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Updated: Aug 28, 2025

Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones
Published on: November 30, 2016
Patterns of metastases progression- The linear parallel ratio
Ofer N Gofrit1, Ben Gofrit2, Yuval Roditi2
1Department of Urology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Metastatic progression can be linear or parallel. This study introduces a method to differentiate these patterns by analyzing metastasis size on CT scans, revealing distinct routes for various cancer types.
Area of Science:
- Oncology
- Medical Imaging
- Cancer Metastasis
Background:
- Metastatic progression is modeled by linear and parallel spread patterns.
- Linear model: genetic alterations accumulate in primary tumor before metastasis.
- Parallel model: disseminated tumor cells spread early, with independent selection at ectopic sites.
Purpose of the Study:
- To propose a simple method to differentiate between linear and parallel metastatic progression models.
- To analyze metastasis size distribution to infer the predominant metastatic route.
Main Methods:
- Retrospective analysis of chest CT scans from 503 cancer patients with lung metastases.
- Measurement of metastasis number and largest diameters.
- Calculation of the linear/parallel ratio (LPR) based on metastasis size similarity.
Main Results:
- A total of 12,887 metastases were analyzed across nine malignancy types.
- Overall median LPR was 0.71, indicating a predominantly linear pattern.
- Thyroid, pancreas, and prostate carcinomas showed a median LPR of 1 (purely linear).
- Renal cell carcinoma, melanoma, colorectal, breast, bladder, and sarcoma showed decreasing LPR values, indicating increasing parallel progression.
Conclusions:
- Metastatic spread of thyroid, pancreas, and prostate tumors is predominantly linear.
- Kidney, melanoma, colorectal, breast, bladder, and sarcoma exhibit both linear and parallel spread, with a greater dominance of the parallel route in this order.
- These findings can help predict tumor features and evaluate metastatic origin.
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