Citations

Introduction The introduction of the purpose‐built single‐port (SP) robotic platform has allowed for the advent of regionalized minimally invasive surgical approaches for robotic radical prostatectomy (RARP), including the extraperitoneal and transvesical techniques. This study sought to develop a best practice consensus on the key principles, patient selection, surgical techniques, expected clinical outcomes, and learning curves pertaining to SP‐RARP. Methods A modified Delphi consensus was developed by a panel of experts, consisting of a 33‐item questionnaire, with each statement rated using a binary Likert scale (‘agree’ or ‘disagree’). The survey was distributed via email to all practicing SP‐RARP surgeons across the USA and Europe. Consensus was defined as ≥75% agreement on a given statement. Results A total of 53 surgeons participated in the Delphi Consensus across the two rounds. Of 33 statements, 28 (84.8%) reached agreement in the first round. The remaining consensus was achieved in the second round, involving 35 surgeons, and with one additional statement. The panel agreed that, although are no absolute contraindications to SP‐RARP, the preference towards specific SP‐RARP techniques should remain guided by the individual patients and surgical expertise. Although the SP extraperitoneal technique is recommended to pursue pelvic lymph node dissections, the panel agreed that transvesical SP‐RARP showed promise in promoting shorter hospital stay, faster recovery of urinary continence, and utility in patients with extensive prior abdominal surgery. Consensus was also reached on the need for a structured training pathway, with most recommending beginning with extraperitoneal SP‐RARP before advancing to the transvesical approach. Conclusion This Delphi consensus demonstrates broad agreement among experienced SP surgeons in the USA and Europe, forming the foundation for future research on the clinical utility and necessary training pathways for SP‐RARP. Nevertheless, empirical validation based on clinical data from large cohort studies remains important.

The NCCN Guidelines for Melanoma: Cutaneous provide strategies for the diagnosis, treatment, and follow-up of patients with cutaneous melanoma, which is among the most common cancers in the United States. The outcome of cutaneous melanoma is significantly influenced by stage at initial presentation. Generally, localized disease and smaller primary tumors have been associated with more favorable prognosis. Thus, the select content featured in this issue focuses on risk factors associated with cutaneous melanoma development and which should be considered during an initial assessment. These include (1) phenotypic predispositions, (2) personal medical history or comorbidities, (3) genetic predispositions, and (4) environmental factors.

PURPOSE In large B-cell lymphoma (LBCL), use of high-dose methotrexate (HD-MTX) to prevent CNS relapse (so-called CNS prophylaxis) remains controversial. International guidelines continue to recommend HD-MTX in patients deemed ultra high risk (UHR) because of a lack of robust data specific to these subgroups. This study was designed to examine the impact of HD-MTX specifically in UHR patients. METHODS Data from two previous retrospective analyses were combined to produce a cohort of UHR patients (≥1 of the following criteria: CNS international prognostic index score 5-6; testicular, renal/adrenal, or breast involvement; ≥3 extranodal sites), treated with or without HD-MTX. The primary outcome was CNS relapse rate (isolated or concurrent with systemic relapse) measured from diagnosis with additional landmark analysis of all responding patients with no progression event at 6 months. RESULTS Analyses were performed on 1,923 patients meeting UHR criteria. No significant difference in 3-year CNS relapse rate was observed between no HD-MTX (n = 1,051) versus HD-MTX patients (n = 872), including in multivariable analyses adjusting for baseline characteristics (3-year rate, 9.3% v 8.1%; adjusted hazard ratio [HR], 1.13 [95% CI, 0.82 to 1.57]). Analyses restricted to isolated CNS relapse also confirmed no difference (5.9% v 5.7%; adjusted HR, 1.03 [95% CI, 0.69 to 1.53]). In the landmark analysis (no HD-MTX n = 782, HD-MTX n = 773), no difference in 3-year CNS relapse was observed (6.7% v 6.6%, adjusted HR, 0.95 [95% CI, 0.62 to 1.44]). CONCLUSION To our knowledge, this is the largest international comparative data set of patients with LBCL with UHR features showing no significant reduction in CNS relapse with HD-MTX in all UHR or in any individual subgroup. Despite inherent limitations of retrospective analyses, the data strongly support previous studies in suggesting HD-MTX prophylaxis has no meaningful benefit for most patients.

Background: The rapid adoption of digital pathology and artificial intelligence (AI) in oncology creates multiple opportunities for precision diagnostics but also creates an urgent need for evidence-based standards to ensure safe and effective implementation. Goal: This article, developed by the Digital Pathology Association, presents recommendations for digital pathology as well as the validation and clinical utility of AI-enabled digital pathology tools in clinical practice. This guidance addresses analytical and clinical validation, algorithm reliability, and criteria for establishing clinical utility attributed to test use. Key recommendations emphasize separate validation of scanning processes and AI algorithms, concordance studies to support interscanner generalizability, rigorous assessment of accuracy and reliability in real-world settings, and clear description of algorithmic use limitations. These recommendations further provide frameworks for when AI may replace or augment existing diagnostic approaches, such as biomarker scoring, cancer diagnosis, and prognostic risk assessment. Conclusions: By considering payer, regulatory, and clinical perspectives, the recommendations promote transparency, trust, and reproducibility in digital pathology while encouraging value-based care delivery. We support responsible innovation in computational pathology, ensuring that AI applications achieve not only technical performance goals but also deliver measurable clinical benefit to patients.