DIGITAL PATHOLOGY
to impact diagnoses in ways that we may not be able to currently fully envision. HALO AP from Indica Labs provides
an AI orchestration platform for anatomic pathology that seamlessly integrates AI tools for diagnostics and clinical research. Whether the source of the AI is a HALO Clinical AI product, a third-party tool, or AI developed in the HALO and HALO AI platform for life sciences, HALO AP provides a single unifying user interface where customers can choose the AI that best fits their needs and workflows. HALO Clinical AI products include tools for slide quality control, macrodissection workflows, and analysis of prostate cancer, breast cancer, and lung cancer. Current third-party AI integrations include products from Paige, Ibex, Lunit, Deep Bio, and more.
References 1 Metter DM, Colgan TJ, Leung ST, et al.
Trends in the US and Canadian Pathologist Workforces from 2007 to 2017. JAMA Netw Open. 2019; 2 (5): e194337. doi: 10.1001/jamanetworkopen.2019.4337
2 Soerjomataram I, Bray F. Planning for tomorrow: global cancer incidence and the role of prevention 2020–2070. Nat Rev Clin Oncol. 2021; 18: 663-672. doi: 10.1038/s41571-021-00514-z
3 Raciti P, Sue J, Retamero JA, et al. Clinical Validation of Artificial Intelligence– Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection. Arch Pathol Lab Med. 2023; 147 (10): 1178-1185. doi: 10.5858/ arpa.2022-0066-OA
4 Steiner DF, MacDonald R, Liu Y, et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am J Surg Pathol. 2018; 42(12): 1636-1646. doi: 10.1097/PAS.0000000000001151
5 Brixtel R, Bougleux S, Lézoray, et al. Whole Slide Image Quality in Digital Pathology: Review and Perspectives. IEEE Access. 2022; 10: 131005-131035. doi: 10.1109/ ACCESS.2022.3227437
6 Janowczyk A, Zuo R, Gilmore H, et al. HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. JCO Clin Cancer Inform. 2019; 3. doi: 10.1200/ CCI.18.00157
7 Marrón-Esquivel JM, Duran-Lopez L, Linares-Barranco A, Dominguez- Morales JP. A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer. Comput Biol Med. 2023; 159: 106856. doi: 10.1016/
j.compbiomed.2023.106856
8 Tolkach Y, Ovtcharov V, Pryalukhin A, et al. An international multi-institutional validation study of the algorithm for
prostate cancer detection and Gleason grading. NPJ Precis Oncol. 2023; 7: 77. doi: 10.1038/s41698-023-00424-6
9 Sandbank J, Bataillon G, Nudelman A, et al. Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. NPJ Breast Cancer. 2022; 8: 129. doi: 10.1038/s41523-022-00496-w
10 Pantanowitz L, Quiroga-Garza GM, Bien L, et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit Health. 2020; 2 (8): E407-E416. doi: 10.1016/S2589- 7500(20)30159-X
11 Chen Y, Zee J, Smith A, et al. Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies. J Pathol. 2021; 253 (3): 268-278. doi: 10.1002/path.5590
12 Wu S, Yue M, Zhang J, et al. The Role of Artificial Intelligence in Accurate Interpretation of HER2 Immunohistochemical Scores 0 and 1+ in Breast Cancer. Mod Pathol. 2023; 36 (3): 100054. doi: 10.1016/j. modpat.2022.100054
13 Bencze J, Szarka M, Kóti B, et al. Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry. Biomolecules. 2022; 12 (1): 19. doi: 10.3390/ biom12010019
14 Halbisen AL, Lu CY. Trends in Availability of Genetic Tests in the United States, 2012- 2022. J Pers Med. 2023; 13 (4): 638. doi: 10.3390/jpm13040638
15 Conroy JM, du Pree G, Wimberger- Friedl R, et al. Automated tissue dissection solution to support comprehensive genomic and immune profiling. Society of Laboratory Automation and Screening 2024 Feb 3-7; Boston, MA
16 Viray H, Li K, Long TA, et al. A prospective, multi-institutional diagnostic trial to determine pathologist accuracy in estimation of percentage of malignant cells. Arch Pathol Lab Med. 2013; 137 (11): 1545–1549. doi: 10.5858/arpa.2012-0561- CP
17 Smits AJJ, Kummer JA, de Bruin PC, et al. The estimation of tumour cell percentage for molecular testing by pathologists is not accurate. Mod Pathol. 2014; 27 (2): 168– 174. doi: 10.1038/modpathol.2013.134
18 Paige. Paige AI Solution for Prostate Cancer Biomarker Detection Receives CE-IVD and UKCA Marks. (Business Wire 2022).
https://paige.ai/paige-ai-solution- for-prostate-cancer-biomarker-detection- receives-ce-ivd-and-ukca-marks/
19 Saillard C, Dubois R, Tchita O, et al. Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from
WWW.PATHOLOGYINPRACTICE.COM FEBRUARY 2025
colorectal cancer histology slides. Nat Commun. 2023; 14: 6695. doi: 10.1038/ s41467-023-42453-6
20 Couture HD. Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review. J Pers Med. 2022; 12 (12). doi: 10.3390/JPM12122022
21 Darbandsari A, Farahani H, Asadi M, et al. AI-based histopathology image analysis reveals a distinct subset of endometrial cancers. Nat Commun. 2024; 15: 4973. doi: 10.1038/s41467-024-49017-2
Dr Kayla Hackman MD Kayla Hackman previously worked as a medical writer at Indica Labs. She earned her BS in Biological Sciences from the University of Rhode Island in 2012 before earning her MD from the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell in 2020. Prior to medical school, she was a researcher at the Oregon Health and Science University working on the development of nerve-targeting fluorophores for interoperative use.
Benjamin Dyer Ben earned his BS in Biochemistry from Campbell University in Buies Creek, North Carolina, and his MS in Cellular and Molecular Biology from the University of Pennsylvania School of Medicine. Before joining Indica Labs in 2022, Ben spent several years as a scientific and technical writer for the United States Government, where he had the opportunity to produce diverse products to support the varied needs of customers across the government. In his current role as Scientific Writer, Ben creates and contributes to print and digital scientific marketing content, hosts webinars, and supports other initiatives, helping to share the word about the latest developments in Indica Labs’ software and services.
Hallie Rane Hallie Rane received a BS in biology from the University of New Mexico and has a background in molecular biology and evolution. Hallie joined Indica Labs in 2023 as the Product Specialist for HALO AP and was promoted to Associate Product Manager in 2024. Prior to Indica Labs, Hallie worked in both industry and academic positions and has held a variety of technical and customer-facing roles, where she collaborated with customers, scientists, and technicians to develop a diverse array of products.
HALO AP is CE-IVDR marked for in vitro diagnostic use in Europe, the UK, and Switzerland. HALO AP is For Research Use Only in the USA and is not FDA cleared for clinical diagnostic use. For further product information, please refer to the Indica Labs website.
Indica Labs
+44 (0) 333 090 1113
info@indicalab.com
www.indicalab.com
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