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Tumor Progression

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.
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Related Experiment Video

Updated: May 5, 2026

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

Published on: August 14, 2012

Novel Bone Scan Features for Predicting Prognosis in Men With Bone Metastatic Prostate Cancer: A Retrospective Study.

Byung Woo Kim^1,2, Jang Hee Han^3,4, Sang Hyun Yoo³

¹Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Korea.

Journal of Korean Medical Science

|August 26, 2025

Summary

Novel bone scan imaging features can improve prognosis prediction for metastatic prostate cancer. Analysis revealed metastasis intensity and lesion size are key indicators, offering more accurate insights than traditional methods.

Keywords:

Bone Metastasis Bone Scan Image Deep Learning Prognosis Prostate Cancer

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Last Updated: May 5, 2026

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

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Published on: August 14, 2012

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Area of Science:

Nuclear Medicine
Oncology
Medical Imaging Analysis

Background:

Bone metastasis is common in prostate cancer, but standardized bone scan image analysis remains a challenge.
Accurate prognosis prediction is crucial for managing metastatic prostate cancer.

Purpose of the Study:

To analyze bone scan imaging features of metastatic prostate cancer.
To assess the prognostic impact of these features on patient outcomes.

Main Methods:

Utilized U-Net architecture for metastatic bone lesion segmentation in 1563 bone scans.
Extracted 18 overall and 32 largest metastatic burden features using computer vision.
Employed Kaplan-Meier survival analysis and Cox proportional risk models for prognostic assessment.

Main Results:

Deep learning model accurately predicted lesion count (correlation coefficient = 0.87).
Metastasis intensity difference and largest metastasis percentage independently predicted disease progression (HRs 0.53 and 0.62, respectively).
Several novel features (e.g., metastasis ratio, lesion percentage, compactness, eccentricity) correlated with progression-free survival.

Conclusions:

Beyond the number of metastases, novel morphological and intensity-based features enhance prognostic accuracy in prostate cancer.
These advanced imaging features offer valuable supplementary information for predicting patient prognosis.