AIMC Topic: Peripheral Nerves

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Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.

Journal of neural engineering
Objective.Precise segmentation and quantification of nerve morphology from imaging data are critical for designing effective and selective peripheral nerve stimulation (PNS) therapies. However, prior studies on nerve morphology segmentation suffer fr...

Machine Learning-Based Pathomics Signature for Perineural Invasion in Colorectal Cancer.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Perineural invasion (PNI) is strongly associated with poor clinical outcomes in colorectal cancer (CRC). However, no machine learning diagnostic model based on pathomics has been established for PNI detection in CRC. To address this issue,...

A novel MRI-based habitat analysis and deep learning for predicting perineural invasion in prostate cancer: a two-center study.

BMC cancer
BACKGROUND: To explore the efficacy of a deep learning (DL) model in predicting perineural invasion (PNI) in prostate cancer (PCa) by conducting multiparametric MRI (mpMRI)-based tumor heterogeneity analysis.

Unraveling the role of perineural invasion in cancer progression across multiple tumor types.

Medical oncology (Northwood, London, England)
Perineural invasion (PNI) refers to the infiltration of tumor cells into the connective tissue of nerves and is increasingly recognized as a pathological hallmark of multiple cancers, including pancreatic, prostate, colorectal, breast, and head and n...

Image-based AI tools in peripheral nerves assessment: Current status and integration strategies - A narrative review.

European journal of radiology
Peripheral Nerves (PNs) are traditionally evaluated using US or MRI, allowing radiologists to identify and classify them as normal or pathological based on imaging findings, symptoms, and electrophysiological tests. However, the anatomical complexity...

Role of Large Language Models for Suggesting Nerve Involvement in Upper Limbs MRI Reports with Muscle Denervation Signs.

Clinical neuroradiology
OBJECTIVES: Determining the involvement of specific peripheral nerves (PNs) in the upper limb associated with signs of muscle denervation can be challenging. This study aims to develop, compare, and validate various large language models (LLMs) to au...

Dual-energy CT combined with histogram parameters in the assessment of perineural invasion in colorectal cancer.

International journal of colorectal disease
PURPOSE: The purpose is to evaluate the predictive value of dual-energy CT (DECT) combined with histogram parameters and a clinical prediction model for perineural invasion (PNI) in colorectal cancer (CRC).

Plexus and Peripheral Nerve MR Imaging: Advances and Applications: MR Neurography: Sequence Possibilities and Recent Advances.

Magnetic resonance imaging clinics of North America
MR neurography (MRN) has become a cornerstone in diagnosing and managing plexus and peripheral nerve disorders. This review explores recent advancements in MRN, emphasizing 3T imaging, coil selection, fat and vascular suppression strategies, and deep...

The value of radiomics and deep learning based on PET/CT in predicting perineural nerve invasion in rectal cancer.

Abdominal radiology (New York)
OBJECTIVE: The objective of this study is to investigate the value of radiomics features and deep learning features based on positron emission tomography/computed tomography (PET/CT) in predicting perineural invasion (PNI) in rectal cancer.

Machine learning model based on preoperative contrast-enhanced CT and clinical features to predict perineural invasion in gallbladder carcinoma patients.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Perineural invasion (PNI) is an independent prognostic risk factor for gallbladder carcinoma (GBC). However, there is currently no reliable method for the preoperative noninvasive prediction of PNI.