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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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Related Experiment Video

Updated: May 11, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Imaging-Based Deep Learning for Predicting Desmoid Tumor Progression.

Rabih Fares¹, Lilian D Atlan¹, Ido Druckmann¹

¹Department of Radiology, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel.

Journal of Imaging

|May 24, 2024

Summary

This summary is machine-generated.

Deep learning models can predict desmoid tumor (DT) progression with 93% accuracy using baseline MRI scans. This artificial intelligence approach aids in risk stratification and clinical decision-making for DT patients.

Keywords:

MRI artificial intelligence decision-making deep learning desmoid tumor

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Desmoid tumors (DTs) are locally aggressive neoplasms causing significant morbidity.
Current monitoring methods like MRI/CT with RECIST 1.1 lack accuracy in detecting DT response and progression.
Accurate prediction of DT clinical course is crucial for effective management.

Purpose of the Study:

To identify unique deep learning features from baseline MRI that correlate with the future clinical course of desmoid tumors.
To develop an AI model for predicting desmoid tumor progression.

Main Methods:

Retrospective analysis of 51 patients with desmoid tumors (DTs) between 2006-2019.
Tumor segmentation on T2 fat-suppressed, treatment-naive MRI sequences.
Application of deep learning software to segmented lesions and comparison with clinical data.

Main Results:

The deep learning model achieved 93% accuracy (±0.04) and an ROC of 0.89 (±0.08) in independently predicting clinical progression from baseline MRI.
The AI model demonstrated significant capability in identifying unique imaging features predictive of DT behavior.

Conclusions:

Deep learning analysis of baseline MRI can accurately predict desmoid tumor progression.
Artificial intelligence offers a promising tool for risk stratification and clinical decision-making in desmoid tumor management.