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Related Experiment Videos

AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With

Alan Liu1, Chong Duan2, Kevin Maresca2

  • 1Pfizer Inc, New York, New York, USA.

CPT: Pharmacometrics & Systems Pharmacology
|June 13, 2026
PubMed
Summary

Related Concept Videos

Tumor Progression02:07

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...
Tumor Progression02:07

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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This summary is machine-generated.

AI-powered tumor growth inhibition models offer a more precise and robust assessment of cancer treatment response compared to standard RECIST 1.1 criteria. Individual lesion analysis, enabled by AI, significantly improves prognostic predictions for progression-free survival (PFS).

Area of Science:

  • Oncology
  • Biostatistics
  • Artificial Intelligence in Medicine

Background:

  • Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is the standard for tumor response but is time-consuming and prone to variability.
  • Existing tumor growth inhibition (TGI) models often aggregate lesion data, potentially missing individual lesion dynamics.

Purpose of the Study:

  • To develop and compare AI-derived TGI models with traditional RECIST-based models for evaluating tumor shrinkage.
  • To investigate the association between lesion-level TGI metrics and progression-free survival (PFS).

Main Methods:

  • An AI tool was used to extract comprehensive lesion and aggregate tumor growth data (longest diameters, 3D volumes).
  • TGI models were developed using both standard and AI-derived measurements.
Keywords:
AIPFStumor‐growth‐inhibition

Related Experiment Videos

  • A parametric time-to-event model was used to describe PFS probability.
  • Stochastic simulation and estimation (SSE) were employed to test model robustness.

    • Main Results:

    • AI-derived TGI models demonstrated superior precision and robustness over RECIST-based models.
    • Mean tumor decay rate (KD) and mean lesion size at week 8 were significantly associated with PFS in lesion-level models.
    • Aggregated TGI models failed to identify significant PFS prognostic factors.

    Conclusions:

    • AI-powered auto-measurement and individual lesion dynamics provide a more comprehensive understanding of tumor growth.
    • This approach can facilitate earlier decision-making for targeted therapies and improve prognostic accuracy.