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Targeted Cancer Therapies02:57

Targeted Cancer Therapies

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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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Related Experiment Video

Updated: Jan 8, 2026

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
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AI-powered pathobiology transformers predict prognosis and targeted therapy benefits in patients with colorectal

Yuanxin Zhang1,2,3, Delin Tan1, Jieru Zhang4

  • 1Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

International Journal of Surgery (London, England)
|December 19, 2025
PubMed
Summary

A new deep learning model integrates digital pathology and RNA data to predict prognosis and targeted therapy benefits for colorectal ovarian metastases. This precision tool aids clinical decisions by identifying patients who benefit from specific treatments.

Keywords:
colorectal neoplasmsdeep learningmutationneoplasm metastasisovarian neoplasmspathologyperitoneal neoplasmsprognosis

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Area of Science:

  • Oncology
  • Pathology
  • Genomics
  • Artificial Intelligence

Background:

  • Individualized management of colorectal ovarian metastases requires precision medicine tools.
  • Current methods lack prognostic heterogeneity consideration and targeted therapy guidance.
  • Genetic testing is costly and has long turnaround times.

Purpose of the Study:

  • To develop and validate an interpretable deep learning model for predicting prognosis and targeted therapy benefits in colorectal ovarian metastases.
  • To integrate digital pathology and RNA data for enhanced predictive accuracy.
  • To identify patients who can benefit from targeted therapies.

Main Methods:

  • Developed and validated a transformer-based transfer learning model using digital pathology and RNA data.
  • Retrospective, prospective multicohort study design.
  • Model performance assessed using AUC, accuracy, sensitivity, specificity, PPV, and NPV.

Main Results:

  • The model accurately predicted peritoneal recurrence (AUCs 0.74-0.90) and stratified prognosis for recurrence-free survival.
  • Identified tumor microenvironment heterogeneity as a factor in prognostic stratification.
  • Predicted BRAF/RAS mutations (AUCs 0.96/0.94) and identified high-risk patients with these mutations who benefit from targeted therapy.
  • Demonstrated cross-cohort generalizability for mutation prediction (AUCs 0.64-0.83).

Conclusions:

  • A pathobiology-based deep learning model can accurately detect prognosis and mutations.
  • The model identifies beneficiaries of targeted therapy, aiding clinical decision-making.
  • This represents a potential precision tool for managing colorectal ovarian metastases.