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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Statistical Learning Algorithm for in situ and invasive breast carcinoma segmentation.

Jagadeesan Jayender1, Eva Gombos, Sona Chikarmane

  • 1Surgical Planning Laboratory, Department of Breast Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. jayender@bwh.harvard.edu

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|May 23, 2013
PubMed
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A new Statistical Learning Algorithm for Tumor Segmentation (SLATS) accurately identifies breast tumors in Dynamic Contrast Enhanced MRI (DCE-MRI) scans. This automated method improves diagnostic accuracy by analyzing perfusion data more effectively than traditional models.

Area of Science:

  • Medical Imaging
  • Radiology
  • Machine Learning in Oncology

Background:

  • Dynamic Contrast Enhanced MRI (DCE-MRI) is sensitive for breast cancer diagnosis but analysis is time-consuming and error-prone.
  • Existing mathematical models for DCE-MRI perfusion quantification are often inaccurate, sensitive to noise, and dependent on numerous external factors.
  • Inaccurate analysis of DCE-MRI data can lead to diagnostic errors in breast cancer assessment.

Purpose of the Study:

  • To develop a novel automated algorithm for accurate tumor segmentation in DCE-MRI data.
  • To improve the efficiency and reliability of breast cancer diagnosis using DCE-MRI.
  • To compare the performance of the novel algorithm against expert radiologists and existing commercial software.

Main Methods:

Keywords:
Computer-aided diagnosisDCE-MRIDuctal Carcinoma In SituHidden Markov ModelsInvasive Ductal CarcinomaStatistical Learning Algorithm

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  • Developed a Statistical Learning Algorithm for Tumor Segmentation (SLATS) utilizing Hidden Markov Models.
  • Trained SLATS to identify tumor voxels using time-intensity curves, first and second derivatives (velocity and acceleration), and a composite vector.
  • Evaluated SLATS performance on 22 Invasive Ductal Carcinoma (IDC) and 19 Ductal Carcinoma In Situ (DCIS) cases.

    • Main Results:

    • The SLATS algorithm demonstrated effective auto-segmentation of tumor regions, corresponding to angiogenesis.
    • SLATS trained using the 'velocity' tuple showed superior performance in tumor delineation.
    • The algorithm's performance surpassed that of a commercially available software (CADstream) and rivaled expert radiologist segmentation.

    Conclusions:

    • The novel SLATS algorithm provides an accurate and efficient method for automated tumor segmentation in DCE-MRI.
    • SLATS offers a promising advancement for improving diagnostic accuracy and reducing analysis time in breast cancer imaging.
    • This machine learning approach holds potential for clinical application in breast cancer diagnosis and characterization.