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Researchers use AI for tumour classification and prognosis CRC patients

Mon, 07/08/2019 - 09:46
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A research team from University Hospital Zurich and the University of Oxford have now developed a method to predict the molecular classification of colorectal cancer from digital pathology slides, which provides valuable information about the molecular subtype of the tumour when providing targeted therapy for colorectal carcinoma.

The study, ‘{{Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning}}’, stated that precise information about the molecular subtype of the tumour using RNA sequencing can support patient stratification for personalised therapy. However, cancer classification through RNA sequencing remains a resource-intensive, costly process: examining a single sample costs over CHF1,000. Further, up to 20 percent of samples cannot be conclusively classified due to insufficient availability of material or ambiguous results.

A research team led by Professor Viktor Kölzer, Institute of Pathology and Molecular Pathology at University Hospital Zurich (UHZ), and Professor Jens Rittscher, Institute of Biomedical Engineering at the University of Oxford, have now developed a much cheaper, faster method: they use artificial intelligence to analyse high-resolution images of histological slides. This allows the subclassification of colorectal tumours into one of four distinct transcriptional subtypes and gives an indication of optimal treatment strategies.

Unlike RNA sequencing, which has been the gold standard thus far, this purely image-based procedure does not require any additional tissue material. It works even with very small tissue fragments and makes it possible to classify tissue samples that were previously inaccessible due to the technical limitations. The procedure also has the potential to lower costs considerably. Image-based procedures could therefore potentially revolutionise personalised therapy in colorectal cancer. In order to use the new technology, the histological slides need to be appropriately prepared: "to use artificial intelligence for tumour analysis in daily diagnostic practice, we need to digitize pathology workflows," added Kölzer.

Kölzer initialised this project on AI-supported cancer classification during his time at the University of Oxford, in a strong interdisciplinary collaboration with the pathologists, bioinformaticians, clinicians and statisticians of the multi-institutional Stratification in COloRecTal Cancer (S-CORT) Consortium.

The study involved the analysis of 1,553 digital tissue slides with data on RNA expression, gene mutations and clinical progression using the latest machine vision and artificial intelligence technologies. This new technology was first published as a preprint in late May 2019 and is recommended for validation in prospective, randomised clinical trials.

"After validation, we will be able to centralize the classification of colorectal tumours and release the technology for use." Scans of histological slides could be sent to university centres, where they would be evaluated and the results returned electronically. In the long term, the method could also be used for other tumour types or even other diseases,” said Kölzer. “

“This research shows that, with the help of computer analysis, it is possible to detect complex biological patterns from the way the cancer looks under the microscope using routine ways to prepare tissue slides,” said Professor Maughan, leader of the S:CORT consortium. “This has great potential for providing information on how the cancer will behave in the individual and use this in the future to guide treatment decisions."