D-PATH AI IHC QUANTIFICATION

AI IHC Quantification in breast cancer, lung cancer, and beyond AI quantitative analysis is utilized for the assessment of biomarkers such as KI67, ER, PR, HER-2, PD-L1, and CD30. It offers objective and accurate quantitative data, which supports molecular subtyping of cancer and contributes to research and development in targeted therapies.

PD-L1 IHC QUANTIFICATION ASSISTANCE PLATFORM

About it

According to the ASCO, CAP, and NCCN guidelines, PD-L1 testing evaluates tumor cell membrane/cytoplasmic staining through standardized IHC procedures and quantifies expression levels using the Tumor Proportion Score (TPS). This scoring system is utilized for immunotherapy stratification in tumor types such as non-small cell lung cancer (NSCLC).

Integration into the NSCLC Pathology Workflow

Effective tools

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Dual-screen
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Dynamic
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  1. Li M et al. Artificial Intelligence Assisted Assessment of HER2-ultralow and HER2-low Immunohistochemical Scoring in Breast Cancer Core Needle Biopsy Specimens. 2024 USCAP: Abstract #178.

  2. Li, H. et al. (2023). Segment Membranes and Nuclei from Histopathological Images via Nuclei Point-Level Supervision. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_52

  3. Ming Li, Hong Lv, Yizhi Zhao, Chenglu Zhu, Hansheng Li, Mingzhen Lin, Wen-Tao Yang; Abstract PO4-26-09: The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer. Cancer Res 1 May 2024; 84 (9_Supplement): PO4–26–09. https://doi.org/10.1158/1538-7445.SABCS23-PO4-26-09

  4. Li H, Kang Y, Yang W, Wu Z, Shi X, Liu F, Liu J, Hu L, Ma Q, Cui L, Feng J, Yang L. A Robust Training Method for Pathological Cellular Detector via Spatial Loss Calibration. Front Med (Lausanne). 2021 Dec 14;8:767625. doi: 10.3389/fmed.2021.767625. PMID: 34970560; PMCID: PMC8712578.

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