. Some parameters like tumor size and metastasis from computed tomography images were not included in this article. Because we think the two parameters may be not objective and may introduce errors in data collection. In general, the result of our study provided a non-invasive method to judge PNET condition and offers a reference for treatment.

Research conclusions

ML is effective in classifying PNET grade by using routine data obtained from the results of biochemical and tumor markers. ML algorithms have shown potential in improving the prediction accuracy of classification of PNET grade using comprehensive data. There is still no effective way to non-invasively determine PNET grade. ML algorithms have shown potential in improving the prediction accuracy using comprehensive data. The combination of imaging and serological outcomes may improve the classification power of ML models. A novel method of minimum P for the MPCST was used to divide the continuous variables into binary variables. Patients of G3 showed more significant differences than grade 1 (G1) and grade 2 (G2). ML is effective in classifying the grade of PNETs by using routine data obtained from the results of biochemical and tumor markers. ML may be a promising method to non-invasively predict PNET grades and has the potential to be widely used in clinical settings.

Research perspectives

Some very simple and routine clinical data may play an important role in medical classification tasks by using ML methods. The combination of imaging and serological outcomes may improve the classification power of ML models. More effective ML models could be utilized in this classification task. The combination of clinical data and experience will help build new ML models.