Since the first laparoscopic gastrectomy for cancer was reported in 1994, minimally invasive surgery is enjoying its wide acceptance. Numerous procedures of this approach have developed, and many patients have benefited from its effectiveness, which has been recently demonstrated for early gastric cancer. However, since laparoscopic surgery is not exempt from some limitations, the robotic surgery system was introduced as a solution by the late 1990's. Many experienced surgeons have embraced this new emerging method that provides undoubted technical and minimally invasive advantages. To date, several studies have concentrated to this new system, and have compared it with open and laparoscopic approach. Most of them have reported satisfactory results concerning the post-operative short-term outcomes, but almost all believe that the role of robotic gastrectomy is still out of focus, especially because long-term outcomes that can prove robotic oncologic equivalency are lacking, and operative costs and time are higher in comparison to the open and laparoscopic ones. This article is a review about the current status of robotic surgery for the treatment of gastric cancer, especially, focusing on the technical aspects, comparisons to other approaches and future prospects.
Gastrectomy ; Humans ; Laparoscopy ; Stomach Neoplasms

We present a rare case of a gastric mixed adenoneuroendocrine tumor and review the related English literature. A 77-year-old Caucasian woman was admitted to our department with nausea, anorexia, weight loss, and anemia. Esophagogastroduodenoscopy showed a large (>7 cm) ulcerative mass in the greater curvature of the stomach. Biopsy showed the presence of an adenocarcinoma with moderate differentiation. The patient underwent D2 subtotal gastrectomy. Histopathological analysis revealed a diagnosis of mixed gastric adenoneuroendocrine carcinoma. The post-operative course was uneventful, and at the 6-month follow-up, the patient was alive without evidence of recurrence. Our review of the English literature suggested that such cases are most often reported from eastern countries. Multimodal treatment should be the aim for these patients because of the neuroendocrine component of the tumor.
Adenocarcinoma ; Aged ; Anemia ; Anorexia ; Biopsy ; Combined Modality Therapy ; Diagnosis ; Endoscopy, Digestive System ; Female ; Follow-Up Studies ; Gastrectomy ; Humans ; Nausea ; Recurrence ; Stomach ; Stomach Neoplasms ; Ulcer ; Weight Loss

Purpose: To explore the role of artificial intelligence and machine learning (ML) techniques in oncological urology. In recent years, our group investigated the prostate cancer gene 3 (PCA3) score, prostate-specific antigen (PSA), and free-PSA predictive role for prostate cancer (PCa), using the classical binary logistic regression (LR) modeling. In this research, we approached the same clinical problem by several different ML algorithms, to evaluate their performances and feasibility in a real-world evidence PCa detection trial. Materials and Methods: The occurrence of a positive biopsy has been studied in a large cohort of 1,246 Italian men undergoing first or repeat biopsy. Seven supervised ML algorithms were selected to build biomarkers-based predictive models: generalized linear model, gradient boosting machine, eXtreme gradient boosting machine (XGBoost), distributed random forest/ extremely randomized forest, multilayer artificial Deep Neural Network, naïve Bayes classifier, and an automatic ML ensemble function. Results: All the ML models showed better performances in terms of area under curve (AUC) and accuracy, when compared to LR model. Among them, an XGBoost model tuned by the autoML function reached the best metrics (AUC, 0.830), well overtaking LR results (AUC, 0.738). In the variable importance ranking coming from this XGBoost model (accuracy, 0.824), the PCA3 score importance was 3-fold and 4-fold larger, when compared to that of free-PSA and PSA, respectively. Conclusions The ML approach proved to be feasible and able to achieve good predictive performances with reproducible results: it may thus be recommended, when applied to PCa prediction based on biomarkers fluctuations.

Purpose: To explore the role of artificial intelligence and machine learning (ML) techniques in oncological urology. In recent years, our group investigated the prostate cancer gene 3 (PCA3) score, prostate-specific antigen (PSA), and free-PSA predictive role for prostate cancer (PCa), using the classical binary logistic regression (LR) modeling. In this research, we approached the same clinical problem by several different ML algorithms, to evaluate their performances and feasibility in a real-world evidence PCa detection trial. Materials and Methods: The occurrence of a positive biopsy has been studied in a large cohort of 1,246 Italian men undergoing first or repeat biopsy. Seven supervised ML algorithms were selected to build biomarkers-based predictive models: generalized linear model, gradient boosting machine, eXtreme gradient boosting machine (XGBoost), distributed random forest/ extremely randomized forest, multilayer artificial Deep Neural Network, naïve Bayes classifier, and an automatic ML ensemble function. Results: All the ML models showed better performances in terms of area under curve (AUC) and accuracy, when compared to LR model. Among them, an XGBoost model tuned by the autoML function reached the best metrics (AUC, 0.830), well overtaking LR results (AUC, 0.738). In the variable importance ranking coming from this XGBoost model (accuracy, 0.824), the PCA3 score importance was 3-fold and 4-fold larger, when compared to that of free-PSA and PSA, respectively. Conclusions The ML approach proved to be feasible and able to achieve good predictive performances with reproducible results: it may thus be recommended, when applied to PCa prediction based on biomarkers fluctuations.

Objective: Owing to the availability of 3D software, scanners, and printers, clinicians are encouraged to produce in-office aligners. Recently, a new directprinting resin (Tera Harz TC-85DAC) has been introduced. Studies on its mechanical characteristics and biological effects have been published; however, evidence on its efficacy in orthodontic treatment remains scarce. This pilot study aimed to investigate the accuracy of teeth movement achieved with directprinted aligners. Methods: Seventeen patients (eight males and nine females) with a mean age of 27.67 ± 8.95 years, presenting with dental rotations < 30° and spaces/crowding < 5 mm, were recruited for this study. The teeth movement was planned starting from a T0 digital dental cast. The 3D direct-printed aligners were produced using Tera Harz TC-85DAC resin. Once the orthodontic treatment was completed, a final digital cast was obtained (T1). The planned teeth positions were then superimposed onto the T0 and T1 digital models.The differences between the programmed movements and the achieved overall torque, tip, rotation, and transverse dimensions were assessed using the paired t test or Wilcoxon’s signed rank test. Results: The overall accuracies for torque, tip, and rotation were 67.6%, 64.2%, and 72.0%, respectively. The accuracy of the change in transverse diameter was 99.6%. Conclusions Within the limits of the present pilot study (difficulties with abnormally shaped teeth and use of attachments), it can be concluded that 3D printed aligners can be successfully printed in-house and utilized for mildly crowded cases, with a comparable accuracy of tooth movement to that of other aligners.

PURPOSE: The aim of the present study was to retrospectively investigate the influence of potential risk indicators on the development of peri-implant disease. METHODS: Overall, 103 patients referred for implant treatment from 2000 to 2012 were randomly enrolled. The study sample consisted of 421 conventional-length (>6 mm) non-turned titanium implants that were evaluated clinically and radiographically according to pre-established clinical and patient-related parameters by a single investigator. A non-parametric Mann-Whitney U test or Kruskal-Wallis rank test and a logistic regression model were used for the statistical analysis of the recorded data at the implant level. RESULTS: The diagnosis of peri-implant mucositis and peri-implantitis was made for 173 (41.1%) and 19 (4.5%) implants, respectively. Age (≥65 years), patient adherence (professional hygiene recalls <2/year) and the presence of plaque were associated with higher peri-implant probing-depth values and bleeding-on-probing scores. The logistic regression analysis indicated that age (P=0.001), patient adherence (P=0.03), the absence of keratinized tissue (P=0.03), implants placed in pristine bone (P=0.04), and the presence of peri-implant soft-tissue recession (P=0.000) were strongly associated with the event of peri-implantitis. CONCLUSIONS: Within the limitations of this study, patients aged ≥65 years and non-adherent subjects were more prone to develop peri-implant disease. Therefore, early diagnosis and a systematic maintenance-care program are essential for maintaining peri-implant tissue health, especially in older patients.
Cohort Studies* ; Dental Implants ; Dental Prosthesis, Implant-Supported ; Diagnosis ; Early Diagnosis ; Humans ; Hygiene ; Logistic Models ; Mucositis ; Patient Compliance ; Peri-Implantitis ; Periodontal Diseases ; Research Personnel ; Retrospective Studies* ; Risk Factors ; Titanium

BACKGROUNDStress index at post-recruitment maneuvers could be a method of positive end-expiratory pressure (PEEP) titration in acute respiratory distress syndrome (ARDS) patients. However, airway pressure (Paw) stress index may not reflect lung mechanics in the patients with high chest wall elastance. This study was to evaluate the Pawstress index on lung mechanics and the correlation between Pawstress index and transpulmonary pressure (PL) stress index in acute respiratory failure (ARF) patients.

METHODSTwenty-four ARF patients with mechanical ventilation (MV) were consecutively recruited from July 2011 to April 2013 in Zhongda Hospital, Nanjing, China and Ospedale S. Giovanni Battista-Molinette Hospital, Turin, Italy. All patients underwent MV with volume control (tidal volume 6 ml/kg) for 20 min. PEEP was set according to the ARDSnet study protocol. The patients were divided into two groups according to the chest wall elastance/respiratory system elastance ratio. The high elastance group (H group, n = 14) had a ratio ≥30%, and the low elastance group (L group, n = 10) had a ratio <30%. Respiratory elastance, gas-exchange, Pawstress index, and PLstress index were measured. Student's t-test, regression analysis, and Bland-Altman analysis were used for statistical analysis.

RESULTSPneumonia was the major cause of respiratory failure (71.0%). Compared with the L group, PEEP was lower in the H group (5.7 ± 1.7 cmH2O vs. 9.0 ± 2.3 cmH2O, P < 0.01). Compared with the H group, lung elastance was higher (20.0 ± 7.8 cmH2O/L vs. 11.6 ± 3.6 cmH2O/L, P < 0.01), and stress was higher in the L group (7.0 ± 1.9 vs. 4.9 ± 1.9, P = 0.02). A linear relationship was observed between the Pawstress index and the PLstress index in H group (R2 = 0.56, P < 0.01) and L group (R2 = 0.85, P < 0.01).

CONCLUSIONIn the ARF patients with MV, Pawstress index can substitute for PLto guide ventilator settings.

TRIAL REGISTRATIONClinicalTrials.gov NCT02196870 (https://clinicaltrials.gov/ct2/show/NCT02196870).

Adult ; China ; Female ; Humans ; Lung ; physiology ; Lung Compliance ; physiology ; Male ; Middle Aged ; Positive-Pressure Respiration ; Regression Analysis ; Respiratory Distress Syndrome, Adult ; therapy ; Respiratory Mechanics ; Tidal Volume ; physiology