47 research outputs found
Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone
Lymphatic spread determines treatment decisions in prostate cancer (PCa) patients. 68Ga-PSMA-PET/CT can be performed, although cost remains high and availability is limited. Therefore, computed tomography (CT) continues to be the most used modality for PCa staging. We assessed if convolutional neural networks (CNNs) can be trained to determine 68Ga-PSMA-PET/CT-lymph node status from CT alone. In 549 patients with 68Ga-PSMA PET/CT imaging, 2616 lymph nodes were segmented. Using PET as a reference standard, three CNNs were trained. Training sets balanced for infiltration status, lymph node location and additionally, masked images, were used for training. CNNs were evaluated using a separate test set and performance was compared to radiologists' assessments and random forest classifiers. Heatmaps maps were used to identify the performance determining image regions. The CNNs performed with an Area-Under-the-Curve of 0.95 (status balanced) and 0.86 (location balanced, masked), compared to an AUC of 0.81 of experienced radiologists. Interestingly, CNNs used anatomical surroundings to increase their performance, "learning" the infiltration probabilities of anatomical locations. In conclusion, CNNs have the potential to build a well performing CT-based biomarker for lymph node metastases in PCa, with different types of class balancing strongly affecting CNN performance
Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning
Background: We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference.
Methods: This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists.
Results: All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75-0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit.
Conclusions: Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool
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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 ÎČ
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 ÎČ (CBFÎČ), 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 CBFÎČ into JunB-deficient ECs rescued the tube formation defect and MMP-13 expression, indicating an important role for CBFÎČ 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 âblack boxesâ, meaning that users are unable to understand how they are able to make decisions. This âblack boxâ 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.ComputergestĂŒtzte 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 âDeep-Learning-
Modelleâ, zeigen eine auĂerordentliche LeistungsfĂ€higkeit, und könnten daher in der
Zukunft Eingang in eine weitreichende klinische Praxis finden. EinschrÀnkend muss
jedoch bemerkt werden, dass diese neuen Methoden aufgrund ihrer hohen
KomplexitĂ€t essentiell âBlack Boxesâ darstellen; in anderen Worten, es ist zur Zeit fĂŒr
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
MaĂnahmen. Zum Nachweis lymphatischer PCa Metastasen wird in zunehmendem
MaĂe 68Ga-PSMA-PET/CT angewandt. Angesichts der BeschrĂ€nkungen hinsichtlich
Kosten und VerfĂŒgbarkeit ist jedoch zweifelhaft ob 68Ga-PSMA-PET/CT fĂŒr weite Teile
der Bevölkerung eingesetzt werden kann. Aus diesem Grund verbleibt die
Computertomographie (CT), trotz geringer SensitivitÀt und SpezifitÀt, 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 fĂŒr Lymphknotenbefall
zugeordnet. FĂŒnf 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 LeistungsfÀhigkeit 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 FlÀche 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 âExplainability Methodenâ wurden hinzugezogen, um die hohe
Klassifizierungsleistung der CNNs zu analysieren. Eine dieser Methoden, die
Erstellung von âsaliency mapsâ, ergab aussagekrĂ€ftige Resultate, die darauf
hinwiesen, dass das CNN die anatomische Umgebung der Lymphknoten hinzuzog,
um die Unterscheidung zwischen âmetastatisch-befallenâ und ânormalâ zu treffen.
DemgegenĂŒber erbrachte die andere Methode, Merkmalsvisualisierung (âfeature
visualizationâ), keine nĂŒtzlichen 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
gewÀhrleisten
Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer
Objectives: To assess the discriminatory power of lexicon terms used in PI-RADS version 2 to describe MRI features of prostate lesions.
Methods: Four hundred fifty-four patients were included in this retrospective, institutional review board-approved study. Patients received multiparametric (mp) MRI and subsequent prostate biopsy including MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy. PI-RADS lexicon terms describing lesion characteristics on mpMRI were assigned to lesions by experienced readers. Positive and negative predictive values (PPV, NPV) of each lexicon term were assessed using biopsy results as a reference standard.
Results: From a total of 501 lesions, clinically significant prostate cancer (csPCa) was present in 175 lesions (34.9%). Terms related to findings of restricted diffusion showed PPVs of up to 52.0%/43.9% and NPV of up to 91.8%/89.7% (peripheral zone or PZ/transition zone or TZ). T2-weighted imaging (T2W)-related terms showed a wide range of predictive values. For PZ lesions, high PPVs were found for "markedly hypointense," "lenticular," "lobulated," and "spiculated" (PPVs between 67.2 and 56.7%). For TZ lesions, high PPVs were found for "water-drop-shaped" and "erased charcoal sign" (78.6% and 61.0%). The terms "encapsulated," "organized chaos," and "linear" showed to be good predictors for benignity with distinctively low PPVs between 5.4 and 6.9%. Most T2WI-related terms showed improved predictive values for TZ lesions when combined with DWI-related findings.
Conclusions: Lexicon terms with high discriminatory power were identified (e.g., "markedly hypointense," "water-drop-shaped," "organized chaos"). DWI-related terms can be useful for excluding TZ cancer. Combining T2WI- with DWI findings in TZ lesions markedly improved predictive values
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 MRIâA 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 (ÎabsNELM; ÎabsHTL) and relative changes (ÎrelNELM; ÎrelHTL) 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 (Ï): 0.76/0.95, respectively) with higher Ï in larger NELM volume (Ï = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (Ï = 0.86) and livers (Ï = 0.96). MCC decisions were significantly differentiated by all response variables (ÎabsNELM; ÎabsHTL; ÎrelNELM; ÎrelHTL) (p < 0.001). ÎrelNELM and ÎrelHTL 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