Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BackgroundPancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.MethodsWe conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.Results3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.ConclusionMost prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.     

Seroprevalence and trends of transfusion transmissible infections among retrospective blood donors in Western Azerbaijan Regional Blood Transfusion Center,Iran: A ten-years evaluation

Transfusion transmissible infections (TTIs) have been a public health challenge for the accessibility, quality and safety of blood transfusion. The present study aimed to consider the prevalence and the trends of hepatitis B virus (HBV), hepatitis C virus (HCV), Human T-cell leukemia virus type 1 (HTLV-1), human immunodeficiency virus (HIV) and syphilis across the ten years among retrospective blood donors. A retrospective investigation of blood donors’ data covering the period from 22 May 2009 to 22 May 2019 was done. Data was accumulated and analyzed from Blood Transfusion Center records, pertaining to all donors who were screened for various TTIs using respective immunological techniques. Out of the 682,171 screened donors in the 2009–2019 study period, 2470 (0.36 %) were infected with at least one infectious agent. The overall prevalence of HBV, HCV, HTLV-1, HIV and syphilis were 1700 (0.25 %), 184 (0.027 %), 335 (0.05 %), 4 (0.0.05 %) and 247 (0.036 %), respectively. The study showed male dominated donor pool (96.79 %) with higher prevalence (0.34 %) of TTIs compared to female donors (0.02 %) with 3.21 % population. Despite the low prevalence of TTIs in our study, HBV, HCV, syphilis and HIV have remained a big threat to safe blood transfusion in Iran. Strict adherence to selection criteria, algorithm of donor screening, use of highly sensitive and specific methods for detection of TTIs, regular consultation and health education programs, prevention and sanitization strategies to reduce the risk of TTIs are recommended to reduce the risk of TTIs and ensure the safety of blood transfusion for recipient.     

Quantifying and leveraging predictive uncertainty for medical image assessment

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy. Finally, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.     

Directional-TV algorithm for image reconstruction from limited-angular-range data

Investigation of image reconstruction from data collected over a limited-angular range in X-ray CT remains a topic of active research because it may yield insight into the development of imaging workflow of practical significance. This reconstruction problem is well-known to be challenging, however, because it is highly ill-conditioned. In the work, we investigate optimization-based image reconstruction from data acquired over a limited-angular range that is considerably smaller than the angular range in short-scan CT. We first formulate the reconstruction problem as a convex optimization program with directional total-variation (TV) constraints applied to the image, and then develop an iterative algorithm, referred to as the directional-TV (DTV) algorithm for image reconstruction through solving the optimization program. We use the DTV algorithm to reconstruct images from data collected over a variety of limited-angular ranges for breast and bar phantoms of clinical- and industrial-application relevance. The study demonstrates that the DTV algorithm accurately recovers the phantoms from data generated over a significantly reduced angular range, and that it considerably diminishes artifacts observed otherwise in reconstructions of existing algorithms. We have also obtained empirical conditions on minimal-angular ranges sufficient for numerically accurate image reconstruction with the DTV algorithm.     

Biomechanical comparison of lateral collateral ligament reconstruction with and without additional internal bracing using a three-dimensional elbow simulator

BackgroundAlthough an additional internal bracing significantly increases stability in a repair of the lateral ulnar collateral ligament, it remains unclear whether it also does in reconstruction. Aim of this study was to implement a three-dimensional elbow simulator for testing posterolateral rotatory instability. We hypothesized that (1) reconstruction with and without internal bracing is comparable in biomechanical properties, and (2) there would be higher load-to-failure with internal bracing.MethodsPosterolateral rotatory instability was tested by imitating the lateral pivot shift test in 16 elbows. Valgus and supination torques were simultaneously increased stepwise up to 1.2 Nm. Specimens were tested at 30°, 60°, 90°, and 120° elbow flexion with an intact lateral collateral ligament complex, dissected complex, and after reconstruction with or without internal bracing. Outcome measures included joint gapping, laxity, and load to failure.FindingsWith the implemented elbow simulator no significant difference was observed for gapping or laxity between both treatment groups. Comparing treatment and native ligament, gapping was reduced, especially with increased elbow flexion. Laxity was also reduced at some flexion angles. The mean load-to-failure was 8.1 ± 2.7 Nm without and 9.6 ± 3.6 Nm with internal bracing (P = 0.645).InterpretationBoth treatments were comparable in biomechanical properties but did not fully restore the native state. Although the additional augmentation of the LUCL reconstruction tends to increase the maximum load to failure, this difference was not statistically significant. Still, reconstruction with internal bracing seems to be a reasonable option in selected primary reconstructions. It could also be useful in revision reconstruction.