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Human behavior is intricately shaped by social influences that arise from interactions with others in diverse contexts. These influences not only mold beliefs and attitudes but also drive the regulation of behaviors through both direct communication and observational learning. The study of these processes falls within the domain of social psychology, which seeks to understand how individuals are affected by and affect those around them.Mechanisms of Social InfluenceDirect social influence...
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Voluntary Breath-hold Technique for Reducing Heart Dose in Left Breast Radiotherapy
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An image-based deep learning framework for individualizing radiotherapy dose.

Bin Lou1, Semihcan Doken2, Tingliang Zhuang3

  • 1755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.

The Lancet. Digital Health
|August 27, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Deep Profiler, an AI tool that analyzes lung CT scans to predict radiotherapy failure. It enables personalized radiation doses (iGray) for improved lung cancer treatment outcomes.

Keywords:
artificial intelligencepersonalized medicineprecision oncologytumor heterogeneity

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Radiotherapy is often standardized, lacking personalization based on individual tumor traits.
  • Precise radiotherapy requires identifying parameters predicting treatment failure.

Purpose of the Study:

  • To leverage lung CT imaging features for predicting radiotherapy response.
  • To develop a method for individualizing radiotherapy dosage based on predicted sensitivity.

Main Methods:

  • A deep neural network (Deep Profiler) analyzed pre-therapy lung CT images from 849 lung cancer patients.
  • The model generated an image fingerprint to predict treatment outcomes and radiomic features.
  • Validation was performed on an independent cohort (n=95), and Deep Profiler was integrated with clinical data to create iGray for individualized dosing.

Main Results:

  • Deep Profiler scores correlated with local failure rates; high scores indicated significantly higher failure rates (3-year local failure: 20.3% vs. 5.7%).
  • Deep Profiler independently predicted local failure (HR 1.65, p=0.04) and improved treatment failure prediction models (C-index 0.72).
  • The model demonstrated strong performance in an external validation cohort (C-index 0.77) and iGray suggested dose reductions in 23.3% of patients.

Conclusions:

  • Distinct patient subpopulations exhibit varying radiotherapy sensitivity, identifiable through imaging.
  • This deep learning framework represents a novel approach to individualize radiotherapy dosage using medical images.