The Non-Small Cell Lung Cancer (NSCLC) IHC Cancer Cell Phenotyper App is a pre-trained HALO AI phenotyper designed to detect and quantify membrane DAB-positive and negative cancer cells across whole slide images of primary NSCLC. Outputs include total cell count, other cell counts, cancer cell count, and the number/percentage of cancer cells that are biomarker-negative and biomarker-positive. The App can classify tissues within regions of interest, fields of view, or across entire images and can be combined with HALO® Analysis Modules and other HALO AI Apps and to derive further quantitative outputs and measurements. In the examples shown below, the App is used to analyze biomarker intensity in cancer cells using the HALO Multiplex IHC module. As an RUO product, the App can be further trained, tuned, and improved by end users using their own images and data.
Intended Use
For Research Use Only and not intended for clinical diagnostic use.
What's Included?
- NSCLC IHC Cancer Cell Phenotyper App
- Nuclear Segmenter – optimized and trained for use with the NSCLC IHC Cell Phenotyper App
Training Information
- The NSCLC IHC Cancer Cell Phenotyper training was performed using 145,000+ annotations from hundreds of DAB and hematoxylin-stained whole slide images of primary NSCLC.
- Tissues used for training and validation were probed with PD-L1 and the final training annotation set included both biomarker-positive and negative samples. Although not included in the training set, cancer cell phenotyping with the App was successful on NSCLC tissues probed with biomarkers localizing to all cellular compartments in subsequent testing.
- Tissues were obtained from multiple institutes using different scanner types to improve overall generalizability of cancer cell detection. Note, not all of the file formats shown under ‘File Format Compatibility’ tab were included in the training set.
Prerequisites
All pre-trained HALO AI Apps require an existing license of HALO and HALO AI upgraded to version 4.0.5.
File formats supported by the HALO image analysis platform:
- Non-proprietary (JPG, TIF, OME.TIFF)
- Nikon (ND2)
- 3D Histech (MRXS)
- Akoya (QPTIFF, component TIFF)
- Olympus / Evident (VSI)
- Hamamatsu (NDPI, NDPIS)
- Aperio (SVS, AFI)
- Zeiss (CZI)
- Leica (SCN, LIF)
- Ventana (BIF)
- Philips (iSyntax, i2Syntax)
- KFBIO (KFB, KFBF)
- DICOM (DCM*)
*whole-slide images
Accelerate your AI Development
AI is a powerful tool in your image analysis toolbox, but AI development is time consuming and data intensive. Pre-trained using hundreds of images and training annotations, HALO AI Apps give users a jumpstart to accelerate AI development.
Refine with your Training Data
For most applications, HALO AI Apps will work right out-of-the-box, but we realize that it is impossible to test every application. Importantly, HALO AI Apps are ‘open’ and can be further trained by users to optimize and refine performance for specific applications and stains.
Complement your Expertise
HALO AI Apps are designed to handle time-consuming and tedious tasks and providing consistent, standardized measurements. You are free to apply your scientific expertise where it’s needed most – in the interpretation of data to make informed decisions.
Generalizability and Flexibility
Having been trained and tested on tissues with variable staining from different scanners and with multi-institutional data, our HALO AI Apps are designed to achieve the highest level of generalizability right out-of-the-box, but with the flexibility to be further trained with your own data. Here we see the NSCLC IHC Cancer Cell Phenotyper App used out-of-the-box to accurately detect and phenotype cancer cells in tissues without DAB staining, as well as tissues with moderate and strong DAB staining across different tissue classes.
Seamlessly Integrate with Other HALO AI Apps and HALO Image Analysis Modules
The NSCLC IHC Cancer Cell Phenotyper App can be used in conjunction with other HALO AI Apps or HALO modules to derive additional quantitative outputs. As shown to the right, the NSCLC IHC Cancer Cell Phenotyper App can be added to the HALO Multiplex IHC module to measure biomarker intensity specifically in cancer cells. The cell phenotype markup is in the middle column and in the third column, the HALO Multiplex IHC module is used to assign a 0 to +3 intensity score to cancer cell membranes specifically.
Please note: HALO AI Apps and associated applications are intended for research use only. Please visit our Clinical Products page to discover clinical AI products deployable through our HALO AP® platform.
Free Proof-of-Concept Analysis
See HALO AI Apps in action on up to three of your images with a free proof-of-concept analysis.
Related HALO AI Apps
The NSCLC IHC Tumor Detection App is a pre-trained HALO AI classifier designed to detect, segment, and quantify tumor area and non-tumor area across hematoxylin and DAB-stained whole-slide digital images of NSCLC.
Learn More
The NSCLC H&E Cancer Cell Phenotyper App is a pre-trained object phenotyper designed to detect, quantify, and segment cancer cells from non-cancer cells across H&E-stained whole-slide digital images of NSCLC.
Learn More
The Non-Small Cell Lung Cancer (NSCLC) H&E Tumor Tissue Detection App is a pre-trained HALO AI masking classifier designed to segment tumor, stroma, necrosis/other, and glass area across H&E-stained whole slide images of NSCLC.
Learn MoreUse the arrows above to view additional related AI apps
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