scholarly journals Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Li-Yeh Chuang ◽  
Guang-Yu Chen ◽  
Sin-Hua Moi ◽  
Fu Ou-Yang ◽  
Ming-Feng Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Predictive Accuracy ◽  
Cancer Prognosis ◽  
University Hospital ◽  
Health Concern ◽  
Survival Models ◽  
Survival Prediction ◽  
Prediction Ability ◽  
Minimum Number

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.

scholarly journals Incidence trends and survival prediction of urothelial cancer of the bladder: a population-based study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hairong He ◽  
Tianjie Liu ◽  
Didi Han ◽  
Chengzhuo Li ◽  
Fengshuo Xu ◽  
...  
Keyword(s):  
Urothelial Cancer ◽  
Predictive Accuracy ◽  
Population Based ◽  
Calibration Plot ◽  
Survival Prediction ◽  
Operating Characteristics ◽  
Prediction Ability ◽  
Term Survival ◽  
Incidence Trends ◽  
Cancer Of The Bladder

Abstract Background The aim of this study is to determine the incidence trends of urothelial cancer of the bladder (UCB) and to develop a nomogram for predicting the cancer-specific survival (CSS) of postsurgery UCB at a population-based level based on the SEER database. Methods The age-adjusted incidence of UCB diagnosed from 1975 to 2016 was extracted, and its annual percentage change was calculated and joinpoint regression analysis was performed. A nomogram was constructed for predicting the CSS in individual cases based on independent predictors. The predictive performance of the nomogram was evaluated using the consistency index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), a calibration plot and the receiver operating characteristics (ROC) curve. Results The incidence of UCB showed a trend of first increasing and then decreasing from 1975 to 2016. However, the overall incidence increased over that time period. The age at diagnosis, ethnic group, insurance status, marital status, differentiated grade, AJCC stage, regional lymph nodes removed status, chemotherapy status, and tumor size were independent prognostic factors for postsurgery UCB. The nomogram constructed based on these independent factors performed well, with a C-index of 0.823 and a close fit to the calibration curve. Its prediction ability for CSS of postsurgery UCB is better than that of the existing AJCC system, with NRI and IDI values greater than 0 and ROC curves exhibiting good performance for 3, 5, and 8 years of follow-up. Conclusions The nomogram constructed in this study might be suitable for clinical use in improving the clinical predictive accuracy of the long-term survival for postsurgery UCB.

Development of prognostic nomograms using institutional data for patients with triple-negative breast cancer

2021 ◽  
Author(s):  
Jie-Yu Zhou ◽  
Kang-Kang Lu ◽  
Wei-Da Fu ◽  
Hao Shi ◽  
Jun-Wei Gu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Disease Free Survival ◽  
Discriminative Ability ◽  
Free Survival ◽  
Disease Free

Background: Triple-negative breast cancer (TNBC) is an aggressive disease. Nomograms can predict prognosis of patients with TNBC. Methods: A total of 745 eligible TNBC patients were recruited and randomly divided into training and validation groups. Endpoints were disease-free survival and overall survival. Concordance index, area under the curve and calibration curves were used to analyze the predictive accuracy and discriminative ability of nomograms. Results: Based on the training cohort, neutrophil-to-lymphocyte ratio, positive lymph nodes, tumor size and tumor-infiltrating lymphocytes were used to construct a nomogram for disease-free survival. In addition, age was added to the overall survival nomogram. Conclusion: The current study developed and validated well-calibrated nomograms for predicting disease-free survival and overall survival in patients with TNBC.

Overall survival according to tumoral clusterin expression in breast cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e11579-e11579
Author(s):  
Guillaume Mouillet ◽  
Loic Chaigneau ◽  
Thierry Michy ◽  
Cristian Villanueva ◽  
Fernando Bazan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Cancer Progression ◽  
Univariate Analysis ◽  
University Hospital ◽  
Breast Cancer Cell Lines ◽  
Rank Test ◽  
Breast Tumorigenesis ◽  
Clusterin Expression

e11579 Background: Clusterin (CLU) is a glycoprotein expressed constitutively in many tissues and involved in various physiopathological processes. Despite CLU expression is dysregulated in many types of cancer, the specific role of CLU in tumorigenesis remains unclear. The identification of several forms of the protein, with multiple roles is an explanation for these conflicting results. Cytoplasmic CLU (cCLU) has a role in breast tumorigenesis, cancer progression and is associated with breast cancer cell lines death in vitro. However contradictory data are reported about prognostic value of cCLU on survival and clinical progression. Our objective was to estimate patient’s overall survival (OS) according to the expression of cCLU. Methods: Histological and clinical data of 158 patients diagnosed with breast cancer were retrospectively recorded. Every patients were treated in a single French university hospital between 1993 and 2001. Histological samples had been reviewed to determine hormonal status, HER2 and clusterin expression. Immunohistochemical techniques were based on standards and recommendations applied at the time of analysis. Tumors were defined as cCLU positive (cCLU +) if its expression was superior to 10%. Overall Survival rates along with standard deviations were estimated using the Kaplan-Meier method. Differences in OS according to cCLU expression were tested for significance using the log-rank test. Results: Patients had a median age of 56 years (31 – 82 years). Among the 158 patients analyzed, cCLU was overexpressed in 31 patients (19.62%). The histopathologic and clinical characteristics were not statistically different according to clusterin expression even if a trend favouring less favourable tumoural characteristics were observed in cCLU positive tumour. The median follow-up was 14.1 years (11.3 - 19.3). In univariate analysis, cCLU overexpession were not related to OS (HR = 0.86; CI95%: 0.43 - 1.70). Ten-year OS was 76% (± 4) among patients with cCLU - tumors vs 77% (± 7) in patients with cCLU + tumor (p = 0.66). Conclusions: cCLU expression does not seem to be a pronostic factor of overall survival.

scholarly journals Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3616
Author(s):  
Viet-Huan Le ◽  
Quang-Hien Kha ◽  
Truong Nguyen Khanh Hung ◽  
Nguyen Quoc Khanh Le
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Low Risk ◽  
Survival Prediction ◽  
Clinical Parameters ◽  
Training Set ◽  
Prediction Ability ◽  
Nsclc Patients ◽  
Overall Survival Prediction

This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.

scholarly journals Outcomes of Women with Non-Metastatic Triple-Negative Breast Cancer in Oman: A single-centre experience

2019 ◽  
Vol 19 (3) ◽  
pp. 209
Author(s):  
Shiyam Kumar ◽  
Muhammad Furrukh ◽  
Khalid Al-Baimani ◽  
Adil Al-Ajmi ◽  
Ikram A. Burney ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Neoadjuvant Chemotherapy ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Tumour Size ◽  
Patient Outcome Assessment ◽  
University Hospital ◽  
Stage Iii ◽  
Sultan Qaboos University

Objectives: Triple-negative breast cancer (TNBC) is one of the most aggressive and heterogeneous variants of breast cancer. However, little is known regarding the prevalence and outcome of this entity in the Middle East. This study aimed to evaluate the outcomes of TNBC patients at a university hospital in Oman. Methods: This retrospective study took place at the Sultan Qaboos University Hospital, Muscat, Oman, in May 2017. All patients diagnosed with non-metastatic TNBC between December 2000 and December 2015 were included. The patients’ electronic medical records were reviewed to identify their clinical and pathological characteristics as well as survival outcomes. Results: A total of 79 patients were diagnosed with non-metastatic TNBC during the study period. The median age was 46 years, with approximately one-third of patients (31.6%) under 40 years of age. Almost half had an advanced tumour size (49.4%) or node-positive disease (48.1%) at presentation and only 16.6% demonstrated a complete pathological response (pCR) to neoadjuvant chemotherapy. The median survival for all patients was not reached within the study period; however, the median overall survival for stage III patients was 44.6 months. The five-year overall survival for all patients was 64%, increasing to 100% and 72% for patients with stage I and II, respectively, and dropping to 47% for those with stage III disease. Conclusion: The findings of this study indicate that the majority of women with TNBC in Oman present at an advanced stage; moreover, such women have low rates of pCR to neoadjuvant chemotherapy and poor five-year survival.Keywords: Breast Cancer; Triple-Negative Breast Cancer; Neoadjuvant Therapy; Survival; Patient Outcome Assessment; Oman.

scholarly journals Tomodensitometric aspects of Metastatic Breast Cancer seen in medical imaging in University Hospital Center Luxembourg

2020 ◽  
pp. 15-20
Author(s):  
Camara MA ◽  
Coulibaly S ◽  
Mariko M ◽  
Traore MM ◽  
Ndiaye M ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Medical Imaging ◽  
Ct Scan ◽  
Teaching Hospital ◽  
Cancer Metastasis ◽  
Metastatic Breast ◽  
University Hospital ◽  
Health Concern ◽  
Important Place ◽  
Secondary Lesion

Introduction: Breast cancer is the first cancer in woman worldwide. Its prognosis depends on early diagnosis and treatment. CT-scan has an important place in the diagnostic screening and the surveillance of this disease. The goal of this study was to assess the place of CT-scan in extension screening of breast cancer in the Department of Medical Imaging of the Teaching Hospital Mother-Child “Le Luxembourg”. Methods: It is a descriptive study on the retrospective compilation of CT-scan data of patients in the Department of Medical Imaging of the Teaching Hospital Mother-Child “Le Luxembourg” from May 1st to November 30th 2017. Were enrolled all patients, regardless of gender or age, with breast cancer histologically confirmed and who developed at least one secondary lesion found by CT-scan. CT-scan was performed before and after treatment. CT-scan machines were HITACHI® SUPRIA 16 bars and TOSHIBA® 04 bars, without and with Iodine 350mg intravenous injection. Results: Over seven months, 44 patients were enrolled with a mean age of 49 years and females were predominant. A family history of breast cancer was found in 13% of cases and invasive ductal carcinoma represented 95.54%. The main metastases were multi visceral (31.82%), pleural pulmonary (70.75%), ganglionic (63.63%), hepatic (27.27%) and bone (18.18%). Conclusion: Breast cancer is a public health concern with a clear predominance of women. In our context, CT-scan still has an important place in the research of secondary lesion in addition to the surveillance of this disease. Keywords: CT-scan Breast cancer; Metastasis; UHC Luxembourg

scholarly journals Radiogenomics-based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance

2018 ◽  
Author(s):  
Xiaohua Qian ◽  
Hua Tan ◽  
Wei Chen ◽  
Weiling Zhao ◽  
Michael D. Chan ◽  
...  
Keyword(s):  
Overall Survival ◽  
Tumor Progression ◽  
Survival Rates ◽  
Progression Free Survival ◽  
Risk Groups ◽  
Resection Margins ◽  
Survival Prediction ◽  
High Survival ◽  
Imaging Features ◽  
Prediction Ability

AbstractGBM is the most common and aggressive primary brain tumor. Although the TMZ-based radiochemotherapy improves overall GBM patients’ survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudoprogression is a treatment-related reaction with an increase in contrast-enhancing lesion size at the tumor site or resection margins which mimics tumor recurrence on MRI. Accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or TTP from the Wake dataset. Based on these radiographic features, we then conducted radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were then used as features to construct a 2YS logistic regression model. GBM patients were classified into low-and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent TCGA dataset and found that 2YS scores were significantly associated with the patients’ overall survival. We further used two cohorts of the TCGA data to train and test our model. Our results show that 2YS scores-based classification results from the training and testing TGCA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS, normal cell ratio) and found that these factors were not related or weakly correlated with patients’ survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting clinical outcomes of GBM patients after standard therapies.

Statistical partitioning of Ki-67 distribution can refine Molecular Classifica-tion of invasive Breast Carcinoma (Preprint)

2021 ◽  
Author(s):  
Akhouayri Laila ◽  
Meriem Regragui ◽  
samira benayad ◽  
Nisrine Bennani Guebessi ◽  
Farida Marnissi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Patient Survival ◽  
Quantitative Description ◽  
Molecular Classification ◽  
Cancer Prognosis ◽  
University Hospital ◽  
Molecular Subgroups ◽  
Molecular Subgroup ◽  
Ki 67

BACKGROUND Breast carcinoma is one of the most common histological types of Breast Cancer, exploring a new approach that allows to do a quantitative description in order to characterize its heterogeneity and refine its classification is one of the main interests for pathologists. OBJECTIVE The purpose of our study is to explore further statistically significant subdivisions beyond breast cancer molecular classification that is routinely established in pathology departments. METHODS We conducted a 5-year retrospective study on 1266 invasive breast carcinomas of moroccan pa-tients, collected at the Pathology Department of Ibn-Rochd University Hospital in Casablanca, and followed at King MohammedVI National Centre for the Treatment of Cancers. We elaborated an Estimation-Maximization clustering, based on the main Breast cancer prognosis biomarkers: Ki-67, HER2, oestrogen and progesterone receptors, evaluated by Immunohistochemistry. RESULTS Each molecular subgroup could be partitioned into two further subdivisions: Cluster1, with average Ki-67 of 16.26%(±11.9) across all molecular subgroups and higher frequency within luminal sub-groups, and Cluster2, with average Ki-67 of 68.8%(±18) across all molecular subgroups; and higher frequency in HER2 as well as in triple negative subgroups. Overall Survival of the two clusters was significantly different, with 5-year rates of 52 and 37 months for Cluster1 and Cluster2, respectively (p=0.000001). Moreover, patient survival within the same molecular subgroup varied remarkably depending on cluster membership. Three independent datasets (Algerian, TCGA-BRCA and METABRIC) were also analysed to assess the reproducibility of this new “2-clusters partition” through several clustering methods and validation measures. Two different al-gorithms to evaluate the prognostic importance, VSURF and MinimalDepth, confirmed that this new subdivision is able to predict patient survival better than several histoprognostic features. CONCLUSIONS Our results highlight a new refinement of the breast cancer molecular classification and provide a simple and improved way to classify tumors that could be applied in low to medium income countries. This is the first study of its kind addressed in an African context.

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