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Related Concept Videos

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

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Content-Based 3D Image Retrieval and a ColBERT-Inspired Re-ranking for Tumor Flagging and Staging.

Farnaz Khun Jush1, Steffen Vogler2, Matthias Lenga2

  • 1Radiology R&D, Bayer AG, Müllerstr. 178, 13353, Berlin, Germany. farnaz.khunjush@bayer.com.

Journal of Imaging Informatics in Medicine
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces C-MIR, a novel system for retrieving similar medical images without pre-segmentation. C-MIR improves tumor detection, especially for colon and lung cancers, by adapting advanced AI for 3D medical imaging.

Keywords:
ColBERTContent-based image retrieval (CBIR)Re-rankingTumor flagging and stagingVision embeddings

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Increasing medical image volumes challenge radiologists' case retrieval.
  • Content-based image retrieval (CBIR) offers potential but lacks standardization.
  • Prior CBIR studies focused on tumor characterization, not volumetric data.

Purpose of the Study:

  • Advance CBIR for volumetric medical images.
  • Develop a framework usable with PACS (Picture Archiving and Communication System).
  • Introduce and evaluate C-MIR, a novel volumetric re-ranking method.

Main Methods:

  • Developed a framework independent of pre-segmented or organ-specific data.
  • Introduced C-MIR, adapting ColBERT's late interaction for 3D medical imaging.
  • Conducted comprehensive evaluations across four tumor sites with varied configurations.

Main Results:

  • C-MIR effectively adapts late interaction for context-aware re-ranking in 3D.
  • C-MIR localizes regions of interest, negating pre-segmentation needs.
  • Significant improvements in tumor flagging, especially for colon and lung tumors (p<0.05).

Conclusions:

  • C-MIR offers a computationally efficient alternative to data enrichment.
  • C-MIR shows promise for improving tumor staging.
  • This work bridges advanced retrieval techniques with clinical healthcare applications.