Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs

Automated histopathological analyses at scale

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 68-73).Histopathology is the microscopic examination of processed human tissues to diagnose conditions like cancer, tuberculosis, anemia and myocardial infractions. The diagnostic procedure is, however, very tedious, time-consuming and prone to misinterpretation. It also requires highly trained pathologists to operate, making it unsuitable for large-scale screening in resource-constrained settings, where experts are scarce and expensive. In this thesis, we present a software system for automated screening, backed by deep learning algorithms. This cost-effective, easily-scalable solution can be operated by minimally trained health workers and would extend the reach of histopathological analyses to settings such as rural villages, mass-screening camps and mobile health clinics. With metastatic breast cancer as our primary case study, we describe how the system could be used to test for the presence of a tumor, determine the precise location of a lesion, as well as the severity stage of a patient. We examine how the algorithms are combined into an end-to-end pipeline for utilization by hospitals, doctors and clinicians on a Software as a Service (SaaS) model. Finally, we discuss potential deployment strategies for the technology, as well an analysis of the market and distribution chain in the specific case of the current Indian healthcare ecosystem.by Mrinal Mohit.S.M

Microchannel network hydrogel induced ischemic blood perfusion connection

Angiogenesis induction into damaged sites has long been an unresolved issue. Local treatment with pro-angiogenic molecules has been the most common approach. However, this approach has critical side effects including inflammatory coupling, tumorous vascular activation, and off-target circulation. Here, the concept that a structure can guide desirable biological function is applied to physically engineer three-dimensional channel networks in implant sites, without any therapeutic treatment. Microchannel networks are generated in a gelatin hydrogel to overcome the diffusion limit of nutrients and oxygen three-dimensionally. Hydrogel implantation in mouse and porcine models of hindlimb ischemia rescues severely damaged tissues by the ingrowth of neighboring host vessels with microchannel perfusion. This effect is guided by microchannel size-specific regenerative macrophage polarization with the consequent functional recovery of endothelial cells. Multiple-site implantation reveals hypoxia and neighboring vessels as major causative factors of the beneficial function. This technique may contribute to the development of therapeutics for hypoxia/inflammatory-related diseases.ope