bims-netuvo 2024-09-22 papers

Issue of 2024‒09‒22
two papers selected by
Maksym V. Kopanitsa, Charles River Laboratories

bioRxiv. 2024 Sep 07. pii: 2024.09.03.611089. [Epub ahead of print]

AxonFinder: Automated segmentation of tumor innervating neuronal fibers.

Kaoutar Ait-Ahmad, Cigdem Ak, Guillaume Thibault, Young Hwan Chang, Sebnem Ece Eksi.

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

DOI: https://doi.org/10.1101/2024.09.03.611089

Muscle Nerve. 2024 Sep 19.

Neuroma morphology: A macroscopic classification system.

Floris V Raasveld, Daniel T Weigel, Wen-Chih Liu, Maximilian Mayrhofer-Schmid, Barbara Gomez-Eslava, Vlad Tereshenko, Charles D Hwang, Brian J Wainger, William Renthal, Mark Fleming, Ian L Valerio, Kyle R Eberlin.

INTRODUCTION/AIMS: Neuromas come in different shapes and sizes; yet the correlation between neuroma morphology and symptomatology is unknown. Therefore, we aim to investigate macroscopic traits of excised human neuromas and assess the validity of a morphological classification system and its potential clinical implications.METHODS: End-neuroma specimens were collected from prospectively enrolled patients undergoing symptomatic neuroma surgery. Protocolized images of the specimens were obtained intraoperatively. Pain data (Numeric rating scale, 0-10) were prospectively collected during preoperative interview, patient demographic and comorbidity factors were collected from chart review. A morphological classification is proposed, and the inter-rater reliability (IRR) was assessed. Distribution of neuroma morphology with patient factors, was described.
RESULTS: Forty-five terminal neuroma specimens from 27 patients were included. Residual limb patients comprised 93% of the population, of which 2 were upper (8.0%) and 23 (92.0%) were lower extremity residual limb patients. The proposed morphological classification, consisting of three groups (bulbous, fusiform, atypical), demonstrated a strong IRR (Cohen's kappa = 0.8). Atypical neuromas demonstrated higher preoperative pain, compared with bulbous and fusiform. Atypical morphology was more prevalent in patients with diabetes and peripheral vascular disease.
DISCUSSION: A validated morphological classification of neuroma is introduced. These findings may assist surgeons and researchers with better understanding of symptomatic neuroma development and their clinical implications. The potential relationship of neuroma morphology with the vascular and metabolic microenvironment requires further investigation.

Keywords: amputation; biology; extremity; morphology; neuroma

DOI: https://doi.org/10.1002/mus.28261