AI Medical Compendium Topic

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Neoplasm, Residual

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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
BACKGROUND: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions.

MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis.

Cytometry. Part B, Clinical cytometry
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
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.

Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence.

Journal of neurosurgery
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 ...

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

Nature medicine
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...

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
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...

Measurable residual disease (MRD) dynamics in multiple myeloma and the influence of clonal diversity analyzed by artificial intelligence.

Blood cancer journal
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 ...

[Value of the deep learning automated quantification of tumor-stroma ratio in predicting efficacy and prognosis of neoadjuvant therapy for breast cancer based on residual cancer burden grading].

Zhonghua bing li xue za zhi = Chinese journal of pathology
To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Specimens were collected from 209 breast cancer patients who receive...