bims-netuvo 2025-01-19 papers

Cancer Discov. 2025 Jan 13. 15(1): 52-68

Nerves at Play: The Peripheral Nervous System in Extracranial Malignancies.

Paola D Vermeer, Anthony C Restaino, Jeffrey L Barr, Dan Yaniv, Moran Amit.

The exponential growth of the cancer neuroscience field has shown that the host's immune, vascular, and nervous systems communicate with and influence each other in the tumor microenvironment, dictating the cancer malignant phenotype. Unraveling the nervous system's contributions toward this phenotype brings us closer to cancer cures. In this review, we summarize the peripheral nervous system's contributions to cancer. We highlight the effects of nerve recruitment and tumor innervation, the neuro-immune axis, glial cell activity, and neural regulation on cancer development and progression. We also discuss harnessing the neural control of peripheral cancers as a potential therapeutic approach in oncology. Significance: The continued and growing interest in cancer neuroscience by the scientific and medical communities reflects the rapidly accumulating interdisciplinary understanding of the nervous system's modulation of immune, vascular, and cancer cells' functions in malignancies. Understanding these regulatory functions can identify targets for intervention that may already be clinically available for other indications. This potential brings great excitement and hope for patients with cancer worldwide.

DOI: https://doi.org/10.1158/2159-8290.CD-23-0397

Surgery. 2025 Jan 10. pii: S0039-6060(24)01005-5. [Epub ahead of print]180 109018

Neural invasion severity is a strong predictor of local recurrence in pancreatic ductal adenocarcinoma.

Stephan Schorn, Anouk Fritz, Georgios Kaissis, Matthias M Gaida, Katja Steiger, Carsten Jäger, Anna Melissa Schlitter, Rickmer Braren, Helmut Friess, Ihsan Ekin Demir, Güralp Onur Ceyhan.

BACKGROUND: In pancreatic ductal adenocarcinoma, neural invasion is being increasingly recognized as an unfavorable predictor of patient outcomes. Neural invasion severity seems to have a stronger clinical impact on patient prognosis than neural invasion status alone. Therefore, this study aims to assess the impact of severity of neural invasion on overall survival and disease-free survival in pancreatic ductal adenocarcinoma.
MATERIALS: To assess the impact of intrapancreatic neural invasion severity, tumor specimens resected from patients with pancreatic ductal adenocarcinoma between 2007 and 2014 were systematically re-evaluated, and neural invasion severity was determined using the standardized neural invasion severity score.
RESULTS: In our cohort (n = 216), an increased neural invasion severity score was associated with markedly shorter overall survival in pancreatic head ductal adenocarcinoma (neural invasion severity score low: 22.8 months vs neural invasion severity score high: 17.6 months: P = .001). An external European validation cohort confirmed these results and showed significantly better survival of patients with lower neural invasion (20.5 vs 15.4 months, P = .026). The disease-free survival time was also substantially decreased in patients with pancreatic head pancreatic ductal adenocarcinoma and increased neural invasion severity (neural invasion severity score low: 19.1 months vs neural invasion severity score high: 10.4 months; P = .004). Moreover, the neural invasion severity score was an important independent factor influencing overall survival (hazards ratio 1.024, P = .04) and disease-free survival (hazards ratio 1.03, P = .01) using an adjusted Cox proportional hazards model. Importantly, higher neural invasion severity score leads to significantly more and earlier local recurrence than to distant tumor recurrence.
CONCLUSION: Neural invasion severity is a powerful independent factor influencing overall survival and local recurrence in patients with pancreatic ductal adenocarcinoma. Therefore, individuals with high neural invasion severity score values should be regarded as a specific subgroup of pancreatic ductal adenocarcinoma patients and may benefit from more tailored postoperative oncologic therapy.

DOI: https://doi.org/10.1016/j.surg.2024.109018

Int J Surg. 2024 Dec 01. 110(12): 7656-7670

Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma.

Jieyu Yu, Chengwei Chen, Mingzhi Lu, Xu Fang, Jing Li, Mengmeng Zhu, Na Li, Xiaohan Yuan, Yaxing Han, Li Wang, Jianping Lu, Chengwei Shao, Yun Bian.

BACKGROUND: Extrapancreatic perineural invasion (EPNI) increases the risk of postoperative recurrence in pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop and validate a computed tomography (CT)-based, fully automated preoperative artificial intelligence (AI) model to predict EPNI in patients with PDAC.
METHODS: The authors retrospectively enrolled 1065 patients from two Shanghai hospitals between June 2014 and April 2023. Patients were split into training (n=497), internal validation (n=212), internal test (n=180), and external test (n=176) sets. The AI model used perivascular space and tumor contact for EPNI detection. The authors evaluated the AI model's performance based on its discrimination. Kaplan-Meier curves, log-rank tests, and Cox regression were used for survival analysis.
RESULTS: The AI model demonstrated superior diagnostic performance for EPNI with 1-pixel expansion. The area under the curve in the training, validation, internal test, and external test sets were 0.87, 0.88, 0.82, and 0.83, respectively. The log-rank test revealed a significantly longer survival in the AI-predicted EPNI-negative group than the AI-predicted EPNI-positive group in the training, validation, and internal test sets (P<0.05). Moreover, the AI model exhibited exceptional prognostic stratification in early PDAC and improved assessment of neoadjuvant therapy's effectiveness.
CONCLUSION: The AI model presents a robust modality for EPNI diagnosis, risk stratification, and neoadjuvant treatment guidance in PDAC, and can be applied to guide personalized precision therapy.

DOI: https://doi.org/10.1097/JS9.0000000000001604

Cureus. 2024 Dec;16(12): e75856

A Rare Case of Perineurioma of the Posterior Interosseous Nerve: A Case Report.

Kewithinwangbo Newme, Sumanjit Boro, Deb K Boruah, Yash Mehandiratta.

The perineurioma (PN) is a benign neoplasm with perineural origin. It can be of two types, i.e., intraneural PN and extraneural PN. It is slow-growing in nature and frequently causes hypoesthesia and progressive motor weakness other than swelling. The size of the swelling ranges from small to large. The entity is difficult to distinguish from Schwannoma and other peripheral nerve sheath tumors clinically, pathologically, and radiographically (MRI). An early diagnosis is needed as the PN arises from a nerve and can be salvable if preoperative planning is well executed. Here, in this case, report, the nerve involved was the posterior interosseous nerve (main motor nerve of the finger/wrist extensors), which is a rare phenomenon of occurrence; however, early intervention and the benign nature of the tumor can be handled with a good prognosis, and recurrence is usually rare but could not be ignored as documented.

Keywords: intraneural perineurioma; perineurioma; perineurioma perineurioma; peripheral nerve sheath tumors; schwannoma

DOI: https://doi.org/10.7759/cureus.75856

Curr Med Imaging. 2025 Jan 13.

Machine-Learning Based Computed Tomography Radiomics Nomgram For Predicting Perineural Invasion In Gastric Cancer.

Pei Huang, Sheng Li, Zhikang Deng, Fangfang Hu, Di Jin, Situ Xiong, Bing Fan.

OBJECTIVE: The aim of this study was to develop and validate predictive models for perineural invasion (PNI) in gastric cancer (GC) using clinical factors and radiomics features derived from contrast-enhanced computed tomography (CE-CT) scans and to compare the performance of these models.
METHODS: This study included 205 GC patients, who were randomly divided into a training set (n=143) and a validation set (n=62) in a 7:3 ratio. Optimal radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. A radiomics model was constructed utilizing the optimal among five machine learning filters, and a radiomics score (rad-score) was computed for each participant. A clinical model was built based on clinical factors identified through multivariate logistic regression. Independent clinical factors were combined with the radscore to create a combined radiomics nomogram. The discrimination ability of the models was evaluated by receiver operating characteristic (ROC) curves and the DeLong test.
RESULTS: Independent predictive factors of the clinical model included tumor T stage, N stage, and tumor differentiation, with AUC values of 0.777 and 0.809 in the training and validation sets. The radiomics model was constructed using the support vector machine (SVM) classifier with the best AUC (0.875 in the training set and 0.826 in the validation set). The combined radiomics nomogram, which combines independent clinical predictors and the rad-score, demonstrated better predictive performance (AUC=0.889 in the training set; AUC=0.885 in the validation set).
CONCLUSION: The nomogram integrating independent clinical predictors and CE-CT radiomics was constructed to predict PNI in GC. This model demonstrated favorable performance and could potentially assist in prognosis evaluation and clinical decision-making for GC patients.

Keywords: Contrast-enhanced computed tomography.; Gastric cancer; Machine learning; Nomogram; Perineural invasion; Radiomics

DOI: https://doi.org/10.2174/0115734056323323250102073559

Heliyon. 2025 Jan 15. 11(1): e41209

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 method achieves high performance, with a validation F1-score of 94 % and IoU of 90.78 %. Besides, the morphometric analysis that shows strong alignment between manual annotations and automated segmentation with nerve length and tortuosity closely matching manual measurements. Furthermore, our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories revealing a significant decline in axon density correlating with higher CAPRA-S prostate cancer risk scores. 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.

Keywords: Axon; CyCIF; Deep learning; Image segmentation; Multiplex imaging; Neurosignaling; Prostate cancer

DOI: https://doi.org/10.1016/j.heliyon.2024.e41209