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HALO AI Publications

Indica Labs / HALO AI Publications

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration

Alan M. Race, et al, Analytical Chemistry, 2021
With increasing demand to correlate data across multiple imaging modalities, Race and colleagues demonstrate a mechanism by which annotations can be generated on images from one imaging modality and transferred to a second image modality for data integration (and optionally, back again). Further, they perform this workflow on mass spectrometry images of a pancreatic cancer mouse model and hematoxylin and eosin-stained sections. HALO and HALO AI image analysis software was used to develop...

Maternal obesity during pregnancy leads to adipose tissue ER stress in mice via miR-126-mediated reduction in Lunapark

Juliana de Almeida-Faira, et al, Diabetologia, 2021
In this study, researchers set out to understand how miR-126-3, a microRNA found at increased levels in offspring of maternally obese mice, functioned in adipocyte metabolism. de Almeida-Faria and colleagues used proteomic approaches to identify a novel ER protein that is a direct target of miR-126-3 called Lunapark. HALO and HALO AI were used to train a DenseNet algorithm to selectively identify crown-like structures in H&E-stained fat tissue. Further, de Almeida-Faria and colleagues demonstrate...

Tumoral PD-1hiCD8+ T cells are partially exhausted and predict favorable outcome in triple-negative breast cancer

Liang Guo, et al, Clinical Science, 2020
Prior to this publication it was known that dysfunctional PD-1hi CD8+ T cells infiltrated tumors, although it was unknown if this phenotype played a role in triple-negative breast cancer (TNBC). Guo et al set out to explore this phenotype in triple-negative breast cancer and using HALO and HALO AI demonstrated using both quantitative multiplexed immunohistochemistry and multispectral fluorescence imaging that PD-1hi CD8+ T cells were found in TNBC patient tissue biopsy core analysis but...

Comparing Deep Learning and Immunohistochemistry in Determining the Site of Origin for Well-Differentiated Neuroendocrine Tumors

Jordan Redemann, et al, Journal of Pathology Informatics, 2020
Metastatic neuroendocrine tumors behave differently according to site of origin and it is important clinically to identify the primary site in order to identify an appropriate therapy. The site of origin in neuroendocrine tumors are challenging to identify based on H&E alone and can require an immunohistochemistry (IHC) panel. Redemann and colleagues evaluated the performance of HALO AI, a deep-learning convolutional neural network (CNN) on site of origin identification from a set...

Independent Prognostic Value of Intratumoral Heterogeneity and Immune Response Features by Automated Digital Immunohistochemistry Analysis in Early Hormone Receptor-Positive Breast Carcinoma

Dovile Zilenaite, et al, Frontiers in Oncology, 2020
This study by Zilenaite and colleagues evaluated the prognostic value of digital image analysis using HALO on analysis of hormone receptor positive breast cancer IHC biomarkers including ER, PR, HER2, and Ki67 combined with information on tumor heterogeneity and immune response. HALO AI was used for tissue classification to differentiate tumor, stroma, and background (necrosis, artifacts, glass). For quantitative analysis of breast cancer biomarker expression and localization, the Multiplex IHC module of HALO...

Advanced Prostate Cancer with ATM Loss: PARP and ATR Inhibitors

Antje Neeb, et al, European Urology, 2021
Researchers set out to evaluate the role of the ATM kinase in metastatic castration-resistant prostate cancer (mCRPC) with the long-term goal of improving molecular stratification in patients. HALO and HALO AI were used in the analysis of 800 ATM immunohistochemistry samples. Neeb et al detected ATM loss by IHC in 11% of their patient cohort which was associated with increased genomic instability but was not associated with a worse outcome. An in vitro model...

Two distinct immunopathological profiles in autopsy lungs of COVID-19

Ronny Nienhold, et al, Nature Communications, 2020
An international consortium of researchers characterized lung tissue from patients with COVID-19 using transcriptomic, histologic, and cellular analyses. Nienhold and colleagues report two phenotypes associated with lethal COVID-19 disease. One showed high levels of interferon stimulated genes in the lungs as well as limited lung damage and high levels of cytokines and viral loads. The second phenotype included severe lung damage with low levels of interferon stimulated genes, low viral loads, and high levels...

Pulmonary Mycobacterium tuberculosis control associates with CXCR3- and CCR6-expressing antigen-specific Th1 and Th17 cell recruitment

Uma Shanmugasundaram, et al, JCI Insight, 2020
Shanmugasundaram and colleagues used a nonhuman primate model to study T-cell responses associated with latent tuberculosis infection (LTBI). LTBI patients are asymptomatic and are thought to have contained the M. tuberculosis bacteria within granulomatous lesions in the lung. This research group wanted to characterize the immune responses associated with LTBI in order to understand how to prevent the progression to active tuberculosis (TB). In this JCI Insight publication, researchers report that rhesus macaques with...

Artificial Intelligence-Based Quantification of Epithelial Proliferation in Mammary Glands of Rats and Oviducts of Göttingen Minipigs

Henning Hvid, Mikala Skydsgaard, Nikolai K. Jensen, Birgitte M. Viuff, Henrik E. Jensen, Martin B. Oleksiewicz, Peter H. Kvist Toxicologic Pathology | First Published August 25, 2020 | Research Article | https://doi.org/10.1177/0192623320950633 Quantitative assessment of proliferation can be an important endpoint in toxicologic pathology. Traditionally, cell proliferation is quantified by labor-intensive manual counting of positive and negative cells after immunohistochemical staining for proliferation markers (eg, Ki67, bromo-2′-deoxyuridine, or proliferating cell nuclear antigen). Currently, there is a lot of interest in replacing manual...

Immunogradient Indicators for Antitumor Response Assessment by Automated Tumor-Stroma Interface Zone Detection

Allan Rasmusson, et al, The American Journal of Pathology, 2020
An international consortium of researchers characterized lung tissue from patients with COVID-19 using transcriptomic, histologic, and cellular analyses. Nienhold and colleagues report two phenotypes associated with lethal COVID-19 disease. One showed high levels of interferon stimulated genes in the lungs as well as limited lung damage and high levels of cytokines and viral loads. The second phenotype included severe lung damage with low levels of interferon stimulated genes, low viral loads,...

Prognostic significance of mesothelin expression in colorectal cancer disclosed by area-specific four-point tissue microarrays

Takehiro Shiraishi, Eiji Shinto, Ines P. Nearchou, Hitoshi Tsuda, Yoshiki Kajiwara, Takahiro Einama, Peter D. Caie, Yoji Kishi & Hideki Ueno Virchows Archiv | February 2020 | DOI: 10.1007/s00428-020-02775-y Mesothelin (MSLN) is a cell surface glycoprotein present in many cancer types. Its expression is generally associated with an unfavorable prognosis. This study examined the prognostic significance of MSLN expression in different areas of individual colorectal cancers (CRCs) using tissue microarrays (TMAs) by enrolling 314 patients with stage II (T3–T4, N0, M0) CRCs....

Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach

Hoa Hoang Ngoc Pham, et al, The American Journal of Pathology, 2020
Pham and colleagues set out to address high false positivity of lymph nodes metastasis analysis using deep learning. As characterizing lymph node metastases in breast and lung cancer is of great clinical importance for treatment selection and prognosis, finding a method with high sensitivity and specificity would represent a major advance. Here, the researchers demonstrate a two-step approach with HALO AI where the first deep learning algorithm excludes...

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

David R. Martin, et al, Archives of Pathology & Laboratory Medicine, 2019
Researchers from the University of New Mexico set out to investigate tissue classification using deep learning to evaluate nonneoplastic gastric biopsies. Ground truth diagnosis was established by gastrointestinal pathologists. HALO AI was trained to recognize Helicobacter pylori (H pylori) mediated gastritis, chemical gastropathy, normal mucosa, smooth muscle, and glass. The HALO AI classifier showed high sensitivity and specificity for control biopsies and gastropathy cases and represents the first deep...