Skip to main content
Erschienen in: European Radiology 3/2021

02.09.2020 | Magnetic Resonance

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

verfasst von: Rakesh Shiradkar, Ananya Panda, Patrick Leo, Andrew Janowczyk, Xavier Farre, Nafiseh Janaki, Lin Li, Shivani Pahwa, Amr Mahran, Christina Buzzy, Pingfu Fu, Robin Elliott, Gregory MacLennan, Lee Ponsky, Vikas Gulani, Anant Madabhushi

Erschienen in: European Radiology | Ausgabe 3/2021

Einloggen, um Zugang zu erhalten

Abstract

Objectives

To explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ratios (TCRs) on whole mount histopathology of prostate cancer (PCa) and prostatitis.

Materials and methods

A retrospective, IRB-approved, HIPAA-compliant cohort consisting of 14 PCa patients who underwent 3 T multiparametric MRI along with T1 and T2 MRF maps prior to radical prostatectomy was used. Correspondences between whole mount specimens and MRI and MRF were manually established. Prostatitis, PCa, and normal peripheral zone (PZ) regions of interest (ROIs) on pathology were segmented for TCRs of epithelium, lumen, and stroma using two U-net deep learning models. Corresponding ROIs were mapped to T2-weighted MRI (T2w), apparent diffusion coefficient (ADC), and T1 and T2 MRF maps. Their correlations with TCRs were computed using Pearson’s correlation coefficient (R). Statistically significant differences in means were assessed using one-way ANOVA.

Results

Statistically significant differences (p < 0.01) in means of TCRs and T1 and T2 MRF were observed between PCa, prostatitis, and normal PZ. A negative correlation was observed between T1 and T2 MRF and epithelium (R = − 0.38, − 0.44, p < 0.05) of PCa. T1 MRF was correlated in opposite directions with stroma of PCa and prostatitis (R = 0.35, − 0.44, p < 0.05). T2 MRF was positively correlated with lumen of PCa and prostatitis (R = 0.57, 0.46, p < 0.01). Mean T2 MRF showed significant differences (p < 0.01) between PCa and prostatitis across both transition zone (TZ) and PZ, while mean T1 MRF was significant (p = 0.02) in TZ.

Conclusion

Significant associations between MRF (T1 in the TZ and T2 in the PZ) and tissue compartments on corresponding histopathology were observed.

Key Points

Mean T2 MRF measurements and ADC within cancerous regions of interest dropped with increasing ISUP prognostic groups (IPG).
Mean T1 and T2 MRF measurements were significantly different (p < 0.001) across IPGs, prostatitis, and normal peripheral zone (NPZ).
T2 MRF showed stronger correlations in the peripheral zone, while T1 MRF showed stronger correlations in the transition zone with histopathology for prostate cancer.
Anhänge
Nur mit Berechtigung zugänglich
Literatur
7.
Zurück zum Zitat Chatterjee A, Watson G, Myint E, Sved P, McEntee M, Bourne R (2015) Changes in epithelium, stroma, and lumen space correlate more strongly with Gleason pattern and are stronger predictors of prostate ADC changes than cellularity metrics. Radiology 277:751–762. https://doi.org/10.1148/radiol.2015142414 Chatterjee A, Watson G, Myint E, Sved P, McEntee M, Bourne R (2015) Changes in epithelium, stroma, and lumen space correlate more strongly with Gleason pattern and are stronger predictors of prostate ADC changes than cellularity metrics. Radiology 277:751–762. https://​doi.​org/​10.​1148/​radiol.​2015142414
20.
Zurück zum Zitat Nyúl LG, Udupa JK (1999) On standardizing the MR image intensity scale. Magn Reson Med 42:1072–1081CrossRef Nyúl LG, Udupa JK (1999) On standardizing the MR image intensity scale. Magn Reson Med 42:1072–1081CrossRef
21.
Zurück zum Zitat Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. Springer International Publishing, Cham, pp 234–241CrossRef Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. Springer International Publishing, Cham, pp 234–241CrossRef
24.
Zurück zum Zitat R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
35.
Metadaten
Titel
T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology
verfasst von
Rakesh Shiradkar
Ananya Panda
Patrick Leo
Andrew Janowczyk
Xavier Farre
Nafiseh Janaki
Lin Li
Shivani Pahwa
Amr Mahran
Christina Buzzy
Pingfu Fu
Robin Elliott
Gregory MacLennan
Lee Ponsky
Vikas Gulani
Anant Madabhushi
Publikationsdatum
02.09.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 3/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-07214-9

Weitere Artikel der Ausgabe 3/2021

European Radiology 3/2021 Zur Ausgabe

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.