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Inverse method for quantitative characterisation of breast tumours from surface temperature data.

R Hatwar1, C Herman1

  • 1a Department of Mechanical Engineering , Johns Hopkins University , Baltimore , MD , USA.

International Journal of Hyperthermia : the Official Journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
|May 26, 2017
PubMed
Summary
This summary is machine-generated.

This study presents a computational method using surface temperature data to estimate breast tumor size, location, and blood perfusion. Combining steady-state and transient data improves accuracy for assessing tumor malignancy.

Keywords:
Breast cancerLevenberg–Marquardt methodinverse methodsquantitative tumour characterizationthermography

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Modeling

Background:

  • Accurate characterization of breast tumors is crucial for diagnosis and treatment planning.
  • Non-invasive methods for estimating tumor properties like size, location, and blood perfusion are highly desirable.

Purpose of the Study:

  • To develop and validate a computational method for simultaneously estimating breast tumor size, location, and blood perfusion using surface temperature data.
  • To investigate the efficacy of combining steady-state and transient temperature data for improved tumor characteristic estimation.

Main Methods:

  • A 2D computational phantom of a breast with a spherical tumor was created, incorporating six tissue layers.
  • An inverse algorithm based on the Levenberg-Marquardt method was employed to estimate tumor characteristics from surface temperature distributions.
  • The inverse algorithm was extended to incorporate transient temperature data from dynamic infrared imaging, in addition to steady-state data.

Main Results:

  • Steady-state temperature data alone were insufficient for simultaneously estimating tumor blood perfusion, size, and location due to similar temperature profiles.
  • The integration of transient data with steady-state data enabled the simultaneous estimation of all three tumor characteristics.
  • Estimation errors were below 1% for tumors with depths less than 20 mm, but exceeded 10% for deeper tumors (25 mm).

Conclusions:

  • The combination of transient and steady-state surface temperature data allows for accurate simultaneous estimation of tumor size, location, and blood perfusion.
  • Blood perfusion serves as an indicator of tumor growth rate, potentially aiding in the assessment of tumor malignancy.
  • This computational approach offers a promising non-invasive tool for breast tumor characterization.