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Health & Medicine

SPATNIC enables H&E-grounded cancer-cell identification across spatial and single-cell transcriptomic modalities in colorectal cancer

Tumors contain diverse non-cancer cells that constitute the tumor microenvironment; thus, identifying which cells are cancerous is a foundational and critical first step in cancer research. However, to our knowledge, no transcriptome-based classifier has been trained using pathologist-defined annotations from matched H&E images at single-cell resolution. Moreover, few tools can be shared across…

We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.

Read the original at biorxiv.org →

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AI Detects Half of Lung Cancers Missed on Neck CT Scans, Highlighting New Opportunities for Medical Imaging

Artificial intelligence could help identify lung cancers overlooked during routine interpretation of CT scans performed for other medical purposes, according to a new study highlighting the potential…

  • AI software AVIEW LCS detects 51.3% of lung cancers missed in neck CT scans
  • Study analyzes 81,794 CT exams, finds 80 cancers not documented initially
  • AI uncovers additional diagnostic information without extra imaging exams

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