Principal component analysis of dynamic contrast enhanced mri in human prostate cancer

Erez Eyal, B. Nicolas Bloch, Neil M. Rofsky, Edna Furman-Haran, Elizabeth M. Genega, Robert E. Lenkinski, Hadassa Degani

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Objectives: To develop and evaluate a fast, objective and standardized method for image processing of dynamic contrast enhanced MRI of the prostate based on principal component analysis (PCA). Materials and Methods:The study was approved by the institutional internal review board; signed informed consent was obtained. MRI of the prostate at 3 Tesla was performed in 21 patients with biopsy proven cancers before radical prostatectomy. Seven 3-dimensional gradient echo datesets, 2 pre and 5 post-gadopentetate dimeglumine injection (0.1 mmol/kg), were acquired within 10.5 minutes at high spatial resolution. PCA of dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets and analysis by the 3-time points (3TP) method were applied using the latter method for adjusting the PCA eigenvectors. Results: PCA of 7 IS datasets and 6 ES datasets yielded their corresponding eigenvectors and eigenvalues. The first IS-eigenvector captured the major part of the signal variance because of a signal change between the precontrast and the first postcontrast arising from the inhomogeneous surface coil reception profile. The next 2 IS-eigenvectors and the 2 dominant ES-eigenvectors captured signal changes because of tissue contrast-enhancement, whereas the remaining eigenvectors captured noise changes. These eigenvectors were adjusted by rotation to reach congruence with the wash-in and wash-out kinetic parameters defined according to the 3TP method. The IS and ES-eigenvectors and rotation angles were highly reproducible across patients enabling the calculation of a general rotated eigenvector base that served to rapidly and objectively calculate diagnostically relevant projection coefficient maps for new cases. We found for the a priori selected prostate cancer patients that the projection coefficients of the IS-2nd eigenvector provided a higher accuracy for detecting biopsy proven cancers (94% sensitivity, 67% specificity, 80% ppv, and 89% npv) than the projection coefficients of the ES-2nd rotated and non rotated eigenvectors. Conclusions: PCA adjusted to correlate with physiological parameters selects a dominant eigenvector, free of the inhomogeneous radio-frequency field reception-profile and noise-components. Projection coefficient maps of this eigenvector provide a fast, objective, and standardized means for visualizing prostate cancer.

Original languageEnglish (US)
Pages (from-to)174-181
Number of pages8
JournalInvestigative Radiology
Volume45
Issue number4
DOIs
StatePublished - Apr 2010

Fingerprint

Principal Component Analysis
Prostatic Neoplasms
Noise
Prostate
Biopsy
Gadolinium DTPA
Research Ethics Committees
Prostatectomy
Informed Consent
Radio
Neoplasms
Sensitivity and Specificity
Injections
Datasets

Keywords

  • 3TP method
  • Dynamic contrast enhancement
  • MRI
  • Principal component analysis
  • Prostate cancer

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Eyal, E., Bloch, B. N., Rofsky, N. M., Furman-Haran, E., Genega, E. M., Lenkinski, R. E., & Degani, H. (2010). Principal component analysis of dynamic contrast enhanced mri in human prostate cancer. Investigative Radiology, 45(4), 174-181. https://doi.org/10.1097/RLI.0b013e3181d0a02f

Principal component analysis of dynamic contrast enhanced mri in human prostate cancer. / Eyal, Erez; Bloch, B. Nicolas; Rofsky, Neil M.; Furman-Haran, Edna; Genega, Elizabeth M.; Lenkinski, Robert E.; Degani, Hadassa.

In: Investigative Radiology, Vol. 45, No. 4, 04.2010, p. 174-181.

Research output: Contribution to journalArticle

Eyal, E, Bloch, BN, Rofsky, NM, Furman-Haran, E, Genega, EM, Lenkinski, RE & Degani, H 2010, 'Principal component analysis of dynamic contrast enhanced mri in human prostate cancer', Investigative Radiology, vol. 45, no. 4, pp. 174-181. https://doi.org/10.1097/RLI.0b013e3181d0a02f
Eyal, Erez ; Bloch, B. Nicolas ; Rofsky, Neil M. ; Furman-Haran, Edna ; Genega, Elizabeth M. ; Lenkinski, Robert E. ; Degani, Hadassa. / Principal component analysis of dynamic contrast enhanced mri in human prostate cancer. In: Investigative Radiology. 2010 ; Vol. 45, No. 4. pp. 174-181.
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N2 - Objectives: To develop and evaluate a fast, objective and standardized method for image processing of dynamic contrast enhanced MRI of the prostate based on principal component analysis (PCA). Materials and Methods:The study was approved by the institutional internal review board; signed informed consent was obtained. MRI of the prostate at 3 Tesla was performed in 21 patients with biopsy proven cancers before radical prostatectomy. Seven 3-dimensional gradient echo datesets, 2 pre and 5 post-gadopentetate dimeglumine injection (0.1 mmol/kg), were acquired within 10.5 minutes at high spatial resolution. PCA of dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets and analysis by the 3-time points (3TP) method were applied using the latter method for adjusting the PCA eigenvectors. Results: PCA of 7 IS datasets and 6 ES datasets yielded their corresponding eigenvectors and eigenvalues. The first IS-eigenvector captured the major part of the signal variance because of a signal change between the precontrast and the first postcontrast arising from the inhomogeneous surface coil reception profile. The next 2 IS-eigenvectors and the 2 dominant ES-eigenvectors captured signal changes because of tissue contrast-enhancement, whereas the remaining eigenvectors captured noise changes. These eigenvectors were adjusted by rotation to reach congruence with the wash-in and wash-out kinetic parameters defined according to the 3TP method. The IS and ES-eigenvectors and rotation angles were highly reproducible across patients enabling the calculation of a general rotated eigenvector base that served to rapidly and objectively calculate diagnostically relevant projection coefficient maps for new cases. We found for the a priori selected prostate cancer patients that the projection coefficients of the IS-2nd eigenvector provided a higher accuracy for detecting biopsy proven cancers (94% sensitivity, 67% specificity, 80% ppv, and 89% npv) than the projection coefficients of the ES-2nd rotated and non rotated eigenvectors. Conclusions: PCA adjusted to correlate with physiological parameters selects a dominant eigenvector, free of the inhomogeneous radio-frequency field reception-profile and noise-components. Projection coefficient maps of this eigenvector provide a fast, objective, and standardized means for visualizing prostate cancer.

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