Nerve invasion (NI) is a characteristic feature of pancreatic cancer. Traditional dichotomous statements on the presence of NI are unreasonable because almost all cases exhibit NI when sufficient pathological sections are examined. The critical implications of NI in pancreatic cancer highlight the need for a more effective criterion. This study included 511 patients, who were categorized into a training group and a testing group at a ratio of 7:3. According to the traditional definition, NI was observed in 91.2 % of patients using five pathological slides in our study. The prevalence of NI increased as more pathological slides were used. The criterion of \'two points of intraneural (endoneural) invasion in the case of four pathological slides\' has the highest receiver operating characteristic (ROC) score. Based on this new criterion, NI was proved to be an independent prognostic factor for overall survival (OS) and disease-free survival (DFS) and was also correlated with tumor recurrence (P = 0.004). Interestingly, gemcitabine-based chemotherapy regimen is an independent favorable factor for patients with high NI. In the high NI group, patients who received a gemcitabine-based regimen exhibited a better prognosis than those who did not receive the gemcitabine-based regimen for OS (P = 0.000) and DFS (P = 0.001). In conclusion, this study establishes assessment criteria to evaluate the severity of NI in order to predict patient outcomes.

Perineural invasion (PNI) is a notorious feature of salivary adenoid cystic carcinoma (SACC) and other neurotropic tumors. The pathogenesis of PNI that involves the molecular communication between the tumor and the suffered nerve is elusive. The in vitro co-culture assays of SACC cells with dorsal root ganglia (DRG) or neural cells showed that nerve-derived CCL2 activated CCR2 expression in SACC cells, promoting the proliferation, adhesion, migration, and invasion of SACC cells via the ERK1/2/ITGβ5 pathway. Meanwhile, SACC-derived exosomes delivered ITGβ5 to promote the neurite outgrowth of neural cells or DRG. Blocking of CCL2/CCR2 axis or ITGβ5 inhibited the PNI of SACC cells in models in vitro by 3D co-culture of DRG with SACC cells and in vivo by xenografting SACC cells onto the murine sciatic nerve. High levels of ITGβ5 in tissues or plasma exosomes were significantly correlated with CCL2 and CCR2 expression in the tissues and associated with PNI and poor prognosis of SACC cases. Our findings revealed a novel reciprocal loop between neural and tumor cells driven by the CCL2/CCR2 axis and exosomal ITGβ5 during PNI of SACC. The present study may provide a prospective diagnostic and anti-PNI treatment strategy for SACC patients via targeting the nerve-tumor interactions.

UNASSIGNED: Perineural invasion (PNI) refers to the invasion, encasement, or penetration of tumor cells around or through nerves. Various malignant tumors, including pancreatic cancer, head and neck tumors, and bile duct cancer, exhibit the characteristic of PNI. Particularly, in head and neck-skull base tumors such as adenoid cystic carcinoma (ACC), PNI is a significant factor leading to incomplete surgical resection and postoperative recurrence.
UNASSIGNED: Spatial transcriptomic and single-cell transcriptomic sequencing were conducted on a case of ACC tissue with PNI to identify potential probes targeting PNI. The efficacy of the probes was validated through in vivo and in vitro experiments.
UNASSIGNED: Spatial transcriptomic and single-cell RNA sequencing revealed phenotypic changes in Schwann cells within the PNI region of ACC. Peptide probes were designed based on the antigen-presenting characteristics of Schwann cells in the PNI region, which are dependent on Major Histocompatibility Complex II (MHC-II) molecules. Successful validation in vitro and in vivo experiments confirmed that these probes can label viable Schwann cells in the PNI region, serving as a tool for dynamic in vivo marking of tumor invasion into nerves.
UNASSIGNED: Peptide probes targeting Schwann cells\' MHC-II molecules have the potential to demonstrate the occurrence of PNI in patients with ACC.

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retrospectively collected data from 108 patients with pathologically confirmed rectal adenocarcinoma who underwent preoperative multiparametric MRI at the First Affiliated Hospital of Bengbu Medical College between April 2019 and August 2023. This dataset was subsequently divided into training and validation sets following a ratio of 7:3. Both univariate and multivariate logistic regression analyses were implemented to identify independent clinical risk factors associated with perineural invasion (PNI) in rectal cancer. We manually delineated the region of interest (ROI) layer-by-layer on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences and extracted the image features. Five machine learning algorithms were used to construct radiomics model with the features selected by least absolute shrinkage and selection operator (LASSO) method. The optimal radiomics model was then selected and combined with clinical features to formulate a nomogram model. The model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and its clinical value was assessed via decision curve analysis (DCA). Our final selection comprised 10 optimal radiological features and the SVM model showcased superior predictive efficiency and robustness among the five classifiers. The area under the curve (AUC) values of the nomogram model were 0.945 (0.899, 0.991) and 0.846 (0.703, 0.99) for the training and validation sets, respectively. The nomogram model developed in this study exhibited excellent predictive performance in foretelling PNI of rectal cancer, thereby offering valuable guidance for clinical decision-making. The nomogram could predict the perineural invasion status of rectal cancer in early stage.

OBJECTIVE: To develop and validate a radiomics nomogram utilizing CT data for predicting perineural invasion (PNI) and survival in gastric cancer (GC) patients.
METHODS: A retrospective analysis of 408 GC patients from two institutions: 288 patients from Institution I were divided 7:3 into a training set (n = 203) and a testing set (n = 85); 120 patients from Institution II served as an external validation set. Radiomics features were extracted and screened from CT images. Independent radiomics, clinical, and combined models were constructed to predict PNI. Model discrimination, calibration, clinical utility, and prognostic significance were evaluated using area under the curve (AUC), calibration curves, decision curves analysis, and Kaplan-Meier curves, respectively.
RESULTS: 15 radiomics features and three clinical factors were included in the final analysis. The AUCs of the radiomics model in the training, testing, and external validation sets were 0.843 (95% CI: 0.788-0.897), 0.831 (95% CI: 0.741-0.920), and 0.802 (95% CI: 0.722-0.882), respectively. A nomogram was developed by integrating significant clinical factors with radiomics features. The AUCs of the nomogram in the training, testing, and external validation sets were 0.872 (95% CI: 0.823-0.921), 0.862 (95% CI: 0.780-0.944), and 0.837 (95% CI: 0.767-0.908), respectively. Survival analysis revealed that the nomogram could effectively stratify patients for recurrence-free survival (Hazard Ratio: 4.329; 95% CI: 3.159-5.934; P < 0.001).
CONCLUSIONS: The radiomics-derived nomogram presented a promising tool for predicting PNI in GC and held significant prognostic implications.
RESULTS: The nomogram functioned as a non-invasive biomarker for determining the PNI status. The predictive performance of the nomogram surpassed that of the clinical model (P < 0.05). Furthermore, patients in the high-risk group stratified by the nomogram had a significantly shorter RFS (P < 0.05).

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignant tumor with a high metastatic potential. Perineural invasion (PNI) occurs in the early stages of PDAC with a high incidence rate and is directly associated with a poor prognosis. It involves close interaction among PDAC cells, nerves and the tumor microenvironment. In this review, we detailed discuss PNI-related pain, six specific steps of PNI, and treatment of PDAC with PNI and emphasize the importance of novel technologies for further investigation.

Perineural invasion (PNI), the neoplastic invasion of nerves, is an often overlooked pathological phenomenon in cervical cancer that is associated with poor clinical outcomes. The occurrence of PNI in cervical cancer patients has limited the promotion of Type C1 surgery. Preoperative prediction of the PNI can help identify suitable patients for Type C1 surgery. However, there is a lack of appropriate preoperative diagnostic methods for PNI, and its pathogenesis remains largely unknown. Here, we dissect the neural innervation of the cervix, analyze the molecular mechanisms underlying the occurrence of PNI, and explore suitable preoperative diagnostic methods for PNI to advance the identification and treatment of this ominous cancer phenotype.

The aim of this study was to conduct a comprehensive review of the predictive significance of PNI in HNSCC survival outcomes. A systematic search was conducted across multiple databases, and all studies published in the last decade were screened (Research Registry ID: reviewregistry1853). The included studies were assessed using the Quality in Prognosis Studies tool. Survival outcome data were extracted, combined, and presented as hazard ratios (HR) with a 95% confidence interval (CI). Totally, 74 studies encompassing 27,559 patients were analyzed and revealed a cumulative occurrent rate of 30% for PNI in HNSCC. PNI+ HNSCC patients had a worse overall survival (HR: 1.91, 95% CI: 1.71-2.13), disease-specific survival (HR: 1.79, 95% CI: 1.55-2.07), disease-free survival (HR: 1.82, 95% CI: 1.69-1.96), local recurrence (HR: 2.54, 95% CI: 1.93-3.33), locoregional recurrence (HR: 2.27, 95% CI: 1.82-2.82), locoregional relapse free survival (HR: 1.77, 95% CI: 1.28-2.45), distant metastasis (HR: 1.82, 95% CI: 1.34-2.48), and distant metastasis-free survival (HR: 2.97, 95% CI: 1.82-4.85) compared to those PNI- patients. The available evidence unequivocally establishes PNI as a critical prognostic factor for worse survival in HNSCC patients.

OBJECTIVE: Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa.
METHODS: In this prospective study, 557 PCa patients who underwent preoperative MRI and radical prostatectomy were recruited and randomly divided into the training and the validation cohorts at a ratio of 7:3. Clinical model for predicting PNI was constructed by univariate and multivariate regression analyses on various clinical indicators, followed by logistic regression. Radiomics and deep learning methods were used to develop different MRI-based radiomics and deep learning models. Subsequently, the clinical, radiomics, and deep learning signatures were combined to develop the integrated deep learning-radiomics-clinical model (DLRC). The performance of the models was assessed by plotting the receiver operating characteristic (ROC) curves and precision-recall (PR) curves, as well as calculating the area under the ROC and PR curves (ROC-AUC and PR-AUC). The calibration curve and decision curve were used to evaluate the model\'s goodness of fit and clinical benefit.
RESULTS: The DLRC model demonstrated the highest performance in both the training and the validation cohorts, with ROC-AUCs of 0.914 and 0.848, respectively, and PR-AUCs of 0.948 and 0.926, respectively. The DLRC model showed good calibration and clinical benefit in both cohorts.
CONCLUSIONS: The DLRC model, which integrated clinical, radiomics, and deep learning signatures, can serve as a robust tool for predicting PNI in patients with PCa, thus aiding in developing effective treatment strategies.

OBJECTIVE: To evaluate the prognostic factors and survival outcomes of patients with surgically treated high-grade neuroendocrine carcinoma of the cervix (NECC).
METHODS: This multicenter, retrospective study involved 98 cervical cancer patients with stage IA2-IIA2 and IIIC1/2p high-grade NECC. We divided the patients into two groups based on histology: the pure and mixed groups. All clinicopathologic variables were retrospectively evaluated. Cox regression and Kaplan-Meier methods were used for analysis.
RESULTS: In our study, 60 patients were in the pure group and 38 patients were in the mixed group. Cox multivariate analysis showed that mixed histology was a protective factor impacting overall survival (OS) (P = 0.026) and progression free survival (PFS) (P = 0.018) in surgically treated high-grade NECC. Conversely, survival outcomes were negatively impacted by ovarian preservation (OS: HR, 20.84; 95% CI: 5.02-86.57, P < 0.001), age >45 years (OS: HR, 4.50; 95% CI: 1.0-18.83, P = 0.039), tumor size >4 cm (OS: HR, 6.23; 95% CI: 2.34-16.61, P < 0.001), parity >3 (OS: HR, 4.50; 95% CI: 1.02-19.91, P = 0.048), and perineural invasion (OS: HR, 5.21; 95% CI: 1.20-22.53, P = 0.027). Kaplan-Meier survival curves revealed notable differences in histologic type (OS: P = 0.045; PFS: P = 0.024), chemotherapy (OS: P = 0.0056; PFS: P = 0.0041), ovarian preservation (OS: P = 0.00031; PFS: P = 0.0023), uterine invasion (OS: P < 0.0001; PFS: P < 0.0001), and depth of stromal invasion (OS: P = 0.043; PFS: P = 0.022).
CONCLUSIONS: Patients with mixed histologic types who undergo surgery for high-grade NECC have a better prognosis. Meanwhile, ovarian preservation, tumor size >4 cm, parity >3, age >45 years and perineural invasion were poor prognostic predictors. Therefore, patients with high-risk factors should be considered in clinical practice.