Grade and stage misclassification in intermediate unfavorable-risk prostate cancer radiotherapy candidates

Tumor upgrading among very favorable intermediate-risk prostate cancer patients treated with robot-assisted radical prostatectomy: how can it impact the clinical course?

PURPOSE

We sought to investigate predictors of unfavorable tumor upgrading in very favorable intermediate-risk (IR) prostate cancer (PCa) patients treated with robot-assisted radical prostatectomy, in addition to evaluate how it may affect the risk of disease progression.

METHODS

A very favorable subset of IR PCa patients presenting with prostate-specific antigen (PSA) < 10 ng/mL, percentage of biopsy positive cores (BPC) < 50%, and either International Society of Urological Pathology (ISUP) grade group 1 and clinical stage T2b or ISUP grade group 2 and clinical stage T1c-2b was identified. Unfavorable pathology at radical prostatectomy was defined as the presence of ISUP grade group > 2 (unfavorable tumor upgrading), extracapsular extension (ECE), and seminal vesicle invasion (SVI). Disease progression was defined as the event of biochemical recurrence and/or local recurrence and/or distant metastases. Associations were evaluated by Cox regression and logistic regression analyses.

RESULTS

Overall, 210 patients were identified between January 2013 and October 2020. Unfavorable tumor upgrading was detected in 71 (33.8%) cases, and adverse tumor stage, including ECE or SVI in 18 (8.6%) and 11 (5.2%) patients, respectively. Median (interquartile range) follow-up was 38.5 (16-61) months. PCa progression occurred in 24 (11.4%) patients. Very favorable IR PCa patients with unfavorable tumor upgrading at final pathology showed a persistent risk of disease progression, which hold significance after adjustment for all factors (Hazard Ratio [HR]: 5.95, 95% Confidence Interval [CI]: 1.97-17.92, p = 0.002) of which PSA was an independent predictor (HR: 1.52, 95% CI 1.12-2.08, p = 0.008). Moreover, these subjects were more likely to belong to the biopsy ISUP grade group 2.

CONCLUSIONS

Very favorable IR PCa patients hiding unfavorable tumor upgrading were more likely to experience disease progression. Unfavorable tumor upgrading involved about one-third of cases and was less likely to occur in patients presenting with biopsy ISUP grade group 1. Tumor misclassification is an issue to discuss, when counseling this subset of patients for active surveillance because of the risk of delayed active treatment.

Mortality Risk for Patients with Biopsy Gleason Grade Group 1 Prostate Cancer

BACKGROUND AND OBJECTIVE

We investigated the association of clinical factors at presentation with the presence of unsampled high-risk prostate cancer (PC) and PC-specific mortality (PCSM) and all-cause mortality (ACM) following radical prostatectomy in patients with biopsy Gleason Grade Group (GGG) 1 PC.

METHODS

The study population comprised 10228 patients treated for GGG1 PC diagnosed via transrectal ultrasound (TRUS)-guided systematic biopsy (SBx; n = 9248) or combined biopsy (CBx; SBx + TRUS/magnetic resonance image [MRI] fusion biopsy; n = 980) from a cohort study at a university hospital in Hamburg, Germany. We used logistic, Fine and Grays, and Cox multivariable regression methods to calculate the adjusted odds ratio (aOR) of adverse pathology and adjusted hazard ratios (aHRs) for early prostate-specific antigen (PSA) failure (≤18 mo), PCSM, and ACM in relation to each clinical factor.

KEY FINDINGS AND LIMITATIONS

Irrespective of biopsy approach, percent positive biopsies (PPB) >50% and PSA >20 ng/ml were significantly associated with higher risk of adverse pathology (SBx: aOR 1.71 and 3.49; CBx: aOR 1.81 and 2.82, respectively) and early PSA failure (SBx: aHR 1.54 and 4.37; CBx: aHR 2.88 and 7.81, respectively). PPB >50% and PSA >20 ng/ml were also associated with higher risk of PCSM (aHRs 2.56 and 3.71) and ACM (aHRs 1.47 and 2.00) in the SBx group (all p ≤ 0.04). The study is limited by the single-institution cohort design.

CONCLUSION AND CLINICAL IMPLICATION

Maintaining the "cancer" classification for patients with GGG1 and either PPB >50% or PSA>20 ng/ml and considering rebiopsy to identify unsampled high-grade disease may minimize the risk of mortality for this subgroup.

PATIENT SUMMARY

For patients undergoing non-targeted prostate biopsy, approximately 1 in 12 with a biopsy result of grade group 1 prostate cancer may have more aggressive cancer than the result suggests. A very high PSA (prostate-specific antigen) level (>20 ng/ml) or the presence of grade group 1 cancer in more than 50% of the biopsy samples can identify patients at risk.

The 2012 Briganti nomogram not only predicts lymph node involvement but also disease progression in surgically treated intermediate-risk prostate cancer patients with PSA <10 ng/mL, ISUP grade group 3, and clinical stage up to cT2b

PURPOSE

We assessed the prognostic impact of the 2012 Briganti nomogram on prostate cancer (PCa) progression in intermediate-risk (IR) patients presenting with PSA <10ng/mL, ISUP grade group 3, and clinical stage up to cT2b treated with robot assisted radical prostatectomy eventually associated with extended pelvic lymph node dissection.

MATERIALS AND METHODS

From January 2013 to December 2021, data of surgically treated IR PCa patients were retrospectively evaluated. Only patients presenting with the above-mentioned features were considered. The 2012 Briganti nomogram was assessed either as a continuous and a categorical variable (up to the median, which was detected as 6%, vs. above the median). The association with PCa progression, defined as biochemical recurrence, and/or metastatic progression, was evaluated by Cox proportional hazard regression models.

RESULTS

Overall, 147 patients were included. Compared to subjects with a nomogram score up to 6%, those presenting with a score above 6% were more likely to be younger, had larger/palpable tumors, presented with higher PSA, underwent tumor upgrading, harbored non-organ confined disease, and had positive surgical margins at final pathology. PCa progression, which occurred in 32 (21.7%) cases, was independently predicted by the 2012 Briganti nomogram both considered as a continuous (Hazard Ratio [HR]:1.04, 95% Confidence Interval [CI]:1.01-1.08;p=0.021), and a categorical variable (HR:2.32; 95%CI:1.11-4.87;p=0.026), even after adjustment for tumor upgrading.

CONCLUSIONS

In IR PCa patients with PSA <10ng/mL, ISUP grade group 3, and clinical stage up to cT2b, the 2012 Briganti nomogram independently predicts PCa progression. In this challenging subset of patients, this tool can identify prognostic subgroups, independently by upgrading issues.

The impact of mpMRI-targeted vs systematic biopsy on the risk of prostate cancer downgrading at final pathology

PURPOSE

Although targeted biopsies (TBx) are associated with improved disease assessment, concerns have been raised regarding the risk of prostate cancer (PCa) overgrading due to more accurate biopsy core deployment in the index lesion.

METHODS

We identified 1672 patients treated with radical prostatectomy (RP) with a positive mpMRI and ISUP ≥ 2 PCa detected via systematic biopsy (SBx) plus TBx. We compared downgrading rates at RP (ISUP 4-5, 3, and 2 at biopsy, to a lower ISUP) for PCa detected via SBx only (group 1), via TBx only (group 2), and eventually for PCa detected with the same ISUP 2-5 at both SBx and TBx (group 3), using multivariable logistic regression models (MVA).

RESULTS

Overall, 12 vs 14 vs 6% (n = 176 vs 227 vs 96) downgrading rates were recorded in group 1 vs group 2 vs group 3, respectively (p < 0.001). At MVA, group 2 was more likely to be downgraded (OR 1.26, p = 0.04), as compared to group 1. Conversely, group 3 was less likely to be downgraded at RP (OR 0.42, p < 0.001).

CONCLUSIONS

Downgrading rates are highest when PCa is present in TBx only and, especially when the highest grade PCa is diagnosed by TBx cores only. Conversely, downgrading rates are lowest when PCa is identified with the same ISUP through both SBx and TBx. The presence of clinically significant disease at SBx + TBx may indicate a more reliable assessment of the disease at the time of biopsy potentially reducing the risk of downgrading at final pathology.

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models

OBJECTIVE

To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.

METHODS

Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.

RESULTS

The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.

CONCLUSIONS

Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.

CLINICAL RELEVANCE STATEMENT

The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.

KEY POINTS

• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Rates of metastatic prostate cancer in newly diagnosed patients: Numbers needed to image according to risk level

BACKGROUND

The numbers needed to image to identify pelvic lymph node and/or distant metastases in newly diagnosed prostate cancer (PCa) patients according to risk level are unknown.

METHODS

Relying on Surveillance, Epidemiology, and End Results (2010-2016), we tabulated rates and proportions of patients with (a) lymph node or (b) distant metastases according to National Comprehensive Cancer Network (NCCN) risk level and calculated the number needed to image (NNI) for both endpoints. Multivariable logistic regression analyses were performed.

RESULTS

Of 145,939 newly diagnosed PCa patients assessable for analyses of pelvic lymph node metastases (cN1), 4559 (3.1%) harbored cN1 stage: 13 (0.02%), 18 (0.08%), 63 (0.3%), 512 (2.8%), and 3954 (14.9%) in low, intermediate favorable, intermediate unfavorable, high, and very high-risk levels. These resulted in NNI of 4619, 1182, 319, 35, and 7, respectively. Of 181,109 newly diagnosed PCa patients assessable for analyses of distant metastases (M1_a-c ), 8920 (4.9%) harbored M1_a-c stage: 50 (0.07%), 45 (0.1%), 161 (0.5%), 1290 (5.1%), and 7374 (22.0%) in low, intermediate favorable, intermediate unfavorable, high, and very high-risk. These resulted in NNI of 1347, 602, 174, 20, and 5, respectively.

CONCLUSIONS

Our observations perfectly validated the NCCN recommendations for imaging in newly diagnosed high and very high-risk PCa patients. However, in unfavorable intermediate-risk PCa patients, in whom bone and soft tissue imaging is recommended, the NNI might be somewhat elevated to support routine imaging in clinical practice.