Abstract 1643: Leveraging tumor size and time to death from bevacizumab (BEV) historical data to predict overall survival in ovarian cancer patients treated with vanucizumab (VAN)

2018 ◽  
Author(s):  
Alexandre Sostelly ◽  
Kevin Smart ◽  
Felix Jaminion ◽  
Christophe Boetsch ◽  
Francois Mercier
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Tumor Size ◽  
Historical Data ◽  
Time To Death

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.

Determination of Both TP53 Mutation Status and the Amount of p53 Protein Has Limited Diagnostic Importance for Patients with Ovarian Cancer

2022 ◽  
Vol 29 ◽  
Author(s):  
Sebastian M. Klein ◽  
Maria Bozko ◽  
Astrid Toennießen ◽  
Nisar P. Malek ◽  
Przemyslaw Bozko
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Tp53 Mutation ◽  
P53 Protein ◽  
Mutation Status ◽  
Tumor Tissues ◽  
Tp53 Status ◽  
Diagnostic Importance

Background: Ovarian cancer is one of the most aggressive types of gynecologic cancers. Many patients have a relapse within two years after diagnosis and subsequent therapy. Among different genetic changes generally believed to be important for the development of cancer, TP53 is the most common mutation in the case of ovarian tumors. Objective: Our work aims to compare the outcomes of different comparisons based on the overall survival of ovarian cancer patients, determination of TP53 status, and amount of p53 protein in tumor tissues. Methods: We analyzed and compared a collective of 436 ovarian patient’s data. Extracted data include TP53 mutation status, p53 protein level, and information on the overall survival. Values for p53 protein level in dependence of TP53 mutation status were compared using the Independent-Samples t-Test. Survival analyses were displayed by Kaplan-Meier plots, using the log-rank test to check for statistical significance. Results: We have not found any statistically significant correlations between determination of TP53 status, amount of p53 protein in tumor tissues, and overall survival of ovarian cancer patients. Conclusion: In ovarian tumors both determination of TP53 status as well as p53 protein amount has only limited diagnostic importance.

Correlation between outcomes and tumor size in >7 cm T4 non-small cell lung cancer patients: A tumor size-based comparison study

2021 ◽  
Vol 29 (8) ◽  
pp. 784-791
Author(s):  
Volkan Erdoğu ◽  
Necati Çitak ◽  
Celal B Sezen ◽  
Levent Cansever ◽  
Cemal Aker ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Tumor Size ◽  
Disease Free Survival ◽  
Small Cell ◽  
Small Cell Lung ◽  
Free Survival ◽  
Lung Cancer Patients ◽  
Disease Free

Background We investigated whether all size-based pathological T4N0–N1 non-small cell lung cancer patients with tumors at any size >7 cm had the same outcomes. Methods We reviewed non-small cell lung cancer patients with tumors >7 cm who underwent anatomical lung resection between 2010 and 2016. A total of 251 size-based T4N0–N1 patients were divided into two groups based on tumor size. Group S ( n = 192) included patients with tumors of 7.1–9.9 cm and Group L ( n = 59) as tumor size ≥10 cm. Results The mean tumor size was 8.83 ± 1.7 cm (Group S: 8.06 ± 0.6 cm, Group L: 11.3 ± 1.6 cm). There were 146 patients with pathological N0 and 105 patients with pathological N1 disease. Mean overall survival and disease-free survival were 64.2 and 51.4 months, respectively. The five-year overall survival and disease-free survival rates were 51.2% and 43.5% (five-year OS; pT4N0:52.7%, pT4N1:47.9%, DFS; pT4N0:44.3%, pT4N1: 42.3%). No significant differences were observed between T4N0 and T4N1 patients in terms of five-year OS or DFS ( p = 0.325, p = 0.505 respectively). The five-year overall survival and disease-free survival rates were 52% and 44.6% in Group S, and 48.5% and 38.9% in Group L. No significant difference was observed between the groups in terms of five-year overall survival or disease-free survival ( p = 0.699, p = 0.608, respectively). Conclusions Above 7 cm, any further increase in tumor size in non-small cell lung cancer patients had no significant effect on survival, confirming it is not necessary to further discriminate among patients with tumors in that size class.

scholarly journals A descriptive report of outcomes of primary mucinous ovarian cancer patients receiving either an adjuvant gynecologic or gastrointestinal chemotherapy regimen

2019 ◽  
Vol 29 (5) ◽  
pp. 904-909
Author(s):  
Brooke A Schlappe ◽  
Qin C Zhou ◽  
Roisin O'Cearbhaill ◽  
Alexia Iasonos ◽  
Robert A Soslow ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Progression Free Survival ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Free Survival ◽  
Exact Test ◽  
Clinical Criteria ◽  
Kaplan Meier

ObjectiveWe described progression-free survival and overall survival in patients with primary mucinous ovarian cancer receiving adjuvant gynecologic versus gastrointestinal chemotherapy regimens.MethodsWe identified all primary mucinous ovarian cancer patients receiving adjuvant gynecologic or gastrointestinal chemotherapy regimens at a single institution from 1994 to 2016. Gynecologic pathologists using strict pathologic/clinical criteria determined diagnosis. Adjuvant therapy was coded as gynecologic or gastrointestinal based on standard agents and schedules. Clinical/pathologic/treatment characteristics were recorded. Wilcoxon rank-sum test was used for continuous variables, and Fisher’s exact test for categorical variables. Progression-free and overall survival were calculated using the Kaplan-Meier method, applying landmark analysis.ResultsOf 62 patients identified, 21 received adjuvant chemotherapy: 12 gynecologic, 9 gastrointestinal. Median age (in years) at diagnosis: 58 (range 25–68) gynecologic cohort, 38 (range 32–68) gastrointestinal cohort (p=0.13). Median body mass index at first post-operative visit: 25 kg/m2(range 18–31) gynecologic cohort, 23 kg/m2(range 18–31) gastrointestinal cohort (p=0.23). History of smoking: 6/12 (50%) gynecologic cohort, 3/9 (33%) gastrointestinal cohort (p=0.66). Stage distribution in gynecologic and gastrointestinal cohorts, respectively: stage I: 9/12 (75%) and 3/9 (33%); stage II: 2/12 (17%) and 1/9 (11%); stage III: 1/12 (8%) and 5/9 (56%) (p=0.06). Grade distribution in gynecologic and gastrointestinal cohorts, respectively: grade 1: 8/12 (67%) and 1/9 (13%); grade 2/3: 4/12 (33%) and 7/9 (88%) (p=0.03). Three-year progression-free survival: 90.9% (95% CI 50.8% to 98.7 %) gynecologic, 53.3% (95% CI 17.7% to 79.6%) gastrointestinal. Three-year overall survival: 90.9% (95% CI 50.8% to 98.7%) gynecologic, 76.2% (95% CI 33.2% to 93.5%) gastrointestinal.ConclusionOngoing international collaborative research may further define associations between chemotherapy regimens and survival.

Overall survival with preoperative biliary drainage in patients with resectable pancreatic cancer.

2014 ◽  
Vol 32 (3_suppl) ◽  
pp. 314-314
Author(s):  
Tobin Joel Crill Strom ◽  
Sarah E. Hoffe ◽  
Shivakumar Vignesh ◽  
Jason Klapman ◽  
Cynthia L. Harris ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Tumor Size ◽  
Biliary Drainage ◽  
Cox Regression ◽  
Neoadjuvant Treatment ◽  
Preoperative Biliary Drainage ◽  
Resectable Pancreatic Cancer ◽  
Pathologic Features

314 Background: Resectable pancreatic cancer patients often present with obstructive jaundice necessitating the placement of biliary stents or percutaneouse drainage catheters. We sought to evaluate whether preoperative biliary drainage affects recurrence and survival. Methods: An IRB-approved study was conducted on our institutional tumor registry to identify pancreatic cancer patients who were treated with upfront surgery between 2000 and 2012. Patients were then stratified by preoperative use of endoscopically placed stents (ERCP), percutaneous catheters (PTC), or no biliary drainage (NBD). The primary endpoint was overall survival (OS). Survival curves were calculated using the Kaplan-Meier method and the log-rank test. Multivariate analysis (MVA) was performed with a Cox regression model. Results: We identified 202 patients for the study (21 PTC; 89 ERCP; 92 NBD). Key differences between the 3 groups were mean pathologic tumor size (p=0.005), pathologic T3/4 (p =0.01), and pathologic N1 (p=0.007) status, with more aggressive pathologic features in PTC patients. PTC patients had a non-significant increase in rate of hepatic recurrences compared with ERCP and NBD patients (47.4% vs. 26.6% vs. 28.7%, respectively; p=0.20). PTC patients also had worse median and 3 year survival (21 months and 16%) compared to ERCP (23.3 months and 39%) and NBD patients (29 months and 45%, p=0.02). MVA revealed that PTC was an independent predictor of worse overall survival (HR 2.3[95% CI 1.3-4.0], p=0.005), along with pathologic tumor size (HR 1.1[1.0-1.3], p=0.008), nodes positive (HR 1.1[1.1-1.2], p=0.001), and post-operative CA19-9 >90 (HR 2.6[1.5-4.4], p=0.001). Conclusions: Patients with resectable pancreatic cancer who require a pre-operative PTC drain had a non-significant increase in hepatic recurrence rate and worse overall survival than patients who either had an ERCP stent placed or no biliary decompression prior to surgery. Given their worse prognosis, patients who require PTC placement might also benefit from neoadjuvant treatment with restaging prior to surgery.

Modeling tumor size time course in platinum resistant/refractory ovarian cancer patients treated with vanucizumab.

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. e17042-e17042
Author(s):  
Christophe Boetsch ◽  
Kevin Smart ◽  
Benjamin Ribba ◽  
Francois Mercier ◽  
Oliver Krieter ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Tumor Size ◽  
Time Course ◽  
Resistant Refractory ◽  
Platinum Resistant
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