The Clinical Significance of Lymphovascular Invasion in Gastric Cancer

Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer

Objectives

To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC).

Methods

A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness.

Results

In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction.

Conclusion

CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC.

Radiomics Analysis to Predict Lymphovascular Invasion of Gastric Cancer Based on Iodine-Based Material Decomposition Images and Virtual Monoenergetic Images

OBJECTIVE

This study aimed to investigate the utility of virtual monoenergetic images (VMIs) and iodine-based material decomposition images (IMDIs) in the assessment of lymphovascular invasion (LVI) in gastric cancer (GC) patients.

METHODS

A total of 103 GC patients who underwent dual-energy spectral computed tomography preoperatively were enrolled. The LVI status was confirmed by pathological analysis. The radiomics features obtained from the 70 keV VMI and IMDI were used to build radiomics models. Independent clinical factors for LVI were identified and used to build the clinical model. Then, combined models were constructed by fusing clinical factors and radiomics signatures. The predictive performance of these models was evaluated.

RESULTS

The computed tomography-reported N stage was an independent predictor of LVI, and the areas under the curve (AUCs) of the clinical model in the training group and testing group were 0.750 and 0.765, respectively. The radiomics models using the VMI signature and IMDI signature and combining these 2 signatures outperformed the clinical model, with AUCs of 0.835, 0.855, and 0.924 in the training set and 0.838, 0.825, and 0.899 in the testing set, respectively. The model combined with the computed tomography-reported N stage and the 2 radiomics signatures achieved the best performance in the training (AUC, 0.925) and testing (AUC, 0.961) sets, with a good degree of calibration and clinical utility for LVI prediction.

CONCLUSIONS

The preoperative assessment of LVI in GC is improved by radiomics features based on VMI and IMDI. The combination of clinical, VMI-, and IMDI-based radiomics features effectively predicts LVI and provides support for clinical treatment decisions.

Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer

Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: -0.0094; AUPRC: -0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.

A robust model training strategy using hard negative mining in a weakly labeled dataset for lymphatic invasion in gastric cancer

Gastric cancer is a significant public health concern, emphasizing the need for accurate evaluation of lymphatic invasion (LI) for determining prognosis and treatment options. However, this task is time-consuming, labor-intensive, and prone to intra- and interobserver variability. Furthermore, the scarcity of annotated data presents a challenge, particularly in the field of digital pathology. Therefore, there is a demand for an accurate and objective method to detect LI using a small dataset, benefiting pathologists. In this study, we trained convolutional neural networks to classify LI using a four-step training process: (1) weak model training, (2) identification of false positives, (3) hard negative mining in a weakly labeled dataset, and (4) strong model training. To overcome the lack of annotated datasets, we applied a hard negative mining approach in a weakly labeled dataset, which contained only final diagnostic information, resembling the typical data found in hospital databases, and improved classification performance. Ablation studies were performed to simulate the lack of datasets and severely unbalanced datasets, further confirming the effectiveness of our proposed approach. Notably, our results demonstrated that, despite the small number of annotated datasets, efficient training was achievable, with the potential to extend to other image classification approaches used in medicine.

Profiling of Lymphovascular Space Invasion in Cervical Cancer Revealed PI3K/Akt Signaling Pathway Overactivation and Heterogenic Tumor-Immune Microenvironments

Lymphovascular space invasion (LVSI) is the presence of tumor emboli in the endothelial-lined space at the tumor body's invasive edge. LVSI is one of three Sedlis criteria components-a prognostic tool for early cervical cancer (CC)-essential for indicating poor prognosis, such as lymph node metastasis, distant metastasis, or shorter survival rate. Despite its clinical significance, an in-depth comprehension of the molecular mechanisms or immune dynamics underlying LVSI in CC remains elusive. Therefore, this study investigated tumor-immune microenvironment (TIME) dynamics of the LVSI-positive group in CC. RNA sequencing included formalin-fixed paraffin-embedded (FFPE) slides from 21 CC patients, and differentially expressed genes (DEGs) were analyzed. Functional analysis and immune deconvolution revealed aberrantly enriched PI3K/Akt pathway activation and a heterogenic immune composition with a low abundance of regulatory T cells (Treg) between LVSI-positive and LVSI-absent groups. These findings improve the comprehension of LSVI TIME and immune mechanisms, benefiting targeted LVSI therapy for CC.

The prognostic significance of lymphovascular invasion in cutaneous squamous cell carcinoma

BACKGROUND

The majority of cutaneous squamous cell carcinomas (cSCC) have a favourable prognosis. However, a subset of cases follow an aggressive disease course with progression to metastasis and death. Several histopathological parameters are associated with poor outcomes, but lymphovascular invasion (LVI) has not been well studied.

OBJECTIVE

To assess the prognostic significance of LVI in cSCC and determine associations between LVI and cSCC.

METHODS

A retrospective review of 486 consecutive cases of cSCC over a 5-year period from a single centre was stratified by the presence or absence of LVI. Logistic regression and multivariate survival analysis were used to determine associations of LVI and prognostic significance of LVI, respectively.

FINDINGS

LVI was present in 41 cases (9.2%). LVI was significantly associated with increasing depth of invasion, microanatomical tumour location (subcutis vs. dermis), and tumour dimensions (P < 0.05). Univariate survival analysis revealed significantly lower 2-year overall survival rates for patients with LVI (37.1%) compared with those without (66.6%) (95% CI = 60.6-73.3, P < 0.001). LVI was also found to be an independent marker of poor disease-specific survival (HR = 0.232 (95% CI = 0.090-0.600), P = 0.003), poor overall survival (HR 0.338 (95% CI = 0.184-0.623), P < 0.001) and poor disease-free survival (HR 0.461 (95% CI = 0.230-0.923), P = 0.029) through multivariate analysis.

CONCLUSIONS

This study confirms that LVI is an independent poor prognosticator in cSCC, with significantly worse survival indices at 2 years. Future systems of risk stratification for cSCC should incorporate LVI.

Risk Stratification and Overall Survival Prediction in Advanced Gastric Cancer Patients Based on Whole-Volume MRI Radiomics

BACKGROUND

The prognosis of advanced gastric cancer (AGC) patients has attracted much attention, but there is a lack of evaluation method. MRI-based radiomics has the potential to evaluate AGC patients' prognosis.

PURPOSE

To identify and validate the risk stratification and overall survival (OS) in AGC patients using MRI-based radiomics.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 233 patients (168 males, 63.6 ± 11.1 years; 65 females, 59.7 ± 11.8 years) confirmed AGC were collected. The data were randomly divided into a training (164) and validation set (69).

SEQUENCE

A 3.0 T, axial T2-weighted, diffusion-weighted imaging, and contrast-enhanced T1-weighted (CE-T1WI).

ASSESSMENT

Radiologist 1 segmented 233 patients and radiologist 2 segmented randomly 50 patients on CE-T1WI. The risk score (RS) was summed by each sample based on the radiomics features and correlation coefficients. Patients were followed up for 7-67 months (median 41; 138 dead and 95 alive).

STATISTICAL TESTS

The intraclass correlation coefficient (ICC) and Kappa value were calculated. Differences in survival analysis were assessed by Kaplan-Meier curves and log-rank test. Cox-regression analysis was performed to identify the radiomics features and clinical indicators associated with OS. The calibration curves were built to assess the model. A two-tailed P value < 0.05 was considered statistically significant.

RESULTS

Integrated with age, lymphovascular invasion (LVI) and RS, a survival combined model was built. The area under the curve (AUC) for predicting 3-year and 5-year OS was 0.765 and 0.788 in the training set, 0.757 and 0.729 in the validation set. There was no significant difference between the radiomics model and survival combined model for 3-year (0.690 vs. 0.757, P = 0.425) and 5-year OS (0.687 vs. 729, P = 0.412) in the validation set. The calibration curves showed a high degree of fit for the survival combined model.

DATA CONCLUSION

This study established a survival combined model that might help AGC patients in future clinical decision-making.

EVIDENCE LEVEL

33 TECHNICAL EFFICACY: Stage 5.

Clinical characteristics and long-term prognosis of type 1 gastric neuroendocrine tumors in a large Japanese national cohort

OBJECTIVES

Optimal management of type 1 gastric neuroendocrine tumors (T1-GNETs) remains unknown, with few reports on their long-term prognosis. This study investigated the clinical characteristics and long-term prognosis of T1-GNETs.

METHODS

We reviewed the medical records of patients diagnosed with T1-GNET during 1991-2019 at 40 institutions in Japan.

RESULTS

Among 172 patients, endoscopic resection (ER), endoscopic surveillance, and surgery were performed in 84, 61, and 27, respectively, including 27, 77, and 2 patients with pT1a-M, pT1b-SM, and pT2 tumors, respectively. The median tumor diameter was 5 (range 0.8-55) mm. Four (2.9%) patients had lymph node metastasis (LNM); none had liver metastasis. LNM rates were significantly higher in tumors with lymphovascular invasion (LVI) (15.8%; 3/19) than in those without (1.1%; 1/92) (P = 0.016). For tumors <10 mm, LVI and LNM rates were 18.4% (14/76) and 2.2% (2/90), respectively, which were not significantly different from those of tumors 10-20 mm (LVI 13.3%; 2/15, P = 0.211; and LNM 0%; 0/17, P = 1.0). However, these rates were significantly lower than those of tumors >20 mm (LVI 60%; 3/5, P = 0.021; and LNM 40%; 2/5, P = 0.039). No tumor recurrence or cause-specific death occurred during the median follow-up of 10.1 (1-25) years. The 10-year overall survival rate was 97%.

CONCLUSIONS

Type 1 gastric neuroendocrine tumors showed indolent nature and favorable long-term prognoses. LVI could be useful in indicating the need for additional treatments. ER for risk prediction of LNM should be considered for tumors <10 mm and may be feasible for tumors 10-20 mm.

TRIAL REGISTRATION

The study protocol was registered in the University Hospital Medical Information Network (UMIN) under the identifier UMIN000029927.

Development and validation of a multiphase CT radiomics nomogram for the preoperative prediction of lymphovascular invasion in patients with gastric cancer

AIM

To develop a nomogram to predict lymphovascular invasion (LVI) in gastric cancer by integrating multiphase computed tomography (CT) radiomics and clinical risk factors.

MATERIALS AND METHODS

One hundred and seventy-two gastric cancer patients (121 training and 51 validation) with preoperative contrast-enhanced CT images and clinicopathological data were collected retrospectively. The clinical risk factors were selected by univariate and multivariate regression analysis. Radiomic features were extracted and selected from the arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images of each patient. Clinical risk factors, radiomic features, and integration of both were used to develop the clinical model, radiomic models, and nomogram, respectively.

RESULTS

Radiomic features from AP (n=6), VP (n=6), DP (n=7) CT images and three selected clinical risk factors were used for model development. The nomogram showed better performance than the AP, VP, DP, and clinical models in the training and validation datasets, providing areas under the curves (AUCs) of 0.890 (95% CI: 0.820-0.940) and 0.885 (95% CI:0.765-0.957), respectively. All models indicated good calibration, and decision curve analysis proved that the net benefit of the nomogram was superior to that of the clinical and radiomic models throughout the vast majority of the threshold probabilities.

CONCLUSIONS

The nomogram integrating multiphase CT radiomics and clinical risk factors showed favourable performance in predicting LVI of gastric cancer, which may benefit clinical practice.

Evaluation of Lymphovascular Invasion by CD31 Expression in Gastric Adenocarcinoma

Background & Objective

Lymphovascular tumoral invasion is a typical histopathological feature of gastric carcinomas and supports the recognition of high-risk patients for the recurrence. We aimed to study CD31 expression in diverse subtypes of gastric carcinomas and to show its association with the histopathologic findings of the carcinoma to assess the prognosis.

Methods

This cross-sectional study was conducted on 40 established patients with gastric adenocarcinoma from radical gastrectomy. The patients were classified according to the pathology assessments. Tumoral tissues were assessed by immunohistochemical staining for CD31 expression. Malignant behavior was estimated by histopathological evaluations.

Results

CD31 positivity was described in 23 (57.5%) of all evaluated patients. In assessment of CD31 expression and tumor features presented, no significant association between the CD31 expression and patients' age, sex, tumor site, size, grade and stage, subtypes of carcinoma, perineural invasion, and also lymphovascular invasion was found. (P>0.05).

Conclusion

Lymphovascular invasion may make valuable additional evidence and may be useful to detect gastric carcinoma patients at high risk for recurrence, who could be candidates for more supplementary therapies. However, in our study, CD31 expression did not show any association with the aggressive histopathologic features of this tumor.

Two web-based dynamic prediction models for the diagnosis and prognosis of gastric cancer with bone metastases: evidence from the SEER database

Purpose

Our aim was to identify the clinical characteristics and develop and validate diagnostic and prognostic web-based dynamic prediction models for gastric cancer (GC) with bone metastasis (BM) using the SEER database.

Method

Our study retrospectively analyzed and extracted the clinical data of patients aged 18-85 years who were diagnosed with gastric cancer between 2010 and 2015 in the SEER database. We randomly divided all patients into a training set and a validation set according to the ratio of 7 to 3. Independent factors were identified using logistic regression and Cox regression analyses. Furthermore, we developed and validated two web-based clinical prediction models. We evaluated the prediction models using the C-index, ROC, calibration curve, and DCA.

Result

A total of 23,156 patients with gastric cancer were included in this study, of whom 975 developed bone metastases. Age, site, grade, T stage, N stage, brain metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for the development of BM in GC patients. T stage, surgery, and chemotherapy were identified as independent prognostic factors for GC with BM. The AUCs of the diagnostic nomogram were 0.79 and 0.81 in the training and test sets, respectively. The AUCs of the prognostic nomogram at 6, 9, and 12 months were 0.93, 0.86, 0.78, and 0.65, 0.69, 0.70 in the training and test sets, respectively. The calibration curve and DCA showed good performance of the nomogram.

Conclusions

We established two web-based dynamic prediction models in our study. It could be used to predict the risk score and overall survival time of developing bone metastasis in patients with gastric cancer. In addition, we also hope that these two web-based applications will help physicians comprehensively manage gastric cancer patients with bone metastases.

Prognostic impact of lymphovascular and perineural invasion in squamous cell carcinoma of the tongue

This study aimed to investigate the prognostic impact of lymphovascular and perineural invasions in patients with squamous cell carcinoma of the tongue who received surgery-based treatment at our institution between January 2013 and December 2020. Patients were divided into four groups based on the presence of perineural (P-/P +) and lymphovascular invasions (V-/V +): P-V-, P-V + , P + V-, and P + V + . Log-rank and Cox proportional hazard models were used to evaluate the association between perineural /lymphovascular invasion and overall survival (OS). Altogether, 127 patients were included, and 95 (74.8%), 8 (6.3%), 18 (14.2%), and 6 (4.7%) cases were classified as P-V-, P-V + , P + V-, and P + V + , respectively. Pathologic N stage (pN stage), tumor stage, histological grade, lymphovascular invasion, perineural invasion, and postoperative radiotherapy were significantly associated with OS (p < 0.05). OS was significantly different among the four groups (p < 0.05). Significant between-group differences in OS were detected for node-positive (p < 0.05) and stage III-IV (p < 0.05) cases. OS was the worst in the P + V + group. Lymphovascular and perineural invasions are independent negative prognostic factors for squamous cell carcinoma of the tongue. Patients with lymphovascular and/or perineural invasion may have significantly poorer overall survival than those without neurovascular involvement.

A Nomogram Model for Evaluating the Risk of Lymph Node Metastasis in cT2-cT4N0M0 Gastric Cancer Population

BACKGROUND Neoadjuvant chemotherapy is an important treatment for advanced gastric cancer, but it has been unclear whether neoadjuvant chemotherapy is closely related to lymph node metastasis. Therefore, based on the disease characteristics of the cT2-cT4N0M0 gastric cancer population, this study established a nomogram prediction model of lymph node metastasis risk in this gastric cancer population to help clinicians optimize clinical decision-making. MATERIAL AND METHODS We analyzed the data of 336 patients with advanced gastric cancer with CT imaging stage of cT2-cT4N0M0 admitted to the Third Department of the Fourth Hospital of Hebei Medical University from 2015 to 2021. Combined with the results of univariate and multivariate logistic regression analysis, 7 indicators were selected to establish a nomogram prediction model. The calibration curves, ROC curves, and decision curves were drawn against the nomogram model using R language. RESULTS The results showed that the AUC value of the model and the external validation data set were 0.925 and 0.911, respectively. The P value of the Hosmer-Lemeshow test for the internal validation dataset was 0.082, and the P value of Hosmer-Lemeshow test for the external validation dataset was 0.076.The decision curve results showed that when the threshold probability was 0.1-0.9, this model could benefit patients by predicting the risk of lymph node metastasis in patients with advanced gastric cancer, and formulating appropriate treatment schemes accordingly. CONCLUSIONS This nomogram has shown good discrimination and fit, and can also be combined with imaging examination to screen the populations suitable for neoadjuvant chemotherapy, avoid the risk of misdiagnosis of N staging to the greatest extent, and to assist clinicians to optimize clinical decision-making.

Radiomics in Gastric Cancer: First Clinical Investigation to Predict Lymph Vascular Invasion and Survival Outcome Using 18F-FDG PET/CT Images

Background

Lymph vascular invasion (LVI) is an unfavorable prognostic indicator in gastric cancer (GC). However, there are no reliable clinical techniques for preoperative predictions of LVI. The aim of this study was to develop and validate PET/CT-based radiomics signatures for predicting LVI of GC preoperatively. Radiomics nomograms were also established to predict patient survival outcomes.

Methods

This retrospective study registered 148 GC patients with histopathological confirmation for LVI status, who underwent pre-operative PET/CT scans (Discovery VCT 64 PET/CT system) from December 2014 to June 2019. Clinic-pathological factors (age, gender, and tumor grade, etc.) and metabolic PET data (maximum and mean standardized uptake value, total lesion glycolysis and metabolic tumor volume) were analyzed to identify independent LVI predictors. The dataset was randomly assigned to either the training set or test set in a 7:3 ratios. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with LVI status is built by feature selection. Four models with different modalities (PET-RS: only PET radiomics features; CT-RS: only CT radiomics features; PET/CT-RS: both PET and CT radiomics features; PET/CT-RS plus clinical data) were developed to predict LVI. Patients were postoperatively followed up with PET/CT every 6-12 months for the first two years and then annually up to five years after surgery. The PET/CT radiomics score (Rad-scores) was calculated to assess survival outcome, and corresponding nomograms with radiomics (NWR) or without radiomics (NWOR) were established.

Results

Tumor grade and maximum standardized uptake value (SUVmax) were the independent LVI predictor. 1037 CT and PET 3D radiomics features were extracted separately and reduced to 4 and 5 features to build CT-RS and PET-RS, respectively. PET/CT-RS and PET/CT-RS plus clinical data (tumor grade and SUVmax) were also developed. The ROC analysis demonstrated clinical usefulness of PET/CT-RS plus clinical data (AUC values for training and validation, respectively 0.936 and 0.914) and PET/CT-RS (AUC values for training and validation, respectively 0.881 and 0.854), which both are superior to CT-RS (0.838 and 0.824) and PET-RS (0.821 and 0.812). SUVmax and LVI were independent prognostic indicators of both OS and PFS. Decision curve analysis (DCA) demonstrated NWR outperformed NWOR and was established to assess survival outcomes. For estimation of OS and PFS, the C-indexes of the NWR were 0. 88 and 0.88 in the training set, respectively, while the C-indexes of the NWOR were 0. 82 and 0.85 in the training set, respectively.

Conclusions

The PET/CT-based radiomics analysis might serve as a non-invasive approach to predict LVI status in GC patients and provide effective predictors of patient survival outcomes.

Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables

PURPOSE

Lymphovascular invasion (LVI) is associated with metastasis and poor survival in patients with gastric cancer, yet the noninvasive diagnosis of LVI is difficult. This study aims to develop predictive models using different machine learning (ML) classifiers based on both enhanced CT and PET/CT images and clinical variables for preoperatively predicting lymphovascular invasion (LVI) status of gastric cancer.

METHODS

A total of 101 patients with gastric cancer who underwent surgery were retrospectively recruited, and the LVI status was confirmed by pathological analysis. Patients were randomly divided into a training dataset (n = 76) and a validation dataset (n = 25). By 3D manual segmentation, radiomics features were extracted from the PET and venous phase CT images. Image models, clinical models, and combined models were constructed by selected enhanced CT-based and PET-based radiomics features, clinical factors, and a combination of both, respectively. Three ML classifiers including adaptive boosting (AdaBoost), linear discriminant analysis (LDA), and logistic regression (LR) were used for model development. The performance of these predictive models was evaluated with respect to discrimination, calibration, and clinical usefulness.

RESULTS

Ten radiomics features and eight clinical factors were selected for the development of predictive models. In the validation dataset, the area under curve (AUC) values of clinical models using AdaBoost, LDA, and LR classifiers were 0.742, 0.706, and 0.690, respectively. The image models using AdaBoost, LDA, and LR classifiers achieved an AUC of 0.849, 0.778, and 0.810, respectively. The combined models showed improved performance than the image models and the clinical models, with the AUC values of AdaBoost, LDA, and LR classifier yielding 0.944, 0.929, and 0.921, respectively. The combined models also showed good calibration and clinical usefulness for LVI prediction.

CONCLUSION

ML-based models integrating PET/CT and enhanced CT radiomics features and clinical factors have good discrimination capability, which could serve as a noninvasive, preoperative tool for the prediction of LVI and assist surgical treatment decisions in patients with gastric cancer.

Occurrence and Prognostic Value of Perineural Invasion in Esophageal Squamous Cell Cancer: A Retrospective Study

OBJECTIVE

The aim of this study was to explore the occurrence and prognostic value of perineural invasion (PNI) as a classic tumor pathological feature in esophageal squamous cell carcinoma (ESCC).

METHODS

We retrospectively enrolled 794 ESCC patients who underwent radical esophagectomy at Shanghai Chest Hospital from 2017 to 2018. The incidence, associated factors, and prognosis of PNI were analyzed.

RESULTS

PNI was identified in 15.7% (125/794) of patients. The presence of PNI was significantly associated with depth of invasion (p < 0.001), pN stage (p = 0.008), tumor stage (p < 0.001), and lymphovascular invasion (LVI; p < 0.001). Multivariate logistic regression analysis demonstrated that advanced pT stage and LVI were independently associated with the presence of PNI, while multivariate Cox regression analysis demonstrated that PNI was not an independent risk factor for poor overall survival (OS) or recurrence-free survival (RFS) in ESCC patients (OS hazard ratio [HR] 0.688, 95% confidence interval [CI] 0.448-1.056, p = 0.087; RFS HR 0.837, 95% CI 0.551-1.273, p = 0.406). In the PNI-positive patient subgroup, adjuvant therapy was associated with better OS and RFS.

CONCLUSION

PNI correlates with, and may be a concomitant consequence of, LVI and advanced tumor invasion (T3-4) in ESCC patients. Although PNI was not identified as an independent prognostic indicator, our results suggest ESCC patients with PNI should be considered for adjuvant therapy.