To follow up on an earlier post on the future of artificial intelligence, AI has been making serious inroads in radiological imaging for a while. Unsurprisingly, histological imaging is the next frontier, and AI is conquering that as well. A subset of lung cancer patients will see metastatic spread to their brains. A recent study reports that deep learning algorithms can distinguish which cancers will, and which won’t, metastasize to the brain based on histology of primary tumor sections. Furthermore, the computer predictions are more accurate than trained pathologists. From the abstract: “The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of
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Joel Eissenberg considers the following as important: artificial intelligence, Healthcare, lung cancer, predicting brain metastasis
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To follow up on an earlier post on the future of artificial intelligence, AI has been making serious inroads in radiological imaging for a while. Unsurprisingly, histological imaging is the next frontier, and AI is conquering that as well.
A subset of lung cancer patients will see metastatic spread to their brains. A recent study reports that deep learning algorithms can distinguish which cancers will, and which won’t, metastasize to the brain based on histology of primary tumor sections. Furthermore, the computer predictions are more accurate than trained pathologists. From the abstract:
“The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 58.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algoithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy.”
The study started as an attempt to discover predictive biomarkers and that failed. Instead, AI was able to make predictions about cancer progression using biopsy samples that were already being collected for diagnosis.