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High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors. [ABSTRACT FROM AUTHOR]

Objectives: The intersecting of pathology training and practice and the utilization of information technology has become an increasingly common occurrence, and the most effective means of teaching residents informatics during these invaluable years has yet to be firmly established. Methods: In offering the idea of longitudinal engagement that stresses early and extended trainee involvement, we attempt to provide a different manner of helping address some of the leading time-limited issues surrounding education in informatics. Results: The proposed model is intended to allow building off a base of fundamentals reached through introductory didactics, exposure to and active participation in departmental and hospital-wide administrative bodies, refining of initial skills gained through frequent mentoring and coaching, and combining these cumulative knowledge and experiential underpinnings with graduated responsibility in a particular area of expertise. Conclusions: In transforming the ways in which pathology residencies teach their trainees informatics, the prospects of realizing its potential utility are heightened. [ABSTRACT FROM AUTHOR]

Background: Pathologists and informaticians are becoming increasingly interested in electronic clinical decision support for pathology, laboratory medicine and clinical diagnosis. Improved decision support may optimize laboratory test selection, improve test result interpretation and permit the extraction of enhanced diagnostic information from existing laboratory data. Nonetheless, the field of pathology decision support is still developing. To facilitate the exchange of ideas and preliminary studies, we convened a symposium entitled: Pathology data integration and clinical decision support. Methods: The symposium was held at the Massachusetts General Hospital, on May 10, 2013. Participants were selected to represent diverse backgrounds and interests and were from nine different institutions in eight different states. Results: The day included 16 plenary talks and three panel discussions, together covering four broad areas. Summaries of each presentation are included in this manuscript. Conclusions: A number of recurrent themes emerged from the symposium. Among the most pervasive was the dichotomy between diagnostic data and diagnostic information, including the opportunities that laboratories may have to use electronic systems and algorithms to convert the data they generate into more useful information. Differences between human talents and computer abilities were described; well-designed symbioses between humans and computers may ultimately optimize diagnosis. Another key theme related to the unique needs and challenges in providing decision support for genomics and other emerging diagnostic modalities. Finally, many talks relayed how the barriers to bringing decision support toward reality are primarily personnel, political, infrastructural and administrative challenges rather than technological limitations. [ABSTRACT FROM AUTHOR]