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Related Experiment Video

Updated: May 10, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

A promptable CT foundation model for solid tumor evaluation.

Léo Machado^1,2, Léo Alberge¹, Hélène Philippe^1,2,3

¹Raidium, Paris Biotech Santé, Paris, France.

NPJ Precision Oncology

|April 25, 2025

Summary

This summary is machine-generated.

ONCOPILOT, an interactive AI tool, enhances tumor segmentation and RECIST 1.1 evaluations using CT scans. It improves accuracy and efficiency in oncology research and clinical decision-making.

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Area of Science:

Oncology
Artificial Intelligence
Medical Imaging

Background:

Cancer is complex, leading to heterogeneous tumors with variable outcomes.
Current tumor evaluation methods like RECIST 1.1 are labor-intensive and error-prone.
Existing AI solutions struggle with tumor heterogeneity and lack user interaction.

Purpose of the Study:

To develop an interactive AI tool for 3D tumor segmentation and RECIST 1.1 evaluation.
To refine longitudinal tumor assessment with radiologist engagement.
To improve clinical decision-making and oncology research through advanced AI.

Main Methods:

Developed ONCOPILOT, an interactive CT-based foundation model for 3D tumor segmentation.
Utilized intuitive visual prompts (point-click, bounding boxes, edit-points) for radiologist interaction.
Trained the model on over 8000 CT scans.

Main Results:

ONCOPILOT achieved segmentation accuracy comparable to or exceeding state-of-the-art methods.
Provided radiologist-level precision for RECIST 1.1 measurements, reducing inter-observer variability.
Enhanced workflow efficiency and facilitated access to volumetric tumor analyses.

Conclusions:

Interactive AI, like ONCOPILOT, significantly refines RECIST 1.1 evaluations.
The tool supports improved patient stratification and clinical decision-making.
ONCOPILOT accelerates advancements in oncology research by integrating AI and clinical expertise.