Background: Pancreatic neuroendocrine tumors (pNETs) have a high prevalence in patients with multiple endocrine neoplasia type 1 (MEN1) and are the leading cause of death. Tumor size is still ...regarded as the main prognostic factor and therefore used for surgical decision-making. We assessed reliability and agreement of radiological and pathological tumor size in a population-based cohort of patients with MEN1-related pNETs. Methods: Patients were selected from the Dutch MEN1 database if they had undergone a resection for a pNET between 2003 and 2018. Radiological (MRI, CT, and endoscopic ultrasonography EUS) and pathological tumor size were collected from patient records. Measures of agreement (Bland-Altman plots with limits of agreement LoA and absolute agreement) and reliability (intraclass correlation coefficients ICC and unweighted kappa) were calculated for continuous and categorized (< or ≥2 cm) pNET size. Results: In 73 included patients, the median radiological and pathological tumor sizes measured were 22 (3–160) and 21 (4–200) mm, respectively. Mean bias between radiological and pathological tumor size was −0.2 mm and LoA ranged from −12.9 to 12.6 mm. For the subgroups of MRI, CT, and EUS, LoA of radiological and pathological tumor size ranged from −9.6 to 10.9, −15.9 to 15.8, and −13.9 to 11.0, respectively. ICCs for the overall cohort, MRI, CT, and EUS were 0.80, 0.86, 0.75, and 0.76, respectively. Based on the 2 cm criterion, agreement was 81.5%; hence, 12 patients (18.5%) were classified differently between imaging and pathology. Absolute agreement and kappa values of MRI, CT, and EUS were 88.6, 85.7, and 75.0%, and 0.77, 0.71, and 0.50, respectively. Conclusion: Within a population-based cohort, MEN1-related pNET size was not systematically over- or underestimated on preoperative imaging. Based on agreement and reliability measures, MRI is the preferred imaging modality.
Objective
To develop a CT-based prediction score for anastomotic leakage after esophagectomy and compare it to subjective CT interpretation.
Methods
Consecutive patients who underwent a CT scan for a ...clinical suspicion of anastomotic leakage after esophagectomy with cervical anastomosis between 2003 and 2014 were analyzed. The CT scans were systematically re-evaluated by two radiologists for the presence of specific CT findings and presence of an anastomotic leak. Also, the original CT interpretations were acquired. These results were compared to patients with and without a clinical confirmed leak.
Results
Out of 122 patients that underwent CT for a clinical suspicion of anastomotic leakage; 54 had a confirmed leak. In multivariable analysis, anastomotic leakage was associated with mediastinal fluid (OR = 3.4), esophagogastric wall discontinuity (OR = 4.9), mediastinal air (OR = 6.6), and a fistula (OR = 7.2). Based on these criteria, a prediction score was developed resulting in an area-under-the-curve (AUC) of 0.86, sensitivity of 80%, and specificity of 84%. The original interpretation and the systematic subjective CT assessment by two radiologists resulted in AUCs of 0.68 and 0.75 with sensitivities of 52% and 69%, and specificities of 84% and 82%, respectively.
Conclusion
This CT-based score may provide improved diagnostic performance for diagnosis of anastomotic leakage after esophagectomy.
Key Points
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A CT-based score provides improved diagnostic performance for diagnosis of anastomotic leakage
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Leakage associations include mediastinal fluid, mediastinal air, wall discontinuity, and fistula
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A scoring system yields superior diagnostic accuracy compared to subjective CT assessment
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Radiologists may suggest presence of anastomotic leakage based on a prediction score
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Background and Objectives
Patients with locally advanced pancreatic cancer (LAPC) are increasingly treated with FOLFIRINOX, resulting in improved survival and resection of tumors that were initially ...unresectable. It remains unclear, however, which specific patients benefit from FOLFIRINOX. Two nomograms were developed predicting overall survival (OS) and resection at the start of FOLFIRINOX for LAPC.
Methods
From our multicenter, prospective LAPC registry in 14 Dutch hospitals, LAPC patients starting first‐line FOLFIRINOX (April 2015–December 2017) were included. Stepwise backward selection according to the Akaike Information Criterion was used to identify independent baseline predictors for OS and resection. Two prognostic nomograms were generated.
Results
A total of 252 patients were included, with a median OS of 14 months. Thirty‐two patients (13%) underwent resection, with a median OS of 23 months. Older age, female sex, Charlson Comorbidity Index ≤1, and CA 19.9 < 274 were independent factors predicting a better OS (c‐index: 0.61). WHO ps >1, involvement of the superior mesenteric artery, celiac trunk, and superior mesenteric vein ≥ 270° were independent factors decreasing the probability of resection (c‐index: 0.79).
Conclusions
Two nomograms were developed to predict OS and resection in patients with LAPC before starting treatment with FOLFIRINOX. These nomograms could be beneficial in the shared decision‐making process and counseling of these patients.
In contrast-enhanced computed tomography, total body weight adapted contrast injection protocols have proven successful in achieving a homogeneous enhancement of vascular structures and liver ...parenchyma. However, because solid organs have greater perfusion than adipose tissue, the lean body weight (fat-free mass) rather than the total body weight is theorised to cause even more homogeneous enhancement. We included 102 consecutive patients who underwent a multiphase abdominal computed tomography between March 2016 and October 2019. Patients received contrast media (300 mgI/mL) according to bodyweight categories. Using regions of interest, we measured the Hounsfield unit (HU) increase in liver attenuation from unenhanced to contrast-enhanced computed tomography. Furthermore, subjective image quality was graded using a four-point Likert scale. An artificial intelligence algorithm automatically segmented and determined the body compositions and calculated the percentages of lean body weight. The hepatic enhancements were adjusted for iodine dose and iodine dose per total body weight, as well as percentage lean body weight. The associations between enhancement and total body weight, body mass index, and lean body weight were analysed using linear regression. Patients had a median age of 68 years (IQR: 58-74), a total body weight of 81 kg (IQR: 73 - 90), a body mass index of 26 kg/m
(SD: ±4.2), and a lean body weight percentage of 50% (IQR: 36 - 55). Mean liver enhancements in the portal venous phase were 61 ± 12 HU (≤ 70 kg), 53 ± 10 HU (70 - 90 kg), and 53 ± 7 HU (≥ 90 kg). The majority (93%) of scans were rated as good or excellent. Regression analysis showed significant correlations between liver enhancement corrected for injected total iodine and total body weight (
= 0.53;
< 0.001) and between liver enhancement corrected for lean body weight and the percentage of lean body weight (
= 0.73;
< 0.001). Most benefits from personalising iodine injection using %LBW additive to total body weight would be achieved in patients under 90 kg. Liver enhancement is more strongly associated with the percentage of lean body weight than with the total body weight or body mass index. The observed variation in liver enhancement might be reduced by a personalised injection based on the artificial-intelligence-determined percentage of lean body weight.
Background
Pelvic morphological parameters on magnetic resonance imaging (MRI), such as the membranous urethral length (MUL), can predict urinary incontinence after radical prostatectomy but are ...prone to interobserver disagreement. Our objective was to improve interobserver agreement among radiologists in measuring pelvic parameters using deep learning (DL)-based segmentation of pelvic structures on MRI scans.
Methods
Preoperative MRI was collected from 167 prostate cancer patients undergoing radical prostatectomy within our regional multicentric cohort. Two DL networks (nnU-Net) were trained on coronal and sagittal scans and evaluated on a test cohort using an 80/20% train-test split. Pelvic parameters were manually measured by three abdominal radiologists on raw MRI images and with the use of DL-generated segmentations. Automated measurements were also performed for the pelvic parameters. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC) and the Bland–Altman plot.
Results
The DL models achieved median Dice similarity coefficient (DSC) values of 0.85–0.97 for coronal structures and 0.87–0.98 for sagittal structures. When radiologists used DL-generated segmentations of pelvic structures, the interobserver agreement for sagittal MUL improved from 0.64 (95% confidence interval 0.28–0.83) to 0.91 (95% CI 0.84–0.95). Furthermore, there was an increase in ICC values for the obturator internus muscle from 0.74 (95% CI 0.42–0.87) to 0.86 (95% CI 0.75–0.92) and for the levator ani muscle from 0.40 (95% CI 0.05–0.66) to 0.61 (95% CI 0.31–0.78).
Conclusions
DL-based automated segmentation of pelvic structures improved interobserver agreement in measuring pelvic parameters on preoperative MRI scans.
Relevance statement
The implementation of deep learning segmentations allows for more consistent measurements of pelvic parameters by radiologists. Standardized measurements are crucial for incorporating these parameters into urinary continence prediction models.
Key points
• DL-generated segmentations improve interobserver agreement for pelvic measurements among radiologists.
• Membranous urethral length measurement improved from substantial to almost perfect agreement.
• Artificial intelligence enhances objective pelvic parameter assessment for continence prediction models.
Graphical Abstract
Primary tumors have a high likelihood of developing metastases in the liver, and early detection of these metastases is crucial for patient outcome. We propose a method based on convolutional neural ...networks to detect liver metastases. First, the liver is automatically segmented using the six phases of abdominal dynamic contrast-enhanced (DCE) MR images. Next, DCE-MR and diffusion weighted MR images are used for metastases detection within the liver mask. The liver segmentations have a median Dice similarity coefficient of 0.95 compared with manual annotations. The metastases detection method has a sensitivity of 99.8% with a median of two false positives per image. The combination of the two MR sequences in a dual pathway network is proven valuable for the detection of liver metastases. In conclusion, a high quality liver segmentation can be obtained in which we can successfully detect liver metastases.
Many viruses modify cellular processes for their own benefit. The enterovirus 3A protein inhibits endoplasmic reticulum (ER)-to-Golgi transport, a function previously suggested to be important for ...viral suppression of immune responses. Here, we show that a virus carrying a 3A protein defective in inhibiting ER-to-Golgi transport is indeed less virulent in mice, and we unravel the mechanism by which 3A inhibits this trafficking step. Evidence is provided that 3A inhibits the activation of the GTPase ADP-ribosylation factor 1 (Arf1), which regulates the recruitment of the COP-I coat complex to membranes. 3A specifically inhibits the function of GBF1, a guanine nucleotide exchange factor for Arf1, by interacting with its N terminus. By specifically interfering with GBF1-mediated Arf1 activation, 3A may prove a valuable tool in dissecting the early steps of the secretory pathway.
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