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Lymphadenectomy represents a fundamental step in the staging and treatment of non-small cell lung cancer (NSCLC). To date, the extension of lymphadenectomy in early-stage NSCLC is a debated topic due to its possible complications. The detection of sentinel lymph nodes (SLNs) is a strategy that can improve the selection of patients in which a more extended lymphadenectomy is necessary. This pilot study aimed to refine lymph nodal staging in early-stage NSCLC patients who underwent robotic lung resection through the application of innovative intraoperative sentinel lymph node (SLN) identification and the pathological evaluation using one-step nucleic acid amplification (OSNA). Clinical N0 NSCLC patients planning to undergo robotic lung resection were selected. The day before surgery, all patients underwent radionuclide computed tomography (CT)-guided marking of the primary lung lesion and subsequently Single Photon Emission Computed Tomography (SPECT) to identify tracer migration and, consequently, the area with higher radioactivity. On the day of surgery, the lymph nodal radioactivity was detected intraoperatively using a gamma camera. SLN was defined as the lymph node with the highest numerical value of radioactivity. The OSNA amplification, detecting the mRNA of CK19, was used for the detection of nodal metastases in the lymph nodes, including SLN. From March to July 2021, a total of 8 patients (3 female; 5 male), with a mean age of 66 years (range 48–77), were enrolled in the study. No complications relating to the CT-guided marking or preoperative SPECT were found. An average of 5.3 lymph nodal stations were examined (range 2–8). N2 positivity was found in 3 out of 8 patients (37.5%). Consequently, pathological examination of lymph nodes with OSNA resulted in three upstages from the clinical IB stage to pathological IIIA stage. Moreover, in 1 patient (18%) with nodal upstaging, a positive node was intraoperatively identified as SLN. Comparing this protocol to the usual practice, no difference was found in terms of the operating time, conversion rate, and complication rate. Our preliminary experience suggests that sentinel lymph node detection, in association with the accurate pathological staging of cN0 patients achieved using OSNA, is safe and effective in the identification of metastasis, which is usually undetected by standard diagnostic methods.

IntroductionSurgical-related injuries are frequent, in fact the reported percentage of musculoskeletal disorders in surgeons is between 47% and 87%. These conditions are caused by long periods of standing, incorrect postures, repeated movements, little rest between operations, the lack of integrated operator rooms, the correct number and arrangement of monitors and the use of non-ergonomic instruments. This survey aims to assess the Italian overview both highlighting how prevalent surgical-related injury is in our surgeons and whether there is an operating room ergonomics education program in Italian surgical specialty schools.MethodsAn anonymous questionnaire was designed through SurveyMonkey© web application. This survey was composed of 3 different sections concerning the general characteristics of the participants, their surgical background and any training performed, and any injuries or ailments related to the surgical activity. The survey was carried out in the period 1th of December 2022 and the 6th of February 2023.ResultsAt the close of our survey, 300 responses were collected. Among the participants, the two most represented specialties were Gynecology and Obstetrics (42.3%) and General Surgery (39.7%) and surgeons were mainly employed in the Northern regions of Italy (54.8%). Analyzing the participants’ background, 61.7% of the respondents had laparoscopic training during their training and only 53.1% had a pelvic trainer during their residency. In accordance with 98.7% of the respondents, during surgery we have the feeling of being in an uncomfortable position that causes discomfort or muscle pain, and regarding the frequency of these discomforts, the majority of our study population experiences these problems monthly (46.2%), while in 29.6% it is experienced weekly, 12.1% annually and finally 12.1% daily. The surgical approach that is most correlated with these disorders is laparoscopy (62.7%) while the one that causes the least discomfort is robotic surgery (1.4%). These discomforts cause 43.9% of our population to take a break or do short exercises to reduce pain during surgery, and the body areas most affected are the back (61.6%), neck (40.6%) and shoulders (37.8%).ConclusionDespite this, our survey allows us to highlight some now-known gaps present in the surgical training program of our schools and the lack of protection toward our surgeons during their long career.

Background: The treatment of non-small-cell lung cancer (NSCLC) patients with reduced respiratory function represents a challenge for thoracic surgeons. Minimally invasive surgery seems to be beneficial for these patients because it reduces tissue trauma and its impact on respiratory mechanics. Application of the robotic technique, the use of CO2 insufflation and longer surgical time are factors that could influence the outcomes of marginal pulmonary function patients. The objective of this study was to evaluate the impact of the robotic technique on the postoperative outcomes of patients with poor lung function. Methods: We retrospectively collected and analyzed data from consecutive marginal respiratory function patients who underwent robotic or open lobectomy for NSCLC. Data regarding clinical, operative and postoperative details were compared between the open and robotic approaches. Results: The outcomes of 100 patients with reduced respiratory function were evaluated, of whom 59 underwent open lobectomies and 41 underwent robotic lobectomies. Robotic lobectomy was characterized by a longer operative time, a reduced hospital stay and a lower incidence of postoperative complications (22% vs. 33.9%), when compared to the open approach. Conclusion: Robotic lobectomy is a safe and feasible procedure for patients with marginal pulmonary function.

Locally advanced non-small cell lung cancer (NSCLC) consists of a heterogeneous group, with different pulmonary extension and lymph nodal involvement. Robotic surgery can play a key role in these tumours thanks to its technological features, although open surgery is still considered the gold-standard approach. Our study aims to evaluate the surgical and oncological outcomes of locally advanced NSCLC patients who underwent robotic surgery in a high-volume centre. Data from consecutive patients with locally advanced NSCLC who underwent robotic lobectomy were retrospectively analysed and compared with patients treated with open surgery. Clinical characteristics and surgical and oncological information were evaluated. From 2010 to 2020, 131 patients underwent anatomical lung resection for locally advanced NSCLC. A total of 61 patients were treated with robotic surgery (46.6%); the median hospitalization time was 5.9 days (range 2–27) and the postoperative complication rate was 18%. Open surgery was performed in 70 patients (53.4%); the median length of stay was 9 days (range 4–48) and the postoperative complication rate was 22.9%. The median follow-up time was 70 months. The 5-year overall survival was 34% in the robotic group and 31% in the thoracotomy group. Robotic surgery can be considered safe and feasible not only for early stages but also for the treatment of locally advanced NSCLC.

In a world with data that change rapidly and abruptly, it is important to detect those changes accurately. In this paper we describe an R package implementing a generalized version of an algorithm recently proposed by Hocking, Rigaill, Fearnhead, and Bourque (2020) for penalized maximum likelihood inference of constrained multiple change-point models. This algorithm can be used to pinpoint the precise locations of abrupt changes in large data sequences. There are many application domains for such models, such as medicine, neuroscience or genomics. Often, practitioners have prior knowledge about the changes they are looking for. For example in genomic data, biologists sometimes expect peaks: up changes followed by down changes. Taking advantage of such prior information can substantially improve the accuracy with which we can detect and estimate changes. Hocking et al. (2020) described a graph framework to encode many examples of such prior information and a generic algorithm to infer the optimal model parameters, but implemented the algorithm for just a single scenario. We present the gfpop package that implements the algorithm in a generic manner in R/C++. gfpop works for a user-defined graph that can encode prior assumptions about the types of changes that are possible and implements several loss functions (Gauss, Poisson, binomial, biweight, and Huber). We then illustrate the use of gfpop on isotonic simulations and several applications in biology. For a number of graphs the algorithm runs in a matter of seconds or minutes for 105 data points.

Primary focal hyperhidrosis is an idiopathic condition characterized by excessive sweating, predominantly localized in the hands and armpits. This condition affects about 1% of the general population and it is often associated with a deterioration of the Quality of Life (QoL), especially in younger patients. Medical therapy, usually prescribed as a first approach, is associated with good results, but only in the short term. Surgery, on the other hand, is associated with a definitive resolution of the disease in most patients. Currently, there is no consensus on the timing of treatment and the final decision is often at the discretion of the physician and the patient. The aim of this study is to analyze the post-operative, age-related QoL in patients affected by primary hyperhidrosis treated by surgery by analyzing data of 56 patients who underwent biportal thoracoscopic sympathectomy between January 2016 and October 2019, dividing the patients into two groups: under and over the age of 25. The QoL was studied by administering the IIRS questionnaire pre-operatively and then six months after surgery. Data analysis demonstrated a lower complication rate in younger patients and equal post-operative outcomes in the two age groups.

IntroductionNon-small cell lung cancer (NSCLC) is the leading cause of cancer incidence and mortality worldwide. Neoadjuvant chemo-immunotherapy has led to clinical benefits in resectable NSCLC in comparison to chemo-therapy alone. Major pathological response (MPR) and pathological complete response (pCR) have been used as surrogates of neoadjuvant therapy response and clinical outcomes. However, the factors affecting the pathological response are still controversial. Therefore, in this study we retrospectively examined MPR and pCR in two different cohorts of NSCLC patients, 14 treated by chemotherapy and 12 by chemo-immunotherapy in the neoadjuvant setting.MethodsIn resected tumor specimens, different histological characteristics were evaluated: necrosis, fibrosis, inflammation, presence of organizing pneumonia, granuloma, cholesterol cleft, and reactive epithelial alterations. In addition, we evaluated how MPR impacts on event-free survival (EFS) and overall survival (OS). In a small group of patients treated by chemo-immunotherapy, a gene expression analysis of the Hippo pathway was performed both in preoperative biopsies and matched post-surgical specimens.ResultsWe observed a better pathological response in the chemo-immunotherapy treated cohort: 6/12 patients (50.0%) achieved a MPR ≤10% and 1/12 (8.3%) achieved pCR both on primary tumor and on lymph nodes. On the contrary, no patient treated with chemotherapy alone achieved pCR or MPR ≤10%. A higher amount of stroma in the neoplastic bed was observed in patients treated with immuno-chemotherapy. Moreover, patients achieving better MPR (including pCR) had significantly improved overall survival (OS) and event-free survival (EFS). After neoadjuvant chemo-immunotherapy, residual tumors showed a remarkable upregulation of genes consistent with the activation of YAP/TAZ. Also, alternative checkpoint, such as CTLA-4, were enhanced.DiscussionOur findings showed that neoadjuvant chemo-immunotherapy treatment improves MPR and pCR thus resulting in better EFS and OS. Moreover, a combined treatment could induce different morphological and molecular changes in comparison to chemotherapy alone, thus giving new insights in the assessment of pathological response.

IntroductionThe COVID-19 pandemic has led to the disruption of surgical training. Lack of communication, guidelines for managing clinical activity as well as concerns for safety in the workplace appeared to be relevant issues. This study aims to investigate how surgical training has been reorganized in Italy, almost 2 years after the outbreak of COVID-19 pandemic.Materials and methodsA 16-item-electronic anonymous questionnaire was designed through SurveyMonkey© web application. This survey was composed of different sections concerning demographic characteristics and impacts of the second COVID-19 pandemic wave on surgical and research/didactic activities. Changes applied in the training programme and activities carried out were also investigated. The survey was carried out in the period between June and October 2021.ResultsFour hundred and thirty responses were collected, and 399 were considered eligible to be included in the study analysis. Three hundred and thirty-five respondents continued working in Surgical Units, with a significant reduction (less than one surgical session per week) of surgical sessions in 49.6% of them. With concern to didactic and research activities, 140 residents maintained their usual activity, while 116 reported a reduction. A sub-group analysis on resident moved to COVID-19 departments showed a reduction of research activities in 35% of them. During the period considered in this survey, the surgical training program was not substantially modified for most of participants (74.6%).ConclusionOur survey demonstrated that surgical residency programs haven't improved 2 years after the beginning of the pandemic. Further improvements are needed to guarantee completeness of surgical training, even in emergency conditions.

Background: Robotic surgery is increasingly used in the treatment of lung disease. Intraoperative catastrophes, despite their low incidence, are currently a critical aspect of this approach. This study aims to identify the incidence and management of catastrophic events in patients who underwent robotic anatomical pulmonary resection; (2) Methods: Data from all patients who underwent robotic anatomical pulmonary resection from 2014 to 2021 for lung disease were collected and analyzed. Catastrophic intraoperative events are defined as events that demanded emergency management for life-threatening bleeding, with or without undocking and thoracotomy; (3) Results: Catastrophic events occurred in seven (1.4%) procedures; all of them consisted of vascular damage during lobectomy. Most of the catastrophic events occurred during left upper lobectomies (57%). Patients in this group had a higher ASA class and a higher pathological stage compared to the control group; (4) Conclusions: Intraoperative catastrophes are unpredictable events which also occur in experienced surgical teams. Given the widespread use of robotic surgery, it is essential to develop well-defined crisis management strategies to better manage catastrophic events in robotic thoracic surgery and improve clinical outcomes.

Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R2) was achieved by the support vector machine (R2 = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.