Learning curve for robotic-assisted total mesorectal excision: a multicentre, prospective study.

AIM: Robotic-assisted surgery (RAS) is becoming increasingly important in colorectal surgery. Recognition of the short, safe learning curve (LC) could potentially improve implementation. We evaluated the extent and safety of the LC in robotic resection for rectal cancer. METHOD: Consecutive rectal cancer resections (January 2018 to February 2021) were prospectively included from three French centres, involving nine surgeons. LC analyses only included surgeons who had performed more than 25 robotic rectal cancer surgeries. The primary endpoint was operating time LC and the secondary endpoint conversion rate LC. Interphase comparisons included demographic and intraoperative data, operating time, conversion rate, pathological specimen features and postoperative morbidity. RESULTS: In 174 patients (69% men; mean age 62.6 years) the mean operating time was 334.5 ± 92.1 min. Operative procedures included low anterior resection (n = 143) and intersphincteric resection (n = 31). For operating time, there were two or three (centre-dependent) LC phases. After 12-21 cases (learning phase), there was a significant decrease in total operating time (all centres) and an increase in the number of harvested lymph nodes (two centres). For conversion rate, there were two or four LC phases. After 9-14 cases (learning phase), the conversion rate decreased significantly in two centres; in one centre, there was a nonsignificant decrease despite the treatment of significantly more obese patients and patients with previous abdominal surgery. There were no significant differences in interphase comparisons. CONCLUSION: The LC for RAS in rectal cancer was achieved after 12-21 cases for the operating time and 9-14 cases for the conversion rate. RAS for rectal cancer was safe during this time, with no interphase differences in postoperative complications and circumferential resection margin.

An anatomical variation during para-aortic lymphadenectomy.

Failure to identify anatomical variations may contribute to surgical errors or perioperative complications during abdominal and oncological surgery. We report the case of an anatomical variation of the inferior vena cava revealed during para-aortic lymphadenectomy for advanced ovarian cancer. Due to renal insufficiency, preoperative CT-scan was performed without contrast injection and the variation was not clearly detected. Our clinical case underlines the importance of the preoperative diagnosis of anatomical variations and highlights the need to provide young surgeons with adequate technical training in para-aortic lymphadenectomy.

Major adverse cardiovascular events associated with VEGF-targeted anticancer tyrosine kinase inhibitors: a real-life study and proposed algorithm for proactive management.

BACKGROUND: Vascular endothelial growth factor receptor (VEGFR)-targeted tyrosine kinase inhibitors (TKIs) are widely used in cancer treatment and burdened by cardiovascular toxicity. The majority of data come from clinical trials, thus in selected populations. The aim of our study is to evaluate the cardiotoxicity profile of VEGFR-targeted TKIs and the impact of cardiovascular risk factors in a real-life population. PATIENTS AND METHODS: In this cohort, population-based study, patients treated with VEGFR-targeted TKIs, bevacizumab and trastuzumab between 2009 and 2014 were analyzed. A multi-source strategy for data retrieval through hospital, pharmaceutical and administrative databases of the Lombardy region, Italy, has been adopted. The primary endpoint was to determine the incidence and type of major adverse cardiovascular events (MACEs) along with their temporal trend. The secondary endpoint was to define the impact of cardiovascular risk factors in the occurrence of MACEs. RESULTS: A total of 829 patients were treated with VEGFR-targeted TKIs. Eighty-one MACEs occurred in the first year of follow-up [crude cumulative incidence (CCI): 9.79%] mainly consisting of arterial thrombotic events (ATEs, 31 events, CCI: 3.99%), followed by rhythm disorders (22 events, CCI: 2.66%), pulmonary embolisms and heart failures (13 events each, CCI: 1.57%). While the incidence of most MACEs showed a plateau after 6 months, ATEs kept increasing along the year of follow-up. Hypertension and dyslipidemia were associated with an increase in risk of ATEs [relative risk difference (RRD) +209.8% and +156.2%, respectively], while the presence of previous MACEs correlated with a higher risk of all MACEs in multivariate analysis (RRD 151.1%, 95% confidence interval 53.6% to 310.3%, P < 0.001). CONCLUSIONS: MACEs occur in a clinically significant proportion of patients treated with VEGFR-targeted TKIs, with ATEs being predominant, mainly associated with hypertension and dyslipidemia. A clinical algorithm for effective proactive management of these patients is warranted.

Lymphovascular invasion has a significant prognostic impact in patients with early breast cancer, results from a large, national, multicenter, retrospective cohort study.

BACKGROUND: We determined the prognostic impact of lymphovascular invasion (LVI) in a large, national, multicenter, retrospective cohort of patients with early breast cancer (BC) according to numerous factors. PATIENTS AND METHODS: We collected data on 17 322 early BC patients treated in 13 French cancer centers from 1991 to 2013. Survival functions were calculated using the Kaplan-Meier method and multivariate survival analyses were carried out using the Cox proportional hazards regression model adjusted for significant variables associated with LVI or not. Two propensity score-based matching approaches were used to balance differences in known prognostic variables associated with LVI status and to assess the impact of adjuvant chemotherapy (AC) in LVI-positive luminal A-like patients. RESULTS: LVI was present in 24.3% (4205) of patients. LVI was significantly and independently associated with all clinical and pathological characteristics analyzed in the entire population and according to endocrine receptor (ER) status except for the time period in binary logistic regression. According to multivariate analyses including ER status, AC, grade, and tumor subtypes, the presence of LVI was significantly associated with a negative prognostic impact on overall (OS), disease-free (DFS), and metastasis-free survival (MFS) in all patients [hazard ratio (HR) = 1.345, HR = 1.312, and HR = 1.415, respectively; P < 0.0001], which was also observed in the propensity score-based analysis in addition to the association of AC with a significant increase in both OS and DFS in LVI-positive luminal A-like patients. LVI did not have a significant impact in either patients with ER-positive grade 3 tumors or those with AC-treated luminal A-like tumors. CONCLUSION: The presence of LVI has an independent negative prognostic impact on OS, DFS, and MFS in early BC patients, except in ER-positive grade 3 tumors and in those with luminal A-like tumors treated with AC. Therefore, LVI may indicate the existence of a subset of luminal A-like patients who may still benefit from adjuvant therapy.

Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis.

PURPOSE: To assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images. METHODS: Four different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations. RESULTS: Highest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median ΔQMs values ranging from 10HU and 158HU between different algorithms. CONCLUSIONS: None of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses.

A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients.

PURPOSE: Quantitative metrics in lung computed tomography (CT) images have been widely used, often without a clear connection with physiology. This work proposes a patient-independent model for the estimation of well-aerated volume of lungs in CT images (WAVE). METHODS: A Gaussian fit, with mean (Mu.f) and width (Sigma.f) values, was applied to the lower CT histogram data points of the lung to provide the estimation of the well-aerated lung volume (WAVE.f). Independence from CT reconstruction parameters and respiratory cycle was analysed using healthy lung CT images and 4DCT acquisitions. The Gaussian metrics and first order radiomic features calculated for a third cohort of COVID-19 patients were compared with those relative to healthy lungs. Each lung was further segmented in 24 subregions and a new biomarker derived from Gaussian fit parameter Mu.f was proposed to represent the local density changes. RESULTS: WAVE.f resulted independent from the respiratory motion in 80% of the cases. Differences of 1%, 2% and up to 14% resulted comparing a moderate iterative strength and FBP algorithm, 1 and 3 mm of slice thickness and different reconstruction kernel. Healthy subjects were significantly different from COVID-19 patients for all the metrics calculated. Graphical representation of the local biomarker provides spatial and quantitative information in a single 2D picture. CONCLUSIONS: Unlike other metrics based on fixed histogram thresholds, this model is able to consider the inter- and intra-subject variability. In addition, it defines a local biomarker to quantify the severity of the disease, independently of the observer.

On skin dose estimation software in interventional radiology.

In recent years, a growing interest has been shown in the implementation of software dedicated to the skin dose calculation, since the Fluoroscopically Guided Interventions are expanding in various medical areas. In this regard, a review article recently published by Malchair et al. (2020) is of great importance as it provides the reader with useful references to the software currently available to estimate the patient's skin dose. Despite the usefulness of collecting and summarizing in one paper the different software solutions, a few critical issues have emerged related to some parameters and configurations used in the estimation; additional details concerning patient's size and position can be added to the information cited by the authors, giving greater robustness to the software calculation. Furthermore, software results cited in the benchmarking without reference cause a lack of solid information. Our suggestion is to adopt the given criteria to evaluate every available software solutions thus helping the eventual user to analyse the tool before adopting it.

[Mortality and use of healthcare resources in elderly patients in Seville: Differences between institutionalised patients and those included in a home care program]. / Mortalidad y uso de recursos sanitarios en pacientes ancianos en la provincia de Sevilla: diferencias entre pacientes institucionalizados y en atención domiciliaria.

OBJECTIVES: Society is ageing, and as a consequence, the population with increased frailty and comorbidity is growing. The aim is to analyse the mortality and its potential factors, as well as the use of healthcare resources in elderly patients, and to study the differences between institutionalised patients and those included in a home care program. MATERIALS AND METHODS: An observational, longitudinal and prospective cohort study was conducted in Seville during 2016. The study subjects consisted of 1314 elderly patients (1061 institutionalised and 253 at home). The variables studied included mortality and its potential factors, and the use of healthcare resources. RESULTS: No differences were found in mortality between institutionalised and home care program patients (RR=1.044; 95% CI; 0.74-1.46; P=.799). The leading cause of death was circulatory diseases followed by respiratory diseases. Among the factors explaining the mortality, it is important to highlight: age, dependency and admissions in the hospital or the emergency department. The patient's functional independence is associated with a higher survival rate. Differences were found between both groups in the number of calls to the Critical Care and Emergency Services (P=.022) or the primary care doctor (P<.001) and in the hospital admissions (P<.001), the first 2differences being higher in home care program patients, and the latter in institutionalised patients. CONCLUSIONS: There are no differences between groups either in the mortality or in the cause of death. Age, functional dependency and admissions in the hospital are factors which explain the mortality. The use of healthcare resources is higher in patients at home.