The Non-Small Cell Lung Cancer (NSCLC) IHC Tumor Tissue Detection App is a pre-trained HALO AI masking classifier designed to detect, quantify, and segment tumor area across DAB-stained, hematoxylin-counterstained whole slide images. Outputs include the percentage and area of tissue classified as tumor or other. The App can classify tissues within regions of interest, fields of view, or across entire images and can be combined with other with HALO® image analysis modules and other HALO AI Apps to derive further quantitative outputs and measurements. In the example shown below, the App is used with other HALO modules to measure biomarker expression specifcially within the tumor compartment. As an RUO product, the NSCLC IHC Tumor Tissue Detection 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 Tumor Tissue Detection App
- Benign Epithelia Classifier – Used to identify and exclude benign epithelia from analysis. Please note, the NSCLC IHC Tumor Tissue Detection App can be used with or without the Benign Epithelia Classifier.
Training Information
- The NSCLC IHC Tumor Tissue Detection App training was performed using 5,000+ 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, tumor tissue detection 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.
Optimized for 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 trained further with your data. Shown here, the NSCLC IHC Tumor Tissue Detection App used right out-of-the-box to detect tumor in tissues with membranous DAB stains of variable intensity and with staining across both tissue classes.
Seamlessly Integrate with Other HALO AI Apps and HALO Image Analysis Modules
The NSCLC IHC Tumor Tissue Detection App seamlessly integrates with HALO analysis modules or other HALO AI Apps. In the examples shown here, the NSCLC IHC Tumor Tissue Detection App is used to analyze membraneous DAB staining within the tumor compartment only by integrating with HALO’s Area Quantification BF (top row) and Multiplex IHC Module (bottom row). Note the strong non-specific staining in necrotic regions of the tissue in the top panel which is effectively excluded from analysis by the App. These examples further highlight the flexibility and robustness of the NSCLC IHC Tumor Tissue Detection App to identify tumor with heterogeneous patterns and stain intensities.
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 Cancer Cell Phenotyper App is a pre-trained HALO AI object phenotyper designed to detect, segment, and quantify non-cancer cells, IHC-positive cancer cells and IHC-negative cancer cells 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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