AIMC Topic: Lymphoma
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Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas.
Journal of biophotonics
Aug 23, 2023
The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep l...
Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.
Molecular imaging and biology
Aug 10, 2023
PURPOSE: This study aims to develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients.
Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the SEER database and a multicentre external validation cohort.
Journal of cancer research and clinical oncology
Jul 10, 2023
PURPOSE: Due to the rarity of primary gastrointestinal lymphoma (PGIL), the prognostic factors and optimal management of PGIL have not been clearly defined. We aimed to establish prognostic models using a deep learning algorithm for survival predicti...
Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives.
British journal of haematology
May 11, 2023
Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms to complement or replace current standard of care approaches requires investigation into the methods...
Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.
International journal of computer assisted radiology and surgery
Apr 21, 2023
PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. T...
Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization.
Journal of digital imaging
Apr 14, 2023
The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions identified in 65 whole-body [F]...
Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects.
European radiology
Mar 29, 2023
OBJECTIVES: The objective of this study was to translate a deep learning (DL) approach for semiautomated analysis of body composition (BC) measures from standard of care CT images to investigate the prognostic value of BC in pediatric, adolescent, an...
Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27-altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults.
Journal of neurosurgery
Dec 23, 2022
OBJECTIVE: Preoperative diagnosis of diffuse midline glioma, H3 K27-altered (DMG-A) and midline high-grade glioma without H3 K27 alteration (DMG-W), as well as midline primary CNS lymphoma (PCNSL) in adults, is challenging but crucial. The aim of thi...
Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer.
Human pathology
Dec 5, 2022
Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports o...
Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy.
BMC medicine
Aug 26, 2022
BACKGROUND: Accurate diagnosis of unexplained cervical lymphadenopathy (CLA) using medical images heavily relies on the experience of radiologists, which is even worse for CLA patients in underdeveloped countries and regions, because of lack of exper...
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