AIMC Topic: Lymphoma

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Artificial intelligence based malignant lymphoma type prediction using enhanced super resolution image and hybrid feature extraction algorithm.

Computers in biology and medicine
In the medical field, the most common and frequent type of blood cancer is lymphoma. Accurately predicting and early response to lymphoma treatment will be useful for initiating treatment plans to achieve a greater rate of cure or reduced risk of tre...

Multi-class brain malignant tumor diagnosis in magnetic resonance imaging using convolutional neural networks.

Brain research bulletin
Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiological features, while the accurate and non-invasive dialgnosis is essential for selecting appropriate...

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
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...

Application of Machine-learning based on Radiomics Features in Differential Diagnosis of Superficial Lymphadenopathy.

Current medical imaging
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
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
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
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
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...

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
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...