Italian National Registry of Alopecia Areata: an epidemiological study of 699 Italian patients.

BACKGROUND: Alopecia areata (AA) is an organ-specific autoimmune disease that affects the hair follicles of the scalp and the rest of the body causing hair loss. Due to the unpredictable course of AA and the different degrees of severity of hair loss, only a few well-designed clinical studies with a low number of patients are available. Also, there is no specific cure, but topical and systemic anti-inflammatory and immune system suppressant drugs are used for treatment. The need to create a global registry of AA, comparable and reproducible in all countries, has recently emerged. An Italian multicentric electronic registry is proposed as a model to facilitate and guide the recording of epidemiological and clinical data and to monitor the introduction of new therapies in patients with AA. METHODS: The aim of this study was to evaluate the epidemiological data of patients with AA by collecting detailed information on the course of the disease, associated diseases, concomitant and previous events, and the clinical response to traditional treatments. Estimate the impact on the quality of life of patients. RESULTS: The creation of the National Register of AA has proven to be a valid tool for recording, with a standardized approach, epidemiological data, the trend of AA, response to therapies and quality of life. CONCLUSIONS: AA is confirmed as a difficult hair disease to manage due to its unpredictable course and, in most cases, its chronic-relapsing course, capable of having a significant impact on the quality of life of patients.

Post-printing processing and aging effects on Polyjet materials intended for the fabrication of advanced surgical simulators.

Material Jetting (MJ) 3D printing technology is promising for the fabrication of highly realistic surgical simulators, however, the changes in the mechanical properties of MJ materials after post-printing treatments and over time remain quite unknown. In this study, we investigate the effect of different post-printing processes and aging on the mechanical properties of a white opaque and rigid MJ photopolymer, a white flexible MJ photopolymer and on a combination of them. Tensile and Shore hardness tests were conducted on homogeneous 3D-printed specimens: two different post-printing procedures for support removal (dry and water) and further surface treatment (with glycerol solution) were compared. The specimens were tested within 48 h from printing and after aging (30-180 days) in a controlled environment. All groups of specimens treated with different post-printing processes (dry, water, glycerol) exhibited a statistically significant difference in mechanical properties (i.e. elongation at break, elastic modulus, ultimate tensile strength). Particularly, the treatment with glycerol makes the flexible photopolymer more rigid, but then with aging the initial elongation of the material tends to be restored. For the rigid photopolymer, an increase in deformability was observed as a major effect of aging. The hardness tests on the printed specimens highlighted a significant overestimation of the Shore values declared by the manufacturer. The study findings are useful for guiding the material selection and post-printing processing techniques to manufacture realistic and durable models for surgical training.

Morpho-functional analysis of the temporomandibular joint following mandible-first bimaxillary surgery with mandible-only patient-specific implants.

The aim of this study was to evaluate condylar and glenoid fossa remodeling after bimaxillary orthognathic surgery guided by patient-specific mandibular implants. In total, 18 patients suffering from dentofacial dysmorphism underwent a virtually planned bimaxillary mandibular PSI-guided orthognathic procedure. One month prior to surgery, patients underwent a CBCT scan and optical scans of the dental arches; these datasets were re-acquired 1 month and at least 9 months postsurgery. Three-dimensional models of the condyles, glenoid fossae, and interarticular surface space (IASS) were obtained and compared to evaluate the roto-translational positional discrepancy and surface variation of each condyle and glenoid fossa, and the IASS variation. The condylar position varied by an average of 4.31° and 2.18 mm, mainly due to surgically unavoidable ramus position correction. Condylar resorption remodeling was minimal (average ≤ 0.1 mm), and affected skeletal class III patients the most. Later condylar remodeling was positively correlated with patient age. No significant glenoid fossa remodeling was observed. No postoperative orofacial pain was recorded at clinical follow-up. The procedure was accurate in minimizing the shift in relationship between the bony components of the TMJ and their remodeling, and was effective in avoiding postoperative onset of orofacial pain. An increase in sample size, however, would be useful to confirm our findings.

Dissecting human anatomy learning process through anatomical education with augmented reality: AEducAR 2.0, an updated interdisciplinary study.

Anatomical education is pivotal for medical students, and innovative technologies like augmented reality (AR) are transforming the field. This study aimed to enhance the interactive features of the AEducAR prototype, an AR tool developed by the University of Bologna, and explore its impact on human anatomy learning process in 130 second-year medical students at the International School of Medicine and Surgery of the University of Bologna. An interdisciplinary team of anatomists, maxillofacial surgeons, biomedical engineers, and educational scientists collaborated to ensure a comprehensive understanding of the study's objectives. Students used the updated version of AEducAR, named AEducAR 2.0, to study three anatomical topics, specifically the orbit zone, facial bones, and mimic muscles. AEducAR 2.0 offered two learning activities: one explorative and one interactive. Following each activity, students took a test to assess learning outcomes. Students also completed an anonymous questionnaire to provide background information and offer their perceptions of the activity. Additionally, 10 students participated in interviews for further insights. The results demonstrated that AEducAR 2.0 effectively facilitated learning and students' engagement. Students totalized high scores in both quizzes and declared to have appreciated the interactive features that were implemented. Moreover, interviews shed light on the interesting topic of blended learning. In particular, the present study suggests that incorporating AR into medical education alongside traditional methods might prove advantageous for students' academic and future professional endeavors. In this light, this study contributes to the growing research emphasizing the potential role of AR in shaping the future of medical education.

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.

Augmented reality for orthopedic and maxillofacial oncological surgery: a systematic review focusing on both clinical and technical aspects.

This systematic review offers an overview on clinical and technical aspects of augmented reality (AR) applications in orthopedic and maxillofacial oncological surgery. The review also provides a summary of the included articles with objectives and major findings for both specialties. The search was conducted on PubMed/Medline and Scopus databases and returned on 31 May 2023. All articles of the last 10 years found by keywords augmented reality, mixed reality, maxillofacial oncology and orthopedic oncology were considered in this study. For orthopedic oncology, a total of 93 articles were found and only 9 articles were selected following the defined inclusion criteria. These articles were subclassified further based on study type, AR display type, registration/tracking modality and involved anatomical region. Similarly, out of 958 articles on maxillofacial oncology, 27 articles were selected for this review and categorized further in the same manner. The main outcomes reported for both specialties are related to registration error (i.e., how the virtual objects displayed in AR appear in the wrong position relative to the real environment) and surgical accuracy (i.e., resection error) obtained under AR navigation. However, meta-analysis on these outcomes was not possible due to data heterogenicity. Despite having certain limitations related to the still immature technology, we believe that AR is a viable tool to be used in oncological surgeries of orthopedic and maxillofacial field, especially if it is integrated with an external navigation system to improve accuracy. It is emphasized further to conduct more research and pre-clinical testing before the wide adoption of AR in clinical settings.

Pretreatment Tumor Volume and Tumor Sphericity as Prognostic Factors in Patients with Oral Cavity Squamous Cell Carcinoma: A Prospective Clinical Study in 95 Patients.

The prognostic impact of tumor volume and tumor sphericity was analyzed in 95 patients affected by oral cancer. The pre-operative computed tomography (CT) scans were used to segment the tumor mass with threshold tools, obtaining the corresponding volume and sphericity. Events of recurrence and tumor-related death were detected for each patient. The mean follow-up time was 31 months. A p-value of 0.05 was adopted. Mean tumor volume resulted higher in patients with recurrence or tumor-related death at the Student's t-test (respectively, 19.8 cm3 vs. 11.1 cm3, p = 0.03; 23.3 cm3 vs. 11.7 cm3, p = 0.02). Mean tumor sphericity was higher in disease-free patients (0.65 vs. 0.59, p = 0.04). Recurrence-free survival and disease-specific survival were greater for patients with a tumor volume inferior to the cut-off values of 21.1 cm3 (72 vs. 21 months, p < 0.01) and 22.4 cm3 (85 vs. 32 months, p < 0.01). Recurrence-free survival and disease-specific survival were higher for patients with a tumor sphericity superior to the cut-off value of 0.57 (respectively, 49 vs. 33 months, p < 0.01; 56 vs. 51 months, p = 0.01). To conclude, tumor volume and sphericity, three-dimensional parameters, could add useful information for better stratification of prognosis in oral cancer.

Preclinical Application of Augmented Reality in Pediatric Craniofacial Surgery: An Accuracy Study.

BACKGROUND: Augmented reality (AR) allows the overlapping and integration of virtual information with the real environment. The camera of the AR device reads the object and integrates the virtual data. It has been widely applied to medical and surgical sciences in recent years and has the potential to enhance intraoperative navigation. MATERIALS AND METHODS: In this study, the authors aim to assess the accuracy of AR guidance when using the commercial HoloLens 2 head-mounted display (HMD) in pediatric craniofacial surgery. The Authors selected fronto-orbital remodeling (FOR) as the procedure to test (specifically, frontal osteotomy and nasal osteotomy were considered). Six people (three surgeons and three engineers) were recruited to perform the osteotomies on a 3D printed stereolithographic model under the guidance of AR. By means of calibrated CAD/CAM cutting guides with different grooves, the authors measured the accuracy of the osteotomies that were performed. We tested accuracy levels of ±1.5 mm, ±1 mm, and ±0.5 mm. RESULTS: With the HoloLens 2, the majority of the individuals involved were able to successfully trace the trajectories of the frontal and nasal osteotomies with an accuracy level of ±1.5 mm. Additionally, 80% were able to achieve an accuracy level of ±1 mm when performing a nasal osteotomy, and 52% were able to achieve an accuracy level of ±1 mm when performing a frontal osteotomy, while 61% were able to achieve an accuracy level of ±0.5 mm when performing a nasal osteotomy, and 33% were able to achieve an accuracy level of ±0.5 mm when performing a frontal osteotomy. CONCLUSIONS: despite this being an in vitro study, the authors reported encouraging results for the prospective use of AR on actual patients.

First Ex Vivo Animal Study of a Biological Heart Valve Prosthesis Sensorized with Intravalvular Impedance.

IntraValvular Impedance (IVI) sensing is an innovative concept for monitoring heart valve prostheses after implant. We recently demonstrated IVI sensing feasible in vitro for biological heart valves (BHVs). In this study, for the first time, we investigate ex vivo the IVI sensing applied to a BHV when it is surrounded by biological tissue, similar to a real implant condition. A commercial model of BHV was sensorized with three miniaturized electrodes embedded in the commissures of the valve leaflets and connected to an external impedance measurement unit. To perform ex vivo animal tests, the sensorized BHV was implanted in the aortic position of an explanted porcine heart, which was connected to a cardiac BioSimulator platform. The IVI signal was recorded in different dynamic cardiac conditions reproduced with the BioSimulator, varying the cardiac cycle rate and the stroke volume. For each condition, the maximum percent variation in the IVI signal was evaluated and compared. The IVI signal was also processed to calculate its first derivative (dIVI/dt), which should reflect the rate of the valve leaflets opening/closing. The results demonstrated that the IVI signal is well detectable when the sensorized BHV is surrounded by biological tissue, maintaining the similar increasing/decreasing trend that was found during in vitro experiments. The signal can also be informative on the rate of valve opening/closing, as indicated by the changes in dIVI/dt in different dynamic cardiac conditions.

Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer.

BACKGROUND: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). METHOD: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor's zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. RESULTS: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. CONCLUSIONS: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.

Assessment of a novel patient-specific 3D printed multi-material simulator for endoscopic sinus surgery.

Background: Three-dimensional (3D) printing is an emerging tool in the creation of anatomical models for surgical training. Its use in endoscopic sinus surgery (ESS) has been limited because of the difficulty in replicating the anatomical details. Aim: To describe the development of a patient-specific 3D printed multi-material simulator for use in ESS, and to validate it as a training tool among a group of residents and experts in ear-nose-throat (ENT) surgery. Methods: Advanced material jetting 3D printing technology was used to produce both soft tissues and bony structures of the simulator to increase anatomical realism and tactile feedback of the model. A total of 3 ENT residents and 9 ENT specialists were recruited to perform both non-destructive tasks and ESS steps on the model. The anatomical fidelity and the usefulness of the simulator in ESS training were evaluated through specific questionnaires. Results: The tasks were accomplished by 100% of participants and the survey showed overall high scores both for anatomy fidelity and usefulness in training. Dacryocystorhinostomy, medial antrostomy, and turbinectomy were rated as accurately replicable on the simulator by 75% of participants. Positive scores were obtained also for ethmoidectomy and DRAF procedures, while the replication of sphenoidotomy received neutral ratings by half of the participants. Conclusion: This study demonstrates that a 3D printed multi-material model of the sino-nasal anatomy can be generated with a high level of anatomical accuracy and haptic response. This technology has the potential to be useful in surgical training as an alternative or complementary tool to cadaveric dissection.

Application of Augmented Reality to Maxillary Resections: A Three-Dimensional Approach to Maxillofacial Oncologic Surgery.

In the relevant global context, although virtual reality, augmented reality, and mixed reality have been emerging methodologies for several years, only now have technological and scientific advances made them suitable for revolutionizing clinical care and medical settings through the provision of advanced features and improved healthcare services. Over the past fifteen years, tools and applications using augmented reality (AR) have been designed and tested in the context of various surgical and medical disciplines, including maxillofacial surgery. The purpose of this paper is to show how a marker-less AR guidance system using the Microsoft® HoloLens 2 can be applied in mandible and maxillary demolition surgery to guide maxillary osteotomies. We describe three mandibular and maxillary oncologic resections performed during 2021 using AR support. In these three patients, we applied a marker-less tracking method based on recognition of the patient's facial profile. The surgeon, using HoloLens 2 smart glasses, could see the virtual surgical planning superimposed on the patient's anatomy. We showed that performing osteotomies under AR guidance is feasible and viable, as demonstrated by comparison with osteotomies performed using CAD-CAM cutting guides. This technology has advantages and disadvantages. However, further research is needed to improve the stability and robustness of the marker-less tracking method applied to patient face recognition.

3D renal model for surgical planning of partial nephrectomy: A way to improve surgical outcomes.

Objective: to evaluate the impact of 3D model for a comprehensive assessment of surgical planning and quality of partial nephrectomy (PN). Materials and methods: 195 patients with cT1-T2 renal mass scheduled for PN were enrolled in two groups: Study Group (n= 100), including patients referred to PN with revision of both 2D computed tomography (CT) imaging and 3D model; Control group (n= 95), including patients referred to PN with revision of 2D CT imaging. Overall, 20 individuals were switched to radical nephrectomy (RN). The primary outcome was the impact of 3D models-based surgical planning on Trifecta achievement (defined as the contemporary absence of positive surgical margin, major complications and ≤30% postoperative eGFR reduction). The secondary outcome was the impact of 3D models on surgical planning of PN. Multivariate logistic regressions were used to identify predictors of selective clamping and Trifecta's achievement in patients treated with PN (n=175). Results: Overall, 73 (80.2%) patients in Study group and 53 (63.1%) patients in Control group achieved the Trifecta (p=0.01). The preoperative plan of arterial clamping was recorded as clampless, main artery and selective in 22 (24.2%), 22 (24.2%) and 47 (51.6%) cases in Study group vs. 31 (36.9%), 46 (54.8%) and 7 (8.3%) cases in Control group, respectively (p<0.001). At multivariate logistic regressions, the use of 3D model was found to be independent predictor of both selective or super-selective clamping and Trifecta's achievement. Conclusion: 3D-guided approach to PN increase the adoption of selective clamping and better predict the achievement of Trifecta.

Innovative IntraValvular Impedance Sensing Applied to Biological Heart Valve Prostheses: Design and In Vitro Evaluation.

Subclinical valve thrombosis in heart valve prostheses is characterized by the progressive reduction in leaflet motion detectable with advanced imaging diagnostics. However, without routine imaging surveillance, this subclinical thrombosis may be underdiagnosed. We recently proposed the novel concept of a sensorized heart valve prosthesis based on electrical impedance measurement (IntraValvular Impedance, IVI) using miniaturized electrodes embedded in the valve structure to generate a local electric field that is altered by the cyclic movement of the leaflets. In this study, we investigated the feasibility of the novel IVI-sensing concept applied to biological heart valves (BHVs). Three proof-of-concept prototypes of sensorized BHVs were assembled with different size, geometry and positioning of the electrodes to identify the optimal IVI-measurement configuration. Each prototype was tested in vitro on a hydrodynamic heart valve assessment platform. IVI signal was closely related to the electrodes' positioning in the valve structure and showed greater sensitivity in the prototype with small electrodes embedded in the valve commissures. The novel concept of IVI sensing is feasible on BHVs and has great potential for monitoring the valve condition after implant, allowing for early detection of subclinical valve thrombosis and timely selection of an appropriate anticoagulation therapy.

Breslow thickness: Geometric interpretation, potential pitfalls, and computer automated estimation.

Breslow thickness is one of most important prognostic factor for cutaneous melanoma. To quantify the positions of the melanocytes, the Breslow thickness is defined on a distance metric that is reliable and easy to use in a clinical setting. In this letter, we want to highlight some pitfalls in this distance measurement arising from geometrical issues related to section bending and curling, and their consequences on computer automated estimation.

Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study.

BACKGROUND: Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists' subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM. METHODS: We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed. RESULTS: The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion's silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information. CONCLUSION: In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level.

AEducaAR, Anatomical Education in Augmented Reality: A Pilot Experience of an Innovative Educational Tool Combining AR Technology and 3D Printing.

Gross anatomy knowledge is an essential element for medical students in their education, and nowadays, cadaver-based instruction represents the main instructional tool able to provide three-dimensional (3D) and topographical comprehensions. The aim of the study was to develop and test a prototype of an innovative tool for medical education in human anatomy based on the combination of augmented reality (AR) technology and a tangible 3D printed model that can be explored and manipulated by trainees, thus favoring a three-dimensional and topographical learning approach. After development of the tool, called AEducaAR (Anatomical Education with Augmented Reality), it was tested and evaluated by 62 second-year degree medical students attending the human anatomy course at the International School of Medicine and Surgery of the University of Bologna. Students were divided into two groups: AEducaAR-based learning ("AEducaAR group") was compared to standard learning using human anatomy atlas ("Control group"). Both groups performed an objective test and an anonymous questionnaire. In the objective test, the results showed no significant difference between the two learning methods; instead, in the questionnaire, students showed enthusiasm and interest for the new tool and highlighted its training potentiality in open-ended comments. Therefore, the presented AEducaAR tool, once implemented, may contribute to enhancing students' motivation for learning, increasing long-term memory retention and 3D comprehension of anatomical structures. Moreover, this new tool might help medical students to approach to innovative medical devices and technologies useful in their future careers.

3D Patient-Specific Virtual Models for Presurgical Planning in Patients with Recto-Sigmoid Endometriosis Nodules: A Pilot Study.

Background and Objective: In recent years, 3D printing has been used to support surgical planning or to guide intraoperative procedures in various surgical specialties. An improvement in surgical planning for recto-sigmoid endometriosis (RSE) excision might reduce the high complication rate related to this challenging surgery. The aim of this study was to build novel presurgical 3D models of RSE nodules from magnetic resonance imaging (MRI) and compare them with intraoperative findings. Materials and Methods: A single-center, observational, prospective, cohort, pilot study was performed by enrolling consecutive symptomatic women scheduled for minimally invasive surgery for RSE between November 2019 and June 2020 at our institution. Preoperative MRI were used for building 3D models of RSE nodules and surrounding pelvic organs. 3D models were examined during multi-disciplinary preoperative planning, focusing especially on three domains: degree of bowel stenosis, nodule's circumferential extension, and bowel angulation induced by the RSE nodule. After surgery, the surgeon was asked to subjectively evaluate the correlation of the 3D model with the intra-operative findings and to express his evaluation as "no correlation", "low correlation", or "high correlation" referring to the three described domains. Results: seven women were enrolled and 3D anatomical virtual models of RSE nodules and surrounding pelvic organs were generated. In all cases, surgeons reported a subjective "high correlation" with the surgical findings. Conclusion: Presurgical 3D models could be a feasible and useful tool to support surgical planning in women with recto-sigmoidal endometriotic involvement, appearing closely related to intraoperative findings.

Novel Volumetric and Morphological Parameters Derived from Three-dimensional Virtual Modeling to Improve Comprehension of Tumor's Anatomy in Patients with Renal Cancer.

BACKGROUND: Three-dimensional (3D) models improve the comprehension of renal anatomy. OBJECTIVE: To evaluate the impact of novel 3D-derived parameters, to predict surgical outcomes after robot-assisted partial nephrectomy (RAPN). DESIGN, SETTING, AND PARTICIPANTS: Sixty-nine patients with cT1-T2 renal mass scheduled for RAPN were included. Three-dimensional virtual modeling was achieved from computed tomography. The following volumetric and morphological 3D parameters were calculated: VT (volume of the tumor); VT/VK (ratio between tumor volume and kidney volume); CSA3D (ie, contact surface area); UCS3D (contact to the urinary collecting system); Tumor-Artery3D: tumor's blood supply by tertiary segmental arteries (score = 1), secondary segmental artery (score = 2), or primary segmental/main renal artery (scoren = 3); ST (tumor's sphericity); ConvT (tumor's convexity); and Endophyticity3D (ratio between the CSA3D and the global tumor surface). INTERVENTION: RAPN with a 3D model. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Three-dimensional parameters were compared between patients with and without complications. Univariate logistic regression was used to predict overall complications and type of clamping; linear regression was used to predict operative time, warm ischemia time, and estimated blood loss. RESULTS AND LIMITATIONS: Overall, 11 (15%) individuals experienced overall complications (7.2% had Clavien ≥3 complications). Patients with urinary collecting system (UCS) involvement at 3D model (UCS3D = 2), tumor with blood supply by primary or secondary segmentary arteries (Tumor-Artery3D = 1 and 2), and high Endophyticity3D values had significantly higher rates of overall complications (all p ≤ 0.03). At univariate analysis, UCS3D, Tumor-Artery3D, and Endophyticity3D are significantly associated with overall complications; CSA3D and Endophyticity3D were associated with warm ischemia time; and CSA3D was associated with selective clamping (all p ≤ 0.03). Sample size and the lack of interobserver variability are the main limits. CONCLUSIONS: Three-dimensional modeling provides novel volumetric and morphological parameters to predict surgical outcomes after RAPN. PATIENT SUMMARY: Novel morphological and volumetric parameters can be derived from a three-dimensional model to describe surgical complexity of renal mass and to predict surgical outcomes after robot-assisted partial nephrectomy.

Percutaneous ablation or minimally invasive partial nephrectomy for cT1a renal masses? A propensity score-matched analysis.

OBJECTIVE: Local tumor ablation to treat small renal mass is increasing. The aim of the present study was to compare oncologic outcomes among patients with T1 renal mass treated with partial nephrectomy and local tumor ablation. METHODS: To reduce the inherent differences between patients undergoing laparoscopic or robot-assisted partial nephrectomy (n = 405) and local tumor ablation (n = 137), we used a 1:1 propensity score-matched analysis. Local tumor ablation consisted of radiofrequency ablation and cryoablation. Disease-free survival, overall survival and other causes mortality-free survival rates were estimated using the Kaplan-Meier method. Multivariable logistic regression and competing-risk regression models were used to identify predictors of complications, recurrence and other causes mortality, respectively. RESULTS: Partial nephrectomy had higher disease-free survival estimates, as compared with local tumor ablation (92.8% vs 80.4% at 5 years, P = 0.02), with no significant difference between radiofrequency ablation and cryoablation (P = 0.9). Ablation showed comparable overall survival estimates to partial nephrectomy (91% vs 95.8% at 5 years, P = 0.6). The 5-year recurrence rates were 7.9% versus 23.8% for patients aged ≤70 years, and 2.5% versus 11.9% for patients aged >70 years treated with partial nephrectomy and ablation, respectively; the 5-year other causes mortality rates were 0% and 2.2% for patients treated with partial nephrectomy and ablation aged ≤70 years, and 3% versus 10.9% for patients aged >70 years treated with partial nephrectomy and ablation, respectively. At multivariable analysis, ablation was associated with fewer complications (odds ratio 0.41; P = 0.01). At competing risks analysis, age (hazard ratio 0.96) and ablation (hazard ratio 4.56) were independent predictors of disease recurrence (all P ≤ 0.008). CONCLUSIONS: Local tumor ablation showed a higher risk of recurrence and lower risk of complications compared with partial nephrectomy, with comparable overall survival rates.