Introduction to Artificial Intelligence and Machine Learning for Pathology.

CONTEXT.­: Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. The principles of operation and management of machine learning systems are unfamiliar to pathologists, who anticipate a need for additional education to be effective as expert users and managers of the new tools. OBJECTIVE.­: To provide a background on machine learning for practicing pathologists, including an overview of algorithms, model development, and performance evaluation; to examine the current status of machine learning in pathology and consider possible roles and requirements for pathologists in local deployment and management of machine learning systems; and to highlight existing challenges and gaps in deployment methodology and regulation. DATA SOURCES.­: Sources include the biomedical and engineering literature, white papers from professional organizations, government reports, electronic resources, and authors' experience in machine learning. References were chosen when possible for accessibility to practicing pathologists without specialized training in mathematics, statistics, or software development. CONCLUSIONS.­: Machine learning offers an array of techniques that in recent published results show substantial promise. Data suggest that human experts working with machine learning tools outperform humans or machines separately, but the optimal form for this combination in pathology has not been established. Significant questions related to the generalizability of machine learning systems, local site verification, and performance monitoring remain to be resolved before a consensus on best practices and a regulatory environment can be established.

A data review and re-assessment of ovarian cancer serum proteomic profiling.

BACKGROUND: The early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research. RESULTS: The mass spectrometry serum profiles derived from cancer and controls exhibited numerous statistical differences. For example, use of the Wilcoxon test in comparing the intensity at each of the 15,154 mass to charge (M/Z) values between the cancer and controls, resulted in the detection of 3,591 M/Z values whose intensities differed by a p-value of 10-6 or less. The region containing the M/Z values of greatest statistical difference between cancer and controls occurred at M/Z values less than 500. For example the M/Z values of 2.7921478 and 245.53704 could be used to significantly separate the cancer from control groups. Three other sets of M/Z values were developed using a training set that could distinguish between cancer and control subjects in a test set with 100% sensitivity and specificity. CONCLUSION: The ability to discriminate between cancer and control subjects based on the M/Z values of 2.7921478 and 245.53704 reveals the existence of a significant non-biologic experimental bias between these two groups. This bias may invalidate attempts to use this dataset to find patterns of reproducible diagnostic value. To minimize false discovery, results using mass spectrometry and data mining algorithms should be carefully reviewed and benchmarked with routine statistical methods.