AIMC Topic: Chromosome Aberrations
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Predicting cisplatin response in cholangiocarcinoma patients using chromosome pattern and related gene expression.
Scientific reports
Jul 1, 2025
Cholangiocarcinoma (CCA) is a prevalent bile duct cancer with limited treatment options. Cisplatin-based chemotherapy is a common approach, but response rates vary. Recently, chromosome aberrations have emerged as predictors of chemotherapy response ...
Cancer cytogenetics in the era of artificial intelligence: shaping the future of chromosome analysis.
Future oncology (London, England)
Aug 12, 2024
Artificial intelligence (AI) has rapidly advanced in the past years, particularly in medicine for improved diagnostics. In clinical cytogenetics, AI is becoming crucial for analyzing chromosomal abnormalities and improving precision. However, existin...
Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol.
Reproductive health
Jul 3, 2024
BACKGROUND: For women in the first trimester, amniocentesis or chorionic villus sampling is recommended for screening. Machine learning has shown increased accuracy over time and finds numerous applications in enhancing decision-making, patient care,...
Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.
Skeletal radiology
Jun 27, 2024
OBJECTIVE: To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM).
Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.
International journal of radiation biology
Apr 30, 2024
PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automate...
Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.
Investigative radiology
May 22, 2023
OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be perfor...
Highly Performing Automatic Detection of Structural Chromosomal Abnormalities Using Siamese Architecture.
Journal of molecular biology
Mar 10, 2023
The detection of structural chromosomal abnormalities (SCA) is crucial for diagnosis, prognosis and management of many genetic diseases and cancers. This detection, done by highly qualified medical experts, is tedious and time-consuming. We propose a...
Dicentric chromosome assay using a deep learning-based automated system.
Scientific reports
Dec 21, 2022
The dicentric chromosome assay is the "gold standard" in biodosimetry for estimating radiation exposure. However, its large-scale deployment is limited owing to its time-consuming nature and requirement for expert reviewers. Therefore, a recently dev...
Ultrasound images-based deep learning radiomics nomogram for preoperative prediction of rearrangement in papillary thyroid carcinoma.
Frontiers in endocrinology
Dec 20, 2022
PURPOSE: To create an ultrasound -based deep learning radiomics nomogram (DLRN) for preoperatively predicting the presence of rearrangement among patients with papillary thyroid carcinoma (PTC).
Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.
PloS one
Jun 22, 2022
OBJECTIVE: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.
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