Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

TAF1D promotes tumorigenesis and metastasis by activating PI3K/AKT/mTOR signaling in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor needs more effective treatments. TATA box-binding protein-associated factor RNA polymerase I subunit D (TAF1D) is a member of the selective factor 1 complex and functions in RNA polymerase I-dependent transcription. Higher TAF1D expression was found in ccRCC tumor tissues and indicated worse survival. Our study aimed to investigate the therapeutic potential of TAF1D in ccRCC. The proliferation and migration of ccRCC cells were significantly inhibited after TAF1D knockdown, while TAF1D overexpressing had opposite effects. Moreover, TAF1D knockdown induced cells to undergo G0/G1 cell cycle arrest and blockade of the epithelial-mesenchymal transition (EMT) process. Mechanistically, TAF1D affect the cell cycle and EMT through the PI3K/AKT/mTOR signaling pathway, thereby promoting the proliferation and metastasis of ccRCC cells in vivo and in vitro. The inhibitory effect of TAF1D knockdown could be reverted by the AKT activator SC79 in ccRCC cells, confirming this mechanism. Besides, TAF1D knockdown in ccRCC cells had a sensitizing effect on sunitinib and enhanced tumor cell inhibiting induced by sunitinib. In conclusion, TAF1D may be a promising target for the treatment of ccRCC and for overcoming sunitinib resistance.

Artificial Intelligence Modeling and Priapism

PURPOSE OF REVIEW

This narrative review aims to outline the current available evidence, challenges, and future perspectives of Artificial Intelligence (AI) in the diagnosis and management of priapism, a condition marked by prolonged and often painful erections that presents unique diagnostic and therapeutic challenges.

RECENT FINDINGS

Recent advancements in AI offer promising solutions to face the challenges in diagnosing and treating priapism. AI models have demonstrated the potential to predict the need for surgical intervention and improve diagnostic accuracy. The integration of AI models into medical decision-making for priapism can also predict long-term consequences. AI is currently being implemented in urology to enhance diagnostics and treatment work-up for various conditions, including priapism. Traditional diagnostic approaches rely heavily on assessments based on history, leading to potential delays in treatment with possible long-term sequelae. To date, the role of AI in the management of priapism is understudied, yet to achieve dependable and effective models that can reliably assist physicians in making decisions regarding both diagnostic and treatment strategies.

Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?

This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR's integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR's transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.

Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis

Off-clamp partial nephrectomy represents one of the latest developments in nephron-sparing surgery, with the goal of preserving renal function and reducing ischemia time. The aim of this study was to evaluate and compare the functional, oncologic, and perioperative outcomes between off-clamp robot-assisted partial nephrectomy (off-C RAPN) and off-clamp open partial nephrectomy (off-C OPN) through a propensity score-matched (PSM) analysis. A 1:1 PSM analysis was used to balance variables potentially affecting postoperative outcomes. To report surgical quality, 1 year trifecta was used. Univariable Cox regression analysis was performed to identify predictors of trifecta achievement. The Kaplan-Meier method was used to compare cancer-specific survival (CSS), overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS) probabilities between groups. Overall, 542 patients were included. After PSM analysis, two homogeneous cohorts of 147 patients were obtained. The off-C RAPN cohort experienced shorter length of stay (LoS) (3.4 days vs. 5.4 days; p < 0.001), increased likelihoods of achieving 1 year trifecta (89.8% vs. 80.3%; p = 0.03), lower postoperative Clavien-Dindo ≤ 2 complications (1.3% vs. 18.3%, p < 0.001), and lower postoperative transfusion rates (3.4% vs. 12.2%, p = 0.008). At univariable analysis, the surgical approach (off-C RAPN vs. off-C OPN, OR 2.22, 95% CI 1.09-4.46, p = 0.02) was the only predictor of 1 year trifecta achievement. At Kaplan-Meier analysis, no differences were observed between the two groups in terms of OS (log-rank p = 0.451), CSS (log-rank p = 0.476), DFS (log-rank p = 0.678), and MFS (log-rank p = 0.226). Comparing RAPN and OPN in a purely off-clamp scenario, the minimally invasive approach proved to be a feasible and safe surgical approach, with a significantly lower LoS and minor rate of postoperative complications and transfusions as a result of improved surgical quality expressed by higher 1 year trifecta achievement.

Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience

The aim of "Precision Surgery" is to reduce the impact of surgeries on patients' global health. In this context, over the last years, the use of three-dimensional virtual models (3DVMs) of organs has allowed for intraoperative guidance, showing hidden anatomical targets, thus limiting healthy-tissue dissections and subsequent damage during an operation. In order to provide an automatic 3DVM overlapping in the surgical field, we developed and tested a new software, called "ikidney", based on convolutional neural networks (CNNs). From January 2022 to April 2023, patients affected by organ-confined renal masses amenable to RAPN were enrolled. A bioengineer, a software developer, and a surgeon collaborated to create hyper-accurate 3D models for automatic 3D AR-guided RAPN, using CNNs. For each patient, demographic and clinical data were collected. A total of 13 patients were included in the present study. The average anchoring time was 11 (6-13) s. Unintended 3D-model automatic co-registration temporary failures happened in a static setting in one patient, while this happened in one patient in a dynamic setting. There was one failure; in this single case, an ultrasound drop-in probe was used to detect the neoplasm, and the surgery was performed under ultrasound guidance instead of AR guidance. No major intraoperative nor postoperative complications (i.e., Clavien Dindo > 2) were recorded. The employment of AI has unveiled several new scenarios in clinical practice, thanks to its ability to perform specific tasks autonomously. We employed CNNs for an automatic 3DVM overlapping during RAPN, thus improving the accuracy of the superimposition process.