The impact of prostate volume estimation on the risk-adapted biopsy decision based on prostate-specific antigen density and magnetic resonance imaging score.

PURPOSE: Utility of prostate-specific antigen density (PSAd) for risk-stratification to avoid unnecessary biopsy remains unclear due to the lack of standardization of prostate volume estimation. We evaluated the impact of ellipsoidal formula using multiparametric magnetic resonance (MRI) and semi-automated segmentation using tridimensional ultrasound (3D-US) on prostate volume and PSAd estimations as well as the distribution of patients in a risk-adapted table of clinically significant prostate cancer (csPCa). METHODS: In a prospectively maintained database of 4841 patients who underwent MRI-targeted and systematic biopsies, 971 met inclusions criteria. Correlation of volume estimation was assessed by Kendall's correlation coefficient and graphically represented by scatter and Bland-Altman plots. Distribution of csPCa was presented using the Schoots risk-adapted table based on PSAd and PI-RADS score. The model was evaluated using discrimination, calibration plots and decision curve analysis (DCA). RESULTS: Median prostate volume estimation using 3D-US was higher compared to MRI (49cc[IQR 37-68] vs 47cc[IQR 35-66], p < 0.001). Significant correlation between imaging modalities was observed (τ = 0.73[CI 0.7-0.75], p < 0.001). Bland-Altman plot emphasizes the differences in prostate volume estimation. Using the Schoots risk-adapted table, a high risk of csPCa was observed in PI-RADS 2 combined with high PSAd, and in all PI-RADS 4-5. The risk of csPCa was proportional to the PSAd for PI-RADS 3 patients. Good accuracy (AUC of 0.69 and 0.68 using 3D-US and MRI, respectively), adequate calibration and a higher net benefit when using 3D-US for probability thresholds above 25% on DCA. CONCLUSIONS: Prostate volume estimation with semi-automated segmentation using 3D-US should be preferred to the ellipsoidal formula (MRI) when evaluating PSAd and the risk of csPCa.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Toward confident prostate cancer detection using ultrasound: a multi-center study.

PURPOSE: Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue. METHODS: Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods. RESULTS: PCa detection models achieve performance scores up to 76 % average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as 2 % , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue. CONCLUSION: Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.

Prostate-specific membrane antigen-targeted surgery in prostate cancer: Accurate identification, real-time diagnosis, and precise resection.

Radical prostatectomy (RP) combined with pelvic lymph node dissection (PLND) is the first step in multimodal treatment of prostate cancer (PCa) without distant metastases. For a long time, the surgical resection range has been highly dependent on the surgeon's visualization and experience with preoperative imaging. With the rapid development of prostate-specific membrane antigen positron emission tomography and single-photon emission computed tomography (PSMA-PET and PSMA-SPECT), PSMA-targeted surgery has been introduced for a more accurate pathological diagnosis and complete resection of positive surgical margins (PSMs) and micro-lymph node metastases (LNMs). We reviewed PSMA-targeted surgeries, including PSMA-PET-guided prostatic biopsy (PSMA-TB), PSMA-targeted radio-guided surgery (PSMA-RGS), PSMA-targeted fluorescence-guided surgery (PSMA-FGS), and multi-modality/multi-targeted PSMA-targeted surgery. We also discuss the strengths and challenges of PSMA-targeted surgery, and propose that PSMA-targeted surgery could be a great addition to existing surgery protocols, thereby improving the accuracy and convenience of surgery for primary and recurrent PCa in the near future.

Design, synthesis and evaluation of novel prostate-specific membrane antigen-targeted aryl [18F]fluorosulfate PET tracers.

The expression of prostate-specific membrane antigen (PSMA) in prostate cancer is 100-1000 times higher than that in normal tissues, and it has shown great advantages in the diagnosis and treatment of prostate cancer. The combination of PSMA and PET imaging technology based on the principle of metabolic imaging can achieve high sensitivity and high specificity for diagnosis. Due to its suitable half-life (109 min) and good positron abundance (97%), as well as its cyclotron accelerated generation, 18F has the potential to be commercialize, which has attracted much attention. In this article, we synthesized a series of fluorosulfate PET tracers targeting PSMA. All four analogues have shown high affinity to PSMA (IC50 = 1.85-5.15 nM). After the radioisotope exchange labeling, [18F]L9 and [18F]L10 have PSMA specific cellular uptake (0.65 ± 0.04% AD and 1.19 ± 0.03% AD) and effectively accumulated in 22Rv1 xenograft mice model. This study demonstrates that PSMA-1007-based PSMA-targeted aryl [18F]fluorosulfate novel tracers have the potential for PET imaging in tumor tissues.

Multiparametric MRI and 18F-PSMA-1007 PET/CT for the Detection of Clinically Significant Prostate Cancer.

Background Multiparametric MRI (mpMRI) is effective for detecting prostate cancer (PCa); however, there is a high rate of equivocal Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions and false-positive findings. Purpose To investigate whether fluorine 18 (18F) prostate-specific membrane antigen (PSMA) 1007 PET/CT after mpMRI can help detect localized clinically significant PCa (csPCa), particularly for equivocal PI-RADS 3 lesions. Materials and Methods This prospective study included participants with elevated prostate-specific antigen (PSA) levels referred for prostate mpMRI between September 2020 and February 2022. 18F-PSMA-1007 PET/CT was performed within 30 days of mpMRI and before biopsy. PI-RADS category and level of suspicion (LOS) were assessed. PI-RADS 3 or higher lesions at mpMRI and/or LOS 3 or higher lesions at 18F-PSMA-1007 PET/CT underwent targeted biopsies. PI-RADS 2 or lower and LOS 2 or lower lesions were considered nonsuspicious and were monitored during a 1-year follow-up by means of PSA testing. Diagnostic accuracy was assessed, with histologic examination serving as the reference standard. International Society of Urological Pathology (ISUP) grade 2 or higher was considered csPCa. Results Seventy-five participants (median age, 67 years [range, 52-77 years]) were assessed, with PI-RADS 1 or 2, PI-RADS 3, and PI-RADS 4 or 5 groups each including 25 participants. A total of 102 lesions were identified, of which 80 were PI-RADS 3 or higher and/or LOS 3 or higher and therefore underwent targeted biopsy. The per-participant sensitivity for the detection of csPCa was 95% and 91% for mpMRI and 18F-PSMA-1007 PET/CT, respectively, with respective specificities of 45% and 62%. 18F-PSMA-1007 PET/CT was used to correctly differentiate 17 of 26 PI-RADS 3 lesions (65%), with a negative and positive predictive value of 93% and 27%, respectively, for ruling out or detecting csPCa. One additional significant and one insignificant PCa lesion (PI-RADS 1 or 2) were found at 18F-PSMA-1007 PET/CT that otherwise would have remained undetected. Two participants had ISUP 2 tumors without PSMA uptake that were missed at PET/CT. Conclusion 18F-PSMA-1007 PET/CT showed good sensitivity and moderate specificity for the detection of csPCa and ruled this out in 93% of participants with PI-RADS 3 lesions. Clinical trial registration no. NCT04487847 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.

The role of PSMA PET/CT in predicting downgrading in patients with Gleason score 4+4 prostate cancer in prostate biopsy.

BACKGROUND: To investigate the predictable parameters associated with downgrading in patients with a Gleason score (GS) 8 (4+4) in prostate biopsy after radical prostatectomy. METHODS: We retrospectively analyzed 62 patients with a GS of 4+4 on prostate biopsy who underwent robotic radical prostatectomy between 2017 and 2022. RESULTS: 38 of 62 (61.2%) were downgraded. In multivariable logistic regression model, Ga-68 prostate-specific membrane antigen (PSMA) positron-emission tomography (PET)/computed tomography (CT) SUV max was independent predictor of downgrading (OR 0.904; p = 0.011) and a Logistic Regression model was constructed using the following formula: Y = 1.465-0.95 (PSMA PET/CT SUV max). The model using this variable correctly predicted the downgrading in 72.6% of patients. The AUC for PSMA PET/CT SUV max was 0.709 the cut off being 8.8. A subgroup analysis was performed in 37 patients who had no other European Association of Urology (EAU) high risk features. 25 out of 37 (67.5%) were downgraded, and 21 of these 25 had organ confined disease. Low PSMA SUV max (<8.1) and percentage of GS 4+4 biopsy cores to cancer bearing cores (45.0%) were independently associated with downgrading to GS 7. CONCLUSION: PSMA PET/CT can be used to predict downgrading in patients with GS 4+4 PCa. Patients with GS 4+4 disease, but no other EAU high risk features, low percentage of GS 4+4 biopsy cores to cancer bearing cores, and a low PSMA PET/CT SUV max are associated with a high likelihood of the cancer reclassification to intermediate risk group.

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

PSMA-Targeted Radiopharmaceuticals for Prostate Cancer Diagnosis and Therapy.

ABSTRACT: Prostate cancer (PCa) is the most common noncutaneous malignancy in men. Until recent years, accurate imaging of men with newly diagnosed PCa, or recurrent or low-volume metastatic disease, was limited. Further, therapeutic options for men with advanced, metastatic, castration-resistant disease were increasingly limited as a result of increasing numbers of systemic therapies being combined in the upfront metastatic setting. The advent of urea-based, small-molecule inhibitors of prostate-specific membrane antigen (PSMA) has partially addressed those shortcomings in diagnosis and therapy of PCa. On the diagnostic side, there are multiple pivotal phase III trials with several different agents having demonstrated utility in the initial staging setting, with generally modest sensitivity but very high specificity for determining otherwise-occult pelvic nodal involvement. That latter statistic drives the utility of the scan by allowing imaging interpreters to read with very high sensitivity while maintaining a robust specificity. Other pivotal phase III trials have demonstrated high detection efficiency in patients with biochemical failure, with high positive predictive value at the lesion level, opening up possible new avenues of therapy such as metastasis-directed therapy. Beyond the diagnostic aspects of PSMA-targeted radiotracers, the same urea-based chemical scaffolds can be altered to deliver therapeutic isotopes to PCa cells that express PSMA. To date, one such agent, when combined with best standard-of-care therapy, has demonstrated an ability to improve overall survival, progression-free survival, and freedom from skeletal events relative to best standard-of-care therapy alone in men with metastatic, castration-resistant PCa who are post chemotherapy. Within the current milieu, there are a number of important future directions including the use of artificial intelligence to better leverage diagnostic findings, further medicinal chemistry refinements to the urea-based structure that may allow improved tumor targeting and decreased toxicities, and the incorporation of new radionuclides that may better balance efficacy with toxicities than those nuclides that are available.

A novel model incorporating quantitative contrast-enhanced ultrasound into PI-RADSv2-based nomogram detecting clinically significant prostate cancer.

The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

Functional imaging guided stereotactic ablative body radiotherapy (SABR) with focal dose escalation and bladder trigone sparing for intermediate and high-risk prostate cancer: study protocol for phase II safo trial.

BACKGROUND: Stereotactic ablative body radiotherapy (SABR) is an emerging treatment alternative for patients with localized low and intermediate risk prostate cancer patients. As already explored by some authors in the context of conventional moderate hypofractionated radiotherapy, focal boost of the index lesion defined by magnetic resonance imaging (MRI) is associated with an improved biochemical outcome. The objective of this phase II trial is to determine the effectiveness (in terms of biochemical, morphological and functional control), the safety and impact on quality of life, of prostate SABR with MRI guided focal dose intensification in males with intermediate and high-risk localized prostate cancer. METHODS: Patients with intermediate and high-risk prostate cancer according to NCCN definition will be treated with SABR 36.25 Gy in 5 fractions to the whole prostate gland with MRI guided simultaneous integrated focal boost (SIB) to the index lesion (IL) up to 50 Gy in 5 fractions, using a protocol of bladder trigone and urethra sparing. Intra-fractional motion will be monitored with daily cone beam computed tomography (CBCT) and intra-fractional tracking with intraprostatic gold fiducials. Androgen deprivation therapy (ADT) will be allowed. The primary endpoint will be efficacy in terms of biochemical and local control assessed by Phoenix criteria and post-treatment MRI respectively. The secondary endpoints will encompass acute and late toxicity, quality of life (QoL) and progression-free survival. Finally, the subgroup of high-risk patients will be involved in a prospective study focused on immuno-phenotyping. DISCUSSION: To the best of our knowledge, this is the first trial to evaluate the impact of post-treatment MRI on local control among patients with intermediate and high-risk prostate cancer undergoing SABR and MRI guided focal intensification. The results of this trial will enhance our understanding of treatment focal intensification through the employment of the SABR technique within this specific patient subgroup, particularly among those with high-risk disease, and will help to clarify the significance of MRI in monitoring local responses. Hopefully will also help to design more personalized biomarker-based phase III trials in this specific context. Additionally, this trial is expected to be incorporated into a prospective radiomics study focused on localized prostate cancer treated with radiotherapy. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT05919524; Registered 17 July 2023. TRIAL SPONSOR: IRAD/SEOR (Instituto de Investigación de Oncología Radioterápica / Sociedad Española de Oncología Radioterápica). STUDY SETTING: Clinicaltrials.gov identifier: NCT05919524; Registered 17 July 2023. TRIAL STATUS: Protocol version number and date: v. 5/ 17 May-2023. Date of recruitment start: August 8, 2023. Date of recruitment completion: July 1, 2024.

Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

A PSMA PET/CT-based risk model for prediction of concordance between targeted biopsy and combined biopsy in detecting prostate cancer.

PURPOSE: This study is to investigate the diagnostic value of 68Ga-PSMA-11 in improving the concordance between mpMRI-TB and combined biopsy (CB) in detecting PCa. METHODS: 115 consecutive men with 68Ga-PSMA-11 PET/CT prior to prostate biopsy were included for analysis. PSMA intensity, quantified as maximum standard uptake value (SUVmax), minimum apparent diffusion coefficient (ADCmin) and other clinical characteristics were evaluated relative to biopsy concordance using univariate and multivariate logistic regression analyses. A prediction model was developed based on the identified parameters, and a dynamic online diagnostic nomogram was constructed, with its discrimination evaluated through the area under the ROC curve (AUC) and consistency assessed using calibration plots. To assess its clinical applicability, a decision curve analysis (DCA) was performed, while internal validation was conducted using bootstrapping methods. RESULTS: Concordance between mpMRI-TB and CB occurred in 76.5% (88/115) of the patients. Multivariate logistic regression analyses performed that SUVmax (OR= 0.952; 95% CI 0.917-0.988; P= 0.010) and ADCmin (OR= 1.006; 95% CI 1.003-1.010; P= 0.001) were independent risk factors for biopsy concordance. The developed model showed a sensitivity, specificity, accuracy and AUC of 0.67, 0.78, 0.81 and 0.78 in the full sample. The calibration curve demonstrated that the nomogram's predicted outcomes closely resembled the ideal curve, indicating consistency between predicted and actual outcomes. Furthermore, the decision curve analysis (DCA) highlighted the clinical net benefit achievable across various risk thresholds. These findings were reinforced by internal validation. CONCLUSIONS: The developed prediction model based on SUVmax and ADCmin showed practical value in guiding the optimization of prostate biopsy pattern. Lower SUVmax and Higher ADCmin values are associated with greater confidence in implementing mono-TB and safely avoiding SB, effectively balancing benefits and risks.

Value of perilesional biopsies in multiparametric magnetic resonance imaging-targeted biopsy and systematic biopsy in detection of prostate cancer: results of a prospective, non-randomized, surgeon-blinded study.

PURPOSE: The goal of this study is to address if detection rates of clinically significant prostate cancer (csPCa) can be increased by additional perilesional biopsies (PB) in magnetic resonance (MR)/ultrasound fusion prostate biopsy in biopsy-naïve men. METHODS: This prospective, non-randomized, surgeon-blinded study was conducted between February 2020 and July 2022. Patients were included with PSA levels < 20 ng/ml and ≥ one PI-RADS lesion (grades 3-5) per prostate lobe. Prostate biopsy was performed by two urologists. The first performed the MR-fusion biopsy with 3-5 targeted biopsies (TB) and 6 PB in a standardized pattern. The second performed the systematic (12-fold) biopsy (SB) without knowledge of the MR images. Primary outcome of this study is absence or presence of csPCa (≥ ISUP grade 2) comparing TB, PB and SB, using McNemar test. RESULTS: Analyses were performed for each PI-RADS lesion (n = 218). There was a statistically significant difference in csPC detection rate of TB + SB between PI-RADS 3, 4 and 5 lesions (18.0% vs. 42.5% vs. 82.6%, p < 0.001) and TB + PB (19.7% vs. 29.1% vs. 78.3%). Comparing only maximum ISUP grade per lesion, even SB plus TB plus PB did not detect more csPCa compared to SB plus TB (41.3% vs. 39.9%, p > 0.05). CONCLUSION: We present prospective study data investigating the role of perilesional biopsy in detection of prostate cancer. We detected no statistically significant difference in the detection of csPCa by the addition of PB. Therefore, we recommend continuing 12-fold bilateral SB in addition to TB.

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

PURPOSE: Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. METHODS: A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. RESULTS: Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). CONCLUSION: The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

A mitochondrion-targeted cyanine agent for NIR-II fluorescence-guided surgery combined with intraoperative photothermal therapy to reduce prostate cancer recurrence.

Poorly identified tumor boundaries and nontargeted therapies lead to the high recurrence rates and poor quality of life of prostate cancer patients. Near-infrared-II (NIR-II) fluorescence imaging provides certain advantages, including high resolution and the sensitive detection of tumor boundaries. Herein, a cyanine agent (CY7-4) with significantly greater tumor affinity and blood circulation time than indocyanine green was screened. By binding albumin, the absorbance of CY7-4 in an aqueous solution showed no effects from aggregation, with a peak absorbance at 830 nm and a strong fluorescence emission tail beyond 1000 nm. Due to its extended circulation time (half-life of 2.5 h) and high affinity for tumor cells, this fluorophore was used for primary and metastatic tumor diagnosis and continuous monitoring. Moreover, a high tumor signal-to-noise ratio (up to ~ 10) and excellent preferential mitochondrial accumulation ensured the efficacy of this molecule for photothermal therapy. Therefore, we integrated NIR-II fluorescence-guided surgery and intraoperative photothermal therapy to overcome the shortcomings of a single treatment modality. A significant reduction in recurrence and an improved survival rate were observed, indicating that the concept of intraoperative combination therapy has potential for the precise clinical treatment of prostate cancer.

Prostate intra-fraction motion recorded by transperineal ultrasound.

Infra-fraction motion of the prostate was recorded during 3.423 fractions of image guided radiotherapy (IGRT) in 191 patients, 14 of which were treated by intensity modulated radiation therapy (IMRT), and 177 of which were treated by volumetric arc therapy (VMAT). The prostate was imaged by three-dimensional and time-resolved transperineal ultrasound (4D-US) of type Clarity by Elekta AB, Stockholm, Sweden. The prostate volume was registered and the prostate position (center of volume) was recorded at a frequency of 2.0 samples per second. This raw data set contains a total of 1.985.392 prostate and patient couch positions over a time span of 272 hours, 52 minutes and 34 seconds of life radiotherapy as exported by the instrument software. This data set has been used for the validation of models of prostate intra-fraction motion and for the estimation of the dosimetric impact of actual intra-fraction motion on treatment quality and side effects. We hope that this data set may be reused by other groups for similar purposes.

A bicentric retrospective study of the correlation of EAU BCR risk groups with 18F-PSMA-1007 PET/CT detection in prostate cancer biochemical recurrence.

The European Association of Urology (EAU) has proposed a risk stratification for patients harboring biochemical recurrence (BCR) after radical prostatectomy: ISUP < 4 and PSA doubling time (PSAdt) > 12 months for low risk, and ISUP ≥ 4 or PSAdt ≤ 12 months for high risk. This dual-center retrospective study aims to investigate the correlation between the EAU risk stratification for BCR following radical prostatectomy and the detection rate of lesions using 18F-PSMA-1007 PET/CT. Among the 71 included patients (58 high-risk, 13 low-risk), with a median PSA level of 1.43 ng/ml, PET/CT demonstrated a significantly higher positivity in the high-risk group compared to the low-risk group (72.4% vs. 38.0%, p = 0.026). Analysis of recurrence sites revealed a similar proportion of pelvic-confined disease in both groups (24.1% vs. 23.1%, p = 0.935), but a significantly higher incidence of metastatic disease in the high-risk group (51.7% vs. 15.4%, p = 0.017), with detailed findings indicating an increased prevalence of bone metastases in the high-risk BCR group (37.8% vs. 7.7%, p = 0.048). Therefore, PSMA PET/CT offers valuable insights for treatment decisions, aligning with the evolving landscape of prostate cancer management.

Prostate MRI Was Negative-What's Next?

The primary benefit of prostate MRI in the modern diagnostic pathway for prostate cancer is that many men with elevated serum PSA can safely avoid an immediate biopsy if the MRI is nonsuspicious. It is less clear, though, how these patients should be followed thereafter. Are they to be followed the same as the general population, or do they warrant more attention because of the risk of a cancer missed on MRI? In this issue, Pylväläinen and colleagues report on incidence of clinically significant prostate cancer (csPCa) and clinically insignificant PCa (ciPCa) among patients who were referred for prostate MRI for clinical suspicion of csPCa in Helsinki but had a nonsuspicious MRI (nMRI). Compared with the general population in Finland, patients who had nMRI were approximately 3.4 times more likely to be diagnosed with csPCa and 8.2 times more likely to be diagnosed with ciPCa. Balancing the competing risks of a missed csPCa versus overdiagnosis in patients after nMRI requires integration of MRI and other risk factors, especially age and PSA density. This integration may be facilitated by multivariable models and quantitative pathology and imaging. See related article by Pylväläinen et al., p. 749.

Optimizing PSMA scintigraphy for resource limited settings - a retrospective comparative study.

BACKGROUND: PSMA PET/CT is the most sensitive molecular imaging modality for prostate cancer (PCa), yet much of the developing world has little or no access to PET/CT. [99mTc]Tc-PSMA scintigraphy (PS) is a cheaper and more accessible gamma camera-based alternative. However, many resource-constrained departments have only a single camera without tomographic or hybrid imaging functionality, and camera time is frequently in high demand. Simplifying imaging protocols by limiting the field of view (FOV) and omitting SPECT/CT or even SPECT may provide a partial solution. The aim was thus to determine the adequacy of PS planar-only and/or SPECT-only imaging protocols with a limited FOV. METHODS: The scans of 95 patients with histologically proven PCa who underwent PS with full-body planar and multi-FOV SPECT/CT were reviewed. The detection rates for uptake in the prostate gland/bed and in metastases were compared on planar, SPECT, and SPECT/CT. The agreement between modalities was calculated for the detection of metastases and for staging. The impact of imaging a limited FOV was determined. RESULTS: Pathological prostatic uptake was seen in all cases on SPECT/CT (excluding two post-prostatectomy patients), 90.3% of cases on SPECT, and 15.1% on planar images (p < 0.001). Eleven (11.7%) patients had seminal vesicle involvement on SPECT/CT, which was undetectable/indistinguishable on planar images and SPECT. The agreement between modalities was moderate to good (κ = 0.41 to 0.61) for the detection of nodal metastases, with detection rates that did not differ significantly (SPECT/CT = 11.6%, SPECT = 8.4%, planar = 5.3%). Detection rates for bone metastases were 14.7% (SPECT/CT) and 11.6% (SPECT and planar). Agreement between modalities for the detection of bone metastases was good (κ = 0.73 to 0.77). Three (3.1%) patients had visceral metastases on SPECT/CT, two of which were detected on SPECT and planar. There was good agreement between modalities for the TNM staging of patients (κ = 0.70 to 0.88). No metastatic lesions were missed on the limited FOV images. CONCLUSION: When PS scintigraphy is performed, SPECT/CT is recommended. However, the lack of SPECT/CT capabilities should not preclude the use of PS in the presence of limited resources, as both planar and SPECT imaging are adequate and will correctly stage most PCa patients. Furthermore, time-based optimisations are achievable by limiting the FOV to exclude the distal lower limbs.