Clinical solutions

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. 

 

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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 ² ³. 



1. Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival. Pai, R K. et al. Gastroenterology, Volume 163, Issue 6, 1531–1546.e8 (2022)

2. Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters. Pai R K et al. Histopathology 79, Issue 3, 391–405 (2021)

3. Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC. Wu et al. Clinical Cancer Research, Epub ahead of print. doi: 10.1158/1078-0432.CCR-23-3211 (2024)

Using Prognostic AI Models in Pathology:
Case Colorectal Cancer

Join our upcoming webinar on June 13, 2024 

Potential value in clinical use in the future 

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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.

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Tailored treatment plans

Patients can receive tailored treatment based on their individual prognosis.

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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.

 

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