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Introduction: The effects of nocturnal noninvasive positive pressure ventilation (NIPPV) in patients with stable chronic obstructive pulmonary disease (COPD) remain controversial. Methods: The Cochrane Airways group Register of Trials, MEDLINE, EMBASE and CINAHL were searched up to August 2012. Individual patient data from randomised controlled trials on NIPPV outcomes were selected for two separate meta-analyses: the first with follow-up of 3 months and the second with 12 months of follow-up. Additionally, subgroup analyses within the NIPPV group comparing IPAP levels, compliance and levels of hypercapnia on change in PaCO2 after 3 months were performed. Results: Seven trials (245 patients) were included. All studies were considered of moderate to high quality. No significant difference was found between NIPPV and control groups after 3 or 12 months of follow-up when looking at PaCO2 and PaO2, 6-minute walking distance, healthrelated quality-of-life, forced expiratory volume in 1 s, forced vital capacity, maximal inspiratory pressure and sleep efficiency. Significant differences in change in PaCO2 after 3 months were found for patients ventilated with IPAP levels of at least 18 cm H2O, for patients who used NIPPV for at least 5 h per night as well as for patients with baseline PaCO2 of at least 55 mm Hg when compared to patients with lower IPAP levels, poorer compliance or lower levels of hypercapnia. Discussion: At present, there is insufficient evidence to support the application of routine NIPPV in patients with stable COPD. However, higher IPAP levels, better compliance and higher baseline PaCO2 seem to improve PaCO2. [ABSTRACT FROM AUTHOR]

A letter to the article is presented in response to the article "Nocturnal non-invasive ventilation in COPD patients with prolonged hypercapnia after ventilatory support for acute respiratory failure: a randomised, controlled, parallel-group study" by F.M. Struik et al published in the year 2014.

Objective: This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability., Methods: We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients. After segmenting the relevant structures, our model quantifies vascular involvement by measuring the degree of the vessel wall that is in contact with the tumor using AI-segmented CTs. Based on these measurements, the model classifies the resectability stage using the Dutch Pancreatic Cancer Group criteria as either resectable, borderline resectable, or locally advanced (LA)., Results: We evaluated the performance of the model using a test set containing 60 CTs from 60 patients, consisting of 20 resectable, 20 borderline resectable, and 20 locally advanced cases, by comparing the automated analysis obtained from the model to expert visual vascular involvement assessments. The model concurred with the radiologists on 227/300 (76%) vessels for determining vascular involvement. The model's resectability classification agreed with the radiologists on 17/20 (85%) resectable, 16/20 (80%) for borderline resectable, and 15/20 (75%) for locally advanced cases., Conclusions: This study demonstrates that an AI model may allow automatic quantification of vascular involvement and classification of resectability for PDAC., Relevance Statement: This AI model enables automated vascular involvement quantification and resectability classification for pancreatic cancer, aiding radiologists in treatment decisions, and potentially improving patient outcomes., Key Points: • High inter-observer variability exists in determining vascular involvement and resectability for PDAC. • Artificial intelligence accurately quantifies vascular involvement and classifies resectability for PDAC. • Artificial intelligence can aid radiologists by automating vascular involvement and resectability assessments., (© 2024. The Author(s).)

Background: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula., Methods: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013-2018) to develop the radiomics-based preoperative-Fistula Risk Score. Extracted radiomic features were analysed with four machine learning classifiers. The model was externally validated in a single centre in Italy (Verona, 2020-2021). The radiomics-based preoperative-Fistula Risk Score was compared with the Fistula Risk Score and the updated alternative Fistula Risk Score., Results: Overall, 359 patients underwent a pancreatoduodenectomy, of whom 89 (25 per cent) developed a clinically relevant postoperative pancreatic fistula. The radiomics-based preoperative-Fistula Risk Score model was developed using CT scans of 118 patients, of which three radiomic features were included in the random forest model, and externally validated in 57 patients. The model performed well with an area under the curve of 0.90 (95 per cent c.i. 0.71 to 0.99) and 0.81 (95 per cent c.i. 0.69 to 0.92) in the Amsterdam test set and Verona data set respectively. The radiomics-based preoperative-Fistula Risk Score performed similarly to the Fistula Risk Score (area under the curve 0.79) and updated alternative Fistula Risk Score (area under the curve 0.79)., Conclusion: The radiomics-based preoperative-Fistula Risk Score, which uses only preoperative CT features, is a new and promising radiomics-based score that has the potential to be integrated with hospital CT report systems and improve patient counselling before surgery. The model with underlying code is readily available via www.pancreascalculator.com and www.github.com/PHAIR-Consortium/POPF-predictor., (© The Author(s) 2023. Published by Oxford University Press on behalf of BJS Society Ltd.)

Purpose To evaluate interobserver variability in the morphologic tumor response assessment of colorectal liver metastases (CRLM) managed with systemic therapy and to assess the relation of morphologic response with gene mutation status, targeted therapy, and Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 measurements. Materials and Methods Participants with initially unresectable CRLM receiving different systemic therapy regimens from the randomized, controlled CAIRO5 trial (NCT02162563) were included in this prospective imaging study. Three radiologists independently assessed morphologic tumor response on baseline and first follow-up CT scans according to previously published criteria. Two additional radiologists evaluated disagreement cases. Interobserver agreement was calculated by using Fleiss κ. On the basis of the majority of individual radiologic assessments, the final morphologic tumor response was determined. Finally, the relation of morphologic tumor response and clinical prognostic parameters was assessed. Results In total, 153 participants (median age, 63 years [IQR, 56-71]; 101 men) with 306 CT scans comprising 2192 CRLM were included. Morphologic assessment performed by the three radiologists yielded 86 (56%) agreement cases and 67 (44%) disagreement cases (including four major disagreement cases). Overall interobserver agreement between the panel radiologists on morphology groups and morphologic response categories was moderate (κ = 0.53, 95% CI: 0.48, 0.58 and κ = 0.54, 95% CI: 0.47, 0.60). Optimal morphologic response was particularly observed in patients treated with bevacizumab ( P = .001) and in patients with RAS/BRAF mutation ( P = .04). No evidence of a relationship between RECIST 1.1 and morphologic response was found ( P = .61). Conclusion Morphologic tumor response assessment following systemic therapy in participants with CRLM demonstrated considerable interobserver variability. Keywords: Tumor Response, Observer Performance, CT, Liver, Metastases, Oncology, Abdomen/Gastrointestinal Clinical trial registration no. NCT02162563 Supplemental material is available for this article. © RSNA, 2022.

Background and Objectives: Left-sided pancreatic lesions are often treated surgically. Accurate diagnostic work-up is therefore essential to prevent futile major abdominal surgery. Large series focusing specifically on the preoperative work-up of left-sided pancreatic lesions are lacking. This surgical cohort analysis describes the sensitivity of CT, MRI, and EUS-FNA/B in the diagnostic work-up of left-sided pancreatic lesions., Methods: We performed a post-hoc analysis of patients who underwent surgery for a left-sided pancreatic lesion between April 2010 and August 2017 and participated in the randomized CPR trial. Primary outcome was the sensitivity of CT, MRI, and EUS-FNA/B. Sensitivity was determined as the most likely diagnosis of each modality compared with the postoperative histopathological diagnosis. Additionally, the change in sensitivity of EUS versus EUS-FNA/B (i.e., cyst fluid analysis, and/or tissue acquisition) was measured., Results: Overall, 181 patients were included (benign: 23%, premalignant: 27%, malignant: 50%). Most patients had solid lesions (65%). Preoperative imaging included CT (86%), MRI (41%), EUS (68%). Overall, CT and EUS-FNA/B reached a sensitivity of both 71%, compared with 66% for MRI. When EUS was combined with FNA/B, sensitivity rose from 64% to 71%. For solid lesions, CT reached the highest sensitivity (75%) when compared with MRI (70%) and EUS-FNA/B (69%). For cystic lesions, EUS-FNA/B reached the highest sensitivity (75%) when compared with CT and MRI (both 62%)., Conclusions: CT is the most sensitive diagnostic modality for solid and EUS-FNA/B for cystic left-sided pancreatic lesions. EUS-FNA/B was associated with an increased sensitivity when compared to EUS alone., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Transcranial magnetic stimulation (TMS) is often used to understand the function of individual brain regions, but this ignores the fact that TMS may affect network-level rather than nodal-level processes. We examine the effects of a double perturbation to two frontoparietal network nodes, compared with the effects of single lesions to either node. We hypothesized that Bayesian evidence for the absence of effects that build upon one another indicates that a single perturbation is consequential to network-level processes. Twenty-three humans performed pro-saccades (look toward) and anti-saccades (look away) after receiving continuous theta-burst stimulation (cTBS) to right frontal eye fields (FEFs), dorsolateral prefrontal cortex (DLPFC), or somatosensory cortex (S1; the control region). On a subset of trials, a TMS pulse was applied to right posterior parietal cortex (PPC). FEF, DLPFC, and PPC are important frontoparietal network nodes for generating anti-saccades. Bayesian t tests were used to test hypotheses for enhanced double perturbation effects (cTBS plus TMS pulse) on saccade behaviors, against the alternative hypothesis that double perturbation effects to a network are not greater than single perturbation effects. In one case, we observed strong evidence [Bayes factor (BF 10 ) = 325] that PPC TMS following DLPFC cTBS enhanced impairments in ipsilateral anti-saccade amplitudes over DLPFC cTBS alone, and not over the effect of the PPC pulse alone (BF 10 = 0.75), suggesting that double perturbation effects do not augment one another. Rather, this suggests that computations are distributed across the network, and in some cases there can be compensation for cTBS perturbations., (Copyright © 2020 Cameron et al.)

Objective: To investigate whether body mass index (BMI) is a prognostic factor for biochemical recurrence (BCR) in Dutch men after radical prostatectomy (RP), as although epidemiological studies of obesity in relation to prostate cancer have provided conflicting results, recent studies from the USA suggest that a higher BMI is a risk factor for progression of prostate cancer., Patients and Methods: Of the 1417 patients with prostate cancer who had RP at two University hospitals, 1302 were included in the present study. BMI (kg/m(2)) classes were defined as normal (<25), overweight (25-30) and obese (> or =30). The median follow-up was 59 months and clinical data were obtained retrospectively from charts. BCR was defined as two consecutive prostate-specific antigen (PSA) levels of >0.1 ng/mL., Results: In all, 600 patients were classified as having normal weight (43.9%), 665 as overweight (48.6%) and 103 as obese (7.5%). Overall, 297 patients developed BCR after RP; the 10-year risk (95% confidence interval) of BCR was 31.9 (26.6-37.2)%, 30.5 (25.8-35.2)% and 23.9 (14.9-32.9)% for patients in the three categories, respectively (P = 0.836). Multivariable proportional hazard regression analyses of BMI and established prognostic factors for BCR did not change these results., Conclusion: BMI appeared to have no prognostic value for BCR in Dutch patients with clinically localized prostate cancer and treated with RP.