Diagnosis of early biochemical recurrence after radical prostatectomy or radiation therapy in patients with prostate cancer: State of the art

The cutting edge: Promising oncology radiotracers in clinical development

Molecular imaging moves forward with the development of new imaging agents, and among these are new radiotracers for nuclear medicine applications, particularly positron emission tomography (PET). A number of new targets are becoming accessible for use in oncologic applications. In this review, major new radiotracers in clinical development are discussed. Prominent among these is the family of fibroblast-activation protein-targeted agents that interact with the tumor microenvironment and may show superiority to 2-deoxy-2-[18F]fluoro-d-glucose in a subset of different tumor histologies. Additionally, carbonic anhydrase IX (CAIX) inhibitors are directed at clear cell renal cell carcinoma, which has long lacked an effective PET imaging agent. Those CAIX agents may also have utility in hypoxic tumors. Pentixafor, which binds to a transmembrane receptor, may similarly allow for visualization by PET of low-grade lymphomas, as well as being a second agent for multiple myeloma that opens theranostic possibilities. There are new adrenergic agents aimed at providing a PET-visible replacement to the single-photon-emitting radiotracer meta-[123I]iodobenzylguanidine (MIBG). Finally, in response to a major development in oncologic chemotherapy, there are new radiotracers targeted at assessing the suitability or use of immunotherapeutic agents. All of these and the existing evidence for their utility are discussed.

Comparison of digital and analog [68Ga]Ga-PSMA-11 PET/CT for detecting post-prostatectomy biochemical recurrence in prostate cancer patients: a prospective study

Digital positron emission tomography/computed tomography (PET/CT) has shown enhanced sensitivity and spatial resolution compared with analog PET/CT. The present study compared the diagnostic performance of digital and analog PET/CT with [68Ga]Ga-PSMA-11 in prostate cancer patients who experienced biochemical recurrence (BCR) after prostatectomy. Forty prostate cancer patients who experienced BCR, defined as serum prostate-specific antigen (PSA) concentrations exceeding 0.2 ng/mL after prostatectomy, were prospectively recruited. These patients were stratified into three groups based on their serum PSA levels. [68Ga]Ga-PSMA-11 was injected into each patient, and images were acquired using both analog and digital PET/CT scanners. Analog and digital PET/CT showed comparable lesion detection rate (71.8% vs. 74.4%), sensitivity (85.0% vs. 90.0%), and positive predictive value (PPV, 100.0% vs. 100.0%). However, digital PET/CT detected more lesions (139 vs. 111) and had higher maximum standardized uptake values (SUVmax, 14.3 vs. 10.3) and higher kappa index (0.657 vs. 0.502) than analog PET/CT, regardless of serum PSA levels. On both analog and digital PET/CT, lesion detection rates and interrater agreement increased with increasing serum PSA levels. Compared with analog PET/CT, digital PET/CT detected more lesions with a higher SUVmax and better interrater agreement in prostate cancer patients who experienced BCR after prostatectomy.

Recurrent prostate cancer: combined role for MRI and PSMA-PET in 68Ga-PSMA-11 PET/MRI

OBJECTIVES

To investigate the specific strengths of MRI and PET components in 68Ga-PSMA-11 PET/MRI for staging of patients with biochemically recurrent prostate cancer (PCa).

METHODS

Patients with biochemical recurrence of PCa and contrast-enhanced whole-body 68Ga-PSMA-11 PET/MRI including a dedicated pelvic multiparametric MRI were included in this retrospective study. Imaging datasets of MRI and PET were evaluated separately regarding local PCa recurrence (Tr), pelvic lymph node metastases (N1), distant lymph node metastases (M1a), bone metastases (M1b), and soft tissue metastases (M1c) according to PROMISE version 1. Data evaluation was performed patient- and region-/lesion-based. Cox regression revealed a PSA of 1.69 ng/mL as a cut-off for subgroup analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were evaluated for each image component. Differences in staging accuracy were assessed using the Wilcoxon and McNemar test.

RESULTS

Altogether 102 patients (mean aged 68 ± 8 years, median PSA 1.33 ng/mL) were included. PCa was found in 70/102 (68%) patients. Accuracy of MRI in the detection of Tr, N1, M + , M1a, and M1b was 100%, 79%, 90%, 97%, and 95% for PSA < 1.69 ng/mL and 100%, 87%, 87%, 91%, and 96% for PSA > 1.69 ng/mL. Accuracy of 68Ga-PSMA-11 PET was 93%, 97%, 93%, 98%, and 100% for PSA < 1.69 ng/mL and 87%, 91%, 96%, 100%, and 96% for PSA > 1.69 ng/mL.

CONCLUSIONS

Combined assessment of 68Ga-PSMA-11 PET/MRI improves tumor localization in men with biochemical recurrence. The MRI detected local recurrence of PCa more often whereas 68 Ga-PSMA-11 PET detected lymph node metastases more often, especially for PSA < 1.69 ng/mL.

CLINICAL RELEVANCE STATEMENT

This study gives a scientific baseline to improve the understanding and reading of 68Ga-PSMA-11 PET/MRI imaging in patients with biochemically recurrent PCa by showing the specific strength of each imaging component.

KEY POINTS

• Combining the individual modality strengths of 68Ga-PSMA-11 PET/MRI improves tumor localization in men with biochemical recurrence of prostate cancer. • MRI component of 68 Ga-PSMA-11 PET/MRI shows its strength in detecting local recurrence of prostate cancer, especially at PSA < 1.69 ng/mL. • 68 Ga-PSMA-11 PET component shows its strength in detecting local and distant lymph node metastases, especially at PSA < 1.69 ng/mL.

Predictive model of positive surgical margins after radical prostatectomy based on Bayesian network analysis

Objective

This study aimed to analyze the independent risk factors for marginal positivity after radical prostatectomy and to evaluate the clinical value of the predictive model based on Bayesian network analysis.

Methods

We retrospectively analyzed the clinical data from 238 patients who had undergone radical prostatectomy, between June 2018 and May 2022. The general clinical data, prostate specific antigen (PSA)-derived indicators, puncture factors, and magnetic resonance imaging (MRI) characteristics were included as predictive variables, and univariate and multivariate analyses were conducted. We established a nomogram model based on the independent predictors and adopted BayesiaLab software to generate tree-augmented naive (TAN) and naive Bayesian models based on 15 predictor variables.

Results

Of the 238 patients included in the study, 103 exhibited positive surgical margins. Univariate analysis revealed that PSA density (PSAD) (P = 0.02), Gleason scores for biopsied tissue (P = 0.002) and the ratio of positive biopsy cores (P < 0.001), preoperative T staging (P < 0.001), and location of abnormal signals (P = 0.002) and the side of the abnormal signal (P = 0.009) were all statistically significant. The area under curve (AUC) of the established nomogram model based on independent predictors was 73.80%, the AUC of the naive Bayesian model based on 15 predictors was 82.71%, and the AUC of the TAN Bayesian model was 80.80%.

Conclusion

The predictive model of positive resection margin after radical prostatectomy based on Bayesian network demonstrated high accuracy and usefulness.

MYB exhibits racially disparate expression, clinicopathologic association, and predictive potential for biochemical recurrence in prostate cancer

MYB acts as a potentiator of aggressiveness and castration resistance in prostate cancer (PCa) through aberrant activation of androgen receptor (AR) signaling. Since Black men experience higher PCa incidence and mortality than White men, we examined if MYB was differentially expressed in prostate tumors from patients of these racial backgrounds. The data reveal that aberrant MYB expression starts early in precancerous high-grade prostate intraepithelial neoplastic lesions and increases progressively in malignant cells. PCa tissues from Black patients exhibit higher MYB expression than White patients in overall and grade-wise comparisons. MYB also exhibits a positive correlation with AR expression and both display higher expression in advanced tumor stages. Notably, we find that MYB is a better predictor of biochemical recurrence than AR, pre-treatment PSA, or Gleason's grades. These findings establish MYB as a promising molecular target in PCa that could be used for improved risk prediction and therapeutic planning.

The Role of Salvage Radical Prostatectomy in Patients with Radiation-Resistant Prostate Cancer

There are multiple treatment strategies for patients with localized prostate adenocarcinoma. In intermediate- and high-risk patients, external beam radiation therapy demonstrates effective long-term cancer control rates comparable to radical prostatectomy. In patients who opt for initial radiotherapy but have a local recurrence of their cancer, there is no unanimity on the optimal salvage approach. The lack of randomized trials comparing surgery to other local salvage therapy or observation makes it difficult to ascertain the ideal management. A narrative review of existing prospective and retrospective data related to salvage radical prostatectomy after radiation therapy was undertaken. Based on retrospective and prospective data, post-radiation salvage radical prostatectomy confers oncologic benefits, with overall survival ranging from 84 to 95% at 5 years and from 52 to 77% at 10 years. Functional morbidity after salvage prostatectomy remains high, with rates of post-surgical incontinence and erectile dysfunction ranging from 21 to 93% and 28 to 100%, respectively. Factors associated with poor outcomes after post-radiation salvage prostatectomy include preoperative PSA, the Gleason score, post-prostatectomy staging, and nodal involvement. Salvage radical prostatectomy represents an effective treatment option for patients with biochemical recurrence after radiotherapy, although careful patient selection is important to optimize oncologic and functional outcomes.

Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review

PURPOSE

The purpose of this study was to perform a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Medline, Embase and Web of Science were searched for studies published between January 2018 and September 2022, using a histological reference standard, and assessing prostate cancer characterization/detection by AI-based MRI algorithms in test cohorts composed of more than 40 patients and with at least one of the following independency criteria as compared to the training cohort: different institution, different population type, different MRI vendor, different magnetic field strength or strict temporal splitting.

RESULTS

Thirty-five studies were selected. The overall risk of bias was low. However, 23 studies did not use predefined diagnostic thresholds, which may have optimistically biased the results. Test cohorts fulfilled one to three of the five independency criteria. The diagnostic performance of the algorithms used as standalones was good, challenging that of human reading. In the 12 studies with predefined diagnostic thresholds, radiomics-based computer-aided diagnosis systems (assessing regions-of-interest drawn by the radiologist) tended to provide more robust results than deep learning-based computer-aided detection systems (providing probability maps). Two of the six studies comparing unassisted and assisted reading showed significant improvement due to the algorithm, mostly by reducing false positive findings.

CONCLUSION

Prostate MRI AI-based algorithms showed promising results, especially for the relatively simple task of characterizing predefined lesions. The best management of discrepancies between human reading and algorithm findings still needs to be defined.