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Case Report: Coexistence of Rosai-Dorfman disease and B-cell acute lymphoblastic leukemia in an adolescent.
Front Pediatr
February 2025
Pediatric Congenital Hematologic Disorders Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Rosai-Dorfman disease (RDD) is an unusual, non-malignant proliferative disorder involving non-Langerhans cell histiocytes, characterized by a wide range of clinical presentations and distinctive atypical morphological patterns. The concurrent manifestation of acute lymphoblastic leukemia (ALL) alongside RDD is exceptionally rare. Here, we present the case of a 14-year-old male patient diagnosed with ALL who, during the consolidation phase of chemotherapy, developed multifocal bone, dural, and liver lesions, as confirmed through CT and MRI imaging.
View Article and Find Full Text PDFThe sentinels of coronary artery disease: heterogeneous monocytes.
Front Immunol
March 2025
Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Monocytes are heterogeneous immune cells that play a crucial role in the inflammatory response during atherosclerosis, influencing the progression and outcome of the disease. In the pathogenesis of atherosclerotic diseases, such as coronary artery disease (CAD), monocytes not only serve as the initial sensors of endogenous and exogenous pathogenic factors, but also function as intermediators that bridge the circulatory system and localized lesions. In the bloodstream, heterogeneous monocytes, acting as sentinels, are rapidly recruited to atherosclerotic lesions, where they exhibit a heightened capacity to respond to various pathological stimuli upon detecting signals from damaged vascular endothelial cells.
View Article and Find Full Text PDFHead CT Scan Motion Artifact Correction via Diffusion-Based Generative Models.
Appl Med Artif Intell (2024)
February 2025
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. Image-domain-based motion correction is practical for routine use since it doesn't rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts.
View Article and Find Full Text PDFDeep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer: A multicenter study.
Chin J Cancer Res
January 2025
Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China.
Objective: Early predicting response before neoadjuvant chemotherapy (NAC) is crucial for personalized treatment plans for locally advanced breast cancer patients. We aim to develop a multi-task model using multiscale whole slide images (WSIs) features to predict the response to breast cancer NAC more finely.
Methods: This work collected 1,670 whole slide images for training and validation sets, internal testing sets, external testing sets, and prospective testing sets of the weakly-supervised deep learning-based multi-task model (DLMM) in predicting treatment response and pCR to NAC.
CT-based radiomics-deep learning model predicts occult lymph node metastasis in early-stage lung adenocarcinoma patients: A multicenter study.
Chin J Cancer Res
January 2025
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan 250117, China.
Objective: The neglect of occult lymph nodes metastasis (OLNM) is one of the pivotal causes of early non-small cell lung cancer (NSCLC) recurrence after local treatments such as stereotactic body radiotherapy (SBRT) or surgery. This study aimed to develop and validate a computed tomography (CT)-based radiomics and deep learning (DL) fusion model for predicting non-invasive OLNM.
Methods: Patients with radiologically node-negative lung adenocarcinoma from two centers were retrospectively analyzed.
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