Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study

Shoshana B. Ginsburg*, Ahmad Algohary, Shivani Pahwa, Vikas Gulani, Lee Ponsky, Hannu J. Aronen, Peter J. Boström, Maret Böhm, Anne Maree Haynes, Phillip Brenner, Warick Delprado, James Thompson, Marley Pulbrock, Pekka Taimen, Robert Villani, Phillip Stricker, Ardeshir R. Rastinehad, Ivan Jambor, Anant Madabhushi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

78 Citations (Scopus)

Abstract

Purpose: To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). Materials and Methods: 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. Results: Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC=0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P<0.05). When classifiers were evaluated on MRI data from multiple institutions, statistically similar AUC values (P>0.14) were obtained for all institutions. Conclusion: A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ.

Original languageEnglish
Pages (from-to)184-193
Number of pages10
JournalJournal of Magnetic Resonance Imaging
Volume46
Issue number1
DOIs
Publication statusPublished - Jul 2017
Externally publishedYes

Keywords

  • Magnetic resonance imaging
  • Multi-institutional
  • Prostate cancer
  • Radiomics

Fingerprint

Dive into the research topics of 'Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study'. Together they form a unique fingerprint.

Cite this