Cchek has been validated with 15 cancer types:

  • Breast Cancer Ribbon Breast Cancer
  • Lung Cancer Ribbon Lung Cancer
  • Colon Cancer Ribbon Colon Cancer
  • Prostate Cancer Ribbon Prostate Cancer
  • Melanoma Ribbon Melanoma
  • Ovarian Cancer Ribbon Ovarian Cancer
  • Liver Cancer Ribbon Liver Cancer
  • Thyroid Cancer Ribbon Thyroid Cancer
  • Pancreative Cancer Ribbon Pancreative Cancer
  • Appendiceal Cancer Ribbon Appendiceal Cancer
  • Uterine Cancer Ribbon Uterine Cancer
  • Osteosarcoma Ribbon Osteosarcoma
  • Leiomyosarcoma Ribbon Leiomyosarcoma
  • Lipsarcoma Ribbon Lipsarcoma
  • Vulvar Cancer Ribbon Vulvar Cancer


Total patients tested: 513*

Cancer patients: 341

Healthy patients: 172

*Pre-treatment and post-treatment biopsy verified samples tested with varying protocols

Preliminary Results

**results tested in an unblinded manner and based on 54 samples from patients with 14 types and varying stages of cancer and 34 healthy patients. Testing did not include benign conditions.

Scientific criteria for evaluating diagnostics:

Sensitivity (Accuracy) measures how good a test is at detecting cancer

Specificity (Reliability) measures how good a test is at distinguishing cancer patients from healthy patients

What is Artificial Intelligence?

Artificial intelligence (AI) refers to the ability of a computer system to simulate human intelligence, with the capacity to perform a greater number of concurrent analyses more quickly than a human typically can.

ITUS is developing a software application using a proprietary neural network, an AI system that focuses on complex pattern recognition. Our neural network has been specifically trained to distinguish between the immunological responses of cancer patients and healthy patients, relying on up to 13 quantitative parameters to analyze test results. As more and more samples are processed, the neural network learns to make more accurate and reliable distinctions. In addition to this enhanced efficiency, the neural network is not subject to human error or bias, which may lead to better results.

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