The Ownership Structure Influence on the Dividend Distribution Policy: The Case of Listed French Family Firms

The usefulness and justification of corporate dividend distribution policies are among the most controversial topics in financial theory. This research aims to shed light on this issue by studying the case of French listed family firms. These companies have a specific governance structure that influences the dividend distribution policy. We examined the impact of the family ownership structure on dividend distribution policy and present empirical study results for a sample of listed French family companies. We explain the dividend distribution policy through the family shareholding structure and the presence of institutional investors and their possible influence. The theoretical framework is the agency relationship. The results show that family ownership positively affects dividend distribution; however, institutional investors have a negative influence

Machbarkeit und Sicherheit einer kombinierten Therapie mittels transarterieller Chemoembolisation mit Irinotecan beladenen Mikrosphären (DEBIRI-TACE) und CT- gesteuerter Hochdosis-Brachytherapie (CT-HDRBT) zur Behandlung von großen, nicht resektablen kolorektalen Lebermetastasen

Background: Globally, colorectal cancer is the third most common type of cancer and the incidence is constantly increasing. The presence of liver metastases is related to a poor outcome, especially if there are no surgical options for the removal of the metastases, due to their size or unfavorable anatomical location. Interventional therapies are therefore being increasingly used in palliative settings. Purpose: This prospective trial evaluated feasibility and safety of combined irinotecan Chemoembolization (DEBIRI-TACE) and CT-guided high-dose-rate brachytherapy (CT- HDRBT) in patients with unresectable colorectal liver metastases > 3 cm in diameter. Materials and Methods: 23 patients (age: 70 years ± 11.3) with 47 colorectal liver metastases (size: 62 mm ± 18.7) were recruited in the trial between December 2015 and December 2017. Catheter-related adverse events were reported per Society of Interventional Radiology classification, and treatment toxicities were reported per Common Terminology Criteria for Adverse Events. Liver blood tests before and after intervention were compared using Wilcoxon test, considering results with p < 0.05 as significant. Time to local tumor progression, progression-free survival (PFS), and overall survival (OS) were estimated by Kaplan–Meier method. Results: No catheter-related major or minor complications were recorded. The periinterventioal mortality was zero. There were significant differences in baseline vs. follow-up levels of aspartate aminotransferase (ASAT), alanine aminotransferase (ALAT); both p < 0.001, gamma-glutamyltransferase (GGT; p = 0.013), and hemoglobin (p = 0.014). After therapy, 12 of 23 patients (52,1%) presented with new grade I/II toxicities (bilirubin, n = 3 [13%]; ASAT, n = 16 [70%]; ALAT, n = 18 [78%]; ALP, n = 12 [52%] and hemoglobin, n = 15 [65%]). Moreover, grade III/IV toxicities developed in 10 (43.5%; 1 grade IV): ASAT, n = 6 (26%), grade III, n = 5; grade IV, n = 1; ALAT, n = 3 (13%); GGT, n = 7 (30%); and hemoglobin, n = 1 (4%). However, all new toxicities resolved within 3 months after therapy without additional treatment. Median local tumor control, PFS, and OS were 6, 4, and 8 months, respectively. Conclusion: DEBIRI-TACE and HDRBT is safe and shows low incidence of toxicities, which were self-resolving.Das kolorektale Karzinom ist die dritthäufigste maligne Erkrankung weltweit und die Inzidenz zeigt eine steigende Tendenz. Hepatisch metastasierte kolorektale Karzinome gehen mit einer ungünstigen Prognose einher, da die Lebermetastasen aufgrund ihrer Anzahl, Größe oder anatomischen Lage oft nicht resektabel sind. Aus diesem Grund werden minimal invasive Therapieverfahren zunehmend im Rahmen von multidisziplinären Therapiekonzepten eingesetzt. Fragestellung: Die vorliegende prospektive Studie untersuchte die Machbarkeit und Sicherheit der Kombination einer transarteriellen Chemoembolisation mit Irinotecan beladenen Mikrosphären (DEBIRI-TACE) und einer CT-gesteuerten Hochdosis- Brachytherapie (CT-HDRBT) bei Patienten mit nicht-resektablen kolorektalen Lebermetastasen mit einem Durchmesser > 3 cm. Material und Methodik: 23 Patienten (70 Jahre ± 11.3) mit insgesamt 47 kolorektale Lebermetastasen (62 mm ± 18.7) erhielten im Zeitraum zwischen Dezember 2015 und Dezember 2017 die oben genannte Kombinationstherapie. Die Therapie-assoziierten Komplikationen wurden nach der Klassifikation der Society of Interventional Radiology erfasst, und die Therapietoxizität nach den Kriterien "Common Terminology Criteria for Adverse Events (CTCAE) version 4.0“ eingestuft. Die prä- und postinterventionellen Leberfunktionsparameter wurden mittels Wilcoxon-Tests verglichen und p < 0.05 als Signifikanzniveau festgelegt. Die sekundären Endpunkte (lokale Tumorkontrolle (LTC), progressionsfreies Überleben (PFS) und Gesamtüberleben (OS)) wurden anhand der Kaplan-Meier Methode analysiert. Ergebnisse: Bei keinem der Patienten wurden milde oder schwerwiegende periinterventionelle Komplikationen festgestellt. Es wurden signifikante Unterschiede einzelner Blutwerte prä- und postinterventionell beobachtet: Aspartat-Aminotransferase (ASAT, p < 0.001), Alanin-Aminotransferase (ALAT, p < 0.001), Gamma- Glutamytransferase (GGT, p = 0.013) sowie Hämoglobin (Hb, p = 0.014). Nach dem Kombinationsverfahren zeigte sich bei 12 Patienten (52.1%) eine Lebertoxizität ersten und zweiten Grades (Bilirubin, n = 3 [13%]; ASAT, n = 16 [70%]; ALA T, n = 18 [78 %]; AP, n = 12 [52%] und Hämoglobin, n = 15 [65%]), bei 9 Patienten (43.5%) eine Lebertoxizität dritten Grades, und ausschließlich bei einem Patienten eine Lebertoxizität vierten Grades (ASAT, n = 6 (26%), Grad III, n = 5; Grad IV, n = 1; ALAT, n = 3 (13%); GGT, n = 7 (30%); Hb, n = 1 (4%)). Bei allen Patienten zeigte sich die Lebertoxizität nach drei Monaten vollständig regredient ohne spezifische Therapie. Lokale Tumorkontrolle, progressionsfreies Überleben, sowie Gesamtüberleben betrugen durchschnittlich jeweils 6, 4 und 8 Monate. Schlussfolgerung: Das Kombinationsverfahren mittels transarterieller Chemoembolisation mit Irinotecan beladenen Mikrosphären und CT-gesteuerter Hochdosis-Brachytherapie ist ein sicheres Verfahren. Die Therapie-assoziierte Toxizität ist gering und selbstlimitierend

Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches

ObjectiveTo establish a deep learning model for the detection of hypoxic–ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.Methods168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).ResultsAll optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping.ConclusionOur proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome

Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen

The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively (p = 0.526). On the in-house test dataset, the in-house U-Net achieved a mean DSC of 0.941, whereas the open U-Net obtained a mean DSC of 0.648 (p < 0.001), showing very poor segmentation results in patients with abnormalities in or surrounding the spleen. Thus, for reliable, fully automated spleen segmentation in clinical routine, the training dataset of a deep learning-based algorithm should include conditions that directly or indirectly affect the spleen

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly

Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with cirrhotic portal hypertension versus patients with lymphoma disease. This retrospective study included 149 patients with splenomegaly on computed tomography (CT) images (77 patients with cirrhotic portal hypertension, 72 patients with lymphoma) who underwent a CT scan between October 2020 and July 2021. The dataset was divided into a training (n = 99), a validation (n = 25) and a test cohort (n = 25). In the first stage, the spleen was automatically segmented using a modified U-Net architecture. In the second stage, the CT images were classified into two groups using a 3D DenseNet to discriminate between the causes of splenomegaly, first using the whole abdominal CT, and second using only the spleen segmentation mask. The classification performances were evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Occlusion sensitivity maps were applied to the whole abdominal CT images, to illustrate which regions were important for the prediction. When trained on the whole abdominal CT volume, the DenseNet was able to differentiate between the lymphoma and liver cirrhosis in the test cohort with an AUC of 0.88 and an ACC of 0.88. When the model was trained on the spleen segmentation mask, the performance decreased (AUC = 0.81, ACC = 0.76). Our model was able to accurately segment splenomegaly and recognize the underlying cause. Training on whole abdomen scans outperformed training using the segmentation mask. Nonetheless, considering the performance, a broader and more general application to differentiate other causes for splenomegaly is also conceivable

Data_Sheet_2_Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.PDF

ObjectiveTo establish a deep learning model for the detection of hypoxic–ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.Methods168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).ResultsAll optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping.ConclusionOur proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.</p

Image_1_Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.JPEG

ObjectiveTo establish a deep learning model for the detection of hypoxic–ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.Methods168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).ResultsAll optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping.ConclusionOur proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.</p

Image_2_Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.JPEG

ObjectiveTo establish a deep learning model for the detection of hypoxic–ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.Methods168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).ResultsAll optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping.ConclusionOur proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.</p