Identification and comparison of sex-specific serious adverse drug reactions in spontaneous reports and systematically collected reports (ADRED).

AIMS: Adverse drug reactions (ADRs) are known to show sex-specific differences in occurrence and phenotype. The aim of this study was to analyse sex-specific differences in ADR-drug combinations that required hospitalization based on two different datasets. METHODS: We performed a complementary analysis of (i) spontaneously reported (n = 12 564, female = 51.7%) and (ii) systematically collected ADR reports from a prospective multicentre observational study (ADRED, n = 2355, female = 48.2%) from Germany in the ADR database EudraVigilance (EV). Both datasets were analysed separately concerning the suspected drugs, ADRs and ADR-drug combinations more frequently reported for females or males by calculating reporting odds ratios (ROR) with 95% confidence intervals. ADR-drug combinations more frequently reported for either females or males in EV reports were related to prescription data. Finally, the results from both datasets were discussed with regard to their (dis-)concordance. RESULTS: In both datasets, some antineoplastic agents and nervous system drugs were found to be reported more often for females than males (RORs ranging from 1.5 [1.1-2.1] for quetiapine in spontaneous reports to 41.3 [13.1-130.0] for trastuzumab in spontaneous reports). ADRs of the respiratory system, and haemorrhages were described predominantly for males in both datasets. In spontaneous reports the ADR-drug combination self-injurious behaviour-quetiapine was more often reported for females without and with consideration of drug prescriptions (ROR: 3.8 [1.3-11.0]). Quetiapine and psychiatric disorders (superordinate level) was exclusively reported for females in ADRED reports. CONCLUSIONS: Our results can contribute to raise awareness and further knowledge regarding sex-specific ADRs. The findings require further in-depth investigation.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.