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MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation.

Yuchen Fei1, Fengyu Zhang1, Chen Zu2

  • 1School of Computer Science, Sichuan University, Chengdu, Sichuan, People's Republic of China.

Methods of Information in Medicine
|February 22, 2021
PubMed
Summary

A new modified random forest recursive feature selection (MRF-RFS) algorithm accurately segments nasopharyngeal carcinoma (NPC) in MRI scans. This method improves tumor margin delineation, aiding diagnosis and radiation therapy planning.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate tumor margin delineation is crucial for cancer diagnosis and treatment.
  • Nasopharyngeal carcinoma (NPC) segmentation in MRI is challenging due to image variability and low contrast.
  • Difficulties in identifying tumor boundaries impact clinical management.

Purpose of the Study:

  • To develop a semiautomatic algorithm for NPC image segmentation.
  • To achieve accurate and reproducible tumor margin delineation with minimal human intervention.
  • To enhance the segmentation of nasopharyngeal carcinoma in MRI.

Main Methods:

  • Proposed a novel modified random forest recursive feature selection (MRF-RFS) algorithm.
  • Applied a modified recursive feature selection to handcrafted features for improved discrimination.
  • Combined MRF-RFS with classical random forest (RF) utilizing feature importance measures.

Main Results:

  • The MRF-RFS method was validated on T1-weighted MRI images from 18 NPC patients.
  • Experimental results showed superior performance compared to baseline and deep learning methods.
  • Demonstrated high accuracy and reproducibility in NPC image segmentation.

Conclusions:

  • The developed MRF-RFS method is effective for NPC diagnosis.
  • The algorithm can assist in guiding radiation therapy for nasopharyngeal carcinoma.
  • Highlights the potential of advanced feature selection in medical image analysis.