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.     

肺癌患者合并肺部真菌感染的临床分析

目的 分析肺癌患者并发肺部真菌感染的影响因素及临床特点,以有效预防和控制感染。方法收集新疆肿瘤医院2007年1月~12月出院的872例肺癌患者的临床资料,对其中合并肺部真菌感染的87例资料总结,分析真菌感染的影响因素及真菌种类特点。结果 872例肺癌患者中,肺部真菌感染87例,感染发生率9.9%。真菌类型主要为念珠菌菌属（96.6%）,其中白色念珠菌（81%）为主要菌种,主要影响因素有年龄≥50岁,Ⅲ～Ⅳ期的中晚期肺癌患者、住院时间≥14天、化疗、放疗,侵袭性操作、白细胞减少≥Ⅲ度,长时间使用抗生素及激素(P＜0.05)。而患者的性别,肺癌的病理分型,是否行手术治疗与肺部真菌感染无关(P＞0.05)。结论 减少易感因素,及时治疗是降低肺癌患者真菌感染的有效措施。     

癌症患者院内肺部真菌感染的临床分析

背景与目的:医院内真菌感染是癌症患者的重要并发症之一,其中20%以上都是肺部真菌感染。本研究分析并探讨恶性肿瘤患者并发肺部真菌感染的危险因素及临床特点。方法:采用回顾性调查方法,分析1229例恶性肿瘤患者中并发医院内肺部真菌感染的78例患者的临床资料。结果:1229例恶性肿瘤患者肺部真菌感染率为6.35%;真菌类型主要是白色念珠菌(68.18%)。主要易感因素是患者年龄(≥50岁,P<0.005)、原发疾病部位(肺癌,P<0.001)、疾病分期(Ⅳ期,P<0.005)、曾经接受化疗及胸部放疗(P<0.001)以及住院时间长(>两周,P<0.005)。结论:减少易感因素,早发现,早诊断和及时治疗是减少真菌感染的有效措施。     

晚期肺癌患者肺部感染死亡危险因素分析

目的 分析肺癌伴肺部感染患者死亡的相关危险因素。方法　回顾2010年1月—2017年5月收治的186例晚期肺癌患者临床资料,其中肺部感染死亡者52例,并收集肺部感染死亡的相关危险因素。结果　晚期肺癌患者肺部感染死亡率为27.96%（52/186）。在诸多因素中,年龄（P=0.039）、BMI值（P=0.000）、组织分化程度低（P=0.003）、功能状态评分（P=0.022）、转移部位≥3（P=0.029）、合并症（P=0.034）、多重耐药菌感染（P=0.000）、血红蛋白浓度<90 g/L（P=0.014）等在死亡组和非死亡组之间差异具有统计学意义。结论　年龄（≥60岁）、BMI值（<20 kg/m²）、组织分化程度（低）、抗肿瘤治疗药物（≥3种）、靶向药物治疗、转移部位（≥3）、合并症、多重耐药菌感染、血红蛋白浓度（<90 g/L）等是晚期肺癌患者肺部感染死亡的主要危险因素。     

Novel artificial neural network for early detection of prostate cancer.

PURPOSE: Two artificial neural networks (ANN) for the early detection of prostate cancer in men with total prostate-specific antigen (PSA) levels from 2.5 to 4 ng/mL and from 4 to 10 ng/mL were prospectively developed. The predictive accuracy of the ANN was compared with that obtained by use of conventional statistical analysis of standard PSA parameters. PATIENTS AND METHODS: Consecutive men with a serum total PSA level between 4 and 10 ng/mL (n = 974) and between 2.5 and 4 ng/mL (n = 272) were analyzed. A separate ANN model was developed for each group of patients. Analyses were performed to determine the presence of prostate cancer. RESULTS: The area under the receiver operator characteristic (ROC) curve (AUC) was 87.6% and 91.3% for the 2.5 to 4 ng/mL and 4 to 10 ng/mL ANN models, respectively. For the latter model, the AUC generated by the ANN was significantly higher than that produced by the single variables of total PSA, percentage of free PSA, PSA density of the transition zone (TZ), and TZ volume (P <.01), but not significantly higher compared with multivariate analysis. For the 2.5 to 4 ng/mL model, the AUC of the ANN ROC curve was significantly higher than the AUCs for percentage of free PSA (P =.0239), PSA-TZ (P =.0204), and PSA density and total prostate volume (P <.01 for both). CONCLUSION: The predictive accuracy of the ANN was superior to that of conventional PSA parameters. ANN models might change the way patients referred for early prostate cancer detection are counseled regarding the need for prostate biopsy.     

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.     

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

PURPOSE: New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance. Neuro-fuzzy modeling (NFM), another AI method, has a transparent functional layer and is without many of the drawbacks of ANN. We have compared the predictive accuracies of NFM, ANN, and traditional statistical methods, for the behavior of bladder cancer. EXPERIMENTAL DESIGN: Experimental molecular biomarkers, including p53 and the mismatch repair proteins, and conventional clinicopathological data were studied in a cohort of 109 patients with bladder cancer. For all three of the methods, models were produced to predict the presence and timing of a tumor relapse. RESULTS: Both methods of AI predicted relapse with an accuracy ranging from 88% to 95%. This was superior to statistical methods (71-77%; P < 0.0006). NFM appeared better than ANN at predicting the timing of relapse (P = 0.073). CONCLUSIONS: The use of AI can accurately predict cancer behavior. NFM has a similar or superior predictive accuracy to ANN. However, unlike the impenetrable "black-box" of a neural network, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. This technique could be used widely in a variety of areas of medicine.     

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。     

肺癌患者院内感染微生物学特点及预后因素分析

目的:探讨肺癌患者院内感染的临床特征、微生物学特点以及预后因素。方法:回顾性分析2013年08月至2019年06月在我院肿瘤中心治疗的171例肺癌合并院内感染患者的临床资料,分析院内感染的发生率、微生物学特点、院内死亡率及其影响因素。结果:1 008例肺癌患者中,171例（17.0%）患者发生院内感染,呼吸道感染所占比例最高,为76.0%。分离的病原体主要包括革兰阴性菌、真菌和革兰阳性菌。白色假丝酵母菌是最主要的致病菌,占14.6%,其次是肺炎克雷伯菌（12.9%）、铜绿假单胞菌（10.5%）、肺炎链球菌（9.9%）和流感嗜血杆菌（9.4%）。肺癌患者院内感染院内死亡率为21.1%（36/171）。多因素Logistic回归分析结果显示:住院期间接受过机械通气、存在感染性休克以及低蛋白血症是影响肺癌患者院内感染预后的独立危险因素（P＜0.05）。结论:肺癌患者院内感染发生率及死亡率较高,且受多种因素影响,临床应采取积极有效的措施,以改善患者预后。     

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.     

晚期肿瘤患者交叉感染的特点及临床分析

目的探讨晚期肿瘤患者交叉感染的特点及其相关危险因素。方法采用回顾性分析的方法对986例晚期肿瘤患者进行分析。结果 986例晚期肿瘤患者中,男573例（58.1%）,女413例（41.9%）,中位年龄为65岁。卡式评分（KPS评分）中位数60分,平均住院日为14 d,死亡124例,住院病死率为12.6%,其中231例晚期肿瘤患者发生交叉感染,占23.4%,因感染死亡39例。单因素分析结果显示,感染与年龄、KPS评分、放疗、化疗后粒细胞减少、远处转移、气管切开等因素有关,差异有统计学意义（P〈0.05）。多因素分析结果显示,高龄、白细胞计数（WBC）〈2.0×10^9/L、远处转移以及气管切开与晚期肿瘤患者发生感染显著相关。结论感染是晚期肿瘤患者最主要的死亡原因之一,高龄、WBC〈2.0×10^9/L、远处转移以及气管切开是晚期肿瘤患者发生感染的危险因素。     

The NQO1 C609T polymorphism and risk of lung cancer: a meta-analysis

Objective: NAD(P)H: quinone oxidoreductase 1 (NQO1) is a cytosolic flavoprotein that catalyzes the two-electron reduction of quinoid compounds into hydroquinones. A single base substitution (CgT) polymorphism at 609 in the NQO1 gene reduces quinone reductase activity. Published data on the association between NQO1 C609T polymorphism and lung cancer risk are conflicting. Methods: To derive a more precise estimation of the relationship, a meta-analysis was performed. Results: A total of 23 studies including 5,575 cases and 9,132 controls were assessed. The pooled result showed that the NQO1 polymorphism was not associated with a clear increased risk of lung cancer (OR = 1.009, 95% CI: 0.943-1.078; P heterogeneity=0.049). In the subgroup analysis by ethnicity, no clear increased risk was found among Asians for TT/CT versus CC (OR = 1.005; 95% CI = 0.890-1.135; Pheterogeneity=0.024). However, the TT and CT genotypes combined were associated with significantly increased risk of lung cancer in Chinese (OR = 1.237, 95% CI: 1.029-1.486; Pheterogeneity=0.061) among whom the variant allele is common. The variant genotype of NQO1 was also associated with modestly increased risk of lung cancer among white populations (OR = 1.017, 95% CI: 0.936-1.105; Pheterogeneity=0.101). However, no significant association was found in Africans with all genetic models. Conclusions: Our meta-analysis suggests that the variant NQO1 C609T genotype may affect individual susceptibility to lung cancer. This meta- analysis suggests that the NQO1 609T allele is a low penetrant risk factor for developing lung cancer in Chinese.     

肿瘤专科医院恶性肿瘤院内感染123例临床分析

目的探讨肿瘤专科医院化疗科恶性肿瘤患者院内感染的发生率、相关因素及防治对策。方法对2009年1月-2009年12月本院化疗科住院的3 316例患者中123例发生院内感染的恶性肿瘤患者的资料进行回顾性分析,包括感染部位、感染致病菌、患者年龄、肿瘤期别、住院时间等,并初步探讨院内感染的防治。结果本组院内感染率、病死率为3.8%、17.1%。感染与年龄、肿瘤期别、抗肿瘤治疗和侵袭性操作密切相关。感染主要部位是下呼吸道、泌尿道,分别占53.7%、16.3%。病原体阳性患者112例,共检出致病菌株127例,有9例为合并几种菌感染。感染主要菌种为革兰氏阴性菌占67株(52.8%),其中前3位是肺炎鲍曼不动杆菌18株(14.2%),克雷伯菌16株(12.6%),铜绿色假单胞菌14株(11.0%)。真菌36株(28.3%);革兰氏阳性菌24株(18.9%),主要是表皮葡萄球菌。结论恶性肿瘤患者院内感染常由条件致病菌引起,住院时间长、放化疗结合、老年及晚期肿瘤患者是院内感染的主要危险因素。降低院内感染,应采取综合措施。     

Prediction of survival from carcinoma of oesophagus and oesophago-gastric junction following surgical resection using an artificial neural network.

AIM: The aim of this study was to assess the ability of artificial neural network (ANN) in predicting survival in patients undergoing surgical resection for carcinoma of oesophagus and oesophago-gastric junction. METHODS: From January 1995 to August 2004 patients who underwent surgery for oesophageal and gastric carcinoma were identified. Biographical data, body mass index and pathological minimal cancer dataset were used to design an ANN. Post-operative survival was assessed at 1 and 3 years. Sixty percent of data was used to train and validate the ANN and 40% was used to evaluate the accuracy of trained ANN in predicting survival. This was compared with Union Internacional Contra la Cancrum UICC TNM classification system. RESULTS: Two hundred and sixteen patients underwent resectional surgery for oesophageal and OGJ carcinoma. The accuracy of the ANN in predicting survival at 1 and 3 years was 88% (sensitivity: 92.3%, specificity: 84.5%, DP = 2.3) and 91.5% (sensitivity of 94.61%, specificity: 88%, DP = 2.72), respectively. These figures were significantly better than 1- and 3-year survival predictions using the UICC TNM classification system 71.6% (sensitivity of 66.4%, specificity: 75.5%, and DP < 1) and 74.7% (sensitivity of 70.5%, specificity: 74.9%, DP < 1), respectively (P < 0.01) (P < 0.05). CONCLUSION: ANNs are superior to the UICC TNM classification system in correlating with survival following resection of carcinoma of oesophagus and OG junction and can become valuable tools in the management of patients with oesophageal carcinoma.     

乳腺癌针吸细胞形态定量的人工神经网络诊断模型的建立及应用研究

目的 建立乳腺癌针吸细胞形态定量参数的人工神经网络诊断模型，并验证其在辅助FNA诊断乳腺癌的价值。方法 利用MPIAS-2000系统对60例乳腺癌及30例乳腺良性病变的针吸细胞学涂片进行形态定量测定，对获得的29项形态参数进行人工神经网络建模分析，并用盲法对其鉴别诊断能力进行评价。结果 所建立的网络模型经过14次训练后即可达到误差要求，诊断模型对乳腺癌及乳腺良性病变的诊断正确率为100％，其特异性和敏感性均为100％。结论 乳腺良恶性病变的针吸细胞学涂片进行ANN分析所建立的诊断模型，对乳腺癌及良性病病变的鉴别诊断具有较高的应用价值，为辅助针吸细胞学诊断乳腺良恶性病变提供了新的思路。     

巨块型肺癌的外科治疗及预后因素分析

目的探讨巨块型肺癌的外科治疗及影响预后的因素。方法回顾性分析上海市肺科医院1992年8月至2005年8月经手术治疗的137例巨块型肺癌患者的临床及病理资料,其中根治性手术122例,姑息性手术15例。122例根治性手术中,肺叶切除63例,全肺切除48例,其他手术方式11例。分别对患者性别、肿瘤大小、p-TNM分期、局部淋巴结N分期、原发肿瘤T分期、组织学类型、手术方式和手术性质等因素进行预后分析。Kaplan-Meier计算生存率,Log rank法进行生存率显著性检验,应用Cox比例风险回归模型进行单因素和多因素分析。结果全组1、3、5年生存率分别为76.0%、49.2%和40.1%。单因素分析显示,患者性别(P=0.001)、p-TNM分期(P=0.001)、N分期(P=0.042)、T分期(P=0.006)、手术性质(P=0.026)是影响患者预后的因素。多因素分析则显示,p-TNM分期(P=0.001)是影响患者预后的独立因素。结论p-TNM分期是影响巨块型肺癌患者预后的主要因素。对巨块型肺癌患者,应严格控制手术适应证的选择,争取根治性手术,以获得较高的生存率。     

肺癌患者下呼吸道感染的现状及危险因素分析

目的探讨肺癌患者下呼吸道感染的现状及危险因素。方法选择2009年11月至2011年10月收治的肺癌患者110例,观察患者是否发生下呼吸道感染,分析患者性别、年龄、吸烟史、肺癌分期、住院时间、侵人性操作、抗生素应用是否为下呼吸道感染发生的危险因素。结果110例肺癌患者发生下呼吸道感染者71例,发生率为64．5％,以革兰阴性菌感染为主,其发生明显受到患者年龄、吸烟史、肺癌分期、住院时间、侵入性操作、抗生素应用的影响（P〈0．05）。结论肺癌患者的年龄、吸烟史、肺癌分期、住院时间、侵人性操作、抗生素应用均为发生下呼吸道感染的危险因素,临床工作中应该针对可干预的危险因素采取有效的预防措施,提高患者的生活质量。     

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)     

Gender difference in human papillomarvirus infection for non-small cell lung cancer in Taiwan

Our previous reports have indicated that high risk human papillomarvirus (HPV) 16/18 were much more frequently detected in lung tumors of female patients as compared to that of male patients and HPV 16/18 in lung tumors were evolutionally correlated with those in blood circulation. In the other hand, it is well known that HPV 6/11 are frequently associated with upper aerodigestive and respiratory diseases. HPV 6/11 DNA were detected in lung tumors by nested PCR and in situ hybridization to investigate if any difference in prevalent types of HPV exists between genders. Our data showed that HPV 6 infection was detected in 28.4% (40 of 141) lung tumors, which was significantly higher than that in non-cancer controls (1.7%, 1 of 60; P < 0.0001), however, such high prevalence was not observed for HPV 11. Among studied clinico-pathological parameters, HPV 6 infection was significantly related with gender (P = 0.002) and smoking status (P = 0.014). After being stratified by gender and smoking status, HPV 6 infection rate in lung tumors of non-smoking male patients was much higher than that in non-smoking female patients (33.3% versus 11.1%; P = 0.023), but no difference between smoking and non-smoking male patients (38.1% versus 33.3%). With adjustments for age, tumor type, and tumor stage, smoking male lung cancer patients had a much higher OR value (OR, 7.35; 95%CI, 2.11-25.58) for HPV 6 infection compared with 3.93 (95% CI, 1.17-13.12) of non-smoking male patients. Moreover, a higher prevalence of HPV 6 was detected in lung tumors of smoking male patients with early tumor stage than those with advanced stages (P = 0.008), but not in non-smoking male and female patients. A higher prevalence of HPV 6 in male lung cancer patients, as compared with female lung cancer patients, indicating not only different HPV infection routes for different genders, but also that HPV 6 infections may act as a prospective early risk marker of lung cancer for smoking male patients in Taiwan.     

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.