scholarly journals Assessing the effect of data integration on predictive ability of cancer survival models

2019 ◽  
Vol 26 (1) ◽  
pp. 8-20 ◽  
Author(s):  
Yi Guo ◽  
Jiang Bian ◽  
Francois Modave ◽  
Qian Li ◽  
Thomas J George ◽  
...  
Keyword(s):  
Data Integration ◽  
Cancer Survival ◽  
Prediction Models ◽  
Survival Rates ◽  
Model Performance ◽  
Predictive Ability ◽  
The United States ◽  
Cancer Prognosis ◽  
Survival Models ◽  
Survival Prediction

Cancer is the second leading cause of death in the United States. To improve cancer prognosis and survival rates, a better understanding of multi-level contributory factors associated with cancer survival is needed. However, prior research on cancer survival has primarily focused on factors from the individual level due to limited availability of integrated datasets. In this study, we sought to examine how data integration impacts the performance of cancer survival prediction models. We linked data from four different sources and evaluated the performance of Cox proportional hazard models for breast, lung, and colorectal cancers under three common data integration scenarios. We showed that adding additional contextual-level predictors to survival models through linking multiple datasets improved model fit and performance. We also showed that different representations of the same variable or concept have differential impacts on model performance. When building statistical models for cancer outcomes, it is important to consider cross-level predictor interactions.

scholarly journals Chromosomal copy number heterogeneity predicts survival rates across cancers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Erik van Dijk ◽  
Tom van den Bosch ◽  
Kristiaan J. Lenos ◽  
Khalid El Makrini ◽  
Lisanne E. Nijman ◽  
...  
Keyword(s):  
Copy Number ◽  
Cancer Survival ◽  
Survival Rates ◽  
Cancer Prognosis ◽  
Disease Outcomes ◽  
Distinct Disease ◽  
Cancer Types ◽  
Chromosomal Copy Number ◽  
Chromosomal Copy ◽  
Pan Cancer

AbstractSurvival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing. Here, we introduce a scalable measure of chromosomal copy number heterogeneity (CNH) that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data and live cell imaging we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 37 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.

scholarly journals Association of chemotherapy with survival in stage II colon cancer patients who received radical surgery: a retrospective cohort study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhihao Lv ◽  
Yuqi Liang ◽  
Huaxi Liu ◽  
Delong Mo
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Prediction Models ◽  
Radical Surgery ◽  
Survival Rates ◽  
Stage Ii ◽  
Survival Prediction ◽  
Stage Ii Colon Cancer ◽  
Overall Survival Rates

Abstract Background It remains controversial whether patients with Stage II colon cancer would benefit from chemotherapy after radical surgery. This study aims to assess the real effectiveness of chemotherapy in patients with stage II colon cancer undergoing radical surgery and to construct survival prediction models to predict the survival benefits of chemotherapy. Methods Data for stage II colon cancer patients with radical surgery were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (1:1) was performed according to receive or not receive chemotherapy. Competitive risk regression models were used to assess colon cancer cause-specific death (CSD) and non-colon cancer cause-specific death (NCSD). Survival prediction nomograms were constructed to predict overall survival (OS) and colon cancer cause-specific survival (CSS). The predictive abilities of the constructed models were evaluated by the concordance indexes (C-indexes) and calibration curves. Results A total of 25,110 patients were identified, 21.7% received chemotherapy, and 78.3% were without chemotherapy. A total of 10,916 patients were extracted after propensity score matching. The estimated 3-year overall survival rates of chemotherapy were 0.7% higher than non- chemotherapy. The estimated 5-year and 10-year overall survival rates of non-chemotherapy were 1.3 and 2.1% higher than chemotherapy, respectively. Survival prediction models showed good discrimination (the C-indexes between 0.582 and 0.757) and excellent calibration. Conclusions Chemotherapy improves the short-term (43 months) survival benefit of stage II colon cancer patients who received radical surgery. Survival prediction models can be used to predict OS and CSS of patients receiving chemotherapy as well as OS and CSS of patients not receiving chemotherapy and to make individualized treatment recommendations for stage II colon cancer patients who received radical surgery.

Breast cancer survival rates among Chinese women born in East Asia and the United States

2005 ◽  
Vol 15 (8) ◽  
pp. 634-634
Author(s):  
S CHUANG ◽  
W CHEN ◽  
M HASHIBE ◽  
G LI ◽  
P GANZ ◽  
...  
Keyword(s):  
Breast Cancer ◽  
United States ◽  
East Asia ◽  
Cancer Survival ◽  
Chinese Women ◽  
Survival Rates ◽  
The United States ◽  
Breast Cancer Survival

Abstract W MP37: Novel Prognostic Scores for Early Prediction of Outcome Following Aneurysmal Subarachnoid Hemorrhage

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Blessing Jaja ◽  
Hester Lingsma ◽  
Ewout Steyerberg ◽  
R. Loch Macdonald ◽  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cross Validation ◽  
Prediction Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Model Performance ◽  
Predictor Variable ◽  
Predictive Ability ◽  
Prognostic Scores ◽  
Operating Characteristics ◽  
Fisher Grade

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a cerebrovascular emergency. Currently, clinicians have limited tools to estimate outcomes early after hospitalization. We aimed to develop novel prognostic scores using large cohorts of patients reflecting experience from different settings. Methods: Logistic regression analysis was used to develop prediction models for mortality and unfavorable outcomes according to 3-month Glasgow outcome score after SAH based on readily obtained parameters at hospital admission. The development cohort was derived from 10 prospective studies involving 10936 patients in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository. Model performance was assessed by bootstrap internal validation and by cross validation by omission of each of the 10 studies, using R2 statistic, Area under the receiver operating characteristics curve (AUC), and calibration plots. Prognostic scores were developed from the regression coefficients. Results: Predictor variable with the strongest prognostic strength was neurologic status (partial R2 = 12.03%), followed by age (1.91%), treatment modality (1.25%), Fisher grade of CT clot burden (0.65%), history of hypertension (0.37%), aneurysm size (0.12%) and aneurysm location (0.06%). These predictors were combined to develop 3 sets of hierarchical scores based on the coefficients of the regression models. The AUC at bootstrap validation was 0.79-0.80, and at cross validation was 0.64-0.85. Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities of the outcomes. Conclusions: The novel prognostic scores have good predictive ability and potential for broad application as they have been developed from prospective cohorts reflecting experience from different centers globally.

Cancer Survival Is (Mostly) Improving

Pained ◽  
2020 ◽  
pp. 245-246
Author(s):  
Michael D. Stein ◽  
Sandro Galea
Keyword(s):  
Prostate Cancer ◽  
United States ◽  
Pancreatic Cancer ◽  
Survival Rate ◽  
Cancer Diagnosis ◽  
Cancer Survival ◽  
Prostate Cancer Screening ◽  
Survival Rates ◽  
The United States ◽  
Cervical Cancers

This chapter discusses how the 5-year survival rates for the most common cancers in the United States improved by nearly 20% since the 1970s. While promising overall, low survival rates persist for pancreatic, liver, lung, esophageal, brain, and many other cancers. Meanwhile, 5-year survival for uterine and cervical cancers worsened. Pancreatic cancer has the lowest 5-year survival rate at 8.2%. In contrast, prostate cancer had the greatest 5-year survival increase from 67.8% to 98.6%, most likely reflecting a substantial uptick in prostate cancer screening and early detection. Five-year survival with leukemia also improved significantly, from 34.2% to 60.6%, likely resulting from improved treatments. As such, in both detection and treatment, the United States is making progress. For the millions of Americans who face a cancer diagnosis, this is cause for hope.

scholarly journals Acoustic-Based Prediction of End-Product-Based Fibre Determinates within Standing Jack Pine Trees

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 605
Author(s):  
Peter F. Newton
Keyword(s):  
Wood Density ◽  
Pinus Banksiana ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Microfibril Angle ◽  
Model Performance ◽  
Predictive Ability ◽  
Analytical Framework ◽  
Jack Pine ◽  
Cross Sectional

The objective of this study was to specify, parameterize, and evaluate an acoustic-based inferential framework for estimating commercially-relevant wood attributes within standing jack pine (Pinus banksiana Lamb) trees. The analytical framework consisted of a suite of models for predicting the dynamic modulus of elasticity (me), microfibril angle (ma), oven-dried wood density (wd), tracheid wall thickness (wt), radial and tangential tracheid diameters (dr and dt, respectively), fibre coarseness (co), and specific surface area (sa), from dilatational stress wave velocity (vd). Data acquisition consisted of (1) in-forest collection of acoustic velocity measurements on 61 sample trees situated within 10 variable-sized plots that were established in four mature jack pine stands situated in boreal Canada followed by the removal of breast-height cross-sectional disk samples, and (2) given (1), in-laboratory extraction of radial-based transverse xylem samples from the 61 disks and subsequent attribute determination via Silviscan-3. Statistically, attribute-specific acoustic prediction models were specified, parameterized, and, subsequently, evaluated on their goodness-of-fit, lack-of-fit, and predictive ability. The results indicated that significant (p ≤ 0.05) and unbiased relationships could be established for all attributes but dt. The models explained 71%, 66%, 61%, 42%, 30%, 19%, and 13% of the variation in me, wt, sa, co, wd, ma, and dr, respectively. Simulated model performance when deploying an acoustic-based wood density estimate indicated that the expected magnitude of the error arising from predicting dt, co, sa, wt, me, and ma prediction would be in the order of ±8%, ±12%, ±12%, ±13%, ±20%, and ±39% of their true values, respectively. Assessment of the utility of predicting the prerequisite wd estimate using micro-drill resistance measures revealed that the amplitude-based wd estimate was inconsequentially more precise than that obtained from vd (≈ <2%). A discourse regarding the potential utility and limitations of the acoustic-based computational suite for forecasting jack pine end-product potential was also articulated.

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 Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Paul Riviere ◽  
Christopher Tokeshi ◽  
Jiayi Hou ◽  
Vinit Nalawade ◽  
Reith Sarkar ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Claims Data ◽  
Prediction Models ◽  
Survival Model ◽  
Localized Prostate Cancer ◽  
Survival Models ◽  
Accurate Estimation ◽  
Survival Prediction ◽  
Data Set ◽  
Comorbidity Index

PURPOSE Treatment decisions about localized prostate cancer depend on accurate estimation of the patient’s life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient’s health. METHODS We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model’s predictive capacity was compared with existing survival models with a metric of explained randomness (ρ2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS Our noncancer survival model included 143 covariates and had improved survival prediction (ρ2 = 0.60) compared with the Charlson comorbidity index (ρ2 = 0.26) and Elixhauser comorbidity index (ρ2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ2 = 0.71) to the Memorial Sloan Kettering prediction model (ρ2 = 0.68). CONCLUSION Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.

Surgical management trends of penile cancer in the United States between 2000 and 2010.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 379-379 ◽  
Author(s):  
Elizabeth Kate Ferry ◽  
Hui Zhu
Keyword(s):  
Surgical Management ◽  
Penile Cancer ◽  
Cancer Survival ◽  
Radical Surgery ◽  
Survival Rates ◽  
The United States ◽  
Stage I ◽  
Community Hospitals ◽  
Nccn Guidelines ◽  
Teaching Research

379 Background: Penile cancer may cause significant morbidities to those who are treated with aggressive surgery. Conversely, there has been no improvement in penile cancer survival rates since 1990. While the causes for this stagnation may be multifactorial, one potential cause is undertreatment of high-risk disease. This study aimed to examine both the trend in the stage-dependent surgical treatment for penile cancer from 2000 to 2010 and the patterns of possible over and/or undertreatment during this period. Methods: The National Cancer Database (NCDB) Datalinks Portal was queried. Diagnosis by year, first course surgery by stage, in all hospitals from 2000-2010 was first obtained. Data was grouped into no surgery, penile sparing, and radical surgery. Data was also obtained for first course surgery by stage in community hospitals (C), comprehensive centers (CC), and teaching research hospitals (TR), individually. Groups were similar as above, with the exclusion of the no surgery group. Results: There were 1,405 patients with recorded first course surgical treatments in community hospitals, 3,930 in comprehensive hospitals, and 3,667 in teaching research hospitals from 2000 to 2010. Graphically, there has been no change in the trend in the pattern of surgical management of penile cancer from 2000 to 2010 in high or low stage penile cancers. Consistent overtreatment of stage I penile cancer with radical surgery was observed in all hospitals during this period. A variable, but high, percentage of penile-sparing surgery was observed for advanced stages among all hospital types. Conclusions: Corresponding to the stagnation in the survival rates of penile cancer, the surgical management trends ded not change between 2000 and 2010. Contrary to the 2013 National Comprehensive Cancer Network (NCCN) Guidelines, penile-sparing surgery continues to be performed in all types of treating hospitals for higher stage penile cancers, which may represent an undertreatment of potentially fatal penile cancers. Conversely, there was a persistent pattern of overtreatment of stage I penile cancer. [Table: see text]

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