AIMC Topic: Lymphoma
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Blood-brain barrier crossing biopolymer targeting c-Myc and anti-PD-1 activate primary brain lymphoma immunity: Artificial intelligence analysis.
Journal of controlled release : official journal of the Controlled Release Society
May 10, 2025
Primary Central Nervous System Lymphoma is an aggressive central nervous system neoplasm with poor response to pharmacological treatment, partially due to insufficient drug delivery across blood-brain barrier. In this study, we developed a novel ther...
Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease.
World journal of gastroenterology
Jul 7, 2024
BACKGROUND: Due to similar clinical manifestations and imaging signs, differential diagnosis of primary intestinal lymphoma (PIL) and Crohn's disease (CD) is a challenge in clinical practice.
Application of Machine-learning based on Radiomics Features in Differential Diagnosis of Superficial Lymphadenopathy.
Current medical imaging
Jan 1, 2024
OBJECTIVE: The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes.
Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images.
Medicine
Nov 3, 2023
Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography ima...
Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions).
PET clinics
Jan 1, 2022
Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognost...
Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma.
Nagoya journal of medical science
Feb 1, 2021
Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of this study is to investigate the usefulness of texture features on unenhanced CT for differentiating be...
Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography.
Technology in cancer research & treatment
Jan 1, 2021
This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcinoma (ICCA) and hepatic lymphoma (HL). A total of 28 patients with HL and 101 patients with ICCA were...
Contrast Media
Diagnosis, Differential
Neoplasm Grading
Tumor Burden
Bile Duct Neoplasms
Cholangiocarcinoma
Radiographic Image Enhancement
Middle Aged
ROC Curve
Image Processing, Computer-Assisted
Lymphoma
Female
Reproducibility of Results
Aged
Male
Humans
Machine Learning
Algorithms
Tomography, X-Ray Computed
Predicting lymphoma outcomes and risk factors in patients with primary Sjögren's Syndrome using gradient boosting tree ensembles.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Primary Sjogren's Syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction, where it has been long stated that 5% of pSS patients are prone to lymphoma development. In this work, we use clinical data from 449 pSS patients...
Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning.
Annals of clinical and laboratory science
Mar 1, 2019
Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging, however, they were limited to just predicting a positive or negative finding for a specific neoplasm. We attempted to use Deep Learning wi...
GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.
Mathematical biosciences and engineering : MBE
Feb 20, 2019
For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agonist-LOF" has laid a solid foundation for supporting drug repurposing. In this research, an active ge...
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