Current Oncology

We aimed to develop and internally validate a nomogram to estimate axillary pathological complete response (pCR, ypN0) after neoadjuvant systemic therapy (NAST) in clinically node-positive (cN1–2) breast cancer. In a single-center retrospective cohort of 144 consecutive patients treated with NAST (anti-HER2 as indicated), all underwent standardized pre- and post-NAST 18F-FDG PET/CT and axillary staging (sentinel lymph node biopsy [SLNB], targeted axillary dissection [TAD], or axillary lymph node dissection [ALND]). Axillary pCR occurred in 51.4% (74/144). In a multivariable analysis, independent positive determinants of ypN0 included the triple-negative subtype, Modified PERCIST (SUVmax-based) reduction ≥ 80.70%, pre-NAST tumor-to-axilla SUVmax ratio ≥ 1.21, and residual breast tumor size < 0.5 mm; conversely, conglomerate/matted nodal morphology at diagnosis was inversely associated. The model showed good internal discrimination (AUC 0.857, 95% CI 0.797–0.917) and acceptable calibration (Hosmer–Lemeshow p = 0.425). Exploratory, subtype-restricted signals were observed for inflammatory indices within Luminal B (HER2+) but were not retained in the final model. The resulting nomogram—combining tumor biology, PET/CT response, and pre-NAST nodal features—may support risk stratification for axillary de-escalation after NAST; however, prospective external validation—ideally embedded in ongoing de-escalation frameworks—remains essential before clinical implementation, and the tool should currently be regarded as hypothesis-generating rather than a stand-alone decision aid for routine practice.

Background: The ability to derive growth from a traumatic event, such as a cancer diagnosis, can facilitate effective adaptation to the challenges associated with cancer survivorship. Objective: In two studies, we investigated the possible cognitive mechanisms explaining the relationship between post-traumatic stress and post-traumatic growth in female survivors of breast cancer. Specifically, Study 1 examined the role of interpretation bias, and Study 2 examined the role of cognitive restructuring of trauma. Methods: In Study 1, 113 participants completed questionnaires assessing stress- and anxiety-related symptomatology, post-traumatic stress and growth, perceived cognitive functioning, and positive interpretation bias. In Study 2, 117 participants completed questionnaires assessing stress and anxiety-related symptoms, rumination, perceived cognitive functioning, cognitive restructuring of trauma, and post-traumatic stress and growth. Results: In both studies, post-traumatic stress was negatively related to post-traumatic growth. In Study 1, positive interpretation bias explained a significant amount of variance in the relationship between post-traumatic stress and post-traumatic growth, with perceived cognitive functioning moderating the relationship between interpretation bias and post-traumatic growth. In Study 2, cognitive restructuring explained a significant amount of variance in the relationship between post-traumatic stress and post-traumatic growth, with deliberate rumination moderating the effects of cognitive restructuring on post-traumatic growth. Conclusions: Cognitive mechanisms are key to understanding the relationship between post-traumatic stress and growth and should be targeted in interventions to improve cognitive flexibility and resilience among breast cancer survivors.

Background: Multimodal artificial intelligence (MMAI) is transforming urological oncology by enabling the seamless integration of diverse data sources, including imaging, clinical records and robotic telemetry to facilitate patient-specific decision-making. Methods: This narrative review summarizes the current developments, applications, opportunities and risks of multimodal AI systems throughout the entire perioperative process in uro-oncologic surgery. Results: MMAI demonstrates quantifiable benefits across the entire perioperative pathway. Preoperatively, it improves diagnostics and surgical planning via multimodal data fusion. Intraoperatively, AI-assisted systems provide real-time context-based decision support, risk prediction and skill assessment within the operating theater. Postoperatively, MMAI facilitates automated documentation, early complication detection and personalized follow-up. Generative AI further revolutionizes surgical training through adaptive feedback and simulations. However, critical limitations must be addressed, including data bias, the barrier of closed robotic platforms, insufficient model validation, data security issues, hallucinations and ethical concerns regarding liability and transparency. Conclusions: MMAI significantly enhances the precision, efficiency and patient-centeredness of uro-oncological care. To ensure safe and widespread implementation, resolving the technical and regulatory barriers to real-time integration into robotic platforms is paramount. This must be coupled with standardized quality controls, transparent decision-making processes and responsible integration that fully preserves physician autonomy.