Correction to: Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

The article Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma, written by M. Venianaki, O. Salvetti, E. de Bree, T. Maris, A. Karantanas, E. Kontopodis, K. Nikiforaki, K. Marias, was originally published electronically without open access

Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

The main purpose of this study is to analyze the intrinsic tumor physiologic characteristics in patients with sarcoma through model-free analysis of dynamic contrast enhanced MR imaging data (DCE-MRI). Clinical data were collected from three patients with two different types of histologically proven sarcomas who underwent conventional and advanced MRI examination prior to excision. An advanced matrix factorization algorithm has been applied to the data, resulting in the identification of the principal time-signal uptake curves of DCE-MRI data, which were used to characterize the physiology of the tumor area, described by three different perfusion patterns i.e. hypoxic, well-perfused and necrotic one. The performance of the algorithm was tested by applying different initialization approaches with subsequent comparison of their results. The algorithm was proven to be robust and led to the consistent segmentation of the tumor area in three regions of different perfusion, i.e. well- perfused, hypoxic and necrotic. Results from the model-free approach were compared with a widely used pharmacokinetic (PK) model revealing significant correlations

Cancer tissue classification from DCE-MRI data using pattern recognition techniques

Cancer research has significantly advanced in recent years mainly through developments in medical genomics and bioinformatics. It is expected that such approaches will result in more durable tumor control and fewer side effects compared with conventional treatments such as radiotherapy or chemotherapy. From the imaging standpoint, non-invasive imaging biomarkers (IBs) that assess angiogenic response and tumor environment at an early stage of therapy are of utmost importance since they could provide useful insights into therapy planning. However, the extraction of IBs is still an open problem since there are no standardized imaging protocols yet or established methods for the robust extraction of IBs. DCE-MRI is amongst the most promising non-invasive functional imaging modalities while compartmental pharmacokinetic (PK) modeling is the most common technique used for DCE-MRI data analysis. However, PK models suffer from a number of limitations such as modeling complexity, which often leads to variability in the computed biomarkers. To address these problems, alternative DCE-MRI biomarker extraction strategies coupled with a profound understanding of the physiological meaning of IBs is a sine qua non condition. To this end, a more recent model-free approach has been suggested in literature for the analysis of DCE-MRI data, which relies on the shape classification of the time-signal uptake curves of image pixels in a selected tumor region of interest. This thesis is centered on this new approach and the clinical question whether model-free DCE-MRI data analysis has the potential to provide robust, clinically significant biomarkers using pattern recognition and image analysis techniques

Incarcerated spontaneous transdiaphragmatic intercostal hernia

AbstractIntroductionProlapse of abdominal viscera into the thoracic subcutis through the chest wall is known as transdiaphragmatic intercostal hernia (TIH). Herein, we present the first case of spontaneous TIH presenting as a thoracoabdominal emergency.Presentation of caseA 78-year-old male presented with acute left thoracoabdominal pain following a sudden bulge at the left posterolateral chest wall corresponding to a partially reducible soft tissue mass with ecchymosis at the overlying skin. Paroxysmal cough during the last four days was also reported along with a prolonged daily application of a special tight abdominal belt that used while milking sheep. CT-scan of the abdomen showed intrathoracic proptosis of the splenic flexure through a defect of the left hemidiaphragm and subcutaneous prolapse of the herniated colon through the 7th intercostal space. On laparotomy, the herniated colon showed signs of ischemic necrosis leading to segmental colectomy followed by repair of the diaphragmatic defect.DiscussionThe clinical diagnosis of spontaneous TIH demands very high index of suspicion and thorough patient's history. In this case the daily elevation of the intraabdominal pressure due to an abdominal milking belt might have caused gradual slimming and loosening of the diaphragm and the intercostals muscles rendering them vulnerable to sudden increases of the thoracoabdominal pressure due to violent coughing. Such a hypothesis is reasonable in the absence of traumatic injury in this patient.ConclusionSpontaneous TIH should be suspected in patients presenting with a sudden palpable chest wall bulge and associated thoracoabdominal symptoms in the absence of preceding injury

External Validation of the American College of Surgeons Surgical Risk Calculator in Elderly Patients Undergoing General Surgery Operations

Preoperative risk stratification in the elderly surgical patient is an essential part of contemporary perioperative care and can be done with the use of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). However, data on the generalizability of the ACS-SRC in the elderly is scarce. In this study, we report an external validation of the ACS-RC in a geriatric cohort. A retrospective analysis of a prospectively maintained database was performed including patients aged > 65 who underwent general surgery procedures during 2012–2017 in a Greek academic centre. The predictive ability of the ACS-SRC for post-operative outcomes was tested with the use of Brier scores, discrimination, and calibration metrics. 471 patients were included in the analysis. 30-day postoperative mortality was 3.2%. Overall, Brier scores were lower than cut-off values for almost all outcomes. Discrimination was good for serious complications (c-statistic: 0.816; 95% CI: 0.762–0.869) and death (c-statistic: 0.824; 95% CI: 0.719–0.929). The Hosmer-Lemeshow test showed good calibration for all outcomes examined. Predicted and observed length of stay (LOS) presented significant differences for emergency and for elective cases. The ACS-SRC demonstrated good predictive performance in our sample and can aid preoperative estimation of multiple outcomes except for the prediction of post-operative LOS

A Comparison of the Malnutrition Universal Screening Tool (MUST) and the Mini Nutritional Assessment-Short Form (MNA-SF) Tool for Older Patients Undergoing General Surgery

The optimal malnutrition screening tool in geriatric surgery has yet to be determined. Herein, we compare two main tools in older patients undergoing general surgery operations. Older patients (>65 years old) who underwent general surgery operations between 2012 and 2017 in a tertiary centre were included. The Malnutrition Universal Screening Tool (MUST) and the Mini Nutritional Assessment Short Form (MNA-SF) were used for nutritional risk assessment. Preoperative variables as well as postoperative outcomes were recorded prospectively. Agreement between tools was determined with the weighted kappa (κ) statistic. Multiple regression analysis was used to assess the association of the screening tools with postoperative outcomes. A total of 302 patients (median age 74 years, range: 65–92) were included. A similar number of patients were classified as medium/high risk for malnutrition with the MNA-SF and MUST (26% vs. 36%, p = 0.126). Agreement between the two tools was moderate (weighted κ: 0.474; 95%CI: 0.381–0.568). In the multivariate analysis, MNA-SF was associated significantly with postoperative mortality (p = 0.038) and with postoperative length of stay (p = 0.001). MUST was associated with postoperative length of stay (p = 0.048). The MNA-SF seems to be more consistently associated with postoperative outcomes in elderly patients undergoing general surgery compared with the MUST tool

Extended perfusion protocol for MS lesion quantification

This study aims to examine a time-extended dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocol and report a comparative study with three different pharmacokinetic (PK) models, for accurate determination of subtle blood–brain barrier (BBB) disruption in patients with multiple sclerosis (MS). This time-extended DCE-MRI perfusion protocol, called Snaps, was applied on 24 active demyelinating lesions of 12 MS patients. Statistical analysis was performed for both protocols through three different PK models. The Snaps protocol achieved triple the window time of perfusion observation by extending the magnetic resonance acquisition time by less than 2 min on average for all patients. In addition, the statistical analysis in terms of adj-R2 goodness of fit demonstrated that the Snaps protocol outperformed the conventional DCE-MRI protocol by detecting 49% more pixels on average. The exclusive pixels identified from the Snaps protocol lie in the low ktrans range, potentially reflecting areas with subtle BBB disruption. Finally, the extended Tofts model was found to have the highest fitting accuracy for both analyzed protocols. The previously proposed time-extended DCE protocol, called Snaps, provides additional temporal perfusion information at the expense of a minimal extension of the conventional DCE acquisition time

Heart rate classification using ECG signal processing and machine learning methods

Summarization: Electrocardiogram (ECG) signal constitutes a valuable technique that provides considerable information towards the early diagnosis of several cardiovascular diseases, especially regarding the detection of abnormal heart rate, namely arrhythmias. In this paper, innovative methodologies that allow for the efficient classification of cardiac rhythm are presented. The proposed methods are based on ECG signal analysis, extraction of significant features, as well as classification algorithms. Several clinical, time- and frequency-domain features are either calculated, or automatically extracted by means of a Convolutional Neural Network, while traditional machine learning algorithms, such as k-Nearest Neighbors and Random Forests are employed in order to classify the ECG signals among 7 different cases of abnormal and normal heart rate. The learning methods are carried out within the JADBio software tool, that also performs feature selection prior to classification. The experimental results demonstrate high performance of the deployed methods in terms of relevant statistical metrics, while they yielded an average validation Area Under the Curve (AUC) of 99.9%.Παρουσιάστηκε στο: 2021 IEEE 21st International Conference on Bioinformatics and Bioengineerin

Stoma-free survival after anastomotic leak following rectal cancer resection: worldwide cohort of 2470 patients

Background: The optimal treatment of anastomotic leak after rectal cancer resection is unclear. This worldwide cohort study aimed to provide an overview of four treatment strategies applied. Methods: Patients from 216 centres and 45 countries with anastomotic leak after rectal cancer resection between 2014 and 2018 were included. Treatment was categorized as salvage surgery, faecal diversion with passive or active (vacuum) drainage, and no primary/secondary faecal diversion. The primary outcome was 1-year stoma-free survival. In addition, passive and active drainage were compared using propensity score matching (2: 1). Results: Of 2470 evaluable patients, 388 (16.0 per cent) underwent salvage surgery, 1524 (62.0 per cent) passive drainage, 278 (11.0 per cent) active drainage, and 280 (11.0 per cent) had no faecal diversion. One-year stoma-free survival rates were 13.7, 48.3, 48.2, and 65.4 per cent respectively. Propensity score matching resulted in 556 patients with passive and 278 with active drainage. There was no statistically significant difference between these groups in 1-year stoma-free survival (OR 0.95, 95 per cent c.i. 0.66 to 1.33), with a risk difference of -1.1 (95 per cent c.i. -9.0 to 7.0) per cent. After active drainage, more patients required secondary salvage surgery (OR 2.32, 1.49 to 3.59), prolonged hospital admission (an additional 6 (95 per cent c.i. 2 to 10) days), and ICU admission (OR 1.41, 1.02 to 1.94). Mean duration of leak healing did not differ significantly (an additional 12 (-28 to 52) days). Conclusion: Primary salvage surgery or omission of faecal diversion likely correspond to the most severe and least severe leaks respectively. In patients with diverted leaks, stoma-free survival did not differ statistically between passive and active drainage, although the increased risk of secondary salvage surgery and ICU admission suggests residual confounding

Stoma-free Survival After Rectal Cancer Resection With Anastomotic Leakage: Development and Validation of a Prediction Model in a Large International Cohort.

Objective:To develop and validate a prediction model (STOMA score) for 1-year stoma-free survival in patients with rectal cancer (RC) with anastomotic leakage (AL).Background:AL after RC resection often results in a permanent stoma.Methods:This international retrospective cohort study (TENTACLE-Rectum) encompassed 216 participating centres and included patients who developed AL after RC surgery between 2014 and 2018. Clinically relevant predictors for 1-year stoma-free survival were included in uni and multivariable logistic regression models. The STOMA score was developed and internally validated in a cohort of patients operated between 2014 and 2017, with subsequent temporal validation in a 2018 cohort. The discriminative power and calibration of the models' performance were evaluated.Results:This study included 2499 patients with AL, 1954 in the development cohort and 545 in the validation cohort. Baseline characteristics were comparable. One-year stoma-free survival was 45.0% in the development cohort and 43.7% in the validation cohort. The following predictors were included in the STOMA score: sex, age, American Society of Anestesiologist classification, body mass index, clinical M-disease, neoadjuvant therapy, abdominal and transanal approach, primary defunctioning stoma, multivisceral resection, clinical setting in which AL was diagnosed, postoperative day of AL diagnosis, abdominal contamination, anastomotic defect circumference, bowel wall ischemia, anastomotic fistula, retraction, and reactivation leakage. The STOMA score showed good discrimination and calibration (c-index: 0.71, 95% CI: 0.66-0.76).Conclusions:The STOMA score consists of 18 clinically relevant factors and estimates the individual risk for 1-year stoma-free survival in patients with AL after RC surgery, which may improve patient counseling and give guidance when analyzing the efficacy of different treatment strategies in future studies