AIMC Topic: Lymphoma
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Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.
Nature communications
Dec 1, 2020
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through...
Comparison of 11 automated PET segmentation methods in lymphoma.
Physics in medicine and biology
Nov 27, 2020
Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesions and quantifying patient disease burden. Simple thresholding methods remain common despite the large heterogeneity in lymphoma lesion location, size...
Editorial for: "Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning".
Journal of magnetic resonance imaging : JMRI
Aug 7, 2020
Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.
Laboratory investigation; a journal of technical methods and pathology
May 29, 2020
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including ...
Radiomics based on F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study.
Cancer medicine
Nov 25, 2019
PURPOSE: Our study assessed the ability F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine-learning approach.
Diagnosis, Differential
Positron Emission Tomography Computed Tomography
Radiopharmaceuticals
Fluorodeoxyglucose F18
ROC Curve
Image Processing, Computer-Assisted
Follow-Up Studies
Lymphoma
Prognosis
Retrospective Studies
Female
Aged
Aged, 80 and over
Adult
Breast Neoplasms
Middle Aged
Humans
Machine Learning
Texture analysis and multiple-instance learning for the classification of malignant lymphomas.
Computer methods and programs in biomedicine
Oct 23, 2019
BACKGROUND AND OBJECTIVES: Malignant lymphomas are cancers of the immune system and are characterized by enlarged lymph nodes that typically spread across many different sites. Many different histological subtypes exist, whose diagnosis is typically ...
F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.
NeuroImage. Clinical
Jun 27, 2019
The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to...
Robust identification of molecular phenotypes using semi-supervised learning.
BMC bioinformatics
May 28, 2019
BACKGROUND: Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in patient outcomes. Unsupervised ...
Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
Neuroradiology
Apr 4, 2019
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this...
Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.
European journal of radiology
Sep 14, 2018
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).
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