Accurate intraoperative diagnosis is crucial for differentiating between primary CNS lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge. We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS tumor/non-tumor lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. In the prospective test cohort of PCNSL and non-PCNSL entities (n=160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ±0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 78.94%). The additional test cohorts (n=420, n=59) reached balanced accuracy rates of 95.44% ±0.74 and 95.57% ±2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features. RapidLymphoma is valid and reliable in detecting PCNSL and differentiating from other CNS entities within three minutes, as well as visual feedback in an intraoperative setting. This leads to fast clinical decision-making and further treatment strategy planning.
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http://dx.doi.org/10.1101/2024.08.25.24312509 | DOI Listing |
J Struct Biol
December 2024
Program in Cellular and Molecular Medicine, Boston Children's Hospital, 200 Longwood Ave, Boston, MA 02115, USA; Department of Cell Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115, USA. Electronic address:
Cryogenic electron tomography (cryo-ET) has rapidly advanced as a high-resolution imaging tool for visualizing subcellular structures in 3D with molecular detail. Direct image inspection remains challenging due to inherent low signal-to-noise ratios (SNR). We introduce CryoSamba, a self-supervised deep learning-based model designed for denoising cryo-ET images.
View Article and Find Full Text PDFComput Methods Programs Biomed
December 2024
School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China. Electronic address:
Background: The fusion of multi-modal data has been shown to significantly enhance the performance of deep learning models, particularly on medical data. However, missing modalities are common in medical data due to patient specificity, which poses a substantial challenge to the application of these models.
Objective: This study aimed to develop a novel and efficient multi-modal fusion framework for medical datasets that maintains consistent performance, even in the absence of one or more modalities.
J Hepatol
December 2024
Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea; Inocras Inc., San Diego, CA, USA. Electronic address:
Background & Aims: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.
Methods: An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software.
Magn Reson Imaging
December 2024
Weill Cornell Graduate School of Medical Sciences, New York, NY, United States; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Department of Radiology, Memorial Sloan Kettering Cancer Center, NY, New York, USA.
Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI.
View Article and Find Full Text PDFPhys Med
December 2024
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China. Electronic address:
Purpose: Automated treatment plan generation is essential for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART) to ensure standardized treatment-plan quality. We proposed a novel cross-technique transfer learning (CTTL)-based strategy for online MRIgART autoplanning.
Method: We retrospectively analyzed the data from 210 rectal cancer patients.
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