Prognostic Significance of Canine Mammary Tumor Histologic Subtypes: An Observational Cohort Study of 229 Cases

Histopathology is considered the gold standard diagnostic method for canine mammary tumors. In 2011, a new histologic classification for canine mammary tumors was proposed. The present study was a 2-year prospective study that validated the 2011 classification as an independent prognostic indicator with multivariate analysis in a population of 229 female dogs, identifying subtype-specific median survival times (MST) and local recurrence/distant metastasis rates. Dogs with benign tumors and carcinoma arising in benign mixed tumors all had an excellent prognosis. Dogs with complex carcinoma and simple tubular carcinoma also experienced prolonged survival. Those with simple tubulopapillary carcinoma, intraductal papillary carcinoma, and carcinoma and malignant myoepithelioma had a more than 10-fold higher risk of tumor-related death. The prognosis was even worse for adenosquamous carcinoma (MST = 18 months), comedocarcinoma (MST = 14 months), and solid carcinoma (MST = 8 months). The most unfavorable outcome was for anaplastic carcinoma (MST = 3 months) and carcinosarcoma (MST = 3 months), which also had the highest metastatic rates (89% and 100%, respectively). Adenosquamous carcinoma exhibited the highest local recurrence rate (50%). In the same canine population, the tumor diameter was recognized as a strong predictor of local recurrence/distant metastasis and an independent prognosticator of survival in the multivariate analysis. Excision margins were predictive only of local recurrence, whereas lymphatic invasion and histologic grade were predictive of local recurrence/distant metastasis and survival, although only in univariate analyses. In conclusion, this study validated the 2011 classification scheme and provided information to be used in the clinical setting and as the basis for future prognostic studies. </jats:p

Are screening methods useful in feature selection? An empirical study

Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model. While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this paper with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated.Comment: 29 pages, 4 figures, 21 table

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table

Analytic lymph node number establishes staging accuracy by occult tumor burden in colorectal cancer.

BACKGROUND AND OBJECTIVES: Recurrence in lymph node-negative (pN0) colorectal cancer suggests the presence of undetected occult metastases. Occult tumor burden in nodes estimated by GUCY2C RT-qPCR predicts risk of disease recurrence. This study explored the impact of the number of nodes analyzed by RT-qPCR (analytic) on the prognostic utility of occult tumor burden. METHODS: Lymph nodes (range: 2-159) from 282 prospectively enrolled pN0 colorectal cancer patients, followed for a median of 24 months (range: 2-63), were analyzed by GUCY2C RT-qPCR. Prognostic risk categorization defined using occult tumor burden was the primary outcome measure. Association of prognostic variables and risk category were defined by multivariable polytomous and semi-parametric polytomous logistic regression. RESULTS: Occult tumor burden stratified this pN0 cohort into categories of low (60%; recurrence rate (RR) = 2.3% [95% CI 0.1-4.5%]), intermediate (31%; RR = 33.3% [23.7-44.1%]), and high (9%; RR = 68.0% [46.5-85.1%], P \u3c 0.001) risk of recurrence. Beyond race and T stage, the number of analytic nodes was an independent marker of risk category (P \u3c 0.001). When \u3e12 nodes were analyzed, occult tumor burden almost completely resolved prognostic risk classification of pN0 patients. CONCLUSIONS: The prognostic utility of occult tumor burden assessed by GUCY2C RT-qPCR is dependent on the number of analytic lymph nodes

Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data

Partial Least Squares (PLS) is a highly efficient statistical regression technique that is well suited for the analysis of high-dimensional genomic data. In this paper we review the theory and applications of PLS both under methodological and biological points of view. Focusing on microarray expression data we provide a systematic comparison of the PLS approaches currently employed, and discuss problems as different as tumor classification, identification of relevant genes, survival analysis and modeling of gene networks