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Urinary bladder cancer staging in CT urography using machine learning.

Sankeerth S Garapati1, Lubomir Hadjiiski1, Kenny H Cha1

  • 1Department of Radiology, The University of Michigan, Ann Arbor, MI, 48109, USA.

Medical Physics
|August 9, 2017
PubMed
Summary
This summary is machine-generated.

This study developed a computer-aided system using machine learning to accurately stage bladder cancer from CT Urography (CTU) scans. The system shows promise for classifying tumors into clinically relevant stages for treatment decisions.

Keywords:
CT urographybladder cancer stagingclassificationcomputer-aided diagnosisfeature extractionmachine learningradiomicssegmentation

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Area of Science:

  • Radiology
  • Oncology
  • Computer Science

Background:

  • Bladder cancer staging is crucial for treatment decisions, particularly distinguishing between stages below T2 and T2 or above for neoadjuvant chemotherapy.
  • Accurate staging of bladder cancer using Computed Tomography Urography (CTU) remains a challenge.

Purpose of the Study:

  • To assess the feasibility of an objective, computer-aided system for bladder cancer staging.
  • To develop and evaluate machine learning models for classifying bladder cancer into two critical pathological stages (≥T2 vs.

Main Methods:

  • A dataset of 84 bladder cancer lesions from 76 CTU cases was utilized.
  • Lesions were automatically segmented, and morphological and texture features were extracted.
  • Four machine learning classifiers (LDA, NN, SVM, RAF) were trained and evaluated using two-fold cross-validation, comparing performance based on area under the ROC curve (Az).

Main Results:

  • The Random Forest (RAF) classifier achieved the highest test Az of 0.97 on Set 2, while other classifiers also demonstrated high performance (Az ranging from 0.88 to 0.92).
  • Both texture-only and combined feature sets showed comparable and effective classification performance.
  • The developed system demonstrated robust accuracy in differentiating between bladder cancer stages.

Conclusions:

  • The computer-aided system shows significant potential as a tool for stratifying bladder cancer patients into clinically relevant stages (≥T2 vs.
  • This objective approach can aid in treatment planning, particularly for neoadjuvant chemotherapy decisions.