Multiparametric MRI Tumor Probability Model for the Detection of Locally Recurrent Prostate Cancer After Radiation Therapy: Pathologic Validation and Comparison With Manual Tumor Delineations

Catarina Dinis Fernandes, Rita Simões, Ghazaleh Ghobadi, Stijn W.T.P.J. Heijmink, I.G. Schoots, Jeroen de Jong, Iris Walraven, Henk G. van der Poel, Petra J. van Houdt, Milena Smolic, Floris J. Pos, Uulke A. van der Heide (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Purpose: Focal salvage treatments of recurrent prostate cancer (PCa) after radiation therapy require accurate delineation of the target volume. Magnetic resonance imaging (MRI) is used for this purpose; however, radiation therapy–induced changes complicate image interpretation, and guidelines are lacking on the assessment and delineation of recurrent PCa. A tumor probability (TP) model was trained and independently tested using multiparametric magnetic resonance imaging (mp-MRI) of patients with radio-recurrent PCa. The resulting probability maps were used to derive target regions for radiation therapy treatment planning. Methods and Materials: Two cohorts of patients with radio-recurrent PCa were used in this study. All patients underwent mp-MRI (T2 weighted, diffusion-weighted imaging, and dynamic contrast enhanced). A logistic regression model was trained using imaging features from 21 patients with biopsy-proven recurrence who qualified for salvage treatment. The test cohort consisted of 17 patients treated with salvage prostatectomy. The model was tested against histopathology-derived tumor delineations. The voxel-wise TP maps were clustered using k-means to generate a gross tumor volume (GTV) contour for voxel-level comparisons with manual tumor delineations performed by 2 radiologists and with histopathology-validated contours. Later, k-means was used with 3 clusters to define a clinical target volume (CTV), high-risk CTV, and GTV, with increasing tumor risk. Results: In the test cohort, the model obtained a median (range) area under the curve of 0.77 (0.41-0.99) for the whole prostate. The GTV delineation resulted in a median sensitivity of 0.31 (0-0.87) and specificity of 0.97 (0.84-1.0) with no significant differences between model and manual delineations. The 3-level clustering GTV and high-risk CTV delineations had median sensitivities of 0.17 (0-0.59) and 0.49 (0-0.97) and specificities of 0.98 (0.84-1.00) and 0.94 (0.84-0.99), respectively. Conclusions: The TP model had a good performance in predicting voxel-wise presence of recurrent tumor. Model-derived tumor risk levels achieved sensitivity and specificity similar to manual delineations in localizing recurrent tumor. Voxel-wise TP derived from mp-MRI can in this way be incorporated for target definition in focal salvage of radio-recurrent PCa.

Original languageEnglish
Pages (from-to)140-148
Number of pages9
JournalInternational Journal of Radiation Oncology Biology Physics
Volume105
Issue number1
DOIs
Publication statusPublished - 1 Sep 2019
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported by the Dutch Cancer Society (grant number NKI 2013-5937 and 10088 ).

Funding Information:
This study was supported by the Dutch Cancer Society (grant number NKI 2013-5937 and 10088).

Publisher Copyright:
© 2019 Elsevier Inc.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

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