scholarly journals Development and validation of a nomogram to predict synchronous lung metastases in patients with ovarian cancer: a large cohort study

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Yufei Yuan ◽  
Fanfan Guo ◽  
Ruoran Wang ◽  
Yidan Zhang ◽  
Guiqin Bai
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Lung Metastasis ◽  
Cox Regression ◽  
Lung Metastases ◽  
Newly Diagnosed ◽  
Discriminative Ability ◽  
Factors Affecting ◽  
Cox Regression Analysis

Abstract Purpose: Lung metastasis is an independent risk factor affecting the prognosis of ovarian cancer patients. We developed and validated a nomogram to predict the risk of synchronous lung metastases in newly diagnosed ovarian cancer patients. Methods: Data of ovarian cancer patients from the Surveillance, Epidemiology, and Final Results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built on the basis of logistic regression. The consistency index (C-index) was used to evaluate the discernment of the synchronous lung metastasis nomogram. Calibration plots were drawn to analyze the consistency between the observed probability and predicted probability of synchronous lung metastases. The Kaplan–Meier method was used to estimate overall survival rate, and influencing factors were included in multivariate Cox regression analysis (P<0.05) to determine the independent prognostic factors of synchronous lung metastases. Results: Overall, 16059 eligible patients were randomly divided into training (n=11242) and validation cohorts (n=4817). AJCC T, N stage, bone metastases, brain metastases, and liver metastases were evaluated as predictors of synchronous lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was calibrated and showed good discriminative ability. Mixed histological types, chemotherapy, and primary site surgery were factors affecting the overall survival of patients with synchronous lung metastases. Conclusion: The clinical prediction model has high accuracy and can be used to predict lung metastasis risk in newly diagnosed ovarian cancer patients, which can guide the treatment of patients with synchronous lung metastases.

scholarly journals Development and Validation of Nomograms to Predict Lung Metastases in Patients with Ovarian Cancer: A Large Cohort Study

2020 ◽  
Author(s):  
Yufei Yuan ◽  
Fanfan Guo ◽  
Ruoran Wang ◽  
Yidan Zhang ◽  
GuiQin Bai
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Survival Rate ◽  
Cancer Patients ◽  
Lung Metastasis ◽  
Cox Regression ◽  
Validation Cohort ◽  
Lung Metastases ◽  
Overall Survival Rate ◽  
Newly Diagnosed

Abstract Background Lung metastasis, an independent risk factor affecting the prognosis of patients with ovarian cancer, is associated with poor survival. We tried to develop and validate a nomogram to predict the risk of lung metastases in newly diagnosed patients with ovarian cancer.Methods Patients diagnosed with ovarian cancer from the surveillance, epidemiology and final results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built based on logistic regression. The consistency index (C-index) was used to evaluate the discernment of the lung metastasis nomogram. Calibration plots was drawn to analyze the consistency between the observed probability and predicted probability of lung metastases in patients with ovarian cancer. The Kaplan-Meier method was used to estimate the overall survival rate, and the influencing factors were included in the multivariate Cox regression (P<0.05) to analyze the independent prognostic factors of lung metastases.Results A total of 16,059 eligible patients were randomly divided into training (n = 11242) and validation cohort (n = 4817). AJCC T, N stage, bone metastases, brain metastases and liver metastases were evaluated as predictors of lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was well calibrated and showed good discriminative ability. The C index is 0.761 (0.736-0.787) for the training cohort and 0.757（0.718-0.795）for the validation cohort. The overall survival rate of ovarian cancer patients with lung metastases was reduced. Mixed histological types, chemotherapy and primary site surgery were factors that affect the overall survival of ovarian cancer patients with lung metastases.Conclusion: The clinical prediction model had high accuracy and can be used to predict the lung metastasis risk of newly diagnosed patients with ovarian cancer, which can guide the treatment of patients with lung metastases.

Propensity score matching analysis of ovarian cancer patients with multiple primary malignant neoplasms using multicenter real-world data.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e17501-e17501
Author(s):  
Qing-lei Gao ◽  
Xiaofei Jiao ◽  
Ruyuan Li ◽  
Shaoqing Zeng ◽  
Yingjun Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Cancer Patients ◽  
Cox Regression ◽  
Malignant Neoplasms ◽  
Cox Regression Analysis ◽  
Multiple Primary

e17501 Background: Multiple primary malignant neoplasms (MPMNs) in patients with ovarian cancer is rare and has not attracted enough attention. It is unclear how the MPMNs affect the prognosis of ovarian cancer (OC) patients. Methods: This is a multicenter retrospective analysis of 5, 268 ovarian cancer patients from six centers who was diagnosed with ovarian cancer from January 1, 1989 to August 21, 2020. Propensity score matching was used to balance the baseline characteristics between patients with and without MPMNs. Cox regression analysis was utilized to analyze the influence of MPMNs on overall survival (OS). Results: After excluding unqualified medical record, totally 4, 848 patients were analyzed and 240 were concurrent at least one MPMNs other than OC. Ten patients had two MPMNs and one patient had three. The most common concurrent cancer was breast cancer (111/240, 46.25%), followed by endometrial cancer (37/240, 15.42%), and cervical cancer (30/240, 12.50%). Patients with MPMNs were elder than those without MPMNs (52 vs. 51, P = 0.03) when ovarian cancer was diagnosed. And the proportion of early-stage cases was lower in patients with MPMNs (25.8% vs. 27.2%, P < 0.001). Patients with breast cancer had a higher proportion of high-grade serous ovarian cancer (HGSOC) than those without MPMNs. After using the propensity score matching method adjusting age, pathological type, grade, and stage, concurrent MPMNs, including breast cancer, had no effect on OS of ovarian cancer patients. Among 240 patients with MPMNs, patients with breast cancer shared similar age and stage compared with the rest patients, while their proportion of HGSOC was higher than patients with other cancer (68.4% vs. 51.1%, P = 0.028). However, the median OS of those two groups were similar (27.3 m vs.27.1 m, P = 0.744). In addition, 94 patients were diagnosed with breast cancer prior to ovarian cancer, seven diagnosed posteriorly to ovarian cancer, four diagnosed simultaneously, and six had no precise diagnosed dates. There was no remarkable difference in clinical characteristics between the prior and posterior groups, however, the median OS of those seven patients was significantly longer than the prior group (76.0 m vs. 25.4 m, P = 0.002). Conclusions: The MPMNs showed no influence on the overall survival of ovarian cancer patients. The order of diagnosis of ovarian cancer and breast cancer might affect the prognosis.

scholarly journals Alternative splicing acts as an independent prognosticator in ovarian carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yan Ouyang ◽  
Kaide Xia ◽  
Xue Yang ◽  
Shichao Zhang ◽  
Li Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Alternative Splicing ◽  
Ovarian Carcinoma ◽  
Cancer Patients ◽  
Cox Regression ◽  
Excision Repair ◽  
Splicing Factors ◽  
Prognostic Signature ◽  
Cox Regression Analysis

AbstractAlternative splicing (AS) events associated with oncogenic processes present anomalous perturbations in many cancers, including ovarian carcinoma. There are no reliable features to predict survival outcomes for ovarian cancer patients. In this study, comprehensive profiling of AS events was conducted by integrating AS data and clinical information of ovarian serous cystadenocarcinoma (OV). Survival-related AS events were identified by Univariate Cox regression analysis. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to construct the prognostic signatures within each AS type. Furthermore, we established a splicing-related network to reveal the potential regulatory mechanisms between splicing factors and candidate AS events. A total of 730 AS events were identified as survival-associated splicing events, and the final prognostic signature based on all seven types of AS events could serve as an independent prognostic indicator and had powerful efficiency in distinguishing patient outcomes. In addition, survival-related AS events might be involved in tumor-related pathways including base excision repair and pyrimidine metabolism pathways, and some splicing factors might be correlated with prognosis-related AS events, including SPEN, SF3B5, RNPC3, LUC7L3, SRSF11 and PRPF38B. Our study constructs an independent prognostic signature for predicting ovarian cancer patients’ survival outcome and contributes to elucidating the underlying mechanism of AS in tumor development.

scholarly journals Incidence, Survival, and Associated Factors Estimation in Osteosarcoma Patients with Lung Metastasis: A Single-center Experience of 11 Years in Tianjin, China

2021 ◽  
Author(s):  
Chao Zhang ◽  
Haixiao Wu ◽  
Guijun Xu ◽  
Wenjuan Ma ◽  
Lisha Qi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Lung Metastasis ◽  
Associated Factors ◽  
Cox Regression ◽  
Cancer Center ◽  
Single Center ◽  
Cox Regression Analysis

Abstract Background: Osteosarcoma is the most common primary malignant bone tumor. The current study was conducted to describe the general condition of patients with primary osteosarcoma in a single cancer center in Tianjin, China and to investigate the associated factors in osteosarcoma patients with lung metastasis. Methods: From February 2009 to October 2020, patients from Tianjin Medical University Cancer Institute and Hospital, China were retrospectively analyzed. The Kaplan–Meier method was used to evaluate the overall survival of osteosarcoma patients. Prognostic factors of patients with osteosarcoma were identified by the Cox proportional hazard regression analysis. Risk factor of lung metastasis in osteosarcoma were investigated by the logistic regression model. Results: A total of 203 patients were involved and 150 patients were successfully followed up for survival status. The 5-year survival rate of osteo-sarcoma patients was 70.0%. Surgery, bone and lung metastasis were the significant prognostic factors in multivariable Cox regression analysis. Twenty-one (10.3%) patients showed lung metastasis at the diagnosis of osteosarcoma and 67 (33%) lung metastases during the later course. T3 stage (OR=11.415, 95%CI 1.362-95.677, P=0.025) and synchronous bone metastasis (OR=6.437, 95%CI 1.69-24.51, P=0.006) were risk factors of synchronous lung metastasis occurrence. Good necrosis (≥90%, OR=0.097, 95%CI 0.028-0.332, P=0.000) and elevated Ki-67 (≥50%, OR=4.529, 95%CI 1.241-16.524, P=0.022) were proved to be significantly associated with metachronous lung metastasis occurrence. Conclusion: The overall survival, prognostic factors and risk factors for lung metastasis in this single center provided insight about osteosarcoma management.

scholarly journals Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

2021 ◽  
Vol 8 ◽  
Author(s):  
Tingshan He ◽  
Liwen Huang ◽  
Jing Li ◽  
Peng Wang ◽  
Zhiqiao Zhang
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Transcription Factors ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Immune Genes ◽  
Immune Biomarkers

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

Homologous recombination deficiency associated with response to poly (ADP-ribose) polymerase inhibitors in ovarian cancer patients: The first real-word data from China.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e17543-e17543
Author(s):  
Xiaoxiang Chen ◽  
Jing Ni ◽  
Xia Xu ◽  
Wenwen Guo ◽  
Xianzhong Cheng ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Homologous Recombination ◽  
Cancer Patients ◽  
Real World ◽  
Cox Regression ◽  
Wild Type ◽  
Cox Regression Analysis ◽  
Homologous Recombination Deficiency ◽  
Polymerase Inhibitors

e17543 Background: Homologous recombination deficiency (HRD) is the first phenotypically defined predictive biomarker for Poly (ADP-ribose) polymerase inhibitors (PARPi) in ovarian cancer. However, the proportion of HRD positive in real world and the relationship of HRD status with PARPi in Chinese ovarian cancer patients remains unknown. Methods: A total of sixty-four ovarian cancer patients underwent PARPi, both Olaparib and Niraparib, were enrolled from August 2018 to January 2021 in Jiangsu Institute of Cancer Hospital. HRD score which was the sum of loss of heterozygosity (LOH), telomeric allelic imbalance (TAI) and large-scale state transitions (LST) events were calculated using tumor DNA-based next generation sequencing (NGS) assays. HRD-positive was defined by either BRCA1/2 pathogenic or likely pathogenic mutation or HRD score ≥42. Progression-free survival (PFS) was analyzed with a log-rank test using HRD status and summarized using Kaplan-Meier methodology. Univariate and multiple cox-regression analysis were conducted to investigate all possible clinical factors. Results: 71.9% (46/64) patients were HRD positive and the rest 28.1% (18/64) were HRD negative, which was higher than the HRD positive proportion reported in Western countries. The PFS among HRD positive patients was significantly longer than those HRD negative patients (medium PFS 8.9 m vs 3.6 m, hazard ratio [HR]: 0.22, p < 0.001). Among them, 23 patients who were BRCA wild type but HRD positive had longer PFS than those with BRCA wild type and HRD negative (medium PFS 9.2 m vs 3.6 m, HR: 0.20, p < 0.001). Univariate cox-regression analysis found that HRD status, previous treatment lines, secondary cytoreductive surgery (SCS) were significantly associated with PFS after PARPi treatment. After multiple regression correction, HRD status (HR: 0.39, 95% CI: [0.20-0.76], p = 0.006), ECOG score (HR: 2.53, 95% CI: [1.24-5.17], p = 0.011) and SCS (HR: 2.21, 95% CI: [1.09-4.48], p = 0.028) were the independent factors. Subgroup analysis in ECOG = 0 subgroup (N = 36), HRD positive patients had significant longer PFS than HRD negative patients (medium PFS 10.3 m vs 5.8 m, HR: 0.14, p < 0.001). Also in the subgroup of patients without SCS, PFS in patients with HRD was longer than patients without HRD (medium PFS 10.2 m vs 5.7 m, HR: 0.29, p = 0.003). Conclusions: This is the first real-world data of HRD status in ovarian cancer patients from China and demonstrate that HRD is a valid biomarker for PARP inhibitors in Chinese ovarian cancer patients.

scholarly journals Clinical factors affecting prognosis of limb osteosarcoma in China: a multicenter retrospective analysis

2020 ◽  
Vol 48 (8) ◽  
pp. 030006052093085
Author(s):  
Jia Han ◽  
Yiyang Yu ◽  
Sujia Wu ◽  
Zhen Wang ◽  
Weibin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Survival Rate ◽  
Cox Regression ◽  
Survival Rates ◽  
Treatment Method ◽  
Overall Survival Rate ◽  
Clinical Factors ◽  
Standard Chemotherapy ◽  
Factors Affecting ◽  
Cox Regression Analysis

Objective This study was performed to explore the relationship between various clinical factors and the prognosis of limb osteosarcoma. Methods We retrospectively analyzed the clinical data of 336 patients with limb osteosarcoma treated from June 2000 to August 2016 at 7 Chinese cancer centers. Data on the patients’ clinical condition, treatment method, complications, recurrences, metastasis, and prognosis were collected and analyzed. Kaplan–Meier analysis and Cox regression models were used to analyze the data. Results The patients comprised 204 males and 132 females ranging in age from 6 to 74 years (average, 21.1 years). The overall 3- and 5-year survival rates were 65.0% and 55.0%, respectively. The 5-year overall survival rate was 64.0% with standard chemotherapy and 45.6% with non-standard chemotherapy. Cox regression analysis demonstrated that standard chemotherapy, surgery, recurrence, and metastasis were independent factors associated with the prognosis of limb osteosarcoma. Conclusion The survival of patients with limb osteosarcoma can be significantly improved by combining standard chemotherapy and surgery. The overall survival rate can also be improved by adding methotrexate to doxorubicin–cisplatin–ifosfamide triple chemotherapy.

A randomized, phase III study (AGO-OVAR-9, GINECO-TCG, NSGO-OC-0102): Gemcitabine-paclitaxel-carboplatin (TCG) versus paclitaxel-carboplatin (TC) as first-line treatment of ovarian cancer (OC): Survival of FIGO stage I-IIA patients

2009 ◽  
Vol 27 (18_suppl) ◽  
pp. LBA5510-LBA5510 ◽  
Author(s):  
J. Herrstedt ◽  
J. Huober ◽  
F. Priou ◽  
H. Müller ◽  
M. Baekelandt ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Residual Tumor ◽  
Figo Stage ◽  
Stage I ◽  
Phase Iii ◽  
Cox Regression Analysis ◽  
Phase Iii Study ◽  
First Diagnosis

LBA5510 Background: One option to increase the efficacy of TC in pts with first diagnosis of ovarian cancer is to add a not cross-resistant drug. Methods: We conducted a randomized, prospective, stratified, phase III study comparing therapy with TC to TC plus gemcitabine. From 7/02 to 4/04, pts with a histological verified first diagnosis of epithelial OC, FIGO IC-IV were randomized to either TC (paclitaxel [T] 175 mg/m2 3h iv d1 + carboplatin [C] AUC 5 iv d1) or TCG (TC + gemcitabine [G] 800 mg/m2 iv d1+8) for at least 6 cycles every 21 days starting within 6 weeks post-operatively. The randomization was balanced within three strata: 1) FIGO I-IIA, 2) FIGO IIB-IIIC with residual tumor ≤ 10mm, 3) FIGO IIB-IIIC with residual tumor > 10 mm or FIGO IV. Primary endpoint is overall survival. Results: We enrolled 1,742 pts and administered 5,268 cycles TC and 5,129 cycles TCG. All baseline characteristics of the patients in both arms were well balanced. Most pts received 6+ cycles (87.2% TC, 86.2% TCG). Previous interim analyses has shown that TCG was tolerable but induced more hematological toxicity and final analysis has shown that addition of gemcitabine did not improve overall survival in patients with FIGO stage IIB-IV disease. Approximately 11% of the patients (n = 175) had FIGO stage I-IIA disease (stratum I). Most patients received 6+ cycles (93.3% TC, 86.9% TCG). With a median follow-up of 53.8 (range 0 –75) months, and using the log rank test and Cox regression analysis, no relevant differences in progression free survival (first quartile about 57 months and median ≥ 75 months in both groups, HR = 0.90 [95% CI: 0.47–1.72], p = 0.7500) and a negative trend in overall survival (first quartile ≥ 75 months in both groups, HR = 2.19 [95% CI: 0.75–6.41], p = 0.1419) were seen. Conclusions: Addition of G to TC did not improve efficacy in patients with stage I-IIA ovarian cancer. This was also the case for stratum II-III patients (previously reported). The addition of G to TC in patients with first diagnosis of ovarian cancer cannot be recommended. [Table: see text]

Modified IPCW model: A method for adjusting for bias in the estimation of overall survival due to the use of second and third line therapies in locally advanced or metastatic pancreatic cancer patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15787-e15787
Author(s):  
N. E. Iznaga Escobar ◽  
Patricia Lorenzo Luaces ◽  
Lizet Sanchez Valdes ◽  
Carmen Valenzuela Silva ◽  
Tania Crombet Ramos ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Cox Regression ◽  
Locally Advanced ◽  
Secondary Analysis ◽  
Metastatic Pancreatic Cancer ◽  
Fixed Dose ◽  
Line Treatment ◽  
Newly Diagnosed

e15787 Background: Nimotuzumab, a unique and affinity differentiated anti-EGFR antibody had been used in combination with gemcitabine on the treatment of pancreatic cancer patients. The aim of the study was to evaluate overall survival. Methods: Patients with newly diagnosed, locally advanced or metastatic pancreatic cancer, KPS ≥ 70 %, 18-72 years old, with adequate renal and liver function were included. Pts received gemcitabine 1000 mg/m2and nimotuzumab or placebo fixed dose of 400 mg once a wk, for 3 wks, followed by a 1-wk rest (d1, 8, 15, q28) until disease progression or unacceptable toxicity. The primary endpoint was OS and secondary PFS, ORR, CBR, safety and QoL. For OS determination, a KM log-rank test was used and a modified IPCW with a cox regression as a secondary analysis. On this evaluation using a modified IPCW model, 41.7% of pts from treatment arm and 42.7% from control arm who received 2nd and 3rd line treatment were censored after progression, while pts that did not receive 2nd and 3rd line treatment were weighted to compensate for the bias created by censoring switchers to 2nd and 3rd line treatment. Results: 192 pancreatic cancer pts were recruited. Ninety-six pts (62 male and 34 female) with a median age of 67 years, range (31, 83) were randomized to treatment arm and 96 pts (57 male and 39 female) with a median age of 64 years, range (41, 82) were randomized to control arm. In the primary analysis, median OS [95% CI] in the treatment arm was 8.57 mo [5.93, 10.90] vs 6.03 mo [4.97, 7.60] in the control arm. The HR [95% CI], 0.83 [0.62, 1.12] and p = 0.23 and when a modified IPCW model as a secondary analysis was used to remove the effect of 2nd and 3rd line therapies, the median OS was statistically significant with a HR [95% CI], 0.81 [0.67, 0.98] and a p = 0.030. The median PFS [95% CI] was 4.43 mo [3.67, 6.00] in the treatment arm vs 3.47 mo [2.60, 4.03] in the control arm with a HR [95% CI] 0.68 [0.51, 0.92] and p = 0.012. Conclusions: A modified IPCW model had proven that addition of nimotuzumab to gemcitabine increases median overall survival of newly diagnosed chemotherapy-naïve locally advanced or metastatic pancreatic cancer patients. Clinical trial information: NCT00561990.

scholarly journals The Relationship Between Retroperitoneal Lymphadenectomy and Survival in Advanced Ovarian Cancer Patients

2020 ◽  
Author(s):  
Chenyan Fang ◽  
Yingli Zhang ◽  
Lingqin Zhao ◽  
Xi Chen ◽  
Liang Xia ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Postoperative Complications ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Cox Regression ◽  
Advanced Ovarian Cancer ◽  
Residual Tumor ◽  
Retroperitoneal Lymphadenectomy ◽  
Cox Regression Analysis ◽  
Significant Difference

Abstract Background Systematic retroperitoneal lymphadenectomy has been widely used in the surgical treatment of advanced ovarian cancer patients. Nevertheless, the corresponding therapeutic may not provide a survival benefit. The aim of this study was to assess the effect of systematic retroperitoneal lymphadenectomy in such patients. Methods Patients with advanced ovarian cancer (stage III-IV, according to the classification presented by the International Federation of Gynecology and Obstetrics) who were admitted and treated in Zhejiang Cancer Hospital from January 2004 to December 2013 were enrolled and reviewed retrospectively. All patients were optimally or suboptimally debulked (absent or residual tumor <1 cm) and divided into two groups. Group A (no-lymphadenectomy group, n =170): patients did not undergo lymph node resection; lymph nodes resection or biopsy were selective. Group B (n=240): patients underwent systematic retroperitoneal lymphadenectomy. Results A total of 410 eligible patients were enrolled in the study. The patients’ median age was 51 years old (range, 28–72 years old). The 5-year overall survival (OS) and 2-year progression-free survival (PFS) rates were 78% and 24% in the no-lymphadenectomy group and 76% and 26% in the lymphadenectomy group (P=0.385 and 0.214, respectively). Subsequently, there was no significant difference in 5-year OS and 2-year PFS between the two groups stratified to histological types (serous type or non-serous type), the clinical evaluation of negative lymph nodes or with macroscopic peritoneal metastasis beyond pelvic (IIIB-IV). Multivariate Cox regression analysis indicated that systematic retroperitoneal lymphadenectomy was not a significant factor influencing the patients’ survival. Patients in the lymphadenectomy group had a higher incidence of postoperative complications (incidence of infection treated with antibiotics was 21.7% vs. 12.9% [P=0.027]; incidence of lymph cysts was 20.8% vs. 2.4% [P < 0.001]). Conclusions Our study showed that systematic retroperitoneal lymphadenectomy did not significantly improve survival of advanced ovarian cancer patients with residual tumor <1 cm or absent after cytoreductive surgery, and were associated with a higher incidence of postoperative complications.

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