Published online Jul 6, 2019. doi: 10.12998/wjcc.v7.i13.1611
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
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.
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.
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.
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.
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.
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.
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.