Rationale And Objectives: This study aimed to measure apparent diffusion coefficient (ADC) in Chinese patients with newly diagnosed multiple myeloma by whole-body diffusion-weighted magnetic resonance imaging (WB-DWI MRI) and assess the diagnostic accuracy of ADC in the discrimination of deep response to induction chemotherapy.
Materials And Methods: Seventeen patients underwent WB-DWI MRI before and after induction chemotherapy (week 20). DWI images and ADC maps were produced and 89 regions of interest were chosen. ADC percent changes were compared between deep (complete response or very good partial response) and non-deep responders (partial response, minimal response, stable disease, or progressive disease) as International Myeloma Working Group criteria. Diagnostic accuracy of ADC was calculated using specific cut offs. Predictive positive value of ADC was calculated to predict deep response to consolidation therapy.
Results: Lesions reduced in size and number and signal intensity decreased in follow-up DWI, which did not differ between deep and non-deep responders. ADC percent changes were significantly higher in deep responders (36.79%) than in non-deep responders (11.50%) after induction therapy (P = .02) in per lesion analysis. ADC percent increases by 46.96%, 78.0% yielded specificity at 81.4%, 90.7% in discriminating deep response to induction therapy. Predictive positive value predicting deep response to consolidation therapy was 60.5% by using ADC cutoff >1.00 × 10 mm/s at week 20.
Conclusions: ADC from WB-DWI MRI increased remarkably in patients who achieved deep response at the end of induction chemotherapy, which represented a confirmatory diagnostic tool to discriminate deep response to induction therapy for patients with multiple myeloma. ADC may have a potential to predict deep response to consolidation therapy.
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http://dx.doi.org/10.1016/j.acra.2017.12.008 | DOI Listing |
Sci Rep
December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.
View Article and Find Full Text PDFNat Commun
December 2024
Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Physical Oceanography, Ministry of Education, the College of Oceanic and Atmospheric Sciences, Ocean University of China, and Laoshan Laboratory, Qingdao, China.
A shift in depth range enables marine organisms to adapt to marine heatwaves (MHWs). Subsurface MHWs could limit this pathway, yet their response to climate warming remains unclear. Here, using an eddy-resolving Earth system model forced under a high emission scenario, we project a robust global increase in subsurface MHWs driven by rising subsurface mean temperatures and enhanced temperature variability.
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December 2024
Oncology Bioinformatics, Genentech, South San Francisco, CA, USA.
Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins.
View Article and Find Full Text PDFElife
December 2024
Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, United States.
Background: Individuals with Down syndrome (DS), the genetic condition caused by trisomy 21 (T21), display clear signs of immune dysregulation, including high rates of autoimmunity and severe complications from infections. Although it is well established that T21 causes increased interferon responses and JAK/STAT signaling, elevated autoantibodies, global immune remodeling, and hypercytokinemia, the interplay between these processes, the clinical manifestations of DS, and potential therapeutic interventions remain ill defined.
Methods: We report a comprehensive analysis of immune dysregulation at the clinical, cellular, and molecular level in hundreds of individuals with DS, including autoantibody profiling, cytokine analysis, and deep immune mapping.
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