G-TRACE: rapid Gal4-based cell lineage analysis in Drosophila.

We combined Gal4-UAS and the FLP recombinase-FRT and fluorescent reporters to generate cell clones that provide spatial, temporal and genetic information about the origins of individual cells in Drosophila melanogaster. We named this combination the Gal4 technique for real-time and clonal expression (G-TRACE). The approach should allow for screening and the identification of real-time and lineage-traced expression patterns on a genomic scale

JunB is required for endothelial cell morphogenesis by regulating core-binding factor β

The molecular mechanism triggering the organization of endothelial cells (ECs) in multicellular tubules is mechanistically still poorly understood. We demonstrate that cell-autonomous endothelial functions of the AP-1 subunit JunB are required for proper endothelial morphogenesis both in vivo in mouse embryos with endothelial-specific ablation of JunB and in in vitro angiogenesis models. By cDNA microarray analysis, we identified core-binding factor β (CBFβ), which together with the Runx proteins forms the heterodimeric core-binding transcription complex CBF, as a novel JunB target gene. In line with our findings, expression of the CBF target MMP-13 was impaired in JunB-deficient ECs. Reintroduction of CBFβ into JunB-deficient ECs rescued the tube formation defect and MMP-13 expression, indicating an important role for CBFβ in EC morphogenesis

Prediction of lymph node infiltration by prostate cancer using deep learning on CT imaging

Computer aided diagnostic tools have been developed for many decades but are only widely used in very specific diagnostic areas. New algorithmic tools, specifically deep learning, have achieved high performance and may find their way into broader clinical practice in the near future. However, the high complexity of these algorithmic tools renders them effectively ‘black boxes’, meaning that users are unable to understand how they are able to make decisions. This ‘black box’ nature of deep learning severely inhibits their introduction into high risk fields such as medicine. In this dissertation, deep learning models were used to test the feasibility of using deep learning to aid in the diagnosis of lymphatic infiltration by prostate cancer (PCa). In order to detect the presence of PCa metastasis into the lymphatic system, 68Ga-PSMA- PET/CT is increasingly being performed. However, due to limitations of cost and availability, it is unlikely that 68Ga-PSMA-PET/CT will be useful for large segments of the population. For this reason, computed tomography (CT) has remained the most important modality for PCa staging, despite low sensitivity and specificity being reported. The goal of this work was to train deep learning models to distinguish normal from PCa-infiltrated lymph nodes based on conventional CT scan. From 549 patients where 68Ga-PSMA-PET/CT was performed, a dataset of 2616 segmented lymph nodes was used. A label of positive or negative for infiltration was generated for each lymph node on the basis of the PET reference standard. Five convolutional neural networks (CNNs), a type of deep learning model, were trained. In order to assess radiologist performance, a zero-footprint web based radiological viewer was developed. Using this viewer, the performance two radiologist reader was assessed. The CNNs performed with an Area-Under-the-Curve between 0.95 and 0.86, compared to an average AUC of 0.81 for the experience radiologists. Of note is that CNNs were able to use anatomical surroundings to increase performance, effectively learning probabilities of infiltration by anatomical location. Two neural network explainability methods were employed to attempt understanding how CNNs achieve high classification performance. One of these methods, namely saliency map generation, provided valuable information, showing that one CNN used anatomical surroundings to increase performance. The other, known as feature visualization, did not provide useful information. From this study, we find that CNNs have the potential to form the basis of a CT-based biomarker for lymph node metastasis in PCa. Additionally, segmentation masks are not required to achieve high classification performance.Computergestützte diagnostische Methoden sind bereits seit mehreren Jahrzehnten in der Entwicklung, finden aber bisher nur in sehr begrenzten Gebieten Anwendung. Neue algorithmische Methoden der letzten zehn Jahre, speziell “Deep-Learning- Modelle”, zeigen eine außerordentliche Leistungsfähigkeit, und könnten daher in der Zukunft Eingang in eine weitreichende klinische Praxis finden. Einschränkend muss jedoch bemerkt werden, dass diese neuen Methoden aufgrund ihrer hohen Komplexität essentiell “Black Boxes” darstellen; in anderen Worten, es ist zur Zeit für den Benutzter nicht nachvollziehbar, wie ein Deep-Learning-Modell zu bestimmten Entscheidungen gelangt. Dieser Umstand limitiert die Anwendung von Deep-Learning- Modellen in risikobehafteten medizinischen Gebieten. Zielsetzung und Problematik In der vorliegenden Dissertation wurden Deep-Learning-Modelle daraufhin getestet, ob sie zur radiologischen Diagnose von Lymphknoteninfiltration durch Prostatakarzinome (PCa) tauglich sind. Die lymphatische Ausdehnung eines Prostatakarzinoms ist ein wesentlicher Faktor bei der Auswahl therapeutischer Maßnahmen. Zum Nachweis lymphatischer PCa Metastasen wird in zunehmendem Maße 68Ga-PSMA-PET/CT angewandt. Angesichts der Beschränkungen hinsichtlich Kosten und Verfügbarkeit ist jedoch zweifelhaft ob 68Ga-PSMA-PET/CT für weite Teile der Bevölkerung eingesetzt werden kann. Aus diesem Grund verbleibt die Computertomographie (CT), trotz geringer Sensitivität und Spezifität, die wichtigste Methode zur Stadienbestimmung des PCa. Zielsetzung der vorliegenden Dissertation war es, Deep-Learning-Modelle unter Benutzung herkömmlicher Computertomographie in die Lage zu versetzen, normale von PCa-infiltrierten Lymphknoten zu unterscheiden. Methodik Es wurden ein Datenatz von 2616 Lymphknoten aus 68Ga-PSMA-PET/CT Aufnahmen von 549 PCa Patienten verwendet. Auf der Basis des PET Referenzstandards wurde jedem dieser Lymphknoten die Beurteilung positiv oder negativ für Lymphknotenbefall zugeordnet. Fünf konvolutionelle Netzwerke (CNNs; eine spezielle Art von Deep- Learning Modellen) mit identischer Architektur wurden getestet. Der Unterschied der CNNs bestand in der Verwendung verschiedener Trainingsdaten, was es ermöglichte, die Leistungsfähigkeit der CNNs mit der Art der Eingabedaten, speziell der An- oder Abwesenheit einer Segmentierungsmaske, zu korrelieren. Zur Bestimmung der diagnostischen Treffsicherheit menschlicher Experten im Vergleich zu den CNNs wurde ein zero-footprint webbasierter radiologischer Viewer entwickelt. Ergebnisse Die CNNs erzielten eine Fläche unter der Kurve (AUC) zwischen 0.95 und 0.86, im Vergleich zu einem Durchschnittswert von 0.81, der von den Radiologen erreicht wurde. Interessanterweise waren CNNs in der Lage, den anatomischen Kontext zur Optimierung ihrer Leistung zu nutzen, wobei sie die Wahrscheinlichkeit des Lymphknotenbefalls in Relation zur anatomischen Lage der Lymphknoten erlernten. Zwei ‚Explainability Methoden‘ wurden hinzugezogen, um die hohe Klassifizierungsleistung der CNNs zu analysieren. Eine dieser Methoden, die Erstellung von „saliency maps“, ergab aussagekräftige Resultate, die darauf hinwiesen, dass das CNN die anatomische Umgebung der Lymphknoten hinzuzog, um die Unterscheidung zwischen “metastatisch-befallen” und “normal” zu treffen. Demgegenüber erbrachte die andere Methode, Merkmalsvisualisierung (“feature visualization”), keine nützlichen Erkenntnisse. Schlussfolgerung Unsere Studie ergibt, dass CNNs das Potential aufweisen, unter Verwendung von CTDaten eine Beurteilung von Lymphknoten im Hinblick auf Metastasen vornehmen zu können. Des Weiteren zeigen unsere Resultate, dass Segmentierungsmasken nicht erforderlich sind, um eine hohe diagnostische Treffsicherheit der CNNs zu gewährleisten

Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer

The purpose of this study is to compare diagnostic performance of Prostate Imaging Reporting and Data System (PI-RADS) version (v) 2.1 and 2.0 for detection of Gleason Score (GS)>= 7 prostate cancer on MRI. Three experienced radiologists provided PI-RADS v2.0 scores and at least 12 months later v2.1 scores on lesions in 333 prostate MRI examinations acquired between 2012 and 2015. Diagnostic performance was assessed retrospectively by using MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy as the reference. From a total of 359 lesions, GS >= 7 tumor was present in 135 lesions (37.60%). Area under the ROC curve (AUC) revealed slightly lower values for peripheral zone (PZ) and transition zone (TZ) scoring in v2.1, but these differences did not reach statistical significance. A significant number of score 2 lesions in the TZ were downgraded to score 1 in v2.1 showing 0% GS >= 7 tumor (0/11). The newly introduced diffusion-weighted imaging (DWI) upgrading rule in v2.1 was applied in 6 lesions from a total of 143 TZ lesions (4.2%). In summary, PI-RADS v2.1 showed no statistically significant differences in overall diagnostic performance of TZ and PZ scoring compared to v2.0. Downgraded BPH nodules showed favorable cancer frequencies. The new DWI upgrading rule for TZ lesions was applied in only few cases

Optimizing size thresholds for detection of clinically significant prostate cancer on MRI: Peripheral zone cancers are smaller and more predictable than transition zone tumors

Purpose: To evaluate if size-based cut-offs based on MR imaging can successfully assess clinically significant prostate cancer (csPCA). The goal was to improve the currently applied size-based differentiation criterion in PIRADS. Methods and materials: MRIs of 293 patients who had undergone 3 T MR imaging with subsequent confirmation of prostate cancer on systematic and targeted MRI/TRUS-fusion biopsy were re-read by three radiologists. All identifiable tumors were measured on T2WI for lesions originating in the transition zone (TZ) and on DWI for lesions from the peripheral zone (PZ) and tabulated against their Gleason grade. Results: 309 lesions were analyzed, 213 (68.9 %) in the PZ and 96 (31.1 %) in the TZ. ROC-Analysis showed a stronger correlation between lesion size and clinically significant (defined as Gleason Grade Group= 2) prostate cancer (PCa) for the PZ (AUC= 0.73) compared to the TZ (AUC= 0.63). The calculated Youden index resulted in size cut-offs of 14mm for PZ and 21mm for TZ tumors. Conclusion: Size cut-offs can be used to stratify prostate cancer with different optimal size thresholds in the peripheral zone and transition zone. There was a clearer separation of clinically significant tumors in peripheral zone cancers compared to transition zone cancers. Future iterations of PI-RADS could therefore take different size-based cut-offs for peripheral zone and transition zone cancers into account

Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). Results: Internal validation of the model's accuracy showed a high overlap for NELM and livers (Matthew's correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). Conclusion: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model's measurements correlated well with MCC's evaluation of therapeutic response