Behavior of Various Machine Learning Models in the Face of Noisy Data

Although a great deal of attention has been focused on the future potential for molecular-based cancer diagnosis, histologic examination of tissue specimens remains the mainstay of diagnosis. The process of histologic diagnosis entails the identification of visual features from a slide, followed by the recognition of a feature pattern to which the case belongs. The combination of image analysis and machine learning imitates this process and in certain circumstances may be able to aid the pathologist. However, there is a great deal of variability and noise inherent in such an approach. Therefore, a classification model developed from data at one institution is likely to perform acceptably at other institutions, only if the model can handle such variability. This paper compares the performance of machine learning models based on fuzzy rules (FR), fuzzy decision trees (FDT), artificial neural networks (aNN) and logistic regression (LR) and examines how these models behave in the face of noisy and variant data. Results suggest that FDT models may be more resistant to data noise. Background Although a great deal of attention has been focused on the future potential of molecular-based cancer identification, histologic examination of tissue specimens remains the mainstay of diagnosis. The process of histologic diagnosis entails the identification of visual features from a slide, followed by the recognition of a feature pattern to which the case belongs. The pattern is associated with a high or low probability of cancer. For example a pathologist examining a breast biopsy may identify breast epithelial cells with large, irregular shaped nuclei, irregularly clumped chromatin, growing in poorly arranged sheets and showing invasion into the surrounding connective tissue with an associated fibrotic reaction. These findings compose a pattern that is highly correlated with malignancy and would warrant such a diagnosis. Imaging equipment and image analysis software can partially, and perhaps eventually, completely automate the process of feature extraction. 2 Given a list of previously identified visual features for a large number of cases, machine learning techniques can be used to discern patterns relevant to the separation of cancer from benign. The process of discerning such patterns from data results in a model of the domain. Diagnostic predictions can be made by applying such models to the data generated from new cases. Wolberg, et al., demonstrated the correspondence between human histologic diagnosis and the combined techniques of image analysis and machine learning using the cytologic diagnosis of breast cancer for illustration. Breast cancer is the most common cancer in women and the second leading cause of female cancer deaths. Cancer screening involves mammography followed by tissue sampling and histologic examination of any mammographically worrisome area. Tissue samples are also obtained without mammography in the setting of palpable breast lumps. Initial tissue sampling in either situation is typically by needle core biopsy or fine needle aspiration (FNA). Core biopsy provides more tissue and retains tissue architecture for evaluation, while FNA typically yields a smaller sample and destroys or severely alters the tissue architecture. Although more invasive, core biopsy is the initial tissue procurement technique of choice in most situations. However, FNA is less invasive, can be performed in the physician’s office at a moments notice, is less expensive and therefore is still widely used. In addition, FNA is used more extensively for cancer diagnosis and screening in many other organ systems. The histologic features used to diagnose breast cancer fall into 2 major categories; architectural and cytologic. Architectural features include those that describe how groups of cells relate to one another and to the surrounding connective tissue. They include characteristics such as the presence or absence of irregular, distorted or excessively cellular glands, too many glandular structures and the presence of single epithelial cells invading into connective tissue. By and large, these features cannot be reliably ascertained in FNA specimens. Cytologic features describe characteristics of single cells and include cell size, nuclear size, nuclear membrane irregularity and nuclear chromatin distribution to name a few. The FNA diagnosis of Harvard-MIT Division of Health Sciences and Technology HST.951J: Medical Decision Support, Fall 2005 Instructors: Professor Lucila Ohno-Machado and Professor Staal Vinterbo