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Updated: May 3, 2026

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

Published on: December 23, 2022

Multimodal image driven patient specific tumor growth modeling.

Yixun Liu¹, Samira M Sadowski², Allison B Weisbrod²

¹Radiology and Imaging Sciences, NIH, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|February 8, 2014

Summary

This study presents a personalized tumor growth model using CT and PET scans. The model accurately predicts tumor size and characteristics, aiding in cancer staging and treatment planning.

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Published on: July 8, 2014

Area of Science:

Medical Imaging
Computational Biology
Oncology

Background:

Personalized tumor growth models are crucial for accurate cancer staging and therapy planning.
Integrating clinical imaging data enhances model specificity and predictive power.

Purpose of the Study:

To develop a patient-specific tumor growth model using longitudinal dual-phase CT and FDG-PET imaging.
To integrate cancerous cell proliferation, infiltration, metabolic rate, and biomechanical responses into a reaction-advection-diffusion framework.
To bridge the model with multimodal radiologic images via intracellular volume fraction (ICVF) and Standardized Uptake Value (SUV).

Main Methods:

Construction of a patient-specific tumor growth model using a reaction-advection-diffusion equation.
Integration of multimodal radiologic data (CT and FDG-PET) through ICVF and SUV.
Validation of the model by comparing predicted tumors against observed tumors in six patients.

Main Results:

The model achieved an average surface distance (ASD) of 2.5 +/- 0.7 mm between predicted and reference tumors.
Root mean square difference (RMSD) of the ICVF map was 4.3 +/- 0.6%.
Average ICVF difference (AICVFD) of the tumor surface was 2.6 +/- 0.8%, and tumor relative volume difference (RVD) was 7.7 +/- 1.9%.

Conclusions:

The developed personalized tumor growth model accurately predicts tumor progression using multimodal imaging.
The model shows significant potential for improving tumor staging and guiding therapy planning in neuroendocrine tumors.