Prediction of outcome in patients with urothelial carcinoma of the bladder following radical cystectomy using artificial neural networks

Aim

The outcome of patients with urothelial carcinoma of the bladder (UCB) after radical cystectomy (RC) shows remarkable variability. We evaluated the ability of artificial neural networks (ANN) to perform risk stratification in UCB patients based on common parameters available at the time of RC.

Methods

Data from 2111 UCB patients that underwent RC in eight centers were analysed; the median follow-up was 30 months (IQR: 12–60). Age, gender, tumour stage and grade (TURB/RC), carcinoma in situ (TURB/RC), lymph node status, and lymphovascular invasion were used as input data for the ANN. Endpoints were tumour recurrence, cancer-specific mortality (CSM) and all-cause death (ACD). Additionally, the predictive accuracies (PA) of the ANNs were compared with the PA of Cox proportional hazards regression models.

Results

The recurrence-, CSM-, and ACD- rates after 5 years were 36%, 33%, and 46%, respectively. The best ANN had 74%, 76% and 69% accuracy for tumour recurrence, CSM and ACD, respectively. Lymph node status was one of the most important factors for the network's decision. The PA of the ANNs for recurrence, CSM and ACD were improved by 1.6% (p = 0.247), 4.7% (p < 0.001) and 3.5% (p = 0.007), respectively, in comparison to the Cox models.

Conclusions

ANN predicted tumour recurrence, CSM, and ACD in UCB patients after RC with reasonable accuracy. In this study, ANN significantly outperformed the Cox models regarding prediction of CSM and ACD using the same patients and variables. ANNs are a promising approach for individual risk stratification and may optimize individual treatment planning.     

Prediction of radiation induced liver disease using artificial neural networks

OBJECTIVE: To evaluate the efficiency of predicting radiation induced liver disease (RILD) with an artificial neural network (ANN) model. METHODS AND MATERIALS: From August 2000 to November 2004, a total of 93 primary liver carcinoma (PLC) patients with single lesion and associated with hepatic cirrhosis of Child-Pugh grade A, were treated with hypofractionated three-dimensional conformal radiotherapy (3DCRT). Eight out of 93 patients were diagnosed RILD. Ninety-three patients were randomly divided into two subsets (training set and verification set). In model A, the ratio of patient numbers was 1:1 for training and verification set, and in model B, the ratio was 2:1. RESULTS: The areas under receiver-operating characteristic (ROC) curves were 0.8897 and 0.8831 for model A and B, respectively. Sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV) were 0.875 (7/8), 0.882 (75/85), 0.882 (82/93), 0.412 (7/17) and 0.987 (75/76) for model A, and 0.750 (6/8), 0.800 (68/85), 0.796 (74/93), 0.261 (6/23) and 0.971 (68/70) for model B. CONCLUSION: ANN was proved high accuracy for prediction of RILD. It could be used together with other models and dosimetric parameters to evaluate hepatic irradiation plans.     

Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma

Currently, patients with neuroblastoma are classified into risk groups (e.g., according to the Children's Oncology Group risk-stratification) to guide physicians in the choice of the most appropriate therapy. Despite this careful stratification, the survival rate for patients with high-risk neuroblastoma remains <30%, and it is not possible to predict which of these high-risk patients will survive or succumb to the disease. Therefore, we have performed gene expression profiling using cDNA microarrays containing 42,578 clones and used artificial neural networks to develop an accurate predictor of survival for each individual patient with neuroblastoma. Using principal component analysis we found that neuroblastoma tumors exhibited inherent prognostic specific gene expression profiles. Subsequent artificial neural network-based prognosis prediction using expression levels of all 37,920 good-quality clones achieved 88% accuracy. Moreover, using an artificial neural network-based gene minimization strategy in a separate analysis we identified 19 genes, including 2 prognostic markers reported previously, MYCN and CD44, which correctly predicted outcome for 98% of these patients. In addition, these 19 predictor genes were able to additionally partition Children's Oncology Group-stratified high-risk patients into two subgroups according to their survival status (P = 0.0005). Our findings provide evidence of a gene expression signature that can predict prognosis independent of currently known risk factors and could assist physicians in the individual management of patients with high-risk neuroblastoma.     

Outcome assessment of patients with metastatic renal cell carcinoma under systemic therapy using artificial neural networks

Background

The outcome of patients with advanced renal cell carcinoma (RCC) under systemic therapy shows remarkable variability, and there is a need to identify prognostic parameters that allow individual prognostic stratification and selection of optimal therapy. Artificial neural networks (ANN) are software systems that can be trained to recognize complex data patterns. In this study, we used ANNs to identify poor prognosis of patients with RCC based on common clinical parameters available at the beginning of systemic therapy.

Patients and Methods

Data from patients with RCC who started systemic therapy were collected prospectively in a single center database; 175 data sets with follow-up data (median, 36 months) were available for analysis. Age, sex, body mass index, performance status, histopathologic parameters, time interval between primary tumor and detection of metastases, type of systemic therapy, number of metastases, and metastatic sites were used as input data for the ANN. The target variable was overall survival after 36 months. Logistic regression models were constructed by using the same variables.

Results

Death after 36 months occurred in 26% of the patients in the tyrosine kinase inhibitors group and in 37% of the patients in the immunotherapy group (P = .22). ANN achieved 95% overall accuracy and significantly outperformed logistic regression models (78% accuracy). Pathologic T classification, invasion of vessels, and tumor grade had the highest impact on the network's decision.

Conclusion

ANN is a promising approach for individual risk stratification of patients with advanced RCC under systemic therapy, based on clinical parameters, and can help to optimize the therapeutic strategy.     

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.     

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)     

Survival prediction using artificial neural networks in patients with uterine cervical cancer treated by radiation therapy alone

Background: We evaluated the usefulness of artificial neural networks (ANNs) for survival prediction in patients with uterine cervical cancer treated by radiotherapy. Methods: We used data from 134 patients with uterine cervical cancer treated by combined external and high-dose-rate remote afterloading intracavitary radiotherapy between 1978 and 1993. The ANNs were trained using the data from 67 randomly selected patients. Using the trained ANNs, we predicted the 5-year survival in the remaining 67 patients, and compared it with the known 5-year survival. The performance of the ANNs was evaluated using a receiver operating characteristic (ROC) curve and was compared using the area under the ROC curve (Az). Results: When fundamental factors, such as age, performance status, hemoglobin, total protein, International Federation of Gynecology and Obstetrics (FIGO) stage, and histological type were used as inputs in the ANNs, Az was 0.5483 ± 0.0145 (mean ± SD). When the histological grading of radiation effect determined by periodic biopsy examination was used in addition to the fundamental factors, Az was highest (0.7782 ± 0.0105). When the cytological grading of radiation effect by the periodic smear was used in addition to the fundamental factors, Az was 0.5523 ± 0.0135, which was not significantly different from that when only the fundamental factors were used. Conclusion: ANNs allow us to evaluate the importance of prognostic factors, and make it possible to predict the survival of each patient. Using ANNs, the combination of histological grading of radiation effect determined by periodic biopsy examination, in addition to the fundamental factors, is the most effective for prediction of survival in patients with uterine cervical cancer. Received: November 8, 2001 / Accepted: June 7, 2002 Acknowledgments The authors are grateful to Masaji Takahashi, M.D. (Professor Emeritus, Kyoto University), Masahiro Hiraoka, M.D. (Professor, Department of Therapeutic Radiology and Oncology, Kyoto University), and Masaaki Kataoka, M.D. (Shikoku Cancer Center Hospital), for their valuable discussions and advice in carrying out this study. This work was presented, in part, at the 86th RSNA meeting in Chicago, USA, November 26 to December 1, 2000. Correspondence to:T. Ochi     

女性食管癌的临床病理特点及生存分析

目的 探讨女性食管癌患者的临床病理特征及生存预后。方法 回顾性分析2008年1月至2011年12月在我院住院且经手术病理证实为食管癌的261例女性患者的临床病理资料及生存时间,并与同期的797例男性食管癌患者进行比较。采用单因素和多因素方法分析影响女性食管癌患者预后的因素。结果女性食管癌患者的中位发病年龄为63岁,病变位置中颈段及胸上段比例为16.9％,病变长度＜5cm比例为90.0％,浸润深度为pT1～pT2比例为49.8％,病理TNM分期为Ⅰ、Ⅱ期比例为74.7％,淋巴结转移率为30.6％,脉管癌栓率为9.2％。与男性相比较,女性食管癌患者的发病年龄较晚,病变部位较高,病变长度较短,病变浸润深度较浅,病理TNM分期较早,淋巴结转移率及脉管侵犯率较低;而在病理类型、神经侵犯率、组织分化方面与男性患者的差异无统计学意义（P＞0.05）。女性患者术后1、2、3年生存率分别为89.3％、74.0％和66.7％,而男性患者分别为86.5％、65.8％和51.3％,差异有统计学意义（P=0.015）。肿瘤长度、浸润深度、淋巴结转移、病理TNM分期是女性食管癌患者的独立预后因素。结论 女性食管癌在发病年龄、病变部位、病变长度、浸润深度、淋巴结转移、病理TNM分期、脉管癌栓等方面具有与男性食管癌不同的临床病理特点。女性食管癌患者的预后要优于男性,可能与其病变长度较短、浸润深度较浅、淋巴结转移率较低及病理TNM分期较早等因素有关。     

放射性肺炎预测因素的相关性研究

目的前瞻性研究转化生长因子(TGF-β_1)、V_(20)及肺功能与放射性肺炎(RP)的关系,并建立放射性肺炎预测指数模型。方法121例符合研究条件的食管癌及肺癌患者接受15 MV X线常规胸部照射D_T60～68 Gy分30～34次42～46d完成。放疗前均行胸部CT扫描,并经三维TPS生成DVH图,得出V_(20)。放疗前、放疗第20天以及放疗结束时,均行血清TGF-β_1检测及肺功能(PFTs)测定。根据增强CT结合临床症状诊断RP。结果121例中32例发生了RP。Logistic回归模型分析结果显示,V_(20)和TGF-β_1比率对RP的发生有影响,V_(20)≥30%者较易发生RP,TGF-β_1比率≥1者较易发生RP。肺功能差者与肺功能正常者相比,发生RP的概率无差别(P=0.079)。预测方程为PI= 2.941×TGF-β_1比率 2.141×V_(20)-FEV_1/FVC,121例的PI中位值为2.89(1.7～6.8)。按PI值将其划分为预后好组(55例,PI<2.8)、预后中组(37例,2.8≤PI<4.5)、预后差组(29例,PI≥4.5)。在自身样本中的符合率为79.6%。结论TGF-β_1比率与放射物理因素V_(20)的有机结合对放射性肺炎发生有一定预测作用。PI方程可定量确定发生放射性肺炎的高危人群,并在决定某种放疗方案时前瞻性地预测放射性肺炎发生概率及程度。     

食管癌p53的表达及预后分析

目的;探讨食管癌患者的预后因素,了解食管癌细胞的增殖状态.方法:用Cox多因素回归模型对单纯根治性手术治疗的108例患者进行预后因素分析,并用许氏ABC法对其中50例患者的手术标本进行p53表达的检测.结果:食管癌患者的预后与临床分期、淋巴结转移和p53的表达有关(P<0.05);而与病变长度、部位、淋巴结大小、病理分级及弓上或弓下吻合无关(P>0.05).p53在食管癌中阳性表达率为54.0%(27/50),其表达强度与病理分级有关(P<0.05),而与临床分期无关(P>0.05).结论:食管癌患者的临床分期、淋巴结转移和p53的表达是重要的预后因素.     

Posttherapy pathologic stage predicts survival in patients with esophageal carcinoma receiving preoperative chemoradiation

BACKGROUND: In patients with locoregional carcinoma of the esophagus or esophagogastric junction who underwent preoperative chemoradiation, it is unclear whether survival was better predicted by pretherapy clinical stage or by posttherapy pathologic stage. METHODS: The authors studied 235 consecutive patients with pretherapy clinical Stage II, III, or IVA (according to American Joint Committee on Cancer criteria) carcinoma of the esophagus or esophagogastric junction who were treated with chemoradiation followed by esophagectomy. Posttherapy cancer status was classified using pathologic stage and semiquantitative assessment of residual carcinoma. Clinicopathologic features, residual carcinoma status, and pretherapy and posttherapy stage were compared with disease-free and overall survival. RESULTS: Posttherapy pathologic stage was Stage 0 in 29% of patients, Stage I in 11% of patients, Stage II in 34% of patients, Stage III in 20% of patients, and Stage IV in 6% of patients. Cancer downstaging occurred in 56% of patients. In univariate analysis, disease-free and overall survival were predicted by posttherapy pathologic stage (both with P < 0.001), margin status (P = 0.002 and P = 0.01, respectively), extent of residual carcinoma (both with P < 0.001), and downstaging (both with P = 0.001), but not by age, gender, type of cancer, pretherapy clinical stage, or preoperative regimen. However, in multivariate analysis, disease-free and overall survival were independently predicted by posttherapy pathologic stage (both with P = 0.02). Extent of residual carcinoma was a marginally significant predictor of overall survival (P = 0.04). CONCLUSIONS: Posttherapy pathologic stage was the best available predictor of outcome for patients with locoregional carcinoma of the esophagus or esophagogastric junction who underwent chemoradiation therapy followed by esophagectomy. The findings in the current study supported the concept of downstaging by preoperative therapy.     

食管鳞癌基因多态性表达与生存分析

目的:探讨食管鳞癌肿瘤组织中ERCC1、TYMS、TUBB3基因表达与预后生存的关系。方法:选择2012年9月至2016年5月我科收治的68例食管鳞癌患者,术后病理分期IIa-IIIa期;检测肿瘤组织ERCC1、TYMS、TUBB3 mRNA表达水平。术后患者分为个体化化疗组和标准化疗组,个体化组患者根据基因检测结果选择敏感药物化疗方案（CF/DCF/TC方案）进行化疗,标准组应用CF方案化疗,所有患者长期随访,统计化疗不良反应及生存数据。结果:肿瘤组织中ERCC1、TYMS、TUBB3阳性表达率分别为43%、47%、51%,无统计学差异（P＞0.05）。所有患者总一年生存率、两年生存率及三年生存率为分别为90.57%,72.45%和59.77%。生存曲线分析提示:0项基因阳性组预后最佳（P＜0.05）,而1项、2项和3项基因阳性组间无统计学差异（P＞0.05）。个体化化疗组的III/IV级化疗不良反应发生率明显低于标准化疗组（P＜0.05）。结论:食管鳞癌患者中ERCC1、TYMS、TUBB3表达具有特定的临床特征,其高表达患者预后欠佳。根据基因检测结果进行个体化化疗可以获得更好的疗效和耐受性,不良反应更轻。     

不同生存期食管鳞癌患者载脂蛋白D表达水平的研究

  目的  通过对不同生存期食管鳞状细胞癌患者术前血清及组织中载脂蛋白D(ApoD)表达水平差异的研究, 探讨其与该病预后的相关性, 推测其是否可能成为预测食管癌患者预后的生物标记物。  方法  收集河南省食管癌高发地区安阳市肿瘤医院2008年3月至2009年9月行根治性切除食管癌的患者731例, 选择一般资料、血清标本及组织标本建立资料库; 随机从资料库抽取生存期≤3年和生存期≥5年的两个极端生存期患者各34例作为研究对象, 以健康人群作为正常对照组, 使用同位素标记相对和绝对定量法(isobaric tags for relative and absolute quantification, iTRAQ)联合基质辅助激光解吸离子化串联飞行时间质谱(MALDITOF/TOF MS)蛋白质组学技术对食管癌患者术前血清蛋白进行分析比照, 锁定目的蛋白ApoD; 采用Western blot技术验证ApoD在不同生存期食管鳞癌患者术前血清及健康人群血清的表达水平; 使用免疫组织化学技术观察不同生存期食管癌组织及正常组织中ApoD的表达水平。  结果  iTRAQ联合MALDI-TOF/TOF MS结果显示, 有52种蛋白的表达随生存期延长而上调, 具有显著性差异, ApoD是其中之一。Western blot结果显示ApoD在生存期≥5年组血清中表达最高, 正常人血清中表达其次, 而在生存期≤3年组中表达最低, 差异均有统计学意义(P < 0.05)。免疫组织化学结果显示ApoD在正常食管鳞状上皮中表达最高, 在生存期≥5年组表达其次, 在生存期≤3年组中表达最低, 差异均有统计学意义(P < 0.05)。  结论  ApoD表达水平与食管癌患者的生存期呈正相关, 可能与食管癌患者的预后相关, 可能成为预测食管癌患者预后情况的一种生物标记物。      

Significance of allogenic blood transfusion on decreased survival in patients with esophageal carcinoma.

BACKGROUND: To the authors' knowledge, the significance of allogenic blood transfusion in the prognosis of patients with esophageal carcinoma remains controversial. The objective of the current study was to elucidate the correlation, if any, between intraoperative allogenic blood transfusion and prognosis in patients with esophageal carcinoma. METHODS: Two hundred fifty-nine patients with esophageal carcinoma who had undergone esophagectomy and reconstruction were studied. The clinicopathologic data and survival were compared between the 87 patients (33.6%) who received an intraoperative allogenic blood transfusion and the 172 patients (66.4%) who did not. RESULTS: Multivariate analysis demonstrated that the factors that appeared to independently determine prognosis in patients with esophageal carcinoma were the depth of the tumor (P = 0.0001), lymph node metastasis (P < 0.0001), lymphatic invasion (P = 0.0002), venous invasion (P = 0.0008), and the occurrence of postoperative complications (P = 0.034). Intraoperative allogenic blood transfusion was not found to be an independent prognostic indicator. CONCLUSIONS: In the current study, an advanced stage of disease at the time of surgery, which resulted in the need for blood transfusion and the occurrence of postoperative complications, appeared to worsen the prognosis in patients with esophageal carcinoma.     

Meta-analysis of survival benefit with postoperative chemoradiotherapy in patients of lymph node positive esophageal carcinoma

Background

The effect of postoperative chemoradiotherapy (CRT) for esophageal carcinoma (EC) was investigated. Patients who can obtain benefit from this treatment modality have not yet been well identified.

Methods

We searched PubMed, Embase, Web of Science, and the Cochrane Library for studies published from January 1993 to July 2016. Research comparing surgery alone (SA) with postoperative CRT in patients with resectable EC was procured; collected articles were written in English.

Results

Nine studies comparing of postoperative CRT versus SA (n = 1650) in patients with resectable EC met the inclusion criteria. No survival benefit was achieved for postoperative CRT compared with SA. Subgroup analysis was conducted for patients under resection with positive lymph node carcinoma; there was a significant survival benefit at 1 year [risk ratio (RR) = 0.55 95% CI: 0.37–0.82; P = 0.003], 3 years (RR = 0.71 95% CI: 0.61–0.83; P<0.0001), as well as 5 years (RR = 0.86 95% CI: 0.78–0.94; P = 0.0007). Subgroup analysis by tumor histology of squamous cell carcinoma (SCC) was also performed, but there was no significant survival benefit when postoperative CRT was compared with SA. Fail models after surgery were performed; the RR for local control rate and distant metastasis rate were 0.64 (95% CI 0.49–0.85; P = 0.002) and 0.87 (95% CI 0.67–1.15; P = 0.34), which indicates lower local recurrence rates of post-CRT than that of SA.

Conclusion

This meta-analysis demonstrated a survival benefit of postoperative CRT over SA in resectable EC patients with positive lymph nodes. Improvements of local control rates with postoperative CRT were also detected.

     

Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks

Summary The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the ‘El álamo’ Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p<0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1–10 and 11–20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset. Address for offprints and correspondence: Leonardo Franco, Depto. de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Campus de Teatinos S/N, 29071, Málaga, Spain; Tel.: +34-952-133304; Fax: +34-952-133397; E-mail: Leonardo.Franco@psy.ox.ac.uk     

Endosonographic assessment of multimodality therapy predicts survival of esophageal carcinoma patients

BACKGROUND: Standard endosonographic (EUS) staging criteria are unreliable for staging esophageal carcinoma after neoadjuvant therapy; however, measurement of tumor size reduction can identify patients who have achieved a pathologic response. In the current study the authors prospectively compared survival between patients classified as responders and those classified as nonresponders by EUS. METHODS: The maximal transverse cross-sectional area of the tumor was measured before and after neoadjuvant therapy in patients who were candidates for multimodality treatment. Response was defined as a > or = 50% reduction in tumor area. RESULTS: A total of 59 patients at 2 centers were followed for a median of 19 months. EUS assessed response in 34 patients (58%). Overall, responders had a median survival of 17.6 months compared with 14.5 months for nonresponders (P < 0.005). Survival was significantly longer in responders compared with nonresponders in the patient subgroup who underwent surgical resection (19.7 months vs. 14.6 months; P < 0. 005), the patient subgroup with adenocarcinoma (21.4 months vs. 10.8 months; P < 0.005), and the patient subgroup initially classified as having T3N1 disease (17.6 months vs. 14.1 months; P < 0.05). Survival was not found to differ significantly between responders and nonresponders in the subgroup of patients with squamous cell carcinoma. EUS response was the only clinical variable that was associated with survival time in a multivariate analysis (relative hazard = 0.27; P < 0.005). CONCLUSIONS: Patients with esophageal carcinoma who respond to neoadjuvant treatment as identified by EUS measurement of reduction in tumor size have a significantly better prognosis than nonresponders.     

Predicting the survival of patients with breast carcinoma using tumor size

BACKGROUND: Tumor size has long been recognized as the strongest predictor of the outcome of patients with invasive breast carcinoma, although it has not been settled whether the correlation between tumor size and the chance of death is independent of the method of detection, nor is it clear how tumor size at the time of treatment may be translated into a specific expectation of survival. In this report, the authors provide such a method. METHODS: A Kaplan-Meier survival analysis was carried out for a population of 1352 women with invasive breast carcinoma who were treated at the Van Nuys Breast Center between 1966 and 1990, and the data were analyzed together with survival data published by others. RESULTS: The authors found that the survival of patients with invasive breast carcinoma was a direct function of tumor size, independent of the method of detection. The results showed that the correlation between tumor size and survival was well fit by a simple equation, with which survival predictions could be made from information on tumor size. For example, a comparison of three large populations studied over the last 5 decades revealed a marked improvement (approximately 35% absolute) in the survival of patients with invasive breast carcinoma diagnosed on clinical grounds that could be ascribed to a reduction in tumor size. However, the capacity of screening mammography to find smaller tumors remains the best way reduce breast carcinoma deaths, with the potential for adding an additional approximately 20% absolute reduction in breast carcinoma deaths. The mathematic correlation between tumor size and survival is consistent with a biologic mechanism in which lethal distant metastasis occurs by discrete events of spread such that, for every invasive breast carcinoma cell in the primary tumor at the time of surgery, there is approximately a 1-in-1-billion chance that a lethal distant metastasis has formed. CONCLUSIONS: The correlation between tumor size and lethality is well captured by a simple equation that is consistent with breast carcinoma death as the result of discrete events of cellular spread occurring with small but definable probabilities.     

食管癌根治性放化疗长期生存影响因素分析

目的 分析放化疗食管癌疗效及其影响因素,为食管癌根治性放化疗提供最佳结合模式。方法 回顾分析2006—2012年收治的232例接受根治性放疗联合化疗的食管癌患者临床资料,放疗采用3DRT技术,化疗方案以铂类药物为基础。Kaplan-Meier法计算LC率和OS率,Logrank法检验和单因素预后分析,Cox模型多因素预后分析。结果 随访时间满1、3、5年者分别为232、84、35例。全组1、3、5年LC率分别为66.1%、42.2%、38.5%,中位LC时间为24.4个月;1、3、5年OS率分别为73.3%、37.2%、19.5%,中位生存期为21个月。单因素分析显示影响LC和OS因素为T分期、N分期、临床分期、照射范围、≥3周期化疗(P=0.112和P=0.000、P=0.031和P=0.000、P=0.009和P=0.000、P=0.074和P=0.030、P=0.218和P=0.001)。多因素分析N分期、临床分期、≥3周期化疗是影响OS的因素(P=0.006、0.000、0.001)。结论 食管癌放化疗能使临床分期偏早者的LC和长期OS明显改善,而照射范围及≥3周期化疗数有利于改善患者的长期生存。