European journal of nuclear medicine and molecular imaging
-
Eur. J. Nucl. Med. Mol. Imaging · Nov 2020
Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.
In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation. ⋯ The CNN model achieved good liver segmentations on CT images of good image quality, with relatively normal liver shapes and low tumor burden. 87.5% of the 40 CNN segmentations only needed slight adjustments for clinical use. However, the trained model failed on SIRT data with low dose or contrast, lesions with large density difference from their surroundings, and abnormal liver position and shape. The abovementioned scenarios were not adequately represented in the training data. Despite this limitation, the current CNN is already a useful clinical tool which improves inter-observer agreement and therefore contributes to the standardization of the dosimetry. A further improvement is expected when the CNN will be trained with more data from SIRT patients.
-
Eur. J. Nucl. Med. Mol. Imaging · Nov 2020
FDG PET/CT for tumoral and systemic immune response monitoring of advanced melanoma during first-line combination ipilimumab and nivolumab treatment.
We aimed to investigate the role of FDG-PET/CT in monitoring of response and immune-related adverse events (irAEs) following first-line combination-immune checkpoint inhibitor (combination-ICI) therapy for advanced melanoma. ⋯ FDG-PET/CT response evaluation predicts the long-term outcome of patients treated with first-line combination-ICIs. Long-term treatment response monitoring for detection of extracranial secondary progression is feasible by FDG-PET/CT. Beyond response assessment, FDG-PET/CT frequently detects clinically relevant irAEs, which may involve multiple systems contemporaneously or at various time-points and may precede clinical diagnosis.
-
Eur. J. Nucl. Med. Mol. Imaging · Nov 2020
Prospective evaluation of whole-body MRI and 18F-FDG PET/MRI in N and M staging of primary breast cancer patients.
To evaluate and compare the diagnostic potential of whole-body MRI and whole-body 18F-FDG PET/MRI for N and M staging in newly diagnosed, histopathologically proven breast cancer. ⋯ 18F-FDG PET/MRI has a better diagnostic accuracy for N staging in primary breast cancer patients and provides a significantly higher diagnostic confidence in lesion characterization than MRI alone. But both modalities bear the risk to overestimate the M stage.
-
Eur. J. Nucl. Med. Mol. Imaging · Oct 2020
Multicenter StudyEnd-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.
In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time. ⋯ This study provides an efficient recognition method for coronavirus disease 2019 pneumonia, using an end-to-end design to implement targeted and effective isolation for the containment of this communicable disease.
-
Eur. J. Nucl. Med. Mol. Imaging · Oct 2020
Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.
The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. ⋯ Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.