Use of an artificial neural network to determine prognostic factors in colorectal cancer patients

Background & Objectives: The aim of this study was to determine the prognostic factors of Iranian colorectal cancer (CRC) patients and their importance using an artificial neural network (ANN) model. Methods: This study was a historical cohort study and the data gathered from 1,219 registered CRC patients between January2002 and October 2007 at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran. For determining the risk factors and survival prediction of patients, neural network (NN) and Cox regression models were used, utilizing R 2.12.0 software. Results: One, three and five-year estimated survival probability in colon patients were 0.92, 0.71, and 0.48 and for rectum patients were 0.86, 0.71, and 0.42, respectively. By the ANN model, pathologic distant metastasis, pathologic regional lymph nodes, tumor grade, high risk behavior, pathologic primary tumor, familial history and tumor size variables were determined as ordered important factors for colon cancer. Tumor grade, pathologic stage, age at diagnosis, tumor size, high risk behavior, pathologic distant metastasis and first treatment variables were ordered important factors for rectum cancer. The ANN model lead to more accurate predictions compared to the Cox model (true prediction of 89.0% vs. 78.6% for colon and 82.7% vs. 70.7% for rectum cancer patients). Conclusion: This study showed that ANN model is a more powerful tool in survival prediction and influential factors of the CRC patients compared to the Cox regression model. Therefore, this model is recommended for predicting and determining of risk factors of these patients.     

The development of composite circulating biomarker models for use in anticancer drug clinical development

The development of informative composite circulating biomarkers predicting cancer presence or therapy response is clinically attractive but optimal approaches to modeling are as yet unclear. This study investigated multidimensional relationships within an example panel of serum insulin‐like growth factor (IGF) peptides using logistic regression (LR), fractional polynomial (FP), regression, artificial neural networks (ANNs) and support vector machines (SVMs) to derive predictive models for colorectal cancer (CRC). Two phase 2 biomarker validation analyses were performed: controls were ambulant adults (n = 722); cases were: (i) CRC patients (n = 100) and (ii) patients with acromegaly (n = 52), the latter as “positive” discriminators. Serum IGF‐I, IGF‐II, IGF binding protein (IGFBP)‐2 and ‐3 were measured. Discriminatory characteristics were compared within and between models. For the LR, FP and ANN models, and to a lesser extent SVMs, the addition of covariates at several steps improved discrimination characteristics. The optimum biomarker combination discriminating CRC vs. controls was achieved using ANN models [sensitivity, 94%; specificity, 90%; accuracy, 0.975 (95% CIs: 0.948 1.000)]. ANN modeling significantly outperformed LR, FP and SVM in terms of discrimination (p < 0.0001) and calibration. The acromegaly analysis demonstrated expected high performance characteristics in the ANN model [accuracy, 0.993 (95% CIs: 0.977, 1.000)]. Curved decision surfaces generated from the ANNs revealed the potential clinical utility. This example demonstrated improved discriminatory characteristics within the composite biomarker ANN model and a final model that outperformed the three other models. This modeling approach forms the basis to evaluate composite biomarkers as pharmacological and predictive biomarkers in future clinical trials.     

Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents

Background: Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model, multiple logistic regression(MLR) model, and Cox regression model.Methods: This study compared the MLR, Cox, and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010. An estimation dataset was used to train the model, and a validation dataset was used to evaluate model performance. The sensitivity analysis was also used to assess the relative significance of input variables in the prediction model.Results: The ANN model significantly outperformed the MLR and Cox models in predicting 5-year mortality, with higher overall performance indices. The results indicated that the 5-year postoperative mortality of breast cancer patients was significantly associated with age, Charlson comorbidity index (CCI), chemotherapy, radiotherapy, hormone therapy, and breast cancer surgery volumes of hospital and surgeon (all P < 0.05). Breast cancer surgery volume of surgeon was the most influential (sensitive) variable affecting 5-year mortality, followed by breast cancer surgery volume of hospital, age, and CCI.Conclusions: Compared with the conventional MLR and Cox models, the ANN model was more accurate in predicting 5-year mortality of breast cancer patients who underwent surgery. The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.     

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients

Background: The statistical methods to analyze and predict the related dangerous factors of deep fungalinfection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Coxproportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materialsand Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employingStudent’s t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly relatedto the presence of deep fungal infection selected as candidates for input into the final artificial neural networkanalysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used toevaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results:The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696),deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99%(194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albuminconcentrations (≤37.18g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67g /L), long time ofhospitalization (≥14days) were apt to deep fungal infection and the ANN model consisted of the seven factors.The AUC of ANN model(0.829±0.019)was higher than that of LR model (0.756±0.021). Conclusions: The artificialneural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, receivedradiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deepfungal infection in lung cancer.     

Prognostic models in patients with non-small-cell lung cancer using artificial neural networks in comparison with logistic regression

It is difficult to precisely predict the outcome of each individual patient with non-small-cell lung cancer (NSCLC) by using conventional statistical methods and ordinary clinico-pathological variables. We applied artificial neural networks (ANN) for this purpose. We constructed a prognostic model for 125 NSCLC patients with 17 potential input variables, including 12 clinico-pathological variables (age, sex, smoking index, tumor size, p factor, pT, pN, stage, histology) and 5 immunohistochemical variables (p27 percentage, p27 intensity, p53, cyclin D1, retinoblastoma (RB)), by using the parameter-increasing method (PIM). Using the resultant ANN model, prediction was possible in 104 of 125 patients (83%, judgment ratio ( JR )) and accuracy for prediction of survival at 5 years was 87%. On the other hand, JR and survival prediction accuracy in the logistic regression (LR) model were 37% and 78%, respectively. In addition, ANN outperformed LR for prediction of survival at 1 or 3 years. In these cases, PIM selected p27 intensity and cyclin D1 for the 3-year survival model and p53 for the 1-year survival model in addition to clinico-pathological variables. Finally, even in an independent validation data set of 48 patients, who underwent surgery 10 years later, the present ANN model could predict outcome of patients at 5 years with the JR and accuracy of 81% and 77%, respectively. This study demonstrates that ANN is a potentially more useful tool than conventional statistical methods for predicting survival of patients with NSCLC and that inclusion of relevant molecular markers as input variables enhances its predictive ability. (Cancer Sci 2003; 94: 473–477)     

Comparison of the Performance of Log-logistic Regression nd Artificial Neural Networks for Predicting Breast Cancer elapse

Background: Breast cancer is the most common cancers in female populations. The exact cause is notknown, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM)is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificialneural network (ANN) models have been increasingly applied to predict survival data. The present research wasconducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer(BC) survival. Materials and Methods: A historical cohort study was established with 104 patients sufferingfrom BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas underthe receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzedusing R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statisticallyhigher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between theperformance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the abilityof prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction ofsurvival in field of breast cancer is suggested.     

Artificial neural networks – A method for prediction of survival following liver resection for colorectal cancer metastases

Objective

To construct an artificial neural network (ANN) model to predict survival after liver resection for colorectal cancer (CRC) metastases.

Background

CRC liver metastases are fatal if untreated and resection can possibly be curative. Predictive models stratify patients into risk categories to predict prognosis and select those who can benefit from aggressive multidisciplinary treatment and intensive follow-up. Standard linear models assume proportional hazards, whereas more flexible non-linear survival models based on ANNs may better predict individual long-term survival.

Methods

Clinicopathological and perioperative data on patients who underwent liver resection for CRC metastases between 1994 and 2009 were studied retrospectively. A five-fold cross-validated ANN model was constructed. Risk variables were ranked and minimised through calibrated ANNs. Time dependent hazard ratio (HR) was calculated using the ANN. Performance of the ANN model and Cox regression were analysed using Harrell's C-index.

Results

241 patients with a median age of 66 years were included. There were no perioperative deaths and median survival was 56 months. Of 28 potential risk variables, the ANN selected six: age, preoperative chemotherapy, size of largest metastasis, haemorrhagic complications, preoperative CEA-level and number of metastases. The C-index was 0.72 for the ANN model and 0.66 for Cox regression.

Conclusion

For the first time ANNs were used to successfully predict individual long-term survival for patients following liver resection for CRC metastases. In the future, more complex prognostic factors can be incorporated into the ANN model to increase its predictive ability.     

Use of an Artificial Neural Network to Predict Risk Factors of Nosocomial Infection in Lung Cancer Patients

Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student’s t-test or the Mann-Whitney test or the Chi-square test. Variablesthat were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (≥22days, P=0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (≥61year old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors .The artificial neural network model with variablesconsisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.     

Development of a machine learning algorithm to predict complications of total laparoscopic anterior resection and natural orifice specimen extraction surgery in rectal cancer

BackgroundTotal laparoscopic anterior resection (tLAR) and natural orifice specimen extraction surgery (NOSES) has been widely adopted in the treatment of rectal cancer (RC). However, no study has been performed to predict the short-term outcomes of tLAR using machine learning algorithms to analyze a national cohort.MethodsData from consecutive RC patients who underwent tLAR were collected from the China NOSES Database (CNDB). The random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), deep neural network (DNN), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to develop risk models to predict short-term complications of tLAR. The area under the receiver operating characteristic curve (AUROC), Gini coefficient, specificity and sensitivity were calculated to assess the performance of each risk model. The selected factors from the models were evaluated by relative importance.ResultsA total of 4313 RC patients were identified, and 667 patients (15.5%) developed postoperative complications. The machine learning model of XGBoost showed more promising results in the prediction of complication than other models (AUROC 0.90, P < 0.001). The performance was similar when internal and external validation was used. In the XGBoost model, the top four influential factors were the distance from the lower edge of the tumor to the anus, age at diagnosis, surgical time and comorbidities. In risk stratification analysis, the rate of postoperative complications in the high-risk group was significantly higher than in the medium- and low-risk groups (P < 0.001).ConclusionThe machine learning model shows potential benefits in predicting the risk of complications in RC patients after tLAR. This novel approach can provide reliable individual information for surgical treatment recommendations.     

Validation of a rectal cancer outcome prediction model with a cohort of Chinese patients

The risk of local recurrence (LR), distant metastases (DM) and overall survival (OS) of locally advanced rectal cancer after preoperative chemoradiation can be estimated by prediction models and visualized using nomograms, which have been trained and validated in European clinical trial populations. Data of 277 consecutive locally advanced rectal adenocarcinoma patients treated with preoperative chemoradiation and surgery from Shanghai Cancer Center, were retrospectively collected and used for external validation. Concordance index (C-index) and calibration curves were used to assess the performance of the previously developed prediction models in this routine clinical validation population. The C-index for the published prediction models was 0.72 ± 0.079, 0.75 ± 0.043 and 0.72 ± 0.089 in predicting 2-year LR, DM and OS in the Chinese population, respectively. Kaplan-Meier curves indicated good discriminating performance regarding LR, but could not convincingly discriminate a low-risk and medium-risk group for distant control and OS. Calibration curves showed a trend of underestimation of local and distant control, as well as OS in the observed data compared with the estimates predicted by the model.In conclusion, we externally validated three models for predicting 2-year LR, DM and OS of locally advanced rectal cancer patients who underwent preoperative chemoradiation and curative surgery with good discrimination in a single Chinese cohort. However, the model overestimated the local control rate compared to observations in the clinical cohort. Validation in other clinical cohorts and optimization of the prediction model, perhaps by including additional prognostic factors, may enhance model validity and its applicability for personalized treatment of locally advanced rectal cancer.     

基于机器学习预测乳腺癌患者新辅助化疗的病理完全反应

目的 基于乳腺癌电子病历系统收集的临床和病理特征数据构建机器学习模型,预测新辅助化疗(neoadjuvant chemotherapy,NAC)后的病理完全反应(pathological complete response,pCR).方法 回顾性收集2015年1月至2020年12月在本院接受NAC治疗和手术切除的乳腺癌...     

Comparing the predictive value of neural network models to logistic regression models on the risk of death for small-cell lung cancer patients

Cancer is one of the leading causes of mortality in the developed world, and prognostic assessment of cancer patients is indispensable in medical care. Medical researchers are accustomed to using regression models to predict patient outcomes. Neural networks have been proposed as an alternative with great potential. Nonetheless, empirical evidence remains lacking to support the application of this technique as the appropriate method to investigate cancer prognosis. Utilizing data on patients from two National Cancer Institute of Canada clinical trials, we compared predictive accuracy of neural network models and logistic regression models on risk of death of limited-stage small-cell lung cancer patients. Our results suggest that neural network and logistic regression models have similar predictive accuracy. The distributions of individual predicted probabilities are very similar. On occasion, however, the prediction pairs are quite different, suggesting that they do not always give the same interpretations of the same variables.     

Diabetes mellitus and subsite-specific colorectal cancer risks in the Iowa Women's Health Study.

OBJECTIVE: Controversy remains regarding the association between type 2 diabetes mellitus (DM) and colorectal cancer (CRC) risk. To clarify and extend the existing data, we prospectively evaluated the association between self-reported type 2 DM (onset at >30 years of age) and incident CRC, overall and by anatomic subsite, among postmenopausal women in the Iowa Women's Health Study (n = 35,230).METHODS: After 14 years of follow-up, a total of 870 incident CRC cases were identified through annual linkage to the Iowa Cancer Registry. DM was analyzed as reported at baseline and as a time-dependent variable using information obtained during follow-up. CRC risks were estimated using Cox proportional hazards regression models.RESULTS: After adjusting for age, body mass index and other potential confounding variables, the relative risk (RR) for women with DM versus women without DM was modestly increased at 1.4 [95% confidence interval (95% CI), 1.1-1.8]. By anatomic subsite, the RR for proximal colon cancer was statistically significantly increased (RR, 1.9; 95% CI, 1.3-2.6), whereas the RRs for distal colon (RR, 1.1; 95% CI, 0.6-1.8) and rectal cancer (RR, 0.8; 95% CI, 0.4-1.6) were not statistically different from unity. Analyses that included DM ascertained at baseline and follow-up yielded similar results.CONCLUSION: In this large, prospective study of postmenopausal women, the association between DM and incident CRC was found to be subsite specific. If confirmed by others, this finding implies that CRC prevention strategies among type 2 DM patients should include examination of the proximal colon.     

基于MRI放射组学模型预测乳腺癌腋窝淋巴结转移状态的初步研究

目的:研究原发性乳腺癌基于多参数磁共振图像的放射组学特征机器学习模型在预测腋窝淋巴结转移状态中的价值。方法:乳腺癌患者98例,共114个乳腺病变,利用基于直方图、形状和纹理的多参数图像特征分别提取107个放射组学特征。采用方差阈值(方差阈值=0.8)和最小绝对收缩选择算子(lasso)来减少冗余特征。基于所选择的最优特征,建立放射性组学的预测模型,采用了3个分类器,分别是 k 近邻(k-Nearest Neighbor,KNN)、支持向量机(support vector machine,SVM)和Logistic回归模型(logistic regression,LR)。并利用ROC分析方法对测试集中曲线下的面积(under the curve,AUC)进行预测性能评价。结果:有淋巴结转移的乳腺癌46例共56个病灶,无淋巴结转移的乳腺癌52例共58个病灶。在特征选择之后,利用最佳放射组学特征(5倍交叉验证,分别12、10、29、10、16个特征)建立预测模型。在三种基于放射组学的模型中,SUM模型的性能最好,平均AUC为0.805,高于KNN及LR的平均AUC（0.783、0.802）。结论:乳腺癌的 MRI纹理分析可作为预测淋巴结转移状态的非侵袭性指标,值得进一步研究。     

Special issue “The advance of solid tumor research in China”: Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep-learning features of primary lesions and peripheral lymph nodes

Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76 ± 0.08 and 0.91 ± 0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine.     

Development and validation of artificial intelligence models for preoperative prediction of inferior mesenteric artery lymph nodes metastasis in left colon and rectal cancer

BackgroundDissection of lymph nodes at the roots of the inferior mesenteric artery (IMAN) should be offered only to selected patients at a major risk of developing IMAN involvement. The aim of this study is to present the first artificial intelligence (AI) models to predict IMAN metastasis risk in the left colon and rectal cancer patients.MethodsA total of 2891 patients with descending colon including splenic flexure, sigmoid colon and rectal cancer undergoing major primary surgery and IMAN dissection were included as a study cohort, which was then split into a training set (67%) and a testing set (33%). Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression model. Seven AI algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Decision Tree Classifier (DTC), Random Forest (RF) classifier, and Multilayer Perceptron (MLP), as well as traditional multivariate LR model were employed to construct predictive models. The optimal hyperparameters were determined with 5 fold cross-validation. The predictive performance of models and the expert surgeon was assessed and compared in the testing set independently.ResultsThe IMAN involvement incidence was 4.6%. The optimal set of features selected by LASSO included 10 characteristics: neoadjuvant treatment, age, synchronous liver metastasis, synchronous lung metastasis, signet ring adenocarcinoma, neural invasion, lymphovascular invasion, CA199, endoscopic obstruction, T stage evaluated by MRI. The most accurate model derived from MLP showed excellent prediction power with area under the receiver operating characteristic curve (AUROC) of 0.873 and produced 81.0% recognition sensitivity and 82.5% specificity in the testing set independently. In contrast, the judgment of IMAN metastasis by expert surgeon yield rather imprecise and unreliable results with a significantly lower AUROC of 0.509. Additionally, the proposed MLP had the highest net benefits and the largest reduction of unnecessary IMAN dissection without the cost of additional involved IMAN missed.ConclusionMLP model was able to maintain its prediction accuracy in the testing set better than other models and expert surgeons. Our MLP model could be used to help identify IMA nodal metastasis and to select candidates for individual IMAN dissection.     

血清蛋白质质谱模型在大肠癌诊断中的应用

目的 建立蛋白质芯片技术检测血清蛋白质质谱的方法,探讨基于人工神经网络的血清蛋白质质谱模型在大肠癌诊断中的应用价值。方法 应用表面增强激光解吸电离飞行时间质谱仪(SELDI-TOF-MS),测定了147例血清标本(其中大肠癌55例,健康人92例)的蛋白质质谱,用随机抽取的87例标本(大肠癌32例,健康人55例)作为训练组,进行训练与交叉验证,将筛选出来的5910,8930,4476和8817的4个质荷比峰作为输入,建立人工神经网络预测模型。并用另外测试组(大肠癌23例,健康人37例)的血清标本盲法验证该模型。结果 利用从训练组得出的基于人工神经网络的血清蛋白质质谱模型,对测试组的60例(包括Dukes A)未知血清进行预测,得到该方法对大肠癌的检出率为82．6％(19／23),排除率为91．9％(34／37)。结论 蛋白质芯片技术检测血清蛋白质质谱法在大肠癌的诊断中较以往的传统方法具有更高的检出率和排除率,值得进一步研究与应用。     

Artificial neural networks applied to survival prediction in breast cancer

In this study, we evaluated the accuracy of a neural network in predicting 5-, 10- and 15-year breast-cancer-specific survival. A series of 951 breast cancer patients was divided into a training set of 651 and a validation set of 300 patients. Eight variables were entered as input to the network: tumor size, axillary nodal status, histological type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis and age. The area under the ROC curve (AUC) was used as a measure of accuracy of the prediction models in generating survival estimates for the patients in the independent validation set. The AUC values of the neural network models for 5-, 10- and 15-year breast-cancer-specific survival were 0.909, 0.886 and 0.883, respectively.The corresponding AUC values for logistic regression were 0.897, 0.862 and 0.858. Axillary lymph node status (N0 vs. N+) predicted 5-year survival with a specificity of 71% and a sensitivity of 77%. The sensitivity of the neural network model was 91% at this specificity level. The rate of false predictions at 5 years was 82/300 for nodal status and 40/300 for the neural network. When nodal status was excluded from the neural network model, the rate of false predictions increased only to 49/300 (AUC 0. 877). An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction. The consistently high accuracy over time and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction. Copyright Copyright 1999 S. Karger AG, Basel     

Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks

Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for predicting survival of gastric cancer patients in Iran.     

Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements

(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.