Assessment of gastric cancer survival: using an artificial hierarchical neural network

Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach

Introduction

Machine learning (ML)-based facial nerve injury (FNI) forecasting grounded on multicentric data has not been released up to now. Three distinct ML models, random forest (RF), K-nearest neighbor, and artificial neural network (ANN), for the prediction of FNI were evaluated in this mode.

Methods

A retrospective, longitudinal, multicentric study was performed, including patients who went through parotid gland surgery for benign tumors at three different university hospitals.

Results

Seven hundred and thirty-six patients were included. The most compelling aspects related to risk escalation of FNI were as follows: (1) location, in the mid-portion of the gland, near to or above the main trunk of the facial nerve and at the top part, over the frontal or the orbital branch of the facial nerve; (2) tumor volume in the anteroposterior axis; (3) the necessity to simultaneously dissect more than one level; and (4) the requirement of an extended resection compared to a lesser extended resection. By contrast, in accordance with the ML analysis, the size of the tumor (>3 cm), as well as gender and age did not result in a determining favor in relation to the risk of FNI.

Discussion

The findings of this research conclude that ML models such as RF and ANN may serve evidence-based predictions from multicentric data regarding the risk of FNI.

Conclusion

Along with the advent of ML technology, an improvement of the information regarding the potential risks of FNI associated with patients before each procedure may be achieved with the implementation of clinical, radiological, histological, and/or cytological data.

Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal

Survival analysis deals with the expected duration of time until one or more events of interest occur. Time to the event of interest may be unobserved, a phenomenon commonly known as right censoring, which renders the analysis of these data challenging. Over the years, machine learning algorithms have been developed and adapted to right-censored data. Neural networks have been repeatedly employed to build clinical prediction models in healthcare with a focus on cancer and cardiology. We present the first ever attempt at a large-scale review of survival neural networks (SNNs) with prognostic factors for clinical prediction in medicine. This work provides a comprehensive understanding of the literature (24 studies from 1990 to August 2021, global search in PubMed). Relevant manuscripts are classified as methodological/technical (novel methodology or new theoretical model; 13 studies) or applications (11 studies). We investigate how researchers have used neural networks to fit survival data for prediction. There are two methodological trends: either time is added as part of the input features and a single output node is specified, or multiple output nodes are defined for each time interval. A critical appraisal of model aspects that should be designed and reported more carefully is performed. We identify key characteristics of prediction models (i.e., number of patients/predictors, evaluation measures, calibration), and compare ANN's predictive performance to the Cox proportional hazards model. The median sample size is 920 patients, and the median number of predictors is 7. Major findings include poor reporting (e.g., regarding missing data, hyperparameters) as well as inaccurate model development/validation. Calibration is neglected in more than half of the studies. Cox models are not developed to their full potential and claims for the performance of SNNs are exaggerated. Light is shed on the current state of art of SNNs in medicine with prognostic factors. Recommendations are made for the reporting of clinical prediction models. Limitations are discussed, and future directions are proposed for researchers who seek to develop existing methodology.

Integrative Survival Analysis of Breast Cancer with Gene Expression and DNA Methylation Data

Motivation

Integrative multi-feature fusion analysis on biomedical data has gained much attention recently. In breast cancer, existing studies have demonstrated that combining genomic mRNA data and DNA methylation data can better stratify cancer patients with distinct prognosis than using single signature. However, those existing methods are simply combining these gene features in series and have ignored the correlations between separate omics dimensions over time.

Results

In the present study, we propose an adaptive multi-task learning method, which combines the Cox loss task with the ordinal loss task, for survival prediction of breast cancer patients using multi-modal learning instead of performing survival analysis on each feature dataset. First, we use local maximum quasi-clique merging (lmQCM) algorithm to reduce the mRNA and methylation feature dimensions and extract cluster eigengenes respectively. Then, we add an auxiliary ordinal loss to the original Cox model to improve the ability to optimize the learning process in training and regularization. The auxiliary loss helps to reduce the vanishing gradient problem for earlier layers and helps to decrease the loss of the primary task. Meanwhile, we use an adaptive weights approach to multi-task learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. Finally, we build an ordinal cox hazards model for survival analysis and use long short-term memory (LSTM) method to predict patients’ survival risk. We use the cross-validation method and the concordance index (C-index) for assessing the prediction effect. Stringent cross-verification testing processes for the benchmark dataset and two additional datasets demonstrate that the developed approach is effective, achieving very competitive performance with existing approaches.

Availability and implementation

https://github.com/bhioswego/ML_ordCOX.

Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors

(1) Background: Despite the increasing use of intraoperative facial nerve monitoring during parotid gland surgery or the improvement in the preoperative radiological assessment, facial nerve injury (FNI) continues to be the most feared complication; (2) Methods: patients who underwent parotid gland surgery for benign tumors between June 2010 and June 2019 were included in this study aiming to make a proof of concept about the reliability of an artificial neural networks (AAN) algorithm for prediction of FNI and compared with a multivariate linear regression (MLR); (3) Results: Concerning prediction accuracy and performance, the ANN achieved the highest sensitivity (86.53% vs 46.23%), specificity (95.67% vs 92.59%), PPV (87.28% vs 66.94%), NPV (95.68% vs 83.37%), ROC–AUC (0.960 vs 0.769) and accuracy (93.42 vs 80.42) than MLR; and (4) Conclusions: ANN prediction models can be useful for otolaryngologists—head and neck surgeons—and patients to provide evidence-based predictions about the risk of FNI. As an advantage, the possibility to develop a calculator using clinical, radiological and histological or cytological information can improve our ability to generate patients counselling before surgery.

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

BACKGROUND

Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN-based survival prediction model for patients with gastric cancer.

METHODS

Patients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time-dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models.

RESULTS

An ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5-year survival prediction, respectively. In the calibration curve analysis, the ANN-predicted survival had a high consistency with the actual survival. Comparison of the DCA and time-dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts.

CONCLUSIONS

The ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool

Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accuracy of the neural network was 92.7%, which was higher than that of the logistic regression model (86.5%). Our online tool provided 88.2% accuracy, 71.2% sensitivity, and 98.9% specificity. In conclusion, ANN seems to offer a unique decision-making support predicting recurrences and thus adding value for the management of early OTSCC. To the best of our knowledge, this is the first study that applied ANN for prediction of recurrence in early OTSCC and provided a Web-based tool.

Five Years Survival of Patients After Liver Transplantation and Its Effective Factors by Neural Network and Cox Poroportional Hazard Regression Models

BACKGROUND

Transplantation is the only treatment for patients with liver failure. Since the therapy imposes high expenses to the patients and community, identification of effective factors on survival of such patients after transplantation is valuable.

OBJECTIVES

The current study attempted to model the survival of patients (two years old and above) after liver transplantation using neural network and Cox Proportional Hazards (Cox PH) regression models. The event is defined as death due to complications of liver transplantation.

PATIENTS AND METHODS

In a historical cohort study, the clinical findings of 1168 patients who underwent liver transplant surgery (from March 2008 to march 2013) at Shiraz Namazee Hospital Organ Transplantation Center, Shiraz, Southern Iran, were used. To model the one to five years survival of such patients, Cox PH regression model accompanied by three layers feed forward artificial neural network (ANN) method were applied on data separately and their prediction accuracy was compared using the area under the receiver operating characteristic curve (ROC). Furthermore, Kaplan-Meier method was used to estimate the survival probabilities in different years.

RESULTS

The estimated survival probability of one to five years for the patients were 91%, 89%, 85%, 84%, and 83%, respectively. The areas under the ROC were 86.4% and 80.7% for ANN and Cox PH models, respectively. In addition, the accuracy of prediction rate for ANN and Cox PH methods was equally 92.73%.

CONCLUSIONS

The present study detected more accurate results for ANN method compared to those of Cox PH model to analyze the survival of patients with liver transplantation. Furthermore, the order of effective factors in patients' survival after transplantation was clinically more acceptable. The large dataset with a few missing data was the advantage of this study, the fact which makes the results more reliable.

The Application of Classification and Regression Trees for the Triage of Women for Referral to Colposcopy and the Estimation of Risk for Cervical Intraepithelial Neoplasia: A Study Based on 1625 Cases with Incomplete Data from Molecular Tests

OBJECTIVE

Nowadays numerous ancillary techniques detecting HPV DNA and mRNA compete with cytology; however no perfect test exists; in this study we evaluated classification and regression trees (CARTs) for the production of triage rules and estimate the risk for cervical intraepithelial neoplasia (CIN) in cases with ASCUS+ in cytology.

STUDY DESIGN

We used 1625 cases. In contrast to other approaches we used missing data to increase the data volume, obtain more accurate results, and simulate real conditions in the everyday practice of gynecologic clinics and laboratories. The proposed CART was based on the cytological result, HPV DNA typing, HPV mRNA detection based on NASBA and flow cytometry, p16 immunocytochemical expression, and finally age and parous status.

RESULTS

Algorithms useful for the triage of women were produced; gynecologists could apply these in conjunction with available examination results and conclude to an estimation of the risk for a woman to harbor CIN expressed as a probability.

CONCLUSIONS

The most important test was the cytological examination; however the CART handled cases with inadequate cytological outcome and increased the diagnostic accuracy by exploiting the results of ancillary techniques even if there were inadequate missing data. The CART performance was better than any other single test involved in this study.

Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients

The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.

Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models

Background

There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models.

Objectives

This study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model.

Patients and Methods

We studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests.

Results

Survival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P < 0.05) and no significant difference between Cox and the neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer). Probabilities of survival were calculated using three neural network models with 3, 5, and 7 nodes in the hidden layer, and it has been observed that none of the predictions was significantly different from results with the Kaplan-Meier method and they appeared more comparable towards the last months (fifth year). However, we observed better accuracy using the neural network with 5 nodes in the hidden layer. Using the Cox proportional hazards and a neural network with 3 nodes in the hidden layer, we found enhanced accuracy with the neural network model.

Conclusions

Neural networks can provide more accurate predictions for survival probabilities compared to the Cox proportional hazards mode, especially now that advances in computer sciences have eliminated limitations associated with complex computations. It is not recommended in order to adding too many hidden layer nodes because sample size related effects can reduce the accuracy. We recommend increasing the number of nodes to a point that increased accuracy continues (decrease in mean standard error), however increasing nodes should cease when a change in this trend is observed.

Data mining methods in Omics-based biomarker discovery

The advent of Omics technologies as genomics and proteomics has brought the hope of discovering novel biomarkers that can be used to diagnose, predict, and monitor the progress of disease. The importance of data mining to identify biological markers for the diagnostic classification and prognostic assessment in the context of microarray and proteomic data has been increasingly recognized. We present an overview of general data mining methods and their applications to biomarker discovery with particular focus on genomics and proteomics data. Two case studies are exemplarily presented, and relevant data mining terminology and techniques are explained.