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.     

老年肺癌患者呼吸道深部真菌感染因素分析

目的：探讨老年肺癌患者呼吸道深部真菌感染的危险因素及预防措施。方法：对58例老年肺癌患者呼吸道深部真菌感染的临床资料进行回顾性分析。结果：老年肺癌患者呼吸道深部真菌感染的病原菌以白色念珠菌为主，占72，4％（42／58）。肺癌的分期晚、白细胞减少、长期应用多种抗生素、放化疗及各种侵入性诊疗等为真菌感染的危险因素。应用以氟康唑为主的综合治疗后，52例（89．7％）治愈，6例死亡。结论：老年肺癌患者呼吸道深部真菌感染有多种危险因素，应早期预防，早期诊断，确诊后及时应用高效低毒的氟康唑治疗有较好的疗效，同时应加强免疫与支持疗法。     

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。     

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。     

肺癌患者深部真菌感染主要病原菌的FCM分析

背景与目的深部真菌感染是晚期肺癌患者的重要并发症和致死原因之一，研究和分析肺癌患者深部真菌感染病原菌有助于早期诊断和治疗晚期肺癌患者深部真菌感染。本研究在细胞生物学水平对肺癌患者深部真菌感染的主要病原菌进行研究，以探讨肺癌患者深部真菌感染发生发展过程中病原菌的变化及其与发病的关系。方法采用流式细胞技术(FCM)对肺癌患者深部真菌感染的主要病原菌——白念珠菌和对照组白念珠菌细胞总DNA含量、增殖指数(PI)及细胞周期进行测量。结果肺癌组与对照组稳定生长阶段的白念珠菌细胞大多数处于G0／G1期。两组G0／G1、G2／M期的细胞构成比和细胞总DNA含量均无显著性差异；肺癌组S期细胞构成比和增殖指数均显著高于对照组(P=0．040，P=0．038)。结论肺癌患者深部真菌感染主要病原菌——白念珠菌细胞DNA合成期比例增加，增殖活性增强，发生了致病力的变化。     

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.     

Analysis of acute-phase proteins, AHSG, C3, CLI, HP and SAA, reveals distinctive expression patterns associated with breast, colorectal and lung cancer

Early detection, clinical management and disease recurrence monitoring are critical areas in cancer treatment in which specific biomarker panels are likely to be very important in each of these key areas. We have previously demonstrated that levels of alpha-2-heremans-schmid-glycoprotein (AHSG), complement component C3 (C3), clusterin (CLI), haptoglobin (HP) and serum amyloid A (SAA) are significantly altered in serum from patients with squamous cell carcinoma of the lung. Here, we report the abundance levels for these proteins in serum samples from patients with advanced breast cancer, colorectal cancer (CRC) and lung cancer compared to healthy controls (age and gender matched) using commercially available enzyme-linked immunosorbent assay kits. Logistic regression (LR) models were fitted to the resulting data, and the classification ability of the proteins was evaluated using receiver-operating characteristic curve and leave-one-out cross-validation (LOOCV). The most accurate individual candidate biomarkers were C3 for breast cancer [area under the curve (AUC) = 0.89, LOOCV = 73%], CLI for CRC (AUC = 0.98, LOOCV = 90%), HP for small cell lung carcinoma (AUC = 0.97, LOOCV = 88%), C3 for lung adenocarcinoma (AUC = 0.94, LOOCV = 89%) and HP for squamous cell carcinoma of the lung (AUC = 0.94, LOOCV = 87%). The best dual combination of biomarkers using LR analysis were found to be AHSG + C3 (AUC = 0.91, LOOCV = 83%) for breast cancer, CLI + HP (AUC = 0.98, LOOCV = 92%) for CRC, C3 + SAA (AUC = 0.97, LOOCV = 91%) for small cell lung carcinoma and HP + SAA for both adenocarcinoma (AUC = 0.98, LOOCV = 96%) and squamous cell carcinoma of the lung (AUC = 0.98, LOOCV = 84%). The high AUC values reported here indicated that these candidate biomarkers have the potential to discriminate accurately between control and cancer groups both individually and in combination with other proteins.     

Artificial neural network for prediction of distant metastasis in colorectal cancer

Background and Objectives: Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. Methods: The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. Results: The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. Conclusion: The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.     

晚期肺癌伴下呼吸道感染的临床分析

目的：探讨晚期肺癌患者发生肺部感染的病原菌及药敏情况。方法：采用回顾性调查方法,分析１９０例晚期肺癌患者肺部感染、痰菌培养及药敏试验的临床资料。结果：培养出病原菌２５２株,其中真菌４９株,细菌２０３株;细菌中革兰氏阴性菌占６５.５％,革兰氏阳性菌占３４.５％。革兰氏阴性菌主要为大肠杆菌、非发酵菌属、铜绿假单胞菌、肺炎克雷伯菌;革兰氏阳性菌主要为粪肠球菌、表皮葡萄球菌、溶血葡萄球菌。革兰氏阴性菌主要对氨基糖甙类、喹诺酮类、碳青霉烯类敏感;革兰氏阳性菌主要对万古霉素敏感。抗真菌治疗主要用氟康唑或伏立康唑。结论：肺癌患者肺部感染多为革兰氏阴性菌感染,混合性感染以及二重感染比例较高;各种致病菌对常用抗生素存在着不同程度耐药性,临床医生应根据药敏试验结果合理使用抗生素。     

晚期肺癌肺部感染细菌培养及药敏实验

目的:探究晚期肺癌肺部感染患者的细菌感染及药敏实验情况。方法:选择2013年3月-2014年7月收治的100例晚期肺癌并发肺部感染患者进行药敏实验研究,分析细菌感染以及耐药状况。结果:100例晚期肺癌并发肺部感染患者痰液标本中的细菌培养结果显示,共分离出150株病原菌,革兰氏阴性菌77株,比例51.3%,占据首位,其次是真菌53株,比例35.3%,革兰氏阳性菌20株,占据比例最小。革兰氏阴性菌中比例较大的有铜绿假单胞菌、鲍氏不动杆菌、肺炎克雷伯菌以及大肠埃希氏菌等;真菌中比例最大的是白色念珠菌,其次是热带念珠菌与光滑念珠菌;革兰氏阳性菌中比例最大的是金黄色葡萄球菌,为6.7%;通过对铜绿假单胞菌、鲍氏不动杆菌、肺炎克雷伯菌、大肠埃希氏菌、金黄色葡萄球菌以及主要真菌进行药敏实验发现,不同的细菌或真菌对不同的抗菌药物的耐药性不同。结论:晚期肺癌患者发生肺部感染,革兰氏阴性菌比例占据首位,其次是真菌,革兰氏阳性菌比例最小。药敏实验显示不同的细菌或真菌对不同的抗菌药物的耐药性不同,需合理使用抗菌药物。     

基于剂量组学预测肺癌患者放射性肺炎发生的研究

目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法（bootstrap）的最小绝对值收敛和选择算子嵌套逻辑回归（LASSO‐LR）及1000次bootstrap的赤池信息量准则（AIC）向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77（95%CI为0.65~0.87）,独立验证集AUC为0.72（95%CI为0.64~0.81）。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。     

基于剂量组学预测肺癌患者放射性肺炎发生的研究

目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法（bootstrap）的最小绝对值收敛和选择算子嵌套逻辑回归（LASSO‐LR）及1000次bootstrap的赤池信息量准则（AIC）向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77（95%CI为0.65~0.87）,独立验证集AUC为0.72（95%CI为0.64~0.81）。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。     

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.     

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.     

Validation of the Royal Marsden Hospital prognostic score in patients treated in the Phase I Clinical Trials Program at the MD Anderson Cancer Center

BACKGROUND:

The authors validated the Royal Marsden Hospital (RMH) prognostic score in patients with advanced lung, pancreatic, and head and neck cancers who were enrolled on phase 1 trials in the MD Anderson Cancer Center Phase I Clinical Trials Program.

METHODS:

The RMH score uses albumin (≥3.5 g/dL vs <3.5 g/dL), lactate dehydrogenase (less than or equal to the upper limit of normal [≤ULN] vs >ULN), and the number of metastatic sites (≤2 sites vs ≥3 sites) to predict patient survival in phase 1 trials. The authors of this report retrospectively reviewed the outcomes of 229 consecutive patients with lung, pancreatic, and head and neck tumors who were treated on 57 phase 1 trials.

RESULTS:

Two hundred twenty‐nine consecutive patients with lung cancer (N = 85), pancreatic cancer (N = 83), and head and neck tumors (N = 61) were treated. The median patient age was 60 years (range, 26‐85 years), and 63% of the patients were men. Patients with a good RMH prognostic score (0‐1) at baseline had a longer median survival than patients with a poor prognostic score (2‐3; 33.9 weeks vs 21.1 weeks; P < .0001). The RMH score was an independent variable that predicted survival in multivariate analysis. Other independent variables that predicted better survival were hemoglobin level (≥10.5 g/dL), Eastern Cooperative Oncology Group performance status (0‐1), and tumor type. Patients who were treated on first‐in‐human trials did not fare worse compared with those who were not treated on first‐in‐human trials.

CONCLUSIONS:

For patients with lung, pancreatic, and head and neck tumors who were treated on phase 1 trials, survival was predicted accurately by the RMH prognostic score. Cancer 2012;. © 2011 American Cancer Society.     

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。     

Assessing Breast Cancer Risk with an Artificial Neural Network

Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk.Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer.This study aimed to establish a model to aid radiologists in breast cancer risk estimation. This incorporated imagingmethods and fine needle aspiration biopsy (FNAB) for cyto-pathological diagnosis. Methods: An artificial neuralnetwork (ANN) technique was used on a retrospectively collected dataset including mammographic results, riskfactors, and clinical findings to accurately predict the probability of breast cancer in individual patients. Area underthe receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictivevalues were used to evaluate discriminative performance. Result: The network incorporating the selected featuresperformed best (AUC = 0.955). Sensitivity and specificity of the ANN were respectively calculated as 0.82 and 0.90.In addition, negative and positive predictive values were respectively computed as 0.90 and 0.80. Conclusion: ANNhas potential applications as a decision-support tool to help underperforming practitioners to improve the positivepredictive value of biopsy recommendations.     

Disease Characteristics and Completion of Treatment in Patients With Metastatic Castration-Resistant Prostate Cancer Treated With Radium-223 in an International Early Access Program

BackgroundRadium-223 is approved by the US Food and Drug Administration and European Medicines Agency for the treatment of metastatic castration-resistant prostate cancer (mCRPC). There are currently no markers for selecting patients most likely to complete radium-223 treatment.Patients and MethodsIn this phase IIIb, international, single-arm study, patients received radium-223, 55 kBq/kg, every 4 weeks for ≤6 cycles. Primary end points were safety and overall survival. In post hoc analyses patients were grouped according to number of radium-223 injections received (1-4 or 5-6). Associations between baseline covariates and number of injections were investigated.ResultsOf 696 eligible patients, 473 (68%) had received 5 to 6 radium-223 injections and 223 (32%) 1 to 4 injections. Patients with less pain (moderate-severe vs. none-mild, odds ratio [OR], 0.41; P < .0001), lower Eastern Cooperative Oncology Group performance status (≥2 vs. 0-1, OR, 0.51; P = .0074), lower prostate-specific antigen level (>141 μg/L vs. ≤141 μg/L, OR, 0.40; P < .0001), and higher hemoglobin level (<10 g/dL vs. ≥10 g/dL, OR, 0.50; P = .0206) were more likely to receive 5 to 6 than 1 to 4 injections. Median overall survival was not reached and was 6.3 months (95% confidence interval, 5.4-7.4) in patients who had received 5 to 6 and 1 to 4 radium-223 injections, respectively. Adverse events were less common in patients who received 5 to 6 than 1 to 4 injections; anemia was reported in 87 (18%) and 64 (29%) patients, respectively.ConclusionPatients with less advanced mCRPC are more likely to receive 5 to 6 radium-223 injections and to achieve better overall survival. Consideration of baseline and disease characteristics is recommended before initiation of radium-223 treatment.     

周围型肺癌CT征象与Ki-67、TTF-1、p63表达的相关性分析

目的:探讨周围型肺癌CT征象与Ki-67指数、TTF-1、p63表达的相关性。方法:回顾性收集我院73例周围型肺癌患者的临床、病理及MSCT资料,分析免疫组化因子（Ki-67指数、TTF-1、p63）表达情况与CT征象的关系。结果:男性、吸烟、瘤体直径≥3 cm、有深分叶、棘突征、增强CT值≥20 HU或肺门、纵隔淋巴结肿大的肺癌患者,Ki-67指数较高;有钙化、空气支气管征或胸膜凹陷征的肺癌患者,Ki-67指数较低。女性、年龄<60岁、非吸烟者、有毛刺征、增强CT值≥20 HU或肺门、纵隔淋巴结肿大与TTF-1阳性比例高有相关性(P＜0.05)。男性、年龄≥60岁、吸烟、瘤体直径≥3 cm或无远处转移与p63阳性比例高有相关性(P＜0.05)。其他CT征象与Ki-67指数、p63、TTF-1表达的差异均无统计学意义。结论:周围型肺癌的某些CT征象可以在一定程度上推测免疫组化指标Ki-67、TTF-1、p63表达情况,对评价肿瘤生物学行为具有重要意义。     

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...