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New AI tool predicts colon cancer survival, t

New AI tool accurately predicts both overall survival and disease-free survival after colorectal cancer diagnosis.

The model uses visual markers on pathology images to glean insights into a tumor’s genomic profile and predicts tumor behavior, disease progression, treatment response.

The new model could help augment clinical decision-making.

Because the AI tool relies on images alone, it could be particularly valuable for hospitals lacking the technology or expertise to perform sophisticated genomic profiling of tumor tissues. ....

Harvard University , United States , National Cheng Kung University , Tainan Municipality , Dana Farber Cancer Institute , Marios Giannakis , Tsung Lu Michael Lee , Mai Chan Lau , Tomotaka Ugai , Andrew Chan , Jeffrey Meyerhardt , Tsung Hua Lee , Pei Chen Tsai , Kana Wu , Melissa Zhao , Seyed Mousavi Kahaki , Eliana Marostica , Kun Chi Kuo , Shuji Ogino , Kun Hsing Yu , Jonathan Nowak , Yasutoshi Takashima , Fang Yi Su , Blavatnik Center , Nurse Health Study , Cancer Genome Atlas Program ,

Model Provides Actionable Insights for Physicians to Augment Clinical Decisions

Model Provides Actionable Insights for Physicians to Augment Clinical Decisions
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National Cheng Kung University , Tainan Municipality , United States , Dana Farber Cancer Institute , Harvard University , Tsung Lu Michael Lee , Kana Wu , Marios Giannakis , Tomotaka Ugai , Shuji Ogino , Kun Hsing Yu , Seyed Mousavi Kahaki , Mai Chan Lau , Jonathan Nowak , Tsung Hua Lee , Pei Chen Tsai , Fang Yi Su , Andrew Chan , Eliana Marostica , Melissa Zhao , Yasutoshi Takashima , Kun Chi Kuo , Jeffrey Meyerhardt , Google Research Scholar Award , Oulu University Hospital , Nature Communications ,