Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues not only for pathologists, but also for interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library for reading and manipulating digital slides of diverse vendor formats. The library is extensible and easily interfaced to various programming languages. An application written to the OpenSlide interface can transparently handle multiple vendor formats. OpenSlide is in use today by many academic and industrial organizations world-wide, including many research sites in the United States that are funded by the National Institutes of Health.

Background: Analysis of tissue biopsy whole-slide images (WSIs) depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models. Materials and Methods: We use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa) and ovarian serous adenocarcinoma (OvCa) - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades. Results: When compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI), 0.77 average true rate (ATR), 0.55 true positive rate (TPR), and 0.98 true negative rate (TNR); and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method) in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively. Conclusion: The proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction models.

Background: Interfacing complex laboratory equipment to laboratory information systems (LIS) has become a more commonly encountered problem in clinical laboratories, especially for instruments that do not have an interface provided by the vendor. Liquid chromatography-tandem mass spectrometry is a great example of such complex equipment, and has become a frequent addition to clinical laboratories. As the testing volume on such instruments can be significant, manual data entry will also be considerable and the potential for concomitant transcription errors arises. Due to this potential issue, our aim was to interface an AB SCIEX ^TM mass spectrometer to our Sunquest^® LIS. Materials and Methods: We licensed software for the data management interface from the University of Pittsburgh, but extended this work as follows: The interface was designed so that it would accept a text file exported from the AB SCIEX ^TM × 5500 QTrap^® mass spectrometer, pre-process the file (using newly written code) into the correct format and upload it into Sunquest^® via file transfer protocol. Results: The licensed software handled the majority of the interface tasks with the exception of converting the output from the Analyst^® software to the required Sunquest^® import format. This required writing of a "pre-processor" by one of the authors which was easily integrated with the supplied software. Conclusions: We successfully implemented the data management interface licensed from the University of Pittsburgh. Given the coding that was required to write the pre-processor, and alterations to the source code that were performed when debugging the software, we would suggest that before a laboratory decides to implement such an interface, it would be necessary to have a competent computer programmer available.