Preoperative local staging of prostate cancer : aspects on predictive models, magnetic resonance imaging and interdisciplinary teamwork

In prostate cancer surgery the two issues at stake are the removal of the tumour on one hand and functional outcome i.e. urinary continence and sexual function on the other. A nerve preserving procedure will optimise the functional outcome but introduces the risk of positive surgical margins by accidentally leaving small tumour remnants behind, thus risking a poor oncological outcome. Preoperative knowledge of tumour aggressiveness, location and whether local growth is confined to the prostate is of outmost importance for an optimal outcome. Currently available tools that provide the surgeon with preoperative information on which to base the treatment decision and surgical technique are far from perfect. The overall aim of this thesis was to explore ways to improve preoperative local staging of prostate cancer, including the development of a prediction tool and the use of magnetic resonance imaging (MRI) in the decision of surgical method. In Paper 1 we found that of men who underwent surgery with preoperative characteristics implicating very low risk disease, one third had adverse pathology outcome i.e. non‐organ confined tumours and/or more aggressive tumour features at pathology. Sixteen percent had positive surgical margins and only 40% were urinary continent and sexually potent 12 months after surgery. The findings describe both the shortcomings of the preoperative work‐up and the risks linked to surgery. It also gives support to active surveillance, where active treatment is deferred, as an option for men with very low risk, albeit after careful risk stratification where MRI should play an important role to rule out maleficent tumours. Patients with tumour that on clinical examination are classified as organ‐confined will in approximately one third of the cases subsequently be reclassified at pathology as non‐organ confined. In Paper 2 the development of a prediction tool from preoperative variables, predicting non‐organ confined disease, is described. The accuracy of the final model was only moderate and when validated on an external group showed even lower performance. We found that the probable cause of the low performance was due to variability between pathologists in judgement of our primary outcome measure, tumour stage. This underlines the need for validation before the use of an externally derived prediction model. Paper 3 investigated the additional value of a three‐dimensional (3D) T2‐weighted sequence with radial reconstructions, in local staging of patients receiving a preoperative prostate MRI. A radial reconstruction overcomes the partial volume effect encountered at the curved portions of the prostate seen with conventional imaging methods. The outcome however showed no benefits of adding the 3D sequence but rather introduced an uncertainty when comparing assessments from two radiologists, with an inter‐rater correlation of 0.17 (poor agreement) compared to traditional sequences of 0.42 (moderate agreement). In Paper 4 we compared outcome measures from pathology regarding positive surgical margins between (A) men who had performed a preoperative MRI discussed at an interdisciplinary consensus conference between surgeons and a radiologist and (B) men who were operated on without a preoperative MRI. The group receiving MRI and a conference showed a significant reduction in positive surgical margins but at the cost of less nerve sparing procedures, compared to those men not receiving a preoperative MRI. The findings of this thesis highlight the difficulties encountered at prediction of local tumour stage in prostate cancer at all stages of the preoperative investigation. This implicates the need for improvements, with tuning and standardisation of the different preoperative investigational modalities for better oncological and functional outcome in men undergoing treatment with curative intent. This should be carried out in a multi‐disciplinary setting, to optimize and increase the knowledge of all specialists involved in the care of prostate cancer patients

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

Background Aging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age-related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored. Purpose To develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence. Study Type Retrospective. Population Four hundred and sixty-eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low-grade PC (Gleason score ≤6) and 131 negative patients, defined as non-csPC. The model was tested on 90 negative, 52 low-grade (142 non-csPC), and 114 csPC patients. Field Strength/Sequence 3-T, axial T2-weighted spin sequence. Assessment Chronological age was defined as the age of the participant at the time of the visit. Prostate-specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non-csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non-csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI-RADS ≥ 3 score. Statistical Tests T-test, Mann–Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant. Results After adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32–6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non-csPC than that of adjusted PI-RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975–0.987).publishedVersio

Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach

An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools consider exclusively the axial MRI direction in spite of the availability as per acquisition protocols of multi-view data. Further, when multi-view data is exploited, manual annotations and availability at test time for all the views is commonly assumed. In this work, we explore a contrastive approach at training time to leverage multi-view data without annotations and provide flexibility at deployment time in the event of missing views. We propose a triplet encoder and single decoder network based on U-Net, tU-Net (triplet U-Net). Our proposed architecture is able to exploit non-annotated sagittal and coronal views via contrastive learning to improve the segmentation from a volumetric perspective. For that purpose, we introduce the concept of inter-view similarity in the latent space. To guide the training, we combine a dice score loss calculated with respect to the axial view and its manual annotations together with a multi-view contrastive loss. tU-Net shows statistical improvement in dice score coefficient (DSC) with respect to only axial view (91.25+-0.52% compared to 86.40+-1.50%,P<.001). Sensitivity analysis reveals the volumetric positive impact of the contrastive loss when paired with tU-Net (2.85+-1.34% compared to 3.81+-1.88%,P<.001). Further, our approach shows good external volumetric generalization in an in-house dataset when tested with multi-view data (2.76+-1.89% compared to 3.92+-3.31%,P=.002), showing the feasibility of exploiting non-annotated multi-view data through contrastive learning whilst providing flexibility at deployment in the event of missing views.Comment: Under revie

Accuracy in local staging of prostate cancer by adding a three-dimensional T2-weighted sequence with radial reconstructions in magnetic resonance imaging.

BACKGROUND: The evidence supporting the use of magnetic resonance imaging (MRI) in prostate cancer detection has been established, but its accuracy in local staging is questioned. PURPOSE: To investigate the additional value of multi-planar radial reconstructions of a three-dimensional (3D) T2-weighted (T2W) MRI sequence, intercepting the prostate capsule perpendicularly, for improving local staging of prostate cancer. MATERIAL AND METHODS: Preoperative, bi-parametric prostate MRI examinations in 94 patients operated between June 2014 and January 2015 where retrospectively reviewed by two experienced abdominal radiologists. Each patient was presented in two separate sets including diffusion-weighted imaging, without and with the 3D T2W set that included radial reconstructions. Each set was read at least two months apart. Extraprostatic tumor extension (EPE) was assessed according to a 5-point grading scale. Sensitivity and specificity for EPE was calculated and presented as receiver operating characteristics (ROC) with area under the curve (AUC), using histology from whole-mount prostate specimen as gold standard. Inter-rater agreement was calculated for the two different reading modes using Cohen's kappa. RESULTS: The AUC for detection of EPE for Readers 1 and 2 in the two-dimensional (2D) set was 0.70 and 0.68, respectively, and for the 2D + 3D set 0.62 and 0.65, respectively. Inter-rater agreement (Reader 1 vs. Reader 2) on EPE using Cohen's kappa for the 2D and 2D + 3D set, respectively, was 0.42 and 0.17 (i.e. moderate and poor agreement, respectively). CONCLUSION: The addition of 3D T2W MRI with radial reconstructions did not improve local staging in prostate cancer

PCASTt/SPCG-17-a randomised trial of active surveillance in prostate cancer : rationale and design

Introduction Overtreatment of localised prostate cancer is substantial despite increased use of active surveillance. No randomised trials help define how to monitor patients or when to initiate treatment with curative intent. Methods and analysis A randomised, multicentre, intervention trial designed to evaluate the safety of an MRI-based active surveillance protocol, with standardised triggers for repeated biopsies and radical treatment. The aim is to reduce overtreatment of prostate cancer. 2000 men will be randomly allocated to either surveillance according to current practice or to standardised triggers at centres in Sweden, Norway, Finland and the UK. Men diagnosed in the past 12 months with prostate cancer, 0.2ng/mL/cc, any International Society of Urological Pathology (ISUP) grade 1 are eligible. Men with ISUP grade 2 in Ethics and dissemination Ethical approval was obtained in each participating country. Results for the primary and secondary outcome measures will be submitted for publication in peer-reviewed journals. Trial registration number NCT02914873.Peer reviewe

Mapping prostatic microscopic anisotropy using linear and spherical b‐tensor encoding: A preliminary study

Purpose: Tensor-valued diffusion encoding provides more specific information than conventional diffusion-weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa). Methods: Seventeen patients with histologically proven PCa were enrolled. DWI of the prostate was performed with linear and spherical tensor encoding using a maximal b-value of 1.5 ms/µm2 and a voxel size of 3 × 3 × 4 mm3. The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MKI), and the anisotropic kurtosis (MKA). Regions of interest were placed in MR-defined cancerous tissues, as well as in apparently healthy tissues in the peripheral and transitional zones (PZs and TZs). Results: DWI with linear and spherical encoding yielded different image contrasts at high b-values, which enabled the estimation of MKA and MKI. Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P <.05), higher MKI (P < 10−5), and lower MKA (P <.05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10−3) and higher MKA (P < 10−3). No significant differences were found between cancers of different Gleason scores, possibly because of the limited sample size. Conclusion: Tensor-valued diffusion encoding enabled mapping of MKA and MKI in the prostate. The elevated MKI in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies

Accurate prediction tools in prostate cancer require consistent assessment of included variables

Objective: The aim of this study was to create a preoperative prediction model predicting extraprostatic tumour growth in men with clinically organ-confined disease from a prospectively collected Swedish cohort. Materials and methods: The study used data from 3386 men in the prospective multi-centre Laparoscopic Prostatectomy Robot Open (LAPPRO) trial, with 14 participating urological departments. External validation was performed using a cohort of 634 men from the largest study centre with patients who underwent surgery before and after the inclusion period of the LAPPRO study. External validation of the updated Partin table was used for comparison. The prediction models were created by multivariable logistic regression. Nomogram prediction performance, internal, internal–external and external validation are presented as the area under the receiver operating characteristic curve (AUC). Results: The nomogram reached a prediction performance with an AUC of 0.741, with internal and external validation of 0.738 and 0.698, respectively. Internal–external validation showed great divergence between centres, with AUCs ranging from 0.476 to 0.892, indicating inconsistencies in pathological staging or one or more of the included variables in the regression model. When including centre as a variable in the multivariable model it was significantly associated with the outcome of pT3 (p < 0.001). AUC for external validation of the Partin table was 0.694. Conclusions: Accurate prediction tools in prostate cancer require consistent assessment of included variables, and local validation is needed before the use of such tools in clinical practice

Accuracy in local staging of prostate cancer by adding a three-dimensional T2-weighted sequence with radial reconstructions in magnetic resonance imaging.

Background: The evidence supporting the use of magnetic resonance imaging (MRI) in prostate cancer detection has been established, but its accuracy in local staging is questioned. Purpose: To investigate the additional value of multi-planar radial reconstructions of a three-dimensional (3D) T2-weighted (T2W) MRI sequence, intercepting the prostate capsule perpendicularly, for improving local staging of prostate cancer. Material and Methods: Preoperative, bi-parametric prostate MRI examinations in 94 patients operated between June 2014 and January 2015 where retrospectively reviewed by two experienced abdominal radiologists. Each patient was presented in two separate sets including diffusion-weighted imaging, without and with the 3D T2W set that included radial reconstructions. Each set was read at least two months apart. Extraprostatic tumor extension (EPE) was assessed according to a 5-point grading scale. Sensitivity and specificity for EPE was calculated and presented as receiver operating characteristics (ROC) with area under the curve (AUC), using histology from whole-mount prostate specimen as gold standard. Inter-rater agreement was calculated for the two different reading modes using Cohen's kappa. Results: The AUC for detection of EPE for Readers 1 and 2 in the two-dimensional (2D) set was 0.70 and 0.68, respectively, and for the 2D + 3D set 0.62 and 0.65, respectively. Inter-rater agreement (Reader 1 vs. Reader 2) on EPE using Cohen's kappa for the 2D and 2D + 3D set, respectively, was 0.42 and 0.17 (i.e. moderate and poor agreement, respectively). Conclusion: The addition of 3D T2W MRI with radial reconstructions did not improve local staging in prostate cancer

The Capio Prostate Cancer Center Model for Prostate Cancer Diagnostics—Real-world Evidence from 2018 to 2022

Background: The Capio Prostate Cancer Center (Capio PCC) in Stockholm, Sweden, adopts a comprehensive diagnostic approach, utilizing prostate-specific antigen (PSA), Stockholm3, and magnetic resonance imaging (MRI) for prostate cancer risk assessment, followed by targeted and systematic biopsies for high-risk cases. Objective: This study aims to elucidate the clinical process and real-world outcomes of the Capio PCC model for prostate cancer diagnosis at Capio S:t Göran Hospital. Design, setting, and participants: Between 2018 and 2022, a cohort of 12 406 men aged 45–75 yr underwent prostate cancer testing, adhering to Capio PCC's structured diagnostic protocol. Outcome measurements and statistical analysis: We provide a comprehensive description of the Capio PCC model and present results from its implementation, including assessments of PSA, Stockholm3, MRI scans, and biopsies. A comparative analysis is conducted between the diagnostic outcomes obtained at Capio PCC and those obtained at other regions in Sweden. Results and limitations: The median participant age was 61 yr (interquartile range [IQR]: 55–67), with PSA levels at 1.6 ng/ml (IQR: 0.8–3.3) and Stockholm3 scores at 4 (IQR: 3–11). Among 1064 men (8.6%) undergoing biopsies, 611 (57% of biopsied) were diagnosed with International Society of Urological Pathology grade ≥ 2 cancer. Notably, employing a Stockholm3 ≥ 15 cutoff for biopsy, in lieu of PSA ≥ 3 ng/ml, reduced biopsy recommendations by 43%. For men with PSA levels between 1.5 and 2.9 ng/ml, 360 (12%) exhibited Stockholm3 scores of ≥ 15, with 72 (56% of biopsied) diagnosed with clinically significant prostate cancer. A comparative analysis with national Swedish prostate cancer detection data indicated that the Capio PCC model (vs Sweden) revealed a distribution of 14% (vs 25%) low-risk, 59% (vs 42%) intermediate-risk, and 26% (vs 30%) high-risk and advanced cancers. Conclusions: This study underscores the effectiveness of the protocol-driven diagnostic process at Capio PCC, enabling earlier detection of intermediate-risk prostate cancer and reducing the need for MRI assessments compared with standard prostate cancer care in Sweden. Patient summary: At the Capio Prostate Cancer Center, a novel diagnostic approach incorporating prostate-specific antigen, Stockholm3, magnetic resonance imaging, and targeted biopsies has been implemented to enhance prostate cancer testing and diagnosis in Stockholm, Sweden