Developing nomograms and predictive models based on Multiparametric MRI characterised localised prostate cancer

  • Saeed Alqahtani

Student thesis: Doctoral ThesisDoctor of Philosophy


Multiparametric magnetic resonance imaging (MP-MRI) and MRI targeted biopsies (TB) are a new standard in prostate cancer (PCa) screening and diagnosis. Guidelines already include this approach for patients at risk. First, this thesis aimed to assess whether pre-biopsy MRI can narrow the discrepancy of histopathological grades between biopsy and radical prostatectomy (RP) using the Prostate Imaging Reporting and Data System (PIRADS). Second, this thesis aimed to develop a prediction model to identify patients who will benefit from performing systematic random biopsy (SB) at the time of TB.

330 men treated consecutively by RP with localised PCa were included in this study. The MRI and histopathology of the biopsies and RP specimens were assessed respectively. A multivariate model was constructed using logistic regression analysis to assess the ability of MRI to predict upgrading in biopsy Gleason score (GS) in a nomogram. A decision-analysis curve was constructed assessing the impact of the nomogram using different thresholds for probabilities of upgrading. In the SB and TB study, 198 patients with positive MRI findings who underwent both TB and SB were prospectively recruited in this study. The first outcome was to compare the detection rate of clinically significant prostate cancer (csPCa) in SB and TB. For the second outcome, a multivariate analysis using a logistic regression model and nomogram construction were used to identify patients who will benefit from SB in addition to TB. A decision-analysis curve was constructed assessing the impact of the nomogram using different thresholds for probabilities of our model. Statistical analyses were performed using IBM SPSS (version 23.0) and RStudio (version 4.0.3).

Using multivariate analysis, the PIRADS v2.0 score significantly improved the predictive ability of MRI scans for upgrading of biopsy GS (p=0.001, 95% CI [0.06-0.034]), which improved the C-index of predictive nomogram significantly (0.90 vs. 0.64, p<0.05). Moreover, the detection rate of csPCa using SB and TB was 51.0% (101/198) and 56.1% (111/198), respectively, adopting a patients-based biopsy approach. The detection rate of csPCa was higher using a combined biopsy (64.6%; 128/198) compared to a TB (56.1%; 111/198) alone. This was statistically significant (χ2=15.06, df (degree of freedom) = 1, P<0.001). In the multivariate analysis, age, prostate-specific antigen density (PSAD) and PIRADS score were found to predict the detection of significant PCa by SB in addition to TB. A nomogram based on the model showed good discriminative ability (C-index; 78%).

In conclusion, MP-MRI using PIRADS score was shown to be an independent predictor of postoperative GS upgrading, and that this should be taken into consideration while offering treatment options to men with localised PCa. There was a significant difference in the detection of csPCa using a combined biopsy approach. The developed nomogram could help identify those patients at risk of having PCa who will benefit from adding SB biopsy in addition to TB.
Date of Award2022
Original languageEnglish
SupervisorZhihong Huang (Supervisor) & Ghulam Nabi (Supervisor)

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