Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

A Multimodal Generative AI Copilot for Human Pathology.

The field of computational pathology[1,2] has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders[3,4]. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants and copilots[5] tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We build PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and finetuning the whole system on over 456,000 diverse visual language instructions consisting of 999,202 question-answer turns. We compare PathChat against several multimodal vision language AI assistants and GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4[7]. PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases of diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision-language AI Copilot that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making.

Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging.

With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 µm in length by 0.5-1 µm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.

Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions.

The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.

Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications.

In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.

Artificial intelligence algorithm accurately assesses oestrogen receptor immunohistochemistry in metastatic breast cancer cytology specimens: A pilot study.

OBJECTIVE: The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cytology specimens. METHODS: The study cohort comprised 105 consecutive cytology specimens with metastatic breast carcinoma. ER IHC WSIs were seamlessly integrated into the Visiopharm platform from the Image Management System (IMS) during our routine digital workflow, and an AI algorithm was employed for analysis. ER AI scores were compared with pathologists' manual consensus scores. Optimization steps were implemented and evaluated to reduce discordance. RESULTS: The overall concordance between pathologists' scores and AI scores was excellent (99/105, 94.3%). Six cases exhibited discordant results, including two false-negative (FN) cases due to abundant histiocytes incorrectly counted as negatively stained tumour cells by AI, two FN cases owing to weak staining, and two false-positive (FP) cases where pigmented macrophages were erroneously counted as positively stained tumour cells by AI. The Pearson correlation coefficient of ER-positive percentages between pathologists' and AI scores was 0.8483. Optimization steps, such as lowering the cut-off threshold and additional training using higher input magnification, significantly improved accuracy. CONCLUSIONS: The automated ER AI algorithm demonstrated excellent concordance with pathologists' assessments and accurately differentiated ER-positive from ER-negative metastatic breast carcinoma cytology cases. However, precision in identifying tumour cells in cytology specimens requires further enhancement.

Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors.

Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histopathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm's annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity, 100%). Preanalytical factors had no significant impact on whole slide imaging and subsequent application of a CADe software. Prediction accuracy could potentially be further improved by processing biopsy specimens in a centralized histology laboratory and training the Algorithm on WSIs from the same laboratory in order to minimize variations in preanalytical factors and optimize the diagnostic performance of the Algorithm.

Identifying SOX17 as a Sensitive and Specific Marker for Ovarian and Endometrial Carcinomas.

Similar to PAX8, SOX17 was recently identified as a master transcription factor of ovarian cancer based on RNA sequencing data. We explored SOX17 utility in diagnosing ovarian tumors and other gynecologic tumors. We systematically evaluated SOX17 expression on tissue microarrays of 398 ovarian tumors of various types, 93 endometrial carcinomas, 80 cervical carcinomas, and 1371 nongynecologic carcinomas, such as those of kidney, thyroid, breast, colon, bladder, liver, bile duct, adrenal gland, pancreas, brain, and lung and malignant melanoma. In addition, we evaluated SOX17 expression in whole tissue sections from 60 gynecologic carcinomas and 10 angiosarcomas. The results demonstrated that SOX17 was highly expressed in most ovarian and endometrial tumors with strong intensity. However, unlike PAX8, it was predominately negative in other tested tumor types, including kidney and thyroid tumors. In particular, SOX17 was highly expressed in the following pathologic subtypes of ovarian tumors: serous carcinoma, clear cell carcinoma, endometrioid carcinoma, and germ cell tumors. SOX17 was mostly negative in mucinous carcinoma and sex cord stromal tumors. In addition, SOX17 was expressed in vascular endothelial cells and was positive in all tested angiosarcomas. In summary, our results demonstrate that SOX17 is a sensitive and specific marker for ovarian nonmucinous carcinomas and endometrial carcinomas. For ovarian germ cell tumors and angiosarcomas, SOX17 demonstrates higher specificity than PAX8, with comparable sensitivity. Furthermore, SOX17 positivity in endothelial cells serves as an internal positive control, making it an excellent marker.

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.

Incidence, Clinicopathologic Features, HER2 Fluorescence In Situ Hybridization Profile, and Oncotype DX Results of Human Epidermal Growth Factor Receptor 2-Low Breast Cancers: Experience From a Single Academic Center.

Human epidermal growth factor receptor 2 (HER2)-low breast cancer, defined by an immunohistochemical (IHC) score of 1+ or 2+ with negative in situ hybridization, is emerging as a predictive marker for the use of the antibody-drug conjugate. To understand how this category differs from HER2-zero cases, we investigated clinicopathological characteristics and HER2 fluorescence in situ hybridization results in a large cohort of 1309 continuous HER2-negative invasive breast carcinomas from 2018 to 2021 evaluated by the Food and Drug Administration-approved HER2 IHC test. Additionally, we compared Oncotype DX recurrence scores and HER2 mRNA expression between HER-low and HER2-zero cases in a separate cohort of 438 estrogen receptor-positive (ER+) early-stage breast carcinoma cases from 2014 to 2016. Based on the cohort from 2018 to 2021, the incidence of HER2-low breast cancers was approximately 54%. HER2-low cases had less frequent grade 3 morphology, less frequent triple-negative results, ER and progesterone receptor negativity, and a higher mean HER2 copy number and HER2/CEP17 ratio than HER2-zero cases (P < .0001). Among ER+ cases, HER2-low cases showed significantly less frequent Nottingham grade 3 tumors. In the cohort from 2014 to 2016, HER2-low cases showed significantly higher ER+ percentages, fewer progesterone receptor-negative cases, lower Oncotype DX recurrence scores, and higher HER2 mRNA expression scores than HER2-zero cases. In summary, this is the first study, to our knowledge, using a large cohort of continuous cases evaluated by the Food and Drug Administration-approved HER2 IHC companion diagnostic test for HER2-low expression and HER2 fluorescence in situ hybridization profile in a real-world setting. Although HER2-low cases showed a higher HER2 copy number, ratio, and mRNA level than HER2-zero cases statistically, such small differences are unlikely to be biologically or clinically meaningful. However, our study suggests that HER2-low/ER+ early-stage breast carcinoma may represent a less aggressive group of breast carcinoma, given its association with a lower Nottingham grade and Oncotype DX recurrence score.

Relevance of the College of American Pathologists guideline for validating whole slide imaging for diagnostic purposes to cytopathology.

Whole slide imaging (WSI) allows pathologists to view virtual versions of slides on computer monitors. With increasing adoption of digital pathology, laboratories have begun to validate their WSI systems for diagnostic purposes according to reference guidelines. Among these the College of American Pathologists (CAP) guideline includes three strong recommendations (SRs) and nine good practice statements (GPSs). To date, the application of WSI to cytopathology has been beyond the scope of the CAP guideline due to limited evidence. Herein we systematically reviewed the published literature on WSI validation studies in cytology. A systematic search was carried out in PubMed-MEDLINE and Embase databases up to November 2021 to identify all publications regarding validation of WSI in cytology. Each article was reviewed to determine if SRs and/or GPSs recommended by the CAP guideline were adequately satisfied. Of 3963 retrieved articles, 25 were included. Only 4/25 studies (16%) satisfied all three SRs, with only one publication (1/25, 4%) fulfilling all three SRs and nine GPSs. Lack of a suitable validation dataset was the main missing SR (16/25, 64%) and less than a third of the studies reported intra-observer variability data (7/25, 28%). Whilst the CAP guideline for WSI validation in clinical practice helped the widespread adoption of digital pathology, more evidence is required to routinely employ WSI for diagnostic purposes in cytopathology practice. More dedicated validation studies satisfying all SRs and/or GPSs recommended by the CAP are needed to help expedite the use of WSI for primary diagnosis in cytopathology.

Comprehensive analysis of clinicopathologic features and p53 mutation in neuroendocrine neoplasms of the breast: experience from a large academic center.

PURPOSE: The recent WHO classification of breast cancer (2019) categorizes breast carcinoma with neuroendocrine (NE) differentiation into three morphologically distinct subtypes: well-differentiated neuroendocrine tumor (NET), poorly differentiated neuroendocrine carcinoma (NEC), and invasive breast carcinoma, no special type with neuroendocrine differentiation (IBC-NST-NE). Data regarding the prognostic significance of neuroendocrine differentiation are conflicting and an association, if any, between p53 mutation and neuroendocrine differentiation is largely unknown. METHODS: We examined p53 expression and other clinicopathologic characteristics in three types of invasive breast carcinoma with NE differentiation in a cohort of sixty-three patients, including 45 IBC-NST with NE differentiation, 10 NETs, and 8 NECs. RESULTS: No significant difference of clinicopathologic feature was observed between IBC-NST with NE differentiation and NET, but NECs showed significantly lower expressions of hormone receptors, more mutated p53, and higher frequency of distant metastases than IBC-NST with NE differentiation and NETs. CONCLUSION: NECs of the breast are genetically and clinically different from IBC-NST-NEs and NETs of the breast.

An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.

AIM: Allograft rejection is a serious concern in heart transplant medicine. Though endomyocardial biopsy with histological grading is the diagnostic standard for rejection, poor inter-pathologist agreement creates significant clinical uncertainty. The aim of this investigation is to demonstrate that cellular rejection grades generated via computational histological analysis are on-par with those provided by expert pathologists. METHODS AND RESULTS: The study cohort consisted of 2472 endomyocardial biopsy slides originating from three major US transplant centres. The 'Computer-Assisted Cardiac Histologic Evaluation (CACHE)-Grader' pipeline was trained using an interpretable, biologically inspired, 'hand-crafted' feature extraction approach. From a menu of 154 quantitative histological features relating the density and orientation of lymphocytes, myocytes, and stroma, a model was developed to reproduce the 4-grade clinical standard for cellular rejection diagnosis. CACHE-grader interpretations were compared with independent pathologists and the 'grade of record', testing for non-inferiority (δ = 6%). Study pathologists achieved a 60.7% agreement [95% confidence interval (CI): 55.2-66.0%] with the grade of record, and pair-wise agreement among all human graders was 61.5% (95% CI: 57.0-65.8%). The CACHE-Grader met the threshold for non-inferiority, achieving a 65.9% agreement (95% CI: 63.4-68.3%) with the grade of record and a 62.6% agreement (95% CI: 60.3-64.8%) with all human graders. The CACHE-Grader demonstrated nearly identical performance in internal and external validation sets (66.1% vs. 65.8%), resilience to inter-centre variations in tissue processing/digitization, and superior sensitivity for high-grade rejection (74.4% vs. 39.5%, P < 0.001). CONCLUSION: These results show that the CACHE-grader pipeline, derived using intuitive morphological features, can provide expert-quality rejection grading, performing within the range of inter-grader variability seen among human pathologists.

Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper.

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.

M2 tumor-associated macrophages play important role in predicting response to neoadjuvant chemotherapy in triple-negative breast carcinoma.

PURPOSE: Two types of macrophages are present in tumor microenvironment. M1 macrophages exhibit potent anti-tumor properties, while M2 macrophages play the pro-tumoral roles. The presence of M2 macrophages is associated with worsened overall survival in triple-negative breast carcinoma (TNBC) patients. However, the relationship between M2 macrophages and response to neoadjuvant chemotherapy (NAC) is unknown. METHODS: M2 macrophages were investigated on biopsy whole sections from 66 TNBCs treated with NAC by CD163 together with other immune checkpoint markers (PD1, PD-L1 and CD8) using a multi-color immunohistochemical multiplex assay. RESULTS: Incomplete response was significantly associated with older age, lower PD-L1 expression (tumor and stroma), lower levels of CD8-positive TILs in stroma, but higher level of CD163-positive macrophages, with the level of CD163-positive M2 macrophages in peritumoral area as the strongest factor. CONCLUSIONS: Our data have demonstrated that the level of CD163-positive M2 macrophages was significantly higher in TNBC patients with incomplete response than patients with complete response, suggesting M2 macrophages' important role in predicting TNBC patients' response to NAC.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as "interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients." This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

HER2 intratumoral heterogeneity is independently associated with distal metastasis and overall survival in HER2-positive breast carcinomas.

PURPOSE: Human epidermal growth factor receptor 2 (HER2) intratumoral heterogeneity (ITH) occurs in a subset of breast cancers. Our recent study revealed it as an independent predictive factor for the response to anti-HER2 neoadjuvant therapy. In this study, we aimed to investigate its association with distal metastasis. METHODS: HER2 ITH was assessed using HER2 gene protein assay (GPA) on whole tissue sections of pretreatment biopsies from a cohort of 158 HER2-positive invasive breast carcinomas and correlated with patients' clinical follow-up outcomes along with other clinicopathologic characteristics. RESULTS: Fifty-seven cases (36%) showed HER2 ITH including 19 with genetic, 8 with both genetic and non-genetic, and 30 with non-genetic ITH. Multivariate analysis demonstrated larger tumor size, positive resected lymph node(s), negative PR, and the presence of HER2 ITH were independently associated with distal metastasis. Additionally, multivariate analysis demonstrated larger tumor size and the presence of HER2 ITH were the only independent factors associated with decreased overall survival (death). CONCLUSION: The presence of HER2 ITH is an independent factor associated with poor overall survival and increased distal metastasis in HER2-positive breast cancer patients.

Quantitative digital imaging analysis of HER2 immunohistochemistry predicts the response to anti-HER2 neoadjuvant chemotherapy in HER2-positive breast carcinoma.

PURPOSE: Patients with HER2-positive breast cancer commonly receive anti-HER2 neoadjuvant chemotherapy and pathologic complete response (pCR) can be achieved in up to half of the patients. HER2 protein expression detected by immunohistochemistry (IHC) can be quantified using digital imaging analysis (DIA) as a value of membranous connectivity. We aimed to investigate the association HER2 IHC DIA quantitative results with response to anti-HER2 neoadjuvant chemotherapy. METHODS: Digitized HER2 IHC whole slide images were analyzed using Visiopharm HER2-CONNECT to obtain quantitative HER2 membranous connectivity from a cohort of 153 HER2+ invasive breast carcinoma cases treated with anti-HER2 neoadjuvant chemotherapy (NAC). HER2 connectivity and other factors including age, histologic grade, ER, PR, and HER2 fluorescence in situ hybridization (FISH) were analyzed for association with the response to anti-HER2 NAC. RESULTS: Eighty-three cases (54.2%) had pCR, while 70 (45.8%) showed residual tumor. Younger age, negative ER/PR, higher HER2 DIA connectivity, higher HER2 FISH ratio and copy number were significantly associated with pCR in univariate analysis. Multivariate analysis demonstrated only age, HER2 DIA connectivity, PR negativity, and HER2 copy number was significantly associated with pCR, whereas HER2 DIA connectivity had the strongest association. CONCLUSIONS: HER2 IHC DIA connectivity is the most important factor predicting pCR to anti-HER2 neoadjuvant chemotherapy in patients with HER2-positive breast cancer.

Whole Slide Imaging: Technology and Applications.

Pathology has benefited from advanced innovation with novel technology to implement a digital solution. Whole slide imaging is a disruptive technology where glass slides are scanned to produce digital images. There have been significant advances in whole slide scanning hardware and software that have allowed for ready access of whole slide images. The digital images, or whole slide images, can be viewed comparable to glass slides in a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Worldwide usage of whole slide imaging has grown significantly. Pathology regulatory organizations (ie, College of American Pathologists) have put forth guidelines for clinical validation, and the US Food and Drug Administration have also approved whole slide imaging for primary diagnosis. This article will review the digital pathology ecosystem and discuss clinical and nonclinical applications of its use.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.