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Related Concept Videos

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Related Experiment Video

Updated: Apr 28, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
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3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a

Yurong Jiang1,2,3, Jiawei Zhang1,3,4, Zhaochen Liu5

  • 1Department of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People's Hospital, The Affiliated Hospital of Beijing Institute of Technology), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China.

BMC Cancer
|January 21, 2025
PubMed
Summary

Integrating 3D liver parenchyma analysis with tumor data significantly enhances hepatocellular carcinoma (HCC) prognosis prediction. This synergistic approach improves model accuracy and identifies distinct risk groups for better patient management.

Keywords:
3D assessmentAutomatic segmentationHepatocellular carcinomaLiver parenchymaLiver resectionProgression-free survivalTranscatheter arterial chemoembolizationTreatment efficacy predictionTumorVisualized model

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Area of Science:

  • Hepatocellular Carcinoma Research
  • Medical Imaging Analysis
  • Prognostic Biomarkers

Background:

  • Hepatocellular carcinoma (HCC) prognosis can benefit from assessing liver parenchyma beyond tumor characteristics.
  • Current prognostic models for HCC may lack comprehensive predictive power.

Purpose of the Study:

  • To evaluate if 3D synergistic tumor-liver analysis improves prediction accuracy for HCC prognosis.
  • To develop and compare predictive models incorporating tumor, liver parenchyma, and clinical data.

Main Methods:

  • Included 422 HCC patients across six centers, with data split into training and validation sets.
  • Performed 3D assessment of liver parenchyma alongside tumor analysis, extracting morphological and high-dimensional data.
  • Constructed and compared tumor, tumor-liver, clinical, and integrated models for discrimination and calibration.

Main Results:

  • The tumor-liver model outperformed the tumor-only model in discrimination and calibration.
  • The integrated model, combining clinical factors, tumor, and liver parenchyma data, showed superior discrimination compared to other models.
  • The integrated model's predictive performance was robust across various clinical subgroups and unaffected by specific patient factors.

Conclusions:

  • 3D synergistic tumor-liver assessment, when combined with clinical factors, significantly enhances the prediction efficacy for hepatocellular carcinoma.
  • The integrated model demonstrates superior accuracy in predicting HCC prognosis, offering a more robust tool for clinical decision-making.