Aiforia® Colorectal Cancer QuantCRC

The prognostic AI model identifies important histological features of colorectal cancer and provides a recurrence prediction estimate useful for treatment decisions. The AI model was developed and validated in collaboration with the Mayo Clinic.

Aiforia® Colorectal Cancer QuantCRC is currently for Research Use Only (RUO) and for Performance Studies Only (PSO) in all market areas.

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Development and validation

Prognostic AI model identifies different tissue characteristics in colorectal cancer patient samples, and combined with two other clinical parameters, it produces a colorectal cancer recurrence risk score.

Dr. Rish Pai from Mayo Clinic trained a highly advanced AI model for detecting multiple pathologic features of colorectal carcinoma. He used Aiforia^® Create end to end by annotating all the training data himself.
QuantCRC was verified and validated in Aiforia^® Platform using whole slide images and against reviews by independent pathologists from eight different hospitals.
The effectiveness of the AI model has been demonstrated through retrospective analysis in multiple independent patient sample cohorts¹^-³.

1. Pai et al. (2022). Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival. Gastroenterology, 163(6), 1531–1546. https://doi.org/10.1053/j.gastro.2022.08.025

2. Pai et al. (2021). Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters. Histopathology, 79(3), 391–405. https://doi.org/10.1111/his.14353

3. Wu et al. (2024). Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC. Clinical Cancer Research, 30(9), 1811-1821. https://doi.org/10.1158/1078-0432.ccr-23-3211

Using prognostic AI models in pathology:
case colorectal cancer

Watch our on-demand webinar to learn how AI can aid oncologists and pathologists working in the gastrointestinal field.

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Potential value in clinical use in the future

Improved risk stratification

QuantCRC improves accuracy in predicting recurrence-free survival for colorectal cancer patients by identifying patients who might benefit from more intense treatment or closer monitoring.

Tailored treatment plans

Patients can receive tailored treatment based on their individual prognosis.

Cost savings in treatment

Implementing the prognostic QuantCRC AI model can lead to substantial cost savings in treatment by optimizing the targeting of expensive chemotherapy drugs.

Seeing is believing

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Discover the power of AI for image analysis

Find out how to enhance your image analysis work in diagnostic pathology, preclinical studies, or medical research. The demo will be tailored based on your interests.

The demo will help you understand:

How AI-assisted image analysis can increase efficiency, precision, and consistency in the pathology workflow.
The limitless possibilities Aiforia® Platform offers – both for research and clinical diagnostics – and the suitable use cases for your needs.

We can either demonstrate Aiforia’s image analysis solutions on your own images or any of our application examples (i.e., neuron quantification, automated tumor grading, NASH analysis, etc.).

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Aiforia® Colorectal Cancer QuantCRC

Development and validation

Using prognostic AI models in pathology: case colorectal cancer

Potential value in clinical use in the future

Improved risk stratification

Tailored treatment plans

Cost savings in treatment

Request a demo

Discover the power of AI for image analysis

Using prognostic AI models in pathology:
case colorectal cancer