We aimed to evaluate radiomic features’ stability across different region of interest (ROI) sizes in CT and MR images. We chose a phantom with a homogenous internal structure so no differences for a feature extracted from ROIs of different sizes would be expected. For this, we scanned a plastic cup filled with sodium chloride solution ten times in CT and per MR sequence (T1-weighted-gradient-echo and T2-weighted-turbo-inversion-recovery-magnitude). We placed sphere-shaped ROIs of different diameters (4, 8, and 16 mm, and 4, 8, and 16 pixels) into the phantom’s center. Features were extracted using PyRadiomics. We assessed feature stability across ROI sizes with overall concordance correlation coefficients (OCCCs). Differences were tested for significance with the Mann–Whitney U-test. Of 93 features, 87 T1w-derived, 87 TIRM-derived, and 70 CT-derived features were significantly different between ROI sizes. Among MR-derived features, OCCCs showed excellent (>0.90) agreement for mean, median, and root mean squared for ROI sizes between 4 and 16 mm and pixels. We further observed excellent agreement for 10th and 90th percentile in T1w and 10th percentile in T2w TIRM images. There was no excellent agreement among the OCCCs of CT-derived features. In summary, many features indicated significant differences and only few showed excellent agreement across varying ROI sizes, although we examined a homogenous phantom. Since we considered a small phantom in an experimental setting, further studies to investigate this size effect would be necessary for a generalization. Nevertheless, we believe knowledge about this effect is crucial in interpreting radiomics studies, as features that supposedly discriminate disease entities may only indicate a systematic difference in ROI size.

NAGEL S.N., TEICHGRÄBER U.K.M., KAUSCHE S. & LEHMANN A. (2011) European Journal of Cancer Care21, 197–204 Satisfaction and quality of life: a survey-based assessment in patients with a totally implantable venous port system The purpose was to assess the satisfaction and quality of life in patients with a totally implantable central venous port system using a questionnaire-based survey. A self-designed questionnaire to assess patient satisfaction and the impact of the port on daily life was dispatched 180 days after implantation. The questionnaire was combined with the commonly used short form (SF)-12 Health Survey quality of life questionnaire. Of the 98 patients who received a port system, 75 were contacted, and 42 (56%) returned the questionnaire. Most of the responding patients reported high overall satisfaction. The impact of the system on daily life was widely perceived not to be negative. The physical component summary (PCS) and the mental component summary (MCS) scores from the SF-12 were 35.5 and 45.23 respectively (general German population: PCS = 49.6, MCS = 52.3). The multiple stepwise regression showed that the cosmetic result was a predictor of overall satisfaction; the cosmetic result and a painful port together were predictors of the MCS. Overall, it was found that the cosmetic result of the implantation procedure was a predictor of satisfaction and quality of life and should thus not be underestimated.

Purpose: Analysis, evaluation and classification of hepatic arterial supply variants and determination of their frequency distribution in CT-angiographies. Materials and Methods: CT-angiographies of 1,568 patients were evaluated retrospectively for the period between January 1, 2010 and August 30, 2012. The hepatic arterial anatomy was assessed and categorized according to Michels’s classification. So far unclassified variants were considered separately. Results: CT-angiographies of 1297 patients were included in the study. Type I according to Michels was seen in 937 cases (72.2 %), followed by type V in 114 patients (8.8 %) and type III in 83 patients (6.4 %). Type X could not be found in any of the patients. Not yet classified variants were discovered in 26 patients. The most frequent variant in this connection was a right hepatic artery originating from the superior mesenteric artery with the left hepatic artery originating from the left gastric artery (n = 10). Conclusion: Michels’s classification could be largely confirmed on the basis of a radiologically examined patient population. Not yet classified variants were categorized into subgroups of the existing classification. Key points: • Imaging of hepatic arterial supply variants using CT-angiography • Distribution of variations of arterial liver supply in a general patient population • Expansion of Michels’s classification to include new variations of the arterial liver supply Citation Format: • Löschner C, Nagel SN, Kausche S et al. Hepatic Arterial Supply in 1297 CT-Angiographies. Fortschr Röntgenstr 2015; 187: 276 – 282 Ziel: Analyse, Auswertung und Klassifikation von Varianten der arteriellen Leberversorgung und die Bestimmung deren Häufigkeitsverteilung in CT-Angiografien. Material und Methoden: Die CT-Angiografien von 1568 Patienten wurden für den Zeitraum zwischen 1.1.2010 und 30.8.2012 retrospektiv ausgewertet. Die arterielle Leberversorgung wurde untersucht und entsprechend der Klassifikation nach Michels eingeteilt. Bisher nicht klassifizierte Varianten wurden gesondert berücksichtigt. Ergebnisse: Die CT-Angiografien von 1297 Patienten konnten in die Studie eingeschlossen werden. Typ I nach Michels fand sich in 937 Fällen (72,2 %), gefolgt von Typ V bei 114 Patienten (8,8 %) und Typ III bei 83 Patienten (6,4 %). Einzig Typ X konnte bei keinem Patienten gefunden werden. Bei 26 Patienten wurden bisher nicht klassifizierte Varianten gefunden. Die häufigste Variante hierbei war eine Arteria hepatica dextra mit Ursprung aus der Arteria mesenterica superior bei Ursprung der Arteria hepatica sinistra aus der Arteria gastrica sinistra (n = 10). Schlussfolgerung: Die Klassifikation von Michels konnte anhand eines radiologisch untersuchten Patientenkollektivs weitgehend bestätigt werden. Bisher unklassifizierte Varianten wurden in Untergruppen in die bestehende Klassifikation eingeordnet.

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.

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.