Histologic grading of invasive lobular carcinoma: does use of a 2-tiered nuclear grading system improve interobserver variability?

The Nottingham histologic grade (NHG) is a prognostic marker for infiltrating ductal carcinoma. Its usefulness for invasive lobular carcinoma (ILC) has been less clear, given that 2 of the 3 parameters, tubule formation and mitotic activity, show little variation in ILC, placing much of the emphasis on nuclear grade. We have previously reported a trend for improved overall and relapse-free survival in patients with ILC of low nuclear grade, as classified by a 2-tiered nuclear grading system. Given the inherent potential for interobserver variability with any grading system, the goal of this study is to compare interobserver variability in the grading of ILC using a 2-tiered nuclear grade vs the NHG. Thirty-eight cases of ILC were graded independently by 5 pathologists using NHG criteria. Tumors were also categorized by a nuclear grading system as low grade (grade 1 nuclei) or high grade (grades 2-3 nuclei). Pairwise kappa values and interobserver agreement rates were calculated for both NHG and nuclear grade. Results were compared using the paired t test. Mean interobserver agreement rates and kappa values improved with use of the nuclear grading system as compared to NHG (83% vs 70%, 0.4738 vs 0.3228, respectively). The differences between the 2 were statistically significant. Because histologic grade has significant prognostic implications for patients with breast cancer, accurate reporting is paramount. For ILC, where use of the NHG places substantial weight on nuclear pleomorphism, a 2-tiered nuclear grading system may reduce interobserver variability yet still provide useful prognostic information.

An automated tissue preclassification approach for telepathology: implementation and performance analysis.

Telepathology is generally defined as the use of telecommunications technologies in the practice of anatomic or surgical pathology. In the usual telepathology scenario, a remotely located pathologist views images of tissues samples in order to render a diagnosis of the biopsy. Some telepathology systems involve interactive remote control of a microscope-based imaging system which delivers diagnostic quality imagery to the remote pathologist. The usefulness of such interactive systems depends on minimizing the end-to-end delays involved in controlling the robotic microscope, manipulating the tissue sample, and acquiring and transmitting the high-resolution image. An approach to minimizing end-to-end delay involves adding "intelligence" to the image acquisition system so that it can gather, classify, rank, and transmit diagnostically useful images in a semiautonomous fashion. In this research, we develop image analysis and ranking techniques which can improve the end-to-end performance of a robotic telepathology imaging system. Our semiautonomous image collection system uses morphological techniques to extract seed points for suspicious regions, a novel region growing algorithm to segment the regions of interest, and heuristically motivated expert system ranking techniques to select diagnostically relevant "next-step" image acquisitions. Diagnostic relevance of our segmentation and ranking algorithms is established via subjective and objective testing of the system. In subjective testing, pathologists Agree or Strongly Agree that all segmented regions are diagnostically relevant with probability greater than 0.75. In objective testing, 84% of "next-step" images acquired by our algorithms coincide with the areas most likely to be chosen by a pathologist.