Comparison of four different displays for identification of select pathologic features extracted from whole slide images of surgical pathology cases.

BACKGROUND: The establishment of minimum standards for display selection for the whole slide image (WSI) interpretation has not been fully defined. Recently, pathologists have increasingly preferred using remote displays for clinical diagnostics. Our study aims to assess and compare the performance of three fixed work displays and one remote personal display in accurately identifying ten selected pathologic features integrated into WSIs. DESIGN: Hematoxylin and eosin-stained glass slides were digitized using Philips scanners. Seven practicing pathologists and three residents reviewed ninety WSIs to identify ten pathologic features using the LG, Dell, and Samsung and an optional consumer-grade display. Ten pathologic features included eosinophils, neutrophils, plasma cells, granulomas, necrosis, mucin, hemosiderin, crystals, nucleoli, and mitoses. RESULTS: The accuracy of the identification of ten features on different types of displays did not significantly differ among the three types of "fixed" workplace displays. The highest accuracy was observed for the identification of neutrophils, eosinophils, plasma cells, granuloma, and mucin. On the other hand, a lower accuracy was observed for the identification of crystals, mitoses, necrosis, hemosiderin, and nucleoli. Participant pathologists and residents preferred the use of larger displays (>30″) with a higher pixel count, resolution, and luminance. CONCLUSION: Most features can be identified using any display. However, certain features posed more challenges across the three fixed display types. Furthermore, the use of a remote personal consumer-grade display chosen according to the pathologists' preference showed similar feature identification accuracy. Several factors of display characteristics seemed to influence pathologists' display preferences such as the display size, color, contrast ratio, pixel count, and luminance calibration. This study supports the use of standard "unlocked" vendor-agnostic displays for clinical digital pathology workflow rather than purchasing "locked" and more expensive displays that are part of a digital pathology system.

Artificial Intelligence-Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow.

Identifying lymph node (LN) metastasis in invasive breast carcinoma can be tedious and time-consuming. We investigated an artificial intelligence (AI) algorithm to detect LN metastasis by screening hematoxylin and eosin (H&E) slides in a clinical digital workflow. The study included 2 sentinel LN (SLN) cohorts (a validation cohort with 234 SLNs and a consensus cohort with 102 SLNs) and 1 nonsentinel LN cohort (258 LNs enriched with lobular carcinoma and postneoadjuvant therapy cases). All H&E slides were scanned into whole slide images in a clinical digital workflow, and whole slide images were automatically batch-analyzed using the Visiopharm Integrator System (VIS) metastasis AI algorithm. For the SLN validation cohort, the VIS metastasis AI algorithm detected all 46 metastases, including 19 macrometastases, 26 micrometastases, and 1 with isolated tumor cells with a sensitivity of 100%, specificity of 41.5%, positive predictive value of 29.5%, and negative predictive value (NPV) of 100%. The false positivity was caused by histiocytes (52.7%), crushed lymphocytes (18.2%), and others (29.1%), which were readily recognized during pathologists' reviews. For the SLN consensus cohort, 3 pathologists examined all VIS AI annotated H&E slides and cytokeratin immunohistochemistry slides with similar average concordance rates (99% for both modalities). However, the average time consumed by pathologists using VIS AI annotated slides was significantly less than using immunohistochemistry slides (0.6 vs 1.0 minutes, P = .0377). For the nonsentinel LN cohort, the AI algorithm detected all 81 metastases, including 23 from lobular carcinoma and 31 from postneoadjuvant chemotherapy cases, with a sensitivity of 100%, specificity of 78.5%, positive predictive value of 68.1%, and NPV of 100%. The VIS AI algorithm showed perfect sensitivity and NPV in detecting LN metastasis and less time consumed, suggesting its potential utility as a screening modality in routine clinical digital pathology workflow to improve efficiency.

Integrating and validating automated digital imaging analysis of estrogen receptor immunohistochemistry in a fully digital workflow for clinical use.

Background: The Visiopharm automated estrogen receptor (ER) digital imaging analysis (DIA) algorithm assesses digitized ER immunohistochemistry (IHC) by segmenting tumor nuclei and detecting stained nuclei automatically. We aimed to integrate and validate this algorithm in a digital pathology workflow for clinical use. Design: The study cohort consisted of a serial collection of 97 invasive breast carcinoma specimens including 73 biopsies and 24 resections. ER IHC slides were scanned into Philips Image Management System (IMS) during our routine digital workflow and digital images were directly streamed into Visiopharm platform and analyzed using automated ER algorithm to obtain the positively stained tumor nuclei and staining intensity. ER DIA scores were compared with pathologists' manual scores. Results: The overall concordance between pathologists' reads and DIA reads was excellent (91/97, 93.8%). Pearson Correlation Coefficient of the percentage of ER positive nuclei between the original reads and VIS reads was 0.72. Six cases (3 ER-negative and 3 ER-positive) had discordant results. All 3 false negative cases had very weak ER staining and no more than 10% positivity. The causes for false positive DIA were mainly pre-analytic/pre-imaging and included intermixed benign glands in tumor area, ductal carcinoma in-situ (DCIS) components, and tissue folding. Conclusions: Automated ER DIA demonstrates excellent concordance with pathologists' scores and accurately discriminates ER positive from negative cases. Furthermore, integrating automated biomarker DIA into a busy clinical digital workflow is feasible and may save time and labor for pathologists.

Design, synthesis, and evaluation of cisplatin-containing EGFR targeting bioconjugates as potential therapeutic agents for brain tumors.

The aim of this study was to evaluate four different platinated bioconjugates containing a cisplatin (cis-diamminedichloroplatinum [cis-DDP]) fragment and epidermal growth factor receptor (EGFR)-targeting moieties as potential therapeutic agents for the treatment of brain tumors using a human EGFR-expressing transfectant of the F98 rat glioma (F98EGFR) to assess their efficacy. The first two bioconjugates employed the monoclonal antibody cetuximab (C225 or Erbitux(®)) as the targeting moiety, and the second two used genetically engineered EGF peptides. C225-G5-Pt was produced by reacting cis-DDP with a fifth-generation polyamidoamine dendrimer (G5) and then linking it to C225 by means of two heterobifunctional reagents. The second bioconjugate (C225-PG-Pt) employed the same methodology except that polyglutamic acid was used as the carrier. The third and fourth bioconjugates used two different EGF peptides, PEP382 and PEP455, with direct coordination to the Pt center of the cis-DDP fragment. In vivo studies with C225-G5-Pt failed to demonstrate therapeutic activity following intracerebral (ic) convection-enhanced delivery (CED) to F98EGFR glioma-bearing rats. The second bioconjugate, C225-PG-Pt, failed to show in vitro cytotoxicity. Furthermore, because of its high molecular weight, we decided that lower molecular weight peptides might provide better targeting and microdistribution within the tumor. Both PEP382-Pt and PEP455-Pt bioconjugates were cytotoxic in vitro and, based on this, a pilot study was initiated using PEP455-Pt. The end point for this study was tumor size at 6 weeks following tumor cell implantation and 4 weeks following ic CED of PEP455-Pt to F98 glioma-bearing rats. Neuropathologic examination revealed that five of seven rats were either tumor-free or only had microscopic tumors at 42 days following tumor implantation compared to a mean survival time of 20.5 and 26.3 days for untreated controls. In conclusion, we have succeeded in reformatting the toxicity profile of cis-DDP and demonstrated the therapeutic efficacy of the PEP455-Pt bioconjugate in F98 glioma-bearing rats.

Aurora-A inhibition offers a novel therapy effective against intracranial glioblastoma.

Glioblastoma remains a devastating disease for which novel therapies are urgently needed. Here, we report that the Aurora-A kinase inhibitor alisertib exhibits potent efficacy against glioblastoma neurosphere tumor stem-like cells in vitro and in vivo. Many glioblastoma neurosphere cells treated with alisertib for short periods undergo apoptosis, although some regain proliferative activity upon drug removal. Extended treatment, however, results in complete and irreversible loss of tumor cell proliferation. Moreover, alisertib caused glioblastoma neurosphere cells to partially differentiate and enter senescence. These effects were also observed in glioma cells treated with the Aurora-A inhibitor TC-A2317 or anti-Aurora-A siRNA. Furthermore, alisertib extended median survival of mice bearing intracranial human glioblastoma neurosphere tumor xenografts. Alisertib exerted similar effects on glioblastoma neurosphere cells in vivo and resulted in markedly reduced activated phosphoThr288Aurora-A and increased abnormal mitoses and cellular ploidy, consistent with on-target activity. Our results offer preclinical proof-of-concept for alisertib as a new therapeutic for glioma treatment.