Deep learning model predicts risk for distant recurrence of endometrial cancer

A deep learning model accurately identified women with endometrial cancer at low and high risk for distant recurrence, according to research presented at American Association for Cancer Research Annual Meeting.
“[The model] can be put into a standard workflow using only H&E [hematoxylin and eosin] tumor slide and stage,” Sarah Fremond, MSc, a PhD candidate in the department of pathology at Leiden University Medical Center in the Netherlands, said during a presentation.
Surgery remains the primary treatment for endometrial cancer, the most common gynecologic cancer. Adjuvant

A deep learning model accurately identified women with endometrial cancer at low and high risk for distant recurrence, according to research presented at American Association for Cancer Research Annual Meeting.
“[The model] can be put into a standard workflow using only H&E [hematoxylin and eosin] tumor slide and stage,” Sarah Fremond, MSc, a PhD candidate in the department of pathology at Leiden University Medical Center in the Netherlands, said during a presentation.
Surgery remains the primary treatment for endometrial cancer, the most common gynecologic cancer. Adjuvant