Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

Background: The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world.Main text: Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted.Conclusion: AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care

Predicting invasive breast cancer versus DCIS in different age groups.

BackgroundIncreasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.MethodsWe analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).ResultsThe models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.ConclusionsClinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age

Improving Prognostic Models In Breast Cancer With Biostatistical Analysis Of The Phosphatidyl Inositol 3-Kinase Pathway

IMPROVING PROGNOSTIC MODELS IN BREAST CANCER WITH BIOSTATISTICAL ANALYSIS OF THE PI3-KINASE PATHWAY. Elliot James Rapp, Jena P. Giltnane, David L. Rimm, Annette Molinaro. Department of Biostatistics, Yale School of Public Health, Yale University School of Medicine, New Haven, CT. Our hypothesis was that prognostic models for breast cancer that incorporate both clinical variables and biomarkers in the PI3 Kinase molecular pathway will improve upon the clinical models of TNM staging and the Nottingham Prognostic Index (NPI). Our specific aim was to develop models that misclassify fewer patients than TNM and NPI with the outcome of dead of disease at ten years. Our population cohort was the YTMA49 cohort, a series of 688 samples of invasive ductal breast carcinoma collected between 1961 and 1983 by the Yale University Department of Pathology. Tissue MicroArray (TMA) analysis was performed and biomarker expression level was determined using Automated Quantitative Analysis (AQUA) technology for thirteen biomarkers in the PI3 Kinase pathway, including an overall expression level and expression levels by subcellular compartment. Eleven clinical variables were also assembled from our cohort. Exhaustively searching the multivariate space, we used logistic regression to predict our outcome of dead of disease at ten years. Validation was performed using Leave One Out Cross Validation (LOOCV). Misclassification estimates provided the means to compare different models, with lower misclassification estimates indicating superior models. Confidence intervals were constructed using bootstrapping with one thousand iterations. We developed a helper computer program named Combination Magic to enable us to develop sophisticated models that included both interactions between variables and transformations of variables (e.g. logarithm). Overall our best univariate models were NPI (misclassification estimate (ME): 0.326, confidence interval (CI): 0.292 to 0.359), Nodal status (ME: 0.353, CI: 0.322 to 0.493), and TNM (ME: 0.367, CI: 0.313 to 0.447). Our best univariate models from the PI3 Kinase biomarkers were FOX01_NU (ME: 0.369, CI: 0.336 to 0.415), AKT1_TM (ME: 0.373, CI: 0.335 to 0.412), and PI3Kp110_TM (ME: 0.377, CI: 0.343 to 0.431). Our best bivariate models were pTumor*PathER (ME: 0.328, CI: 0.308 to 0.443), pNode + NuGrade (ME: 0.333, CI: 0.305 to 0.434), and AKT1_NN + Fox01_NU (ME: 0.338, CI: 0.307 to 0.391). Our best trivariate models were pTumor + mTOR_NN + PI3Kp110_TM + pTumor*PI3Kp110_TM (ME: 0.296, CI: 0.273 to 0.375), pTumor + AKT1_NU + Fox01_NU + pTumor*AKT1_NU (ME: 0.298, CI: 0.275 to 0.38), and pTumor + mTOR_TM + PI3Kp110_TM + pTumor*PI3Kp110_TM (ME: 0.299, CI: 0.276 to 0.378). Our best multi-variate model was Fox01_NU + AKT1_NU + mTOR_MB + p70S6K_NU + AVG_BCL2_TM + Fox01_NU*AKT1_NU*mTOR_MB (ME: 0.295, CI: 0.274 to 0.393). None of these models was statistically superior to the clinical models of TNM and NPI

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph