Significant interest exists in establishing radiologic imaging as a valid biomarker for assessing the response of cancer to a variety of treatments. To address this problem, we have chosen to study ...patients with metastatic colorectal carcinoma to learn whether statistical learning theory can improve the performance of radiologists using CT in predicting patient treatment response to therapy compared with the more traditional RECIST (Response Evaluation Criteria in Solid Tumors) standard.
Predictions of survival after 8 months in 38 patients with metastatic colorectal carcinoma using the Support Vector Machine (SVM) technique improved 30% when using additional information compared to WHO (World Health Organization) or RECIST measurements alone. With both Logistic Regression (LR) and SVM, there was no significant difference in performance between WHO and RECIST. The SVM and LR techniques also demonstrated that one radiologist consistently outperformed another.
This preliminary research study has demonstrated that SLT algorithms, properly used in a clinical setting, have the potential to address questions and criticisms associated with both RECIST and WHO scoring methods. We also propose that tumor heterogeneity, shape, etc. obtained from CT and/or MRI scans be added to the SLT feature vector for processing.
While the role of survival analysis in medicine has continued to be increasingly essential in making treatment and other health care decisions, the common clinical methods used for performing these ...analyses, such as Cox Proportional Hazard models and Kaplan-Meier curves, have become antiquated. We have developed a new survival analysis technique of the Evolutionary Programming / Evolutionary Strategies Support Vector Regression Hybrid for censored and non-censored event data. This method provides the benefits of optimized statistical learning theory to be used as a replacement for or in addition to existing survival analysis protocols. The technique was tested on an artificially censored data from a well-known benchmark dataset as well as actual clinical data with encouraging results.
Introduction
The aim of this study was to determine if correlations exist between quantitative parameters from dynamic contrast‐enhanced (DCE) and diffusion‐weighted (DW) MRI with National ...Comprehensive Cancer Network (NCCN) risk group, Gleason score (GS), maximum tumour diameter (MTD), pre‐treatment prostate‐specific antigen (PSA), clinical T stage and MRI prostate volume in prostate cancer.
Method
We retrospectively reviewed 3T multiparametric MRI reports on biopsy‐proven prostate cancer patients performed during radiation treatment evaluation or an active surveillance protocol. DCE‐MRI parameters included Ktrans (influx volume transfer coefficient), Kep (efflux reflux rate constant) and iAUC (initial area under the curve). Average DCE and apparent diffusion coefficient (ADC) values were recorded for regions of interest on DW‐MRI. Relationships between MRI metrics and risk group, GS, MTD, PSA, clinical T stage and MRI prostate volume were examined using analysis of variance. Central and peripheral tumours were also analysed separately in a sub‐analysis. Statistical significance was defined as P < 0.0125.
Results
Of 58 patients, 29%, 52% and 19% had low (L), intermediate (I), or high (H) NCCN risk disease, respectively. Ktrans significantly correlated with PSA. For central tumours, Ktrans significantly correlated with MTD and PSA, and Kep significantly correlated with PSA. For peripheral tumours, iAUC was significantly different when stratified by L/I/H risk and GS, and ADC score with L/I/H risk, GS, and clinical T stage.
Conclusions
DCE‐ and DW‐MRI metrics correlate with some risk stratification factors in prostate cancer. Further work is required to determine if MRI metrics are complementary or independent prognostic factors.
The effect of a concurrent herpes simplex virus (HSV) infection on human immunodeficiency virus type 1 (HIV-1) load was evaluated. Sixteen subjects were identified with an active HSV infection and ...had pre-outbreak, acute-phase, and post-outbreak plasma (n = 16) and peripheral blood mononuclear cell (PBMC) (n = 8) samples for evaluation. All subjects were treated for an acute HSV outbreak with acyclovir for 10 days, followed by chronic prophylaxis. HIV-1 plasma RNA levels were determined by branched DNA, and intracellular HIV gag mRNA copy numbers were determined by quantitative reverse transcriptase-polymerase chain reaction ELISA. Plasma virus load increased a median of 3.4-fold during the acute outbreak (range, 0- to 10-fold; P = .002), while post-outbreak levels (30–45 days after the appearance of lesions) remained above pre-outbreak, baseline levels in some subjects. Intracellular HIV gag mRNA increased during the outbreak as well. Thus, an acute HSV episode can result in increased HIV transcription and plasma virus load.
In previous work, we applied an advanced genetic algorithm method for feature subset selection combined with noise perturbation in an attempt to overcome the over-fitting that is typical with ...microarray datasets. The method was applied to a dataset from Moffitt Cancer Center and the clinical outcome to be predicted was cancer recurrence in less than 5 years. By its nature, the method yields multiple gene signatures, each as small as possible and often these signatures will share one or more genes. The question is how to combine the predictions from multiple predictors. In the previous work, we produced an ensemble prediction by a simple majority vote rule, and observed that performance on a validation set was considerably worse than on the learning set. Our conclusion was that the training and validation sets were not equally representative of the same population, and therefore could not provide reliable gene signatures. Here we report on an effort to apply a more sophisticated ensemble method, the Generalized Regression Neural network (GRNN) Oracle, but this did not allow us to reverse our original conclusion.
New advances in medicine have led to a disparity between the existing information about patients and the ability of clinicians to utilize it. Lack of training and incompatibility with clinical ...techniques has made the use of the complex adaptive systems approach difficult. To avoid this, we used statistical learning theory as an inline preprocess between existing data collection methods and clinical analysis of data. Clinicians would be able to use this system without any changes to their techniques, while improving accuracy. We used data from CT scans of patients with metastatic carcinoma to predict prognosis. Specifically, we used the standard for evaluating response to treatment, RECIST, and new qualitative and quantitative features. An Evolutionary Programming trained Support Vector Machine (EP-SVM), was used to preprocess the data for two traditional survival analysis techniques: Cox Proportional Hazard Models and Kaplan Meier curves. This was compared to Logistic Regression (LR) and using cutoff points. Analyses were also done to compare different inputs and different radiologists. The EP-SVM outperformed both LR and the cutoff method significantly and allowed us to both intelligently combine data from multiple sources and identify the most predictive features without necessitating changes in clinical methods.
To establish radiologic imaging as a valid biomarker for assessing the response of cancer to different treatments. We study patients with metastatic colorectal carcinoma to learn whether Statistical ...Learning Theory (SLT) improves the performance of radiologists using Computer Tomography (CT) in predicting patient treatment response to therapy compared with traditional Response Evaluation Criteria in Solid Tumours (RECIST) standard. Preliminary research demonstrated that SLT algorithms can address questions and criticisms associated with both RECIST and World Health Organization (WHO) scoring methods. We add tumour heterogeneity, shape, etc., obtained from CT or MRI scans the feature vector for processing.
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