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Related Experiment Video

Updated: Jul 4, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

Predicting Chemotherapy Response from Staging Laparoscopy Images.

Thomas Schnelldorfer, Janil Castro, Atoussa Goldar-Najafi

    Medrxiv : the Preprint Server for Health Sciences
    |July 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    This study shows a new AI system can predict chemotherapy resistance in gastrointestinal cancers using laparoscopy images. This could help personalize cancer treatment decisions for better patient outcomes.

    Area of Science:

    • Oncology
    • Artificial Intelligence
    • Surgical Technology

    Background:

    • Chemotherapy resistance is a major challenge in treating metastatic gastrointestinal cancers.
    • Predicting resistance allows for personalized treatment strategies.
    • Current methods lack the ability to predict resistance pre-treatment.

    Purpose of the Study:

    • To assess the feasibility of a deep learning computer vision system for predicting chemotherapy resistance.
    • To utilize laparoscopy images of peritoneal surface metastases to infer molecular characteristics related to drug response.
    • To enable individualized treatment decisions for patients with metastatic gastrointestinal cancers.

    Main Methods:

    • A retrospective observational study included 35 adult patients with gastrointestinal adenocarcinoma and peritoneal metastases.

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    Published on: April 18, 2025

    Related Experiment Videos

    Last Updated: Jul 4, 2026

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

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

    Published on: October 10, 2018

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
    07:13

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

    Published on: April 18, 2025

  • Laparoscopy images of metastases were analyzed using a deep learning model (densely connected convolutional neural network).
  • Chemotherapy resistance was determined by cancer-specific survival, and the model was validated using cross-validation.

    • Main Results:

    • The system achieved high accuracy (0.80) in predicting patient-level chemotherapy resistance.
    • The model demonstrated strong performance with a sensitivity of 0.72 and specificity of 0.88.
    • Saliency maps confirmed the reliability and trustworthiness of the AI system's predictions.

    Conclusions:

    • A prototype surgical computer vision system is technically feasible for predicting chemotherapy resistance.
    • The system uses operative images of peritoneal metastases to determine drug response.
    • Further multi-institutional clinical validation is planned for this innovative approach.