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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.
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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Updated: Nov 29, 2025

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

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Published on: September 13, 2022

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data

Joonsang Lee¹, Nicholas Wang¹, Sevcan Turk²

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Scientific Reports

|November 24, 2020

Summary

Distinguishing pseudoprogression from true tumor progression in diffuse infiltrating gliomas is crucial. A deep learning model using multiparametric MRI sequences effectively improved diagnostic accuracy for this challenge.

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Published on: September 13, 2022

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Neuro-oncology
Medical Imaging
Artificial Intelligence

Background:

Differentiating pseudoprogression from true tumor progression in diffuse infiltrating gliomas, especially high-grade gliomas, presents a significant clinical challenge.
This distinction is critical for timely treatment initiation in patients experiencing glioma recurrence, as delays can negatively impact outcomes.

Purpose of the Study:

To develop and evaluate a deep learning model for discriminating between pseudoprogression and true tumor progression in diffuse infiltrating gliomas.
To assess the efficacy of using multiparametric MRI sequences as input for a convolutional neural network-recurrent neural network (CNN-LSTM) structure.

Main Methods:

A dataset of 43 biopsy-proven diffuse infiltrating glioma patients with disease progression or recurrence was utilized.
Five original MRI sequences (T1-weighted pre- and post-contrast, T2-weighted, FLAIR, ADC) and two engineered sequences were processed.
Three CNN-LSTM models with varying input sequence combinations were trained and evaluated using threefold cross-validation.

Main Results:

The CNN-LSTM models achieved higher diagnostic performance compared to VGG16 models, with mean accuracies ranging from 0.62 to 0.75 and mean AUCs from 0.64 to 0.81.
The proposed CNN-LSTM approach using multiparametric MRI sequences outperformed traditional convolutional neural networks (CNNs) utilizing single MRI sequences.
Incorporating all available MRI sequences into the CNN-LSTM model significantly improved the diagnostic performance for distinguishing pseudoprogression from true tumor progression.

Conclusions:

Multiparametric MRI data, when processed through a CNN-LSTM deep learning architecture, offers a promising method for accurately differentiating pseudoprogression from true tumor progression in diffuse infiltrating gliomas.
This advanced imaging analysis approach has the potential to reduce treatment delays and improve patient management for glioma recurrence.