AIMC Topic: Neoplasm, Residual
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Validation of an AI-enabled exome/transcriptome liquid biopsy platform for early detection, MRD, disease monitoring, and therapy selection for solid tumors.
Scientific reports
Jul 1, 2025
Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and w...
Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.
Clinical breast cancer
Feb 4, 2025
PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (N...
Measurable residual disease (MRD) dynamics in multiple myeloma and the influence of clonal diversity analyzed by artificial intelligence.
Blood cancer journal
Aug 7, 2024
Minimal residual disease (MRD) assessment is a known surrogate marker for survival in multiple myeloma (MM). Here, we present a single institution's experience assessing MRD by NGS of Ig genes and the long-term impact of depth of response as well as ...
Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2): International Improvement and Validation Study.
The American journal of surgical pathology
Jul 2, 2024
Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quant...
Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.
Nature medicine
Jun 14, 2024
In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (T...
Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence.
Journal of neurosurgery
May 3, 2024
OBJECTIVE: It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly ...
Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden During Neoadjuvant Chemotherapy in Breast Cancer.
Annals of surgery
Apr 1, 2024
OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer.
MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis.
Cytometry. Part B, Clinical cytometry
Feb 28, 2024
Multiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but...
Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.
Neuroradiology
Feb 19, 2024
PURPOSE: To evaluate the diagnostic value of T1-weighted 3D fast spin-echo sequence (CUBE) with deep learning-based reconstruction (DLR) for depiction of pituitary adenoma and parasellar regions on contrast-enhanced MRI.
Editorial for "Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study".
Journal of magnetic resonance imaging : JMRI
Mar 20, 2023
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