Predicting Pancreatic Cancer Using Support Vector Machine

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately

Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa

Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012. Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark. We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Simple Summary Metastatic colorectal cancer (mCRC) has high incidence and mortality. Nevertheless, innovative biomarkers have been developed for predicting the response to therapy. We have examined the ability of learning methods to build prognostic and predictive models to predict response to chemotherapy, alone or combined with targeted therapy in mCRC patients, by targeting specific narrative publications. After a literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. We showed that all investigations conducted in this field provided generally promising results in predicting the response to therapy or toxic side-effects, using a meta-analytic approach. We found that radiomics and molecular biomarker signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Our study supports the use of computer science for developing personalized treatment decision processes for mCRC patients. Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set

Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

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

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Objectives: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. Setting: A regional cancer centre in Australia. Participants: Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. Primary and secondary outcome measures: Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). Results: The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. Conclusions: Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems

Sex Difference In Identification Of Predictive Tumor Tissue Metabolites Associated With Colorectal Cancer Prognosis

Colorectal cancer (CRC) is the third major cause of cancer-related deaths in the United States in 2020. Sex-related differences in CRC stage, prognosis, and metabolism have become increasingly popular in cancer research. Males have poorer survival for CRC, but females with right-sided colon cancer (RCC) have aberrant metabolism correlated with poor survival. Delay in knowing the condition of CRC in female patients would result in poor prognosis, which could be avoided by predicting prognostic outcomes. Random Survival Forest (RSF) is ideal for exploration and making predictions using metabolomics data with high dimension, strong collinearity, and heterogeneity, which CPH models could not efficiently address. In this retrospective study including 197 patients, we applied an RSF prediction method based on the backward selection algorithm in 5-year overall survival (OS) for 95 female CRC patients and validated its performance. We also investigated Cox proportional hazard models (CPH), lasso penalized Cox regression (Cox-Lasso), and Logistic Regression (LR) and compared their predictive performances. RSF using the backward selection algorithm showed the best performance with the C-index of the training and testing sets reaching 0.81(95% CI: 0.810-0.813) and 0.78 (95% CI: 0.776-0.777) respectively and identified the five most predictive metabolites for female 5-year OS: glutathione, citrulline, phosphoenolpyruvate, lysoPC (16:0), and asparagine. Accordingly, the backward selection algorithm-based Random Survival Forest model using tumor tissue metabolic profile is promising for predicting 5-year OS for female CRC patients. The results could be easily interpreted and applied in preventive medicine and precision medicine, guiding clinicians in choosing targeted treatments by sex for better survival and avoiding unnecessary treatments