2,017 research outputs found
A mobile and evolving tool to predict colorectal cancer survivability
In this work, a tool for the survivability prediction of patients with colon or rectal cancer, up to five years after diagnosis and treatment, is presented. Indeed, an accurate survivability prediction is a difficult task for health care professionals and of high concern to patients, so that they can make the most of the rest of their lives. The distinguishing features of the tool include a balance between the number of necessary inputs and prediction performance, being mobile-friendly, and featuring an online learning component that enables the automatic evolution of the prediction models upon the addition of new cases.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported by a FCT grant with the reference SFRH/BD/85291/2012.info:eu-repo/semantics/publishedVersio
Master of Science
thesisPatients find genetic test results hard to interpret. Information about testing colon cancer (CRC) patients for Lynch syndrome (LS) is particularly complex as it involves several laboratory tests and has to be interpreted along with family/ personal health history information. In this study, the example of LS was used to explore methods of presenting information to patients. Specifically, the tailoring of information was compared to the general didactic presentation in a web-based format for communicating genetic test results to patients. Ninety volunteers, aged 50-75, with ability to read and write English and familiarity with using the Internet were recruited from the Osher Lifelong Learning Institute at The University of Utah and through ResearchMatch.org. Healthcare professionals/ students, people with a professional medical background and the University faculty were excluded. This study was a postintervention, two-group randomized controlled trial. For evaluating the website, a vignette of a typical CRC patient being tested for LS was designed and participants were asked to imagine that they were the patient described in the scenario. They were then asked to interpret the test reports and answer a survey. The primary outcome was genetic knowledge based on interpretation of the test results. The other outcomes were task completion (correct/ incorrect), time to complete the task, usability and usefulness of the website. iv The two groups showed no statistically significant difference in total knowledge score, task completion and usefulness outcomes. Inconsistent differences were found between groups for individual knowledge questions. Time data had to be excluded from our analysis as there were inconsistencies in reporting time. Usability was rated significantly higher for the nontailored website. Our study has demonstrated that online tailored communication of genetic test results is possible and effective, although it could not determine conclusively if tailoring is more effective than nontailoring methods for conveying complex genetics-based testing information to patients. Future research on evaluating the website for its usability through cognitive response methods with actual CRC patients is necessary to get more insights into how the users actually process information and clarify these results
Doctor of Philosophy
dissertationFamily history has been called the "cornerstone of individualized disease prevention" but it is underutilized in clinical practice. In order to use it more effectively, its role in assessing risk for disease needs to be better quantified and understood. Family history has been identified as an important risk factor for colorectal cancer (CRC) and risk prediction in CRC is potentially worthwhile because of the possibility of preventing the disease through application of individualized screening programs tailored to risk. The overall project objective was to explore how family history can be better utilized to predict who will develop CRC. First, we used the Utah Population Database (UPDB) to define familial risk for CRC in more detail than has previously been reported. Second, we explored whether individuals at increased familial risk for CRC or at increased risk based on other risk factors such as a personal history of CRC or adenomatous polyps, are more compliant with screening and surveillance recommendations using colonoscopy than those who are at normal risk. Third, we measured how well family history can predict who will develop CRC over a period of 20 years, using family history by itself as a risk factor, and also in combination with the risk factor, age. We found that increased numbers of affected first-degree relatives influence risk much more than affected relatives from the second or third degrees. However, when combined with a positive firstdegree family history, a positive second- and third-degree family history can significantly increase risk. Next, we found that colonoscopy rates were higher in those with risk factors, according to risk-specific guidelines, but improvements in compliance are still warranted. Lastly, it was determined that family history by itself is not a strong predictor of exactly who will acquire colorectal cancer within 20 years. However, stratification of risk using absolute risk probabilities may be more helpful in focusing screening on individuals who are more likely to develop the disease. Future work includes using these findings as a basis for a cost/benefit analysis to determine optimal screening recommendations and building tools to better capture and utilize family history data in an electronic health record system
Developing an individualized survival prediction model for colon cancer
In this work a 5-year survival prediction model was developed for colon cancer using machine learning methods. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Survival prediction models for colon cancer are not widely and easily available. Results showed that the performance of the model using fewer features is close to that of the model using a larger set of features recommended by an expert physician, which indicates that the first may be a good compromise between usability and performance. The purpose of such a model is to be used in Ambient Assisted Living applications, providing decision support to health care professionals.info:eu-repo/semantics/publishedVersio
Survival Prediction from Imbalance colorectal cancer dataset using hybrid sampling methods and tree-based classifiers
Background and Objective: Colorectal cancer is a high mortality cancer.
Clinical data analysis plays a crucial role in predicting the survival of
colorectal cancer patients, enabling clinicians to make informed treatment
decisions. However, utilizing clinical data can be challenging, especially when
dealing with imbalanced outcomes. This paper focuses on developing algorithms
to predict 1-, 3-, and 5-year survival of colorectal cancer patients using
clinical datasets, with particular emphasis on the highly imbalanced 1-year
survival prediction task. To address this issue, we propose a method that
creates a pipeline of some of standard balancing techniques to increase the
true positive rate. Evaluation is conducted on a colorectal cancer dataset from
the SEER database. Methods: The pre-processing step consists of removing
records with missing values and merging categories. The minority class of
1-year and 3-year survival tasks consists of 10% and 20% of the data,
respectively. Edited Nearest Neighbor, Repeated edited nearest neighbor (RENN),
Synthetic Minority Over-sampling Techniques (SMOTE), and pipelines of SMOTE and
RENN approaches were used and compared for balancing the data with tree-based
classifiers. Decision Trees, Random Forest, Extra Tree, eXtreme Gradient
Boosting, and Light Gradient Boosting (LGBM) are used in this article. Method.
Results: The performance evaluation utilizes a 5-fold cross-validation
approach. In the case of highly imbalanced datasets (1-year), our proposed
method with LGBM outperforms other sampling methods with the sensitivity of
72.30%. For the task of imbalance (3-year survival), the combination of RENN
and LGBM achieves a sensitivity of 80.81%, indicating that our proposed method
works best for highly imbalanced datasets. Conclusions: Our proposed method
significantly improves mortality prediction for the minority class of
colorectal cancer patients.Comment: 19 Pages, 6 Figures, 4 Table
Predictive Model of the Risk of In-Hospital Mortality in Colorectal Cancer Surgery, Based on the Minimum Basic Data Set
Background: Various models have been proposed to predict mortality rates for hospital
patients undergoing colorectal cancer surgery. However, none have been developed in Spain using
clinical administrative databases and none are based exclusively on the variables available upon
admission. Our study aim is to detect factors associated with in-hospital mortality in patients
undergoing surgery for colorectal cancer and, on this basis, to generate a predictive mortality score.
Methods: A population cohort for analysis was obtained as all hospital admissions for colorectal
cancer during the period 2008–2014, according to the Spanish Minimum Basic Data Set. The main
measure was actual and expected mortality after the application of the considered mathematical
model. A logistic regression model and a mortality score were created, and internal validation was
performed. Results: 115,841 hospitalization episodes were studied. Of these, 80% were included
in the training set. The variables associated with in-hospital mortality were age (OR: 1.06, 95%CI:
1.05–1.06), urgent admission (OR: 4.68, 95% CI: 4.36–5.02), pulmonary disease (OR: 1.43, 95%CI:
1.28–1.60), stroke (OR: 1.87, 95%CI: 1.53–2.29) and renal insufficiency (OR: 7.26, 95%CI: 6.65–7.94).
The level of discrimination (area under the curve) was 0.83. Conclusions: This mortality model is
the first to be based on administrative clinical databases and hospitalization episodes. The model
achieves a moderate–high level of discrimination.Carlos III Institute of Health, Madrid (Spain) under the 2013-2016 National Plan for RDI
PI16/01931ISCIII-General Subdirectorate for Evaluation and Promotion of Research, within the European Regional Development Fund (FEDER
Shifts in the Fecal Microbiota Associated with Adenomatous Polyps
BACKGROUND:
Adenomatous polyps are the most common precursor to colorectal cancer, the second leading cause of cancer-related death in the United States. We sought to learn more about early events of carcinogenesis by investigating shifts in the gut microbiota of patients with adenomas.
METHODS:
We analyzed 16S rRNA gene sequences from the fecal microbiota of patients with adenomas (n = 233) and without (n = 547).
RESULTS:
Multiple taxa were significantly more abundant in patients with adenomas, including Bilophila, Desulfovibrio, proinflammatory bacteria in the genus Mogibacterium, and multiple Bacteroidetes species. Patients without adenomas had greater abundances of Veillonella, Firmicutes (Order Clostridia), and Actinobacteria (family Bifidobacteriales). Our findings were consistent with previously reported shifts in the gut microbiota of colorectal cancer patients. Importantly, the altered adenoma profile is predicted to increase primary and secondary bile acid production, as well as starch, sucrose, lipid, and phenylpropanoid metabolism.
CONCLUSIONS:
These data hint that increased sugar, protein, and lipid metabolism along with increased bile acid production could promote a colonic environment that supports the growth of bile-tolerant microbes such as Bilophilia and Desulfovibrio In turn, these microbes may produce genotoxic or inflammatory metabolites such as H2S and secondary bile acids, which could play a role in catalyzing adenoma development and eventually colorectal cancer.
IMPACT:
This study suggests a plausible biological mechanism to explain the links between shifts in the microbiota and colorectal cancer. This represents a first step toward resolving the complex interactions that shape the adenoma-carcinoma sequence of colorectal cancer and may facilitate personalized therapeutics focused on the microbiota
Treating colon cancer survivability prediction as a classification problem
This work presents a survivability prediction model for colon cancer developed
with machine learning techniques. Survivability was viewed as a classification
task where it was necessary to determine if a patient would survive each of
the five years following treatment. The model was based on the SEER dataset
which, after preprocessing, consisted of 38,592 records of colon cancer patients.
Six features were extracted from a feature selection process in order to construct
the model. This model was compared with another one with 18 features
indicated by a physician. The results show that the performance of the sixfeature
model is close to that of the model using 18 features, which indicates
that the first may be a good compromise between usability and performance.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a
Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported
by a FCT grant with the reference SFRH/BD/85291/ 2012.info:eu-repo/semantics/publishedVersio
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