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《Clinical Lymphoma, Myeloma & Leukemia》2020,20(10):e629-e644
IntroductionLenalidomide plus dexamethasone is effective and well tolerated in relapsed/refractory multiple myeloma (RRMM). In this observational, noninterventional European post-authorization safety study, the safety profile of lenalidomide plus dexamethasone was investigated and compared with that of other agents in the treatment of RRMM in a real-world setting.Patients and MethodsPatients had received ≥ 1 prior antimyeloma therapy; prior lenalidomide was excluded. Treatment was per investigator’s routine practice. Adverse events were analyzed by incidence rates per 100 person-years to account for differences in observation length and treatment duration.ResultsIn total, 2150 patients initiated lenalidomide, and 1479 initiated any other antimyeloma therapy, predominately bortezomib (80.3%), which was primarily administered intravenously (74.3%). The incidence rate of neuropathy was lower with lenalidomide (10.5) than with bortezomib (78.9) or thalidomide (38.7). Lenalidomide also had a lower incidence rate of infections (68.7) versus bortezomib (95.9) and thalidomide (76.0). Conversely, the incidence rate of neutropenia was higher with lenalidomide (38.0) than with bortezomib (18.2) or thalidomide (25.7). The incidence rates of thrombocytopenia were 24.4, 40.4, and 14.4 with lenalidomide, bortezomib, and thalidomide, respectively.ConclusionNo new safety signals for lenalidomide were identified in this study, which is the largest prospective real-world European study of lenalidomide in patients with RRMM to date. These results confirm that the safety profile of lenalidomide plus dexamethasone in RRMM in a real-world setting is comparable to that reported in clinical trials. 相似文献
954.
《Clinical breast cancer》2020,20(6):e757-e760
IntroductionWe previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm.Materials and MethodsIn this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases.ResultsArea under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively.ConclusionProspectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity. 相似文献
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Ting Chen MD PhD Andrew C. Vamos BS Seth H. Dailey MD Jack J. Jiang MD PhD 《The Laryngoscope》2016,126(10):2295-2300
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T. Danielle Samulski MD Teresa La Roseann I. Wu MD MPH 《Diagnostic cytopathology》2016,44(11):944-951
Clinical training imposes time and resource constraints on educators and learners, making it difficult to provide and absorb meaningful instruction. Additionally, innovative and personalized education has become an expectation of adult learners. Fortunately, the development of web‐based educational tools provides a possible solution to these challenges. Within this review, we introduce the utility of adaptive eLearning platforms in pathology education. In addition to a review of the current literature, we provide the reader with a suggested approach for module creation, as well as a critical assessment of an available platform, based on our experience in creating adaptive eLearning modules for teaching basic concepts in gynecologic cytopathology. Diagn. Cytopathol. 2016;44:944–951. © 2016 Wiley Periodicals, Inc. 相似文献
958.
ObjectiveThe combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale.MethodsBased on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review.ResultsThe practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources.ConclusionsMachine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine. 相似文献
959.
Raoul Bell Julia Sasse Malte Möller Daniela Czernochowski Susanne Mayr Axel Buchner 《Psychophysiology》2016,53(2):216-228
A sequential prisoner's dilemma game was combined with psychophysiological measures to examine the cognitive underpinnings of reciprocal exchange. Participants played four rounds of the game with partners who either cooperated or cheated. In a control condition, the partners’ faces were shown, but no interaction took place. The partners’ behaviors were consistent in the first three rounds of the game, but in the last round some of the partners unexpectedly changed strategies. In the first round of the game, the feedback about a partner's decision elicited a feedback P300, which was more pronounced for cooperation and cheating in comparison to the control condition, but did not vary as a function of feedback valence. In the last round, both the feedback negativity and the feedback P300 were sensitive to expectancy violations. There was no consistent evidence for a negativity bias, that is, enhanced allocation of attention to feedback about another person's cheating in comparison to feedback about another person's cooperation. Instead, participants focused on both positive and negative information, and flexibly adjusted their processing biases to the diagnosticity of the information. This conclusion was corroborated by the ERP correlates of memory retrieval. Successful retrieval of a partner's reputation was associated with an anterior positivity between 400 and 600 ms after face onset. This anterior positivity was more pronounced for both cooperator and cheater faces in comparison to control faces. The results suggest that it is not the negativity of social information, but rather its motivational and behavioral relevance that determines its processing. 相似文献
960.
Procedural learning is subject to consolidation processes believed to depend on the modulation of functional connections involved in representing the acquired skill. While sleep provides the most commonly studied framework for such consolidation processes, posttraining modulation of oscillatory brain activity may also impact on plasticity processes. Under the hypothesis that consolidation of motor learning is associated with theta band activity, we used EEG neurofeedback (NFB) to enable participants to selectively increase either theta or beta power in their EEG spectra following the acquisition phase of motor sequence learning. We tested performance on a motor task before and after training, right after the NFB session to assess immediate NFB effects, 1 day after NFB to assess interaction between NFB effects and overnight sleep‐dependent stabilization, and 1 week after the initial session, to assess the effects of NFB on long‐term stabilization of motor training. We also explored the extent of the influence of single‐electrode NFB on EEG recorded across the scalp. Results revealed a significantly greater improvement in performance immediately after NFB in the theta group than in the beta group. This effect continued for testing up to 1 week following training. Across participants, post‐NFB improvement correlated positively with theta/beta ratio change achieved during NFB. Additionally, NFB was found to cause widespread band‐power modulation beyond the electrode used for feedback. Thus, upregulating postlearning theta power may yield contributions to the immediate performance and subsequent consolidation of an acquired motor skill. 相似文献