Jianliang Liu

Background: There is a recent surge in interest in the application of artificial intelligence (AI) in prostate cancer (PCa). When evaluating PSMA PET scans, AI has shown promise in detecting intraprostatic cancer and metastatic disease. This systematic review aims to assess the ability of AI to detect or predict the progression of PCa during AS. Methods: This systematic review was registered on PROSPERO (ID CRD42024529354) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A comprehensive literature search was performed on Medline, Embase, Web of Science, and IEEE Xplore. Only studies evaluating AI in AS-eligible patients were included. Results: After screening 842 articles, 12 studies were suitable for inclusion. The included studies comprised of 4622 AS patients of whom 1022 experienced progressions. Only three studies utilised developed their AI purely on clinicopathological variables. The area under curve (AUC) of these three AI ranged between 0.65 and 0.76, and the AI algorithm in one study outperformed traditional logistic regression. The integration of MRI parameters particularly the use of radiomics improves the ability of AI to predict progression as compared to clinicopathological variables alone. AI was also able to analyse serial MRI during AS and performs on par with the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scoring system. The AUC of the AI algorithms which included MRI parameters ranged between 0.65 and 0.95. One of the limitations was the variability in study methodologies and inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used and none of the studies had high risk of bias. Conclusion: AI shows promise in detecting PCa progression during AS. However, this systematic review highlights the need for larger prospective studies with external validation before AI can be integrated into the AS process.

Background/Objectives: Since the proPSMA trial, prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scan has primarily replaced conventional imaging for staging newly diagnosed prostate cancer. The objective of this commentary is to summarise the existing literature on the role of PSMA PET in staging favourable intermediate-risk prostate cancer. Methods: A literature search was conducted on Embase and Ovid MEDLINE, and three retrospective cohort studies were identified. Results: Overall, these studies demonstrated a low prevalence of nodal and distant metastases, as well as modest diagnostic performance of PSMA positron emission tomography-computed tomography (PET-CT) in this patient group. Additionally, PSMA PET did not significantly outperform existing nomograms in predicting lymph node involvement. Conclusions: Given its limited sensitivity, low yield, and cost, the routine use of PSMA PET-CT in favourable intermediate-risk prostate cancer patients is not recommended. Further prospective studies and cost-effectiveness analyses are warranted to clarify its role in this population.

Background/Objectives: Prostate cancer (PCa) is one of the most prevalent cancers in men. While PSA testing aids in early detection, it often identifies clinically insignificant PCa (ciPCa), which may not necessitate treatment. Prostate-specific membrane antigen (PSMA) PET scans have emerged as a promising tool to evaluate of localised PCa. This review aims to assess the current evidence of using PSMA PET scans for localised PCa. Methods: Peer-reviewed publications on PSMA PET scans in localised PCa, from inception to May 2024, were retrieved from PubMed. The outcomes evaluated included diagnostic performance in identifying intraprostatic lesions, detecting csPCa (ISUP GG ≥ 2), and role peri-treatment. Results: The addition of PSMA PET/CT to MRI improved the sensitivity (from 83% to 97%) and NPV (72% to 91%) of detecting csPCa. PSMA PET helped improve risk stratification in active surveillance by identifying MRI-occult lesions in up to 29% of patients, of which up to 10% may harbour underlying unfavourable pathology. In local staging, PSMA PET/MRI outperforms MRI in identifying extra-prostatic extension (77% vs. 73%) and seminal vesicle invasion (90% vs. 87%). PSMA PET scans are also superior to MRI in nodal staging and bone scans in identifying bony metastasis. PSMA PET scans appear useful in guiding treatment of localised PCa and aiding follow-up. Conclusions: PSMA PET scans are valuable for evaluating localised PCa by improving the detection of csPCa and enhancing local staging. However, most available studies are retrospective, and long-term oncological outcomes remain underreported due to the relative novelty of PSMA PET scans.

Background/Objective: Penile cancer is aggressive and rapidly progressive. Early recognition is paramount for overall survival. However, many men delay presentation due to a lack of awareness and social stigma. This pilot study aims to develop a convolutional neural network (CNN) model to differentiate penile cancer from precancerous and benign penile lesions. Methods: The CNN was developed using 136 penile lesion images sourced from peer-reviewed open access publications. These images included 65 penile squamous cell carcinoma (SCC), 44 precancerous lesions, and 27 benign lesions. The dataset was partitioned using a stratified split into training (64%), validation (16%), and test (20%) sets. The model was evaluated using ten trials of 10-fold internal cross-validation to ensure robust performance assessment. Results: When distinguishing between benign penile lesions and penile SCC, the CNN achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.94, with a sensitivity of 0.82, specificity of 0.87, positive predictive value of 0.95, and negative predictive value of 0.72. The CNN showed reduced discriminative capability in differentiating precancerous lesions from penile SCC, with an AUROC of 0.74, sensitivity of 0.75, specificity of 0.65, PPV of 0.45, and NPV of 0.88. Conclusion: These findings demonstrate the potential of artificial intelligence in identifying penile SCC. Limitations of this study include the small sample size and reliance on photographs from publications. Further refinement and validation of the CNN using real-life data are needed.

This review discusses an evidence-based approach to managing renal and adrenal masses.

Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.

Objective: To conduct a systematic review of the current literature to determine the current role of primary retroperitoneal lymph node dissection (RPLND) in stage II testicular seminoma and its associated oncological, functional and peri‐operative outcomes. Materials and Methods: A comprehensive literature search was conducted in Medline, Embase, and Scopus for publications from inception until November 2023. The systematic review was registered on PROSPERO (ID CRD42023449781), was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines and utilised the Methodological Index for Non‐Randomised Studies (MINORS) tool. Results: Six studies involving 385 patients were analysed, with 48.5% clinical stage IIA and 51.5% stage IIB seminomas. The patients’ mean (range) age was 37 (20–64) years. The median operation time was 187 min, median estimated blood loss was 150 mL and median length of hospital stay was 4 days. In all, 6.1% of patients developed complications that were greater or equal to Clavien–Dindo grade 3. Only four studies reported on anejaculation rate (median: 4.9%). Only one study had long‐term data, demonstrating a 92% 5‐year overall survival for stage IIA/B disease treated with RPLND. The remaining five studies had a median follow‐up of between 18.5 and 37 months and reported a mean recurrence rate of 15.6%. Most recurrences (78%) were not within the field of RPLND. Recurrence was associated with higher clinical and pathological lymph node stage, and metachronous or delayed development of retroperitoneal lymphadenopathy (initially stage I disease, as opposed to de novo stage IIA/B disease). Discussion: Primary RPLND, performed by experienced surgeons, has good peri‐operative outcomes. Recurrence is more common than with standard treatment, but long‐term survival and functional data are limited, although promising.

Objectives: To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate‐specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy). Materials and Methods: This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: ‘artificial intelligence’, ‘prostate cancer’, and ‘PSMA PET’. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used. Results: Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high‐grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non‐PSMA‐avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume. Conclusion: Although the current state of AI differentiating high‐grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.