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Radiation: Applications01:17

Radiation: Applications

The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry.

Michalis F Georgiou1, Joshua A Nielsen2,3, Rommel Chiriboga2

  • 1Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.

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Artificial Intelligence (AI) simplifies radioactive iodine therapy (RAIT) dosimetry for differentiated thyroid carcinoma (DTC). An AI system accurately predicts treatment doses using fewer data points, streamlining patient care.

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Area of Science:

  • Endocrinology
  • Nuclear Medicine
  • Oncology

Background:

  • Differentiated thyroid carcinoma (DTC) is a common endocrine malignancy.
  • Radioactive iodine therapy (RAIT) with I-131 is a standard treatment for DTC, aiming to eliminate residual disease and metastases.
  • Accurate dosimetry is crucial for RAIT to maximize efficacy and minimize toxicity, but conventional methods are lengthy and complex.

Purpose of the Study:

  • To explore the application of Artificial Intelligence (AI) in simplifying and optimizing RAIT dosimetry.
  • To develop and validate an AI system for predicting maximum permissible activity (MPA) in RAIT for DTC patients.

Main Methods:

  • Retrospective analysis of 83 adult DTC patients who underwent RAIT dosimetry (1996-2023).
  • Conventional MIRD-based dosimetry involved imaging and blood sampling at multiple time points (4-96 h post-I-131 administration).
  • A deep-learning neural network AI system was developed to predict MPA using only early data (4, 24, 48 h).

Main Results:

  • The AI system accurately predicted MPA values for RAIT.
  • AI-predicted MPA showed no significant difference compared to conventional MIRD-based dosimetry (p = 0.351).
  • The AI approach reduces the need for extensive imaging and blood sampling sessions.

Conclusions:

  • AI-based dosimetry offers a streamlined and efficient method for RAIT planning in differentiated thyroid carcinoma.
  • This approach has the potential to optimize resource allocation and improve patient-specific treatment.
  • AI-based dosimetry represents a promising step towards precision medicine in thyroid cancer treatment.