Recurrent abducens nerve palsy with optic perineuritis

Dear Sir, I am Joo Yeon Kim, from the department of Ophthalmology of Kim’s Eye Hospital in Seoul, Korea. I write to present a case report of recurrent abducens nerve palsy with optic perineuritis Abducens nerve palsy is associated with trauma, viral infection or inoculation, central nervous system tumors, elevated intracranial pressure and idiopathic cause [1]. Optic perineuritis is an uncommon noninfectious inflammation of the optic nerve sheath and perineural fat. Optic perineuritis

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model:A dual-center study

BACKGROUND Perineural invasion(PNI)has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer(RC).Preoperative prediction of PNI status is helpful for individualized treatment of RC.Recently,several radiomics studies have been used to predict the PNI status in RC,demonstrating a good predictive effect,but the results lacked generalizability.The preoperative prediction of PNI status is still challenging and needs further study.AIM To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers.The patients underwent preoperative high-resolution magnetic resonance imaging(MRI)between May 2019 and August 2022.Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging(T2WI)and contrast-enhanced T1WI(T1CE)sequences.The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared(T2WI,T1CE and T2WI+T1CE fusion sequences).A clinical-radiomics(CR)model was established by combining the radiomics features and clinical risk factors.The internal and external validation groups were used to validate the proposed models.The area under the receiver operating characteristic curve(AUC),DeLong test,net reclassification improvement(NRI),integrated discrimination improvement(IDI),calibration curve,and decision curve analysis(DCA)were used to evaluate the model performance.RESULTS Among the radiomics models,the T2WI+T1CE fusion sequences model showed the best predictive performance,in the training and internal validation groups,the AUCs of the fusion sequence model were 0.839[95%confidence interval(CI):0.757-0.921]and 0.787(95%CI:0.650-0.923),which were higher than those of the T2WI and T1CE sequence models.The CR model constructed by combining clinical risk factors had the best predictive performance.In the training and internal and external validation groups,the AUCs of the CR model were 0.889(95%CI:0.824-0.954),0.889(95%CI:0.803-0.976)and 0.894(95%CI:0.814-0.974).Delong test,NRI,and IDI showed that the CR model had significant differences from other models(P<0.05).Calibration curves demonstrated good agreement,and DCA revealed significant benefits of the CR model.CONCLUSION The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively,which facilitates individualized treatment of RC patients.

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment deci-sions.However,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited accuracy.Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion.We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.AIM To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.METHODS This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor regions.Next,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine concentration of equilibrium phase(EP-NIC)were independent influencing factors.The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824,respectively,which were slightly greater than those of CT-T and CT-EMVI(AUC=0.793,0.762).The nomogram combining CT-T stage,CT-EMVI,VP-70 keV and EP-NIC yielded AUCs of 0.918(0.866-0.954)and 0.874(0.784-0.936)in the training and validation cohorts,which are significantly higher than using each of single independent factors(P<0.05).CONCLUSION The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC,with accuracy boosted by integrating clinical markers.

Review on article of preoperative prediction in chronic hepatitis B virus patients using spectral computed tomography and machine learning

This letter comments on the article that developed and tested a machine learning model that predicts lymphovascular invasion/perineural invasion status by combining clinical indications and spectral computed tomography characteristics accurately.We review the research content,methodology,conclusions,strengths and weaknesses of the study,and introduce follow-up research to this work.

Non-coding RNA as future target for diagnose and treatment of perineural invasion in cancers

Perineural invasion(PNI),a particularly insidious form of tumor metastasis distinct from hematogenous or lymphatic spread,has the capacity to extend well beyond the primary tumor site,infiltrating distant regions devoid of lymphatic or vascular structures.PNI often heralds a decrease in patient survival rates and is recognized as an indicator of an unfavorable prognosis across a variety of cancers.Despite its clinical significance,the underlying molecular mechanisms of PNI remain elusive,complicating the development of specific and efficacious diagnostic and therapeutic strategies.In the realm of cancer research,non-coding RNAs(ncRNAs)have attracted considerable attention due to their multifaceted roles and cancer-specific expression profiles,positioning them as promising candidates for applications in cancer diagnostics,prognostics,and treatment.Among the various types of ncRNAs,microRNAs(miRNAs),long non-coding RNAs(lncRNAs),and circular RNAs(circRNAs)have emerged as influential players in PNI.Their involvement is increasingly recognized as a contributing factor to tumor progression and therapeutic resistance.Our study synthesizes and explores the diverse functions and mechanisms of ncRNAs in relation to PNI in cancer.This comprehensive review aims to shed light on cutting-edge perspectives that could pave the way for innovative diagnostic and therapeutic approaches to address the challenges posed by PNI in oncology.

Current status of nerve infiltration by skip metastases in colorectal cancer

Colorectal cancer(CRC)is a prevalent malignant tumor,with the global new cases reaching 1.9316 million and deaths reaching 935,200 in 2022.In China,therewere 555,500 new cases of CRC,with an age-standardized incidence rate of 24.07 per 10million,and 286,200 deaths.China accounts for approximately 30%of new cases and deaths from CRC worldwide,with East Asia accounting for over 75%.Initially,CRC presents as local tumor growth,but it has the potential to spread to other body parts over time.Perineural infiltration(PNI)is a relatively less discussed route of diffusion,yet it plays a crucial role in the progression and prognosis of CRC.PNI often occurs alongside local lymph nodes and distant metastases,posing challenges for treatment and management.Clinical symptoms,radiographic findings,and histopathological examination can be used to diagnose PNI with skipmetastasis.Symptoms commonly include local pain,paresthesia,andmotor impairment.Imaging helps identify the mass’s location and relationship to nerves,whereas histopathological examination confirms the diagnosis.Treatment of PNI skipmetastases is similar to other CRC metastases,including surgical resection,chemotherapy,radiotherapy,and targeted therapy.Surgical resection is the primary therapeutic approach,but the wider range of metastasis in PNI skip transfer may limit its feasibility.In cases where surgical resection is not possible,chemotherapy,radiotherapy,and targeted therapy are used to control tumor metastasis.In conclusion,PNI skip metastases increase the risk of poor prognosis for CRC,requiring a comprehensive approach with multiple treatments to prevent disease progression.Early detection and treatment are vital to improving prognosis.

Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study

BACKGROUND The presence of perineural invasion(PNI)in patients with rectal cancer(RC)is associated with significantly poorer outcomes.However,traditional diagnostic modalities have many limitations.AIM To develop a deep learning radiomics stacking nomogram model to predict preoperative PNI status in patients with RC.METHODS We recruited 303 RC patients and separated them into the training(n=242)and test(n=61)datasets on an 8:2 scale.A substantial number of deep learning and hand-crafted radiomics features of primary tumors were extracted from the arterial and venous phases of computed tomography(CT)images.Four machine learning models were used to predict PNI status in RC patients:support vector machine,k-nearest neighbor,logistic regression,and multilayer perceptron.The stacking nomogram was created by combining optimal machine learning models for the arterial and venous phases with predicting clinical variables.RESULTS With an area under the curve(AUC)of 0.964[95%confidence interval(CI):0.944-0.983]in the training dataset and an AUC of 0.955(95%CI:0.900-0.999)in the test dataset,the stacking nomogram demonstrated strong performance in predicting PNI status.In the training dataset,the AUC of the stacking nomogram was greater than that of the arterial support vector machine(ASVM),venous SVM,and CT-T stage models(P<0.05).Although the AUC of the stacking nomogram was greater than that of the ASVM in the test dataset,the difference was not particularly noticeable(P=0.05137).CONCLUSION The developed deep learning radiomics stacking nomogram was effective in predicting preoperative PNI status in RC patients.

Magnetic resonance imaging evaluation of head and neck involvement in IgG4-related disease

Objective:To evaluate the radiological features of IgG4-related disease(IgG4-RD)in the head and neck region.Methods:In this radiology-based study,radiological features,clinical,laboratory,pathological findings,and prognosis of nine patients with head and neck involvement diagnosed with IgG4-RD were investigated retrospectively.Results:The median age of the patients was 38 years(range:2.5-79 years),and there were six males and three females.The most common symptoms and clinical findings of the patients were eyelid and lacrimal gland swelling,painless exophthalmos,and ophthalmoplegia.The most common site of involvement on MRI was the orbit.Orbital involvement was followed by branches of the trigeminal nerve,sinonasal cavity,cervical lymph nodes,and dural involvement.The most common and remarkable imaging features were T2 hypointensity and diffuse homogeneous contrast enhancement.Conclusions:Head and neck involvement of the IgG4-RD,has specific imaging features that can help with diagnosis.Thus,early diagnosis and better outcomes can be achieved with increasing awareness of these features of this relatively new pathology.

Prediction of the lymphatic,microvascular,and perineural invasion of pancreatic neuroendocrine tumors using preoperative magnetic resonance imaging

BACKGROUND Significant correlation between lymphatic,microvascular,and perineural invasion(LMPI)and the prognosis of pancreatic neuroendocrine tumors(PENTs)was confirmed by previous studies.There was no previous study reported the relationship between magnetic resonance imaging(MRI)parameters and LMPI.AIM To determine the feasibility of using preoperative MRI of the pancreas to predict LMPI in patients with non-functioning PENTs(NFPNETs).METHODS A total of 61 patients with NFPNETs who underwent MRI scans and lymphadenectomy from May 2011 to June 2018 were included in this retrospective study.The patients were divided into group 1(n=34,LMPI negative)and group 2(n=27,LMPI positive).The clinical characteristics and qualitative MRI features were collected.In order to predict LMPI status in NF-PNETs,a multivariate logistic regression model was constructed.Diagnostic performance was evaluated by calculating the receiver operator characteristic(ROC)curve with area under ROC,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV)and accuracy.RESULTS There were significant differences in the lymph node metastasis stage,tumor grade,neuron-specific enolase levels,tumor margin,main pancreatic ductal dilatation,common bile duct dilatation,enhancement pattern,vascular and adjacent tissue involvement,synchronous liver metastases,the long axis of the largest lymph node,the short axis of the largest lymph node,number of the lymph nodes with short axis>5 or 10 mm,and tumor volume between two groups(P<0.05).Multivariate analysis showed that tumor margin(odds ratio=11.523,P<0.001)was a predictive factor for LMPI of NF-PNETs.The area under the receiver value for the predictive performance of combined predictive factors was 0.855.The sensitivity,specificity,PPV,NPV and accuracy of the model were 48.1%(14/27),97.1%(33/34),97.1%(13/14),70.2%(33/47)and 0.754,respectively.CONCLUSION Using preoperative MRI,ill-defined tumor margins can effectively predict LMPI in patients with NF-PNETs.

Orbital inflammatory disease:Pictorial review and differential diagnosis

Orbital inflammatory disease(OID) represents a collec tion of inflammatory conditions affecting the orbit. OID is a diagnosis of exclusion, with the differential diagno sis including infection, systemic inflammatory conditions and neoplasms, among other conditions. Inflammatory conditions in OID include dacryoadenitis, myositis, cel lulitis, optic perineuritis, periscleritis, orbital apicitis, and a focal mass. Sclerosing orbital inflammation is a rare condition with a chronic, indolent course involving dense fibrosis and lymphocytic infiltrate. Previously though to be along the spectrum of OID, it is now considered a distinct pathologic entity. Imaging plays an importan role in elucidating any underlying etiology behind orbita inflammation and is critical for ruling out other condi tions prior to a definitive diagnosis of OID. In this re view, we will explore the common sites of involvemen by OID and discuss differential diagnosis by site and key imaging findings for each condition.

Nerve biopsy findings contribute to diagnosis of multiple mononeuropathy: 78% of findings support clinical diagnosis

Multiple mononeuropathy is an unusual form of peripheral neuropathy involving two or more nerve trunks. It is a syndrome with many different causes. We reviewed the clinical, electrophysi- ological and nerve biopsy findings of 14 patients who suffered from multiple mononeuropathy in our clinic between January 2009 and June 2013. Patients were diagnosed with vasculitic neurop- athy （n = 6）, perineuritis （n = 2）, chronic inflammatory demyelinating polyradiculoneuropathy （n = 2） or Lewis-Sumner syndrome （n = 1） on the basis of clinical features, laboratory data, elec- trophysiological investigations and nerve biopsies. Two patients who were clinically diagnosed with vasculitic neuropathy and one patient who was clinically diagnosed with chronic inflamma- tory demyelinating polyradiculoneuropathy were not confirmed by nerve biopsy. Nerve biopsies confirmed clinical diagnosis in 78.6% of the patients （11/14）. Nerve biopsy pathological diagno- sis is crucial to the etiological diagnosis of multiple mononeuropathy.

Atypical Presentation of Acanthamoeba Keratitis in a Contact Lens Wearer

Acanthamoeba keratitis is a serious infection that can lead to loss of vision. It is highly challenging and often poses a diagnostic dilemma, causing delay in diagnosis and treatment. We report herewith the clinical and histopathology findings of a patient with an atypical presentation of acanthamoeba keratitis in Bahrain. The patient is a 16-year-old Bahraini teenager who was a cosmetic contact lens wearer. She presented with clinical signs and symptoms of microbial keratitis, which was initially misdiagnosed elsewhere as a case of herpetic corneal infection. Her corneal biopsy confirmed the clinical diagnosis as acanthamoeba keratitis. The patient was started on anti amoebic treatment. The infection got eradicated. The cornea healed with a central scar. Eventually, she underwent penetrating keratoplasty. This case report serves to raise awareness of this rare condition. Clinicians should have a high index of suspicion when diagnosing such cases among contact lens wearers. Early diagnosis and treatment are crucial to prevent serious complications.

NGF及其受体在肿瘤生长及侵袭中的作用

神经生长因子（NGF）的受体分为高亲和力受体TrkA和低亲和力受体p75。它们对于神经系统的生长发育以及神经元特性的维持有着不可替代的作用。NGF和非神经源性肿瘤的发生发展及生物学特性方面也有着密切的关系。NGF和TrkA结合可以促进某些肿瘤细胞的生长，而且这种作用主要是通过MAPK这一信号通路；而p75则可以诱导肿瘤细胞的凋亡。NGF及其受体通过不同机制在肿瘤的嗜神经侵袭中也发挥着重要作用。

Pleiotrophin promotes perineural invasion in pancreatic cancer

Perineural invasion(PNI)in pancreatic cancer is an important cause of local recurrence,but little is known about its mechanism.Pleiotrophin(PTN)is an important neurotrophic factor.It is of interest that our recent experimental data showed its involvement in PNI of pancreatic cancer.PTN strongly presents in the cytoplasm of pancreatic cancer cells,and high expression of PTN and its receptor may contribute to the high PNI of pancreatic cancer.Correspondingly,PNI is prone to happen in PTN-positive tumors.We thus hypothesize that,as a neurite growth-promoting factor,PTN may promote PNI in pancreatic cancer.PTN is released at the time of tumor cell necrosis,and binds with its highaffinity receptor,N-syndecan on pancreatic nerves,to promote neural growth in pancreatic cancer.Furthermore,neural destruction leads to a distorted neural homeostasis.Neurons and Schwann cells produce more N-syndecan in an effort to repair the pancreatic nerves.However,the abundance of N-syndecan attracts further PTN-positive cancer cells to the site of injury,creating a vicious cycle.Ultimately,increased PTN and N-syndecan levels,due to the continuous nerve injury,may promote cancer invasion and propagation along the neural structures.Therefore,it is meaningful to discuss the relationship between PTN/N-syndecan signaling and PNI in pancreatic cancer,which may lead to a better understanding of the mechanism of PNI in pancreatic cancer.

Midkine promotes perineural invasion in human pancreatic cancer

AIM: To investigate midkine (MK) and syndecan-3 protein expression in pancreatic cancer by immunohistochemistry, and to analyze their correlation with clinicopathological features, perineural invasion, and prognosis.

Relationship between autophagy and perineural invasion, clinicopathological features, and prognosis in pancreatic cancer

AIM To investigate the relationship between autophagy and perineural invasion(PNI), clinical features, and prognosis in patients with pancreatic cancer. METHODS Clinical and pathological data were retrospectively collected from 109 patients with pancreatic ductal adenocarcinoma who underwent radical resection at the First Affiliated Hospital of Zhengzhou University from January 2011 to August 2016. Expression levels of the autophagy-related protein microtubuleassociated protein 1 A/1 B-light chain 3(LC3) and PNI marker ubiquitin carboxy-terminal hydrolase(UCH) in pancreatic cancer tissues were detected by immunohistochemistry. The correlations among LC3 expression, PNI, and clinical pathological features in pancreatic cancer were analyzed. The patients were followed for further survival analysis. RESULTS In 109 cases of pancreatic cancer, 68.8%(75/109) had evidence of PNI and 61.5%(67/109) had high LC3 expression. PNI was associated with lymph node metastasis, pancreatitis, and CA19-9 levels(P < 0.05). LC3 expression was related to lymph node metastasis(P < 0.05) and was positively correlated with neural invasion(P < 0.05, r = 0.227). Multivariate logistic regression analysis indicated that LC3 expression, lymph node metastasis, pancreatitis, and CA19-9 level were factors that influenced neural invasion, whereas only neural invasion itself was an independent factor for high LC3 expression. Univariate analysis showed that LC3 expression, neural invasion, and CA19-9 level were related to the overall survival of pancreatic cancer patients(P < 0.05). Multivariate COX regression analysis indicated that PNI and LC3 expression were independent risk factors for poor prognosis in pancreatic cancer(P < 0.05). CONCLUSION PNI in patients with pancreatic cancer is positively related to autophagy. Neural invasion and LC3 expression are independent risk factors for pancreatic cancer with a poor prognosis.

Radiomics for predicting perineural invasion status in rectal cancer

BACKGROUND Perineural invasion(PNI),as a key pathological feature of tumor spread,has emerged as an independent prognostic factor in patients with rectal cancer(RC).The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis.However,the preoperative evaluation of PNI status is still challenging.AIM To establish a radiomics model for evaluating PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019.These patients were classified as the training cohort(n=242)and validation cohort(n=61)at a ratio of 8:2.A large number of intraand peritumoral radiomics features were extracted from portal venous phase images of computed tomography(CT).After deleting redundant features,we tested different feature selection(n=6)and machine-learning(n=14)methods to form 84 classifiers.The best performing classifier was then selected to establish Rad-score.Finally,the clinicoradiological model(combined model)was developed by combining Rad-score with clinical factors.These models for predicting PNI were compared using receiver operating characteristic curve(ROC)analysis and area under the ROC curve(AUC).RESULTS One hundred and forty-four of the 303 patients were eventually found to be PNIpositive.Clinical factors including CT-reported T stage(cT),N stage(cN),and carcinoembryonic antigen(CEA)level were independent risk factors for predicting PNI preoperatively.We established Rad-score by logistic regression analysis after selecting features with the L1-based method.The combined model was developed by combining Rad-score with cT,cN,and CEA.The combined model showed good performance to predict PNI status,with an AUC of 0.828[95%confidence interval(CI):0.774-0.873]in the training cohort and 0.801(95%CI:0.679-0.892)in the validation cohort.For comparison of the models,the combined model achieved a higher AUC than the clinical model(cT+cN+CEA)achieved(P<0.001 in the training cohort,and P=0.045 in the validation cohort).CONCLUSION The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3

BACKGROUND Pancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1 A/1 B-light chain 3(LC3) and perineural invasion(PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIM To identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODS Two sets of gene expression profiles of pancreatic cancer/normal tissue(GSE16515 and GSE15471) were collected from the Gene Expression Omnibus.Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTS Based on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition,65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene,which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSION Three hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.

Perineural invasion of hilar cholangiocarcinoma in Chinese population: One center’s experience

BACKGROUND Hilar cholangiocarcinoma(HCCA)often produces perineural invasion(PNI)extending to extra-biliary sites,while significant confusion in the incidence of PNI in HCCA has occurred in the literature,and the mechanism of that procedure remains unclear.AIM To summarize the incidence of PNI in HCCA and to provide the distribution of nerve plexuses around hepatic portal to clinical surgeons.METHODS Reported series with PNI in HCCA since 1996 were reviewed.A clinicopathological study was conducted on sections from 75 patients with HCCA to summarize the incidence and modes of PNI.Immunohistochemical stains for CD34 and D2-40 in the cancer tissue were performed to clarify the association of PNI with microvessel and lymph duct.Sections of the hepatoduodenal ligament from autopsy cases were scanned and handled by computer to display the distribution of nerve plexuses around the hepatic portal.RESULTS The overall incidence of PNI in this study was 92%(69 of 75 patients),while the rate of PNI in HCCA in the literature ranging from 38%to 100%.The incidence of PNI did not show any remarkable differences among various differentiated groups and Bismuth-Corlette classification groups.Logistic regression analysis identified the depth of tumor invasion was the only factor that correlated significantly with PNI(P<0.01).In spite of finding tumor cells that could invade microvessels and lymph ducts in HCCA,we did not find tumor cells invaded nerves via microvessels or lymph ducts.Three nerve plexuses in the hepatoduodenal ligament and Glisson’s sheath were classified,and they all surrounded the great vessels very closely.CONCLUSION The incidence of PNI of HCCA in Chinese population is around 92%and correlated significantly with a depth of tumor invasion.It also should be considered when stratifying HCCA patients for further treatment.

Neural invasion in pancreatic carcinoma

Background: Neural invasion is a special metastatic route in pancreatic cancer and responsible for the high recurrence in curatively resected cases. Objective: To summarize the characteristics and mechanisms of neural invasion in pancreatic carcino- ma for the better treatment of this disease. Data sources: The international literatures were re- viewed about the definition, incidence and mecha- nisms of neural invasion and its clinicopathology, di- agnosis and treatment. Data synthesis: Neural invasion is defined when the medial perineurium is involved by cancer cells, ac- counting for 45 %--100 % of all cases. It can be divid- ed into different kinds or stages according to its loca- tions and the number of nerve fascicles involved. In- vasion along vascularity, lymphatic vessels, perineu- ral space and neurotropism is considered as its pri- mary mechanisms. No clinicopathologic factors are correlated with neural invasion. Intravascular ultra- sound, CT scan and immunostaining K-ras gene a- nalysis can be used to diagnose neural invasion pre-, intra- or postoperatively. Conclusion: Neural invasion is an important prognos- tic factor for the recurrence of pancreatic carcinoma after pancreatectomy. Because of its high incidence, pancreatectomy with extended radical retroperitoneal dissection should be considered as a basic procedure in the treatment of pancreatic carcinoma.