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Background: Human papilloma virus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) is rising in prevalence and is associated with better survival than HPV-negative OPSCC. In surgically treated HPV-negative OPSCCs, adverse pathologic features such as positive surgical margins, extra-nodal extension (ENE) and perineural invasion are well described to portend worse clinical outcomes. These adverse pathological features, however, are not validated prognostic markers among surgically treated HPV-positive OPSCCs. To that end, we pooled all available evidence to address the prognostic significance of these histologic features. Patients and Methods: This meta-analysis was performed according to PRISMA guidelines. PubMed, Web of Science and Embase databases were systematically searched for articles evaluating 13 known adverse histopathological prognostic factors of surgically treated HPV-associated OPSCC. Data analysis was done using R v4.0.5. Results: A total of 32 studies (n = 31,535) fulfilled the inclusion criteria. ENE and advanced pT stage were associated with poorer overall survival (OS) [hazard ratio (HR):1.80, 95% confidence interval (CI) [1.59–2.03], p < 0.0001, HR: 3.28, 95% CI [2.20–4.87], p = 0.0025]; disease-specific survival (DSS) (HR: 3.14, 95% CI [1.20–8.26], p = 0.0327, HR: 3.49, 95% CI [2.45–4.96], p = 0.0043) and disease-free survival (DFS) (HR: 2.03, 95% CI [1.05–3.94], p = 0.0397, HR: 3.66, 95% CI [2.81–4.77], p = 0.0001) respectively. The presence of lymphovascular invasion (HR: 1.46, 95% CI [1.22–1.75], p = 0.0018) and positive margins (HR: 1.50, 95% CI [1.185–1.899], p = 0.0069) significantly worsen OS. Conclusion: ENE, advanced pT stage, positive margins and lymphovascular invasion were adverse histologic prognostic marker among surgically treated HPV-positive OPSCC. The presence of these factors should be carefully evaluated in order to select the optimal patients for surgical treatment. [ABSTRACT FROM AUTHOR]

Background: Lymphovascular invasion (LVI) or pT1b is noncurative after endoscopic resection (ER) for esophageal squamous cell carcinoma (ESCC), and therefore surgery or chemoradiotherapy (CRT) is recommended. However, there has been debate regarding which treatment has better outcomes and whether individual risks should be considered. Methods: This was a multicenter, retrospective study conducted at 65 hospitals in Japan. The inclusion criteria were patients with ESCC who underwent ER between January 2006 and December 2015, with pT1a-muscularis mucosa (MM) with LVI or pT1b, with negative vertical margins, cN0M0, and who underwent surgery or CRT. A 1:1 propensity score-matched analysis was performed between two groups. The primary and secondary end points were overall survival (OS) and relapse-free survival (RFS). OS and RFS were also compared between two subgroups: low risk (pT1a-MM with LVI and pT1b without LVI) and high risk (pT1b with LVI) for metastatic recurrence. Results: Among 472 patients, 160 patients were selected from each group. The OS and RFS did not differ between surgery and CRT groups (hazard ratio, 0.887; P =.635 and hazard ratio, 1.036; P =.876, respectively). Subgroup analysis showed that CRT had a better prognosis in the low-risk group, and conversely, surgery had a better prognosis in the high-risk group. But these were not significant. The high-risk CRT group had a significant worse prognosis than the low-risk CRT group. Conclusions: In patients with noncurative ER for ESCC, surgery and CRT showed no difference in long-term outcomes. Indications for CRT in the high-risk group need further investigation because of poor prognosis. [ABSTRACT FROM AUTHOR]

To investigate the value of dual-energy computed tomography (DECT) in predicting lymphovascular invasion (LVI) and the accuracy of preoperative T-staging of rectal cancer (RC). METHODS Forty-nine patients with RC who had not received radiotherapy were enrolled to undergo a DECT scan. All patients underwent surgical tumor resection within 3-5 days after the DECT scan. Preoperative T-staging of RC based on images was performed by experienced radiologists. The normalized iodine concentrations (NIC) of the tumor and the perirectal adipose tissue (PAT) from the arterial phase (AP) and venous phase (VP) were measured using DECT. The tumor LVI and T-staging confirmed by pathology were used as the gold standard for grouping (group A, LVI-; group B, LVI+; group C, T1-2; and group D, T3-4a). The NIC values between two groups were compared using the Mann-Whitney U test, with P < 0.05 indicating a statistically significant difference. The accuracy of NIC in predicting LVI and distinguishing T1-2 RC from T3-4a RC were determined via receiver operating characteristic curve analysis, and the optimal cut-off of NIC was determined using the area under the curve. RESULTS The tumor NIC values were significantly higher in the LV+ group than in the LVI- group in the VP (0.728 ± 0.031 vs. 0.669 ± 0.034, P < 0.001). The NIC values of PAT were significantly higher in the T3-4a group than in the T1-2 group in both the AP (4.034 ± 0.991 vs. 3.115 ± 0.581, P < 0.05) and the VP (5.481 ± 1.054 vs. 3.450 ± 0.980, P < 0.001). The accuracy of using NIC values to distinguish between the LVI+ group and the LVI- group and to diagnose the T3-4a group were 85.7% and 89.8%, respectively. However, there was no statistically significant difference between the NIC value in the LVI+ group and in the LVI- group in the AP. There was also no statistical difference in the tumor NIC value between the T1-2 group and the T3-4a group. CONCLUSION The tumor and PAT NIC are valuable indicators in RC that can preoperatively predict LVI and improve the accuracy of preoperative RC T-staging. CLINICAL SIGNIFICANCE The use of DECT improves the T-staging and LVI prediction of RC, which is helpful in guiding the clinical selection of appropriate treatment modalities and improving prognostic outcomes. [ABSTRACT FROM AUTHOR]

Background: To explore the potential impact of lymphovascular invasion (LVI) on overall survival (OS) of pN0 stage gastric cancer (GC) after curative resection. Methods: A total of 497 GC patients who underwent curative gastrectomy and postoperative pathology proved negative lymph node metastasis between January 2015 and December 2018 in our center were enrolled in this study. All patients were divided into two groups according to the status of LVI. Their clinical and pathological features were compared and potential prognostic factors were analyzed using the propensity score matching analysis (PSM). Results: Ninety-nine (19.9%) patients had LVI. The presence of LVI was associated with significantly worse survival outcomes in both the overall and PSM cohorts (χ2 = 19.635, p < 0.001; χ2 = 9.367, p = 0.002). After PSM, data of 99 pairs of patients were extracted. Multivariate analysis revealed that number of examined lymph nodes (LNs), and LVI were independent predictors of OS (all p < 0.05). Following stratified analysis, patients with LNs 11–25 and those without LVI tended to have better OS than those with LVI (LNs 11–15: χ2 = 5.019, p = 0.025; LNs 16–25: χ2 = 11.876, p = 0.001). Conclusions: pN0 stage GC patients with LVI have poor prognosis. More than 15 lymph nodes need to be dissected to reduce the influence of LVI on the prognosis of pN0 stage GC patients. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Gangliocytic paragangliomas are benign neuro‐endocrine tumors of the ampulla of Vater. Their preoperative diagnosis is hampered by the low yield of biopsies and non‐specific imaging. Their management relies then on resection. But the type of resection is controversial. Radical resection is indicated in case of lymph node involvement. [ABSTRACT FROM AUTHOR]

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics had been used to evaluate lymphovascular invasion (LVI) in patients with breast cancer. However, no studies had explored the associations between features from delayed phase as well as multiphases DCE-MRI and the LVI status. Thus, we aimed to develop an efficient nomogram based on multiphases DCE-MRI to predict the LVI status in invasive (IBC) breast cancer patients. A retrospective analysis was conducted on preoperative clinical, pathological, and DCE-MRI data of 173 breast cancer patients. All patients were randomly assigned into training set (n = 121) and validation set (n = 52) in 7:3 ratio. The clinical, pathologic, and conventional MRI characteristics were then subjected to univariate and multivariate logistic regression analysis, and the clinical risk factors with P < 0.05 in the multivariate logistic regression were used to build clinical models. Different single-phase models (early phase, peak phase, and terminal phase), as well as a multiphases model integrating radiomics features from multiple phases, were established. Furthermore, a preoperative radiomics nomogram model was constructed by combining the rad-score of the multiphases model with clinicopathologic independent risk factors. Finally, the performance of the multiphases model, clinical model, and rad-score was compared using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, and decision curve analysis (DCA). The clinical utility of the rad-score was evaluated using calibration curves, and Delong test was used to compare the differences in AUC values among the different models. The axillary lymph nodes (ALN) status and Ki-67 had been identified as clinicopathologic independent predictors and a clinical model had been constructed. Image features that were extracted from the terminal phase of the DCE-MRI exhibited notably superior predictive performances compared to features from the other single phases. Particularly, in the multiphases model, terminal phase features were identified as potentially providing more predictive information. Among the nine features that were found to be associated with LVI in the multiphase model, one was derived from the early phase, two from the peak phase, and six from the terminal phase, indicating that terminal phase features contributed significantly more information towards predicting LVI. Evaluation of the nomogram performance revealed promising results in both the training set (AUCs: clinical model vs. multiphase model vs. nomogram = 0.734 vs. 0.840 vs. 0.876) and the validation set (AUCs: clinical model vs. multiphase model vs. nomogram = 0.765 vs. 0.753 vs. 0.832). The DCE-MRI-based radiomics model demonstrated utility in predicting LVI status, features of the terminal phase offered more valuable information particularly. The preoperative radiomics nomogram enhanced the diagnostic capability of identifying LVI status in IBC patients, and might aid clinicians in making personalized treatment decisions. [ABSTRACT FROM AUTHOR]

Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC. [ABSTRACT FROM AUTHOR]

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis. [ABSTRACT FROM AUTHOR]

Background: Although visceral pleural invasion, lymphovascular invasion, tumor spread through air spaces, and poor differentiation are pathological risk factors associated with unfavorable prognosis in patients with lung adenocarcinoma, the cumulative impact of these factors on prognosis remains unclear. Methods: We enrolled 1532 patients with stage I lung adenocarcinoma. Patients were divided according to the number of risk factors as follows: Group A (without risk factors), Group B (one risk factor), and Group C (multiple risk factors). Moreover, we stratified patients into two subgroups based on tumor size (≤ 3 cm, 3–4 cm). Kaplan–Meier analysis was used to evaluate 5-year disease-free survival (DFS) and overall survival (OS). Results: Overall, 949, 404, and 179 patients were included in Groups A, B, and C, respectively. Group C had a larger tumor size and more cases of extrathoracic recurrence than the other groups. The 5-year DFS and OS gradually decreased across Groups A to C (DFS: 94.3%, 80.6%, and 64.3%, respectively, p < 0.001; OS: 97.2%, 92.7%, and 77%, respectively, p < 0.001). A similar trend was observed for tumors ≤ 3 cm in size (DFS: 95.2%, 83.2%, and 68.5%, respectively, p < 0.001; OS: 97.6%, 94.1%, and 79.6%, respectively, p < 0.001), but a less pronounced trend was observed for tumors between 3 and 4 cm in size (DFS: 72.1, 60.8, and 43.3%, respectively, p = 0.054; OS: 85.7, 82.1, and 64.7%, respectively, p = 0.16). Conclusions: Postoperative survival worsened with increasing pathological risk factors in patients with stage I lung adenocarcinoma, especially those with tumor size ≤ 3 cm. [ABSTRACT FROM AUTHOR]

Prognostic factors by definition, are capable of providing information on clinical outcomes at the time of diagnosis, independent of therapy. The number of positive lymph nodes (number of ipsilateral axillary nodes with metastatic tumour deposits) is a strong and independent prognostic factor in breast cancer. In a meta-analysis (New England Journal of Medicine, 2017) of over 62,000 patients, the risk of distant recurrence over years 5 to 20 for those with T1 tumours was 13% in the absence of lymph node involvement, 20% among those with one to three involved lymph nodes, and 34% among those with four to nine involved nodes. In this study, we analyzed the association of clinicopathological factors and breast cancer subtypes with axillary lymph node (ALN) positivity in women with breast cancer in Rajasthan. A multivariate Logistic (Ordinal) Regression Model was used to predict the number of positive lymph nodes based on independent variables that showed 90% significance in bivariate analysis, such as total number of lymph nodes dissected, tumour necrosis, and lymphovascular invasion. The Wald criterion indicated that only LVI had a significant impact on the prediction (p < 0.05), while tumour necrosis and the total number of lymph nodes dissected were not significant predictors (p > 0.05). Patients with LVI had a 43.47 times higher risk of having positive lymph nodes (p < 0.05). Early prediction of lymph node metastasis through LVI testing can help in prognostication. Breast cancer subtypes should not be a criterion while deciding lymph nodal management. [ABSTRACT FROM AUTHOR]

PURPOSE: To investigate the value of dual-energy computed tomography (DECT) in predicting lymphovascular invasion (LVI) and the accuracy of preoperative T-staging of rectal cancer (RC). METHODS: Forty-nine patients with RC who had not received radiotherapy were enrolled to undergo a DECT scan. All patients underwent surgical tumor resection within 3–5 days after the DECT scan. Preoperative T-staging of RC based on images was performed by experienced radiologists. The normalized iodine concentrations (NIC) of the tumor and the perirectal adipose tissue (PAT) from the arterial phase (AP) and venous phase (VP) were measured using DECT. The tumor LVI and T-staging confirmed by pathology were used as the gold standard for grouping (group A, LVI−; group B, LVI+; group C, T1-2; and group D, T3-4a). The NIC values between two groups were compared using the Mann–Whitney U test, with P < 0.05 indicating a statistically significant difference. The accuracy of NIC in predicting LVI and distinguishing T1-2 RC from T3-4a RC were determined via receiver operating characteristic curve analysis, and the optimal cut-off of NIC was determined using the area under the curve. RESULTS: The tumor NIC values were significantly higher in the LV+ group than in the LVI− group in the VP (0.728 ± 0.031 vs. 0.669 ± 0.034, P < 0.001). The NIC values of PAT were significantly higher in the T3-4a group than in the T1-2 group in both the AP (4.034 ± 0.991 vs. 3.115 ± 0.581, P < 0.05) and the VP (5.481 ± 1.054 vs. 3.450 ± 0.980, P < 0.001). The accuracy of using NIC values to distinguish between the LVI+ group and the LVI− group and to diagnose the T3-4a group were 85.7% and 89.8%, respectively. However, there was no statistically significant difference between the NIC value in the LVI+ group and in the LVI− group in the AP. There was also no statistical difference in the tumor NIC value between the T1-2 group and the T3-4a group. CONCLUSION: The tumor and PAT NIC are valuable indicators in RC that can preoperatively predict LVI and improve the accuracy of preoperative RC T-staging. CLINICAL SIGNIFICANCE: The use of DECT improves the T-staging and LVI prediction of RC, which is helpful in guiding the clinical selection of appropriate treatment modalities and improving prognostic outcomes.

Abstract Background To explore the potential impact of lymphovascular invasion (LVI) on overall survival (OS) of pN0 stage gastric cancer (GC) after curative resection. Methods A total of 497 GC patients who underwent curative gastrectomy and postoperative pathology proved negative lymph node metastasis between January 2015 and December 2018 in our center were enrolled in this study. All patients were divided into two groups according to the status of LVI. Their clinical and pathological features were compared and potential prognostic factors were analyzed using the propensity score matching analysis (PSM). Results Ninety-nine (19.9%) patients had LVI. The presence of LVI was associated with significantly worse survival outcomes in both the overall and PSM cohorts (χ2 = 19.635, p

Abstract Background Lymphovascular invasion (LVI) has been identified as a prognostic factor in various cancers, but its significance in node-negative gastric cancer remains unclear. Gastric cancer prognosis is notably affected by lymph node metastasis, with LVI potentially indicating metastatic spread. Methods A retrospective review was conducted on 5,699 patients who underwent curative radical gastrectomy for gastric cancer between 1989 and 2018. The median follow-up duration was 62 months (0–362 months). Overall, disease-specific, and disease-free survival were compared based on LVI status and stratified by T stage. Additionally, patients with stage IIA or T2N0 were further evaluated to clarify the clinical significance of LVI in the T2N0 group. Results The T2N0 LVI-positive group exhibited significantly poor prognosis than those in the T2N0 LVI-negative group, with no significant differences observed on comparing the T2N0 LVI-positive group with the T2N1 LVI-negative or LVI-positive groups. Furthermore, although the T2N0 LVI-negative group demonstrated better prognosis compared to the IIA group, the T2N0 LVI-positive group exhibited worse survival. In addition, LVI positivity was an independent risk factor for overall survival in T2N0 patients. Conclusions LVI in node-negative gastric cancer has clinical significance as a prognostic indicator, indicating an increased risk of disease recurrence and poor survival especially in T2 cohort. This indicates an increased likelihood of lymph node involvement and may influence treatment decisions and follow-up strategies.

Objective: To identify clinicopathologic and genomic features associated with brain metastasis after resection of lung adenocarcinoma (LUAD) and to evaluate survival after brain metastasis. Methods: Patients who underwent complete resection of stage I-IIIA LUAD between 2011 and 2020 were included. A subset of patients had broad-based panel next-generation sequencing performed on their tumors. Fine-Gray models for the development of brain metastasis were constructed, with death without brain metastasis as a competing risk. Results: A total of 2660 patients were included. The median duration of follow-up was 71 months (95% confidence interval [CI], 69-73 months). The cumulative incidence of brain metastasis at 10 years was 9.8%. Among patients who developed a brain metastasis, the median time from surgery to brain metastasis was 21 months (interquartile range, 10-42 months). Higher maximum standardized uptake value of the primary tumor, neoadjuvant therapy, lymphovascular invasion, and stage III disease were associated with the development of brain metastasis. Among patients who underwent next-generation sequencing, a multivariable analysis identified neoadjuvant therapy, pathologic stage, and TP53 mutations as associated with development of brain metastasis. The median survival after brain metastasis was 18 months (95% CI, 13-24 months). Better performance status, lack of extracranial metastasis, stereotactic radiosurgery, and targeted therapy were associated with better survival after brain metastasis. Conclusions: Brain metastasis is common after complete resection of LUAD and often occurs within 2 years. Markers of aggressive tumor biology, including higher maximum standardized uptake value, lymphovascular invasion, and TP53 mutations, and neoadjuvant therapy are associated with brain metastasis.

Abstract Backgroud Lymphovascular invasion (LVI) was histological factor that was closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC, and validating the predictive efficacy of a clinical model for LVI in patients with lung adenocarcinoma with lesions ≤ 3 cm. Methods A total of 450 patients with LAC were retrospectively enrolled. Clinical data and CT features were analyzed to identify independent predictors of LVI. A nomogram incorporating the independent predictors of LVI was built. The performance of the nomogram was evaluated by assessing its discriminative ability and clinical utility.We took 321 patients with tumours ≤ 3 cm in diameter to continue constructing the clinical prediction model, which was labelled subgroup clinical model. Results Carcinoembryonic antigen (CEA) level, maximum tumor diameter, spiculation, and vacuole sign were independent predictors of LVI. The LVI prediction nomogram showed good discrimination in the training set [area under the curve (AUC), 0.800] and the test set (AUC, 0.790), the subgroup clinical model also owned the stable predictive efficacy for preoperative prediction of LVI in lung adenocarcinoma patients, and both training and test set AUC reached 0.740. Conclusions The nomogram developed in this study could predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-makin, and the subgroup clinical model also had good predictive performance for lung cancer patients with lesion ≤ 3 cm in diameter.

Abstract Objective Lymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC. Methods A total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the models. Results Eighteen radiomic features were retained to construct the radiomic signature. Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively. Conclusion The eight machine learning models based on MRI radiomics serve as reliable indicators for identifying LVI status, and the KNN model demonstrated superior performance.This model offers substantial clinical utility, facilitating timely intervention in invasive BC and ultimately aiming to enhance patient survival rates.

Background and objective: Lymphovascular invasion (LVI) is a significant histopathological feature in prostate cancer (PCa) associated with higher risk of biochemical recurrence (BCR) and other adverse outcomes. Our aim was to assess the association of LVI found in radical prostatectomy (RP) specimens with BCR and adverse clinicopathological findings. Methods: A systematic literature search was conducted using the PubMed, Embase, and Web of Science databases in July 2023, with an additional search in May 2024. We included 94 prospective and retrospective studies reporting on LVI in RP specimens and its association with the specified outcomes. Key findings and limitations: Meta-analyses revealed that LVI is significantly associated with higher BCR risk (hazard ratio 1.96, 95% confidence interval [CI] 1.73–2.21), higher pathological tumour stage (odds ratio [OR] 5.77; 95% CI 3.96–8.40), higher Gleason score (OR 5.19, 95% CI 4.12–6.54), lymph node metastasis (OR 11.52, 95% CI 7.65–17.34), distant metastasis (OR 9.10, 95% CI 5.46–15.17), positive surgical margins (OR 2.38, 95% CI 1.83–3.09), extraprostatic extension (OR 5.01, 95% CI 3.11–8.06), seminal vesicle invasion (OR 7.50, 95% CI 3.47–16.23), and perineural invasion (OR 133.71, 95% CI 65.93–271.15). Major limitations of this study include high heterogeneity of the data and the reliance on nonrandomised studies. Conclusions and clinical implications: Our findings reveal that LVI is associated with nearly twofold higher risk of BCR, highlighting its potential role as a critical prognostic marker. Patient summary: We analysed data from multiple studies to understand the impact of the spread of prostate cancer into the lymph or blood vessels, called lymphovascular invasion (LVI). We found that LVI is linked to a higher risk of cancer recurrence after surgery and other negative outcomes. Our findings highlight the importance of considering LVI in treatment decisions for better management of prostate cancer.

Abstract Background To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC). Methods Two hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors’ 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA). Results There were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model. Conclusions Both 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.

INTRODUCTION: Differentiated thyroid cancer (DTC) is the most common pediatric endocrine cancer but studies are scarce. Latest recommendations advocate for an individualized risk-based approach to select patients for additional therapy. Lymphovascular invasion is not considered, despite being a well-known risk factor in the adult population. The aim of this study was to describe the outcomes of a cohort of DTC patients diagnosed at pediatric age and to evaluate the impact of lymphovascular invasion on the risk of persistence/recurrence. METHODS: A retrospective study of patients diagnosed with DTC at pediatric age from 2010 to 2022 at a single center was performed. All patients had total thyroidectomy. Radioactive iodine therapy (RAI) was used in selected patients. The response to therapy and occurrence of persistent/recurrent disease were evaluated. RESULTS: A total of 21 DTC were diagnosed, mostly papillary thyroid carcinoma (PTC) (81.0%, n=17). Six patients (28.6%) had nodal involvement and one (4.8%) had lung metastasis at the time of the diagnosis. Lymphovascular invasion was present in 11 patients (52.4%). After surgery, 13 patients (61.9%) underwent RAI. The mean follow-up time was 5.7±3.1 years. In total, 6 patients (31.6%) experienced persistent/recurrent disease during the follow-up time. Among PTC patients, persistent/recurrent disease was more frequent in the presence of lymphovascular invasion [55.6% (5/9) vs. 0.0% (0/6), p=0.031]. DISCUSSION AND CONCLUSION: An individualized risk-based approach is recommended. Our study suggests that lymphovascular invasion may be associated with a higher risk of persistence/recurrence and should therefore be considered for decision making in children and adolescents with PTC.

Abstract Background The presence of lymphovascular invasion (LVI) in cases with breast cancer is considered a bad prognostic sign. The purpose of this study is to compare the efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) versus unenhanced magnetic resonance imaging (UE-MRI + DBT) in predicting LVI in women with pathologically confirmed breast cancer. Methods This prospective self-controlled study enrolled a total of 70 cases of pathologically proven breast cancer. All the patients underwent tomosynthesis, non-contrast, and post-contrast MRI. Depending on the broken halo sign seen in tomosynthesis, peritumoral edema, dark rim diffusion at diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) values evaluated in MRI. Results The accuracy of LVI detection by tomosynthesis was 58%; unenhanced and enhanced MRI had the same results at 60%. The accuracy of detecting LVI was raised to 64% by combining the tomosynthesis results with unenhanced MRI. Conclusions Tomosynthesis parameters are promising tools in detecting LVI in breast cancer with better diagnostic accuracy in combination with unenhanced MRI.

Backgroud: Lymphovascular invasion (LVI) was histological factor that was closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC, and validating the predictive efficacy of a clinical model for LVI in patients with lung adenocarcinoma with lesions ≤ 3 cm. Methods: A total of 450 patients with LAC were retrospectively enrolled. Clinical data and CT features were analyzed to identify independent predictors of LVI. A nomogram incorporating the independent predictors of LVI was built. The performance of the nomogram was evaluated by assessing its discriminative ability and clinical utility.We took 321 patients with tumours ≤ 3 cm in diameter to continue constructing the clinical prediction model, which was labelled subgroup clinical model. Results: Carcinoembryonic antigen (CEA) level, maximum tumor diameter, spiculation, and vacuole sign were independent predictors of LVI. The LVI prediction nomogram showed good discrimination in the training set [area under the curve (AUC), 0.800] and the test set (AUC, 0.790), the subgroup clinical model also owned the stable predictive efficacy for preoperative prediction of LVI in lung adenocarcinoma patients, and both training and test set AUC reached 0.740. Conclusions: The nomogram developed in this study could predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-makin, and the subgroup clinical model also had good predictive performance for lung cancer patients with lesion ≤ 3 cm in diameter. [ABSTRACT FROM AUTHOR]

Objective: Lymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC. Methods: A total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the models. Results: Eighteen radiomic features were retained to construct the radiomic signature. Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively. Conclusion: The eight machine learning models based on MRI radiomics serve as reliable indicators for identifying LVI status, and the KNN model demonstrated superior performance.This model offers substantial clinical utility, facilitating timely intervention in invasive BC and ultimately aiming to enhance patient survival rates. [ABSTRACT FROM AUTHOR]

Simple Summary: The prognostic value of lymphovascular or perineural invasion in prostate cancer specimens regarding oncological outcomes after radical prostatectomy is unclear. Within a contemporary study cohort of 822 prostate cancer patients, 78 (9%) exhibited lymphovascular invasion and 633 (77%) exhibited perineural invasion in RP specimens. In univariable Cox regression models, lymphovascular invasion and perineural invasion were both associated with higher rates of biochemical recurrence (BCR). However, after multivariable adjustment for standard pathologic tumor characteristics, lymphovascular or perineural invasion was not found to be an independent predictor for BCR. These phenomes may be explained by the strong association between the Gleason Grade Group and pathologic tumor stage with lymphovascular as well as perineural invasion. Objective: The aim of this study was to test for the association between lymphovascular invasion or perineural invasion in radical prostatectomy (RP) specimens and biochemical recurrence (BCR). Methods: Relying on a tertiary-care database, we identified prostate cancer patients treated with RP between January 2014 and June 2023. Of these, the majority underwent robotic-assisted RP (81%). Kaplan–Meier survival analyses and Cox regression models addressed BCR according to either lymphovascular invasion or perineural invasion in RP specimens. Additionally, the linear trend test assessed the association between the Gleason Grade Group or pathologic tumor stage and lymphovascular or perineural invasion. Results: Of 822 patients, 78 (9%) exhibited lymphovascular invasion and 633 (77%) exhibited perineural invasion in RP specimens. In survival analyses, the five-year BCR-free survival rates were 62% in patients with lymphovascular invasion vs. 70% in patients without lymphovascular invasion (p = 0.04) and 64% in patients with perineural invasion vs. 82% in patients without perineural invasion (p = 0.01). In univariable Cox regression models, lymphovascular invasion (hazard ratio 1.58, 95% confidence interval 1.01–2.47; p = 0.045) and perineural invasion (hazard ratio 1.77, 95% confidence interval 1.13–2.77; p = 0.013) were both associated with a higher BCR rate. After accounting for age at surgery, PSA value, pathologic tumor stage, Gleason Grade Group, lymph node invasion, positive surgical margin, surgical approach, and adjuvant radiation therapy, lymphovascular (p = 0.740) or perineural invasion (p = 0.341) were not significantly associated with a higher BCR since the Gleason Grade Group and pathologic tumor stage highly correlated with lymphovascular as well as perineural invasion. Conclusions: In univariable models, lymphovascular or perineural invasion is associated with BCR. After adjustment for standard pathologic tumor characteristics, lymphovascular or perineural invasion is not an independent predictor for BCR. [ABSTRACT FROM AUTHOR]

Background: To assess the distribution characteristics of immune infiltration and lymphovascular invasion in breast cancer skin recurrence patients. Methods: We retrospectively analyzed the clinicopathological data of patients who underwent radical surgery for primary breast cancer and experienced skin recurrence between January 2001 and April 2019. Immune and lymphovascular biomarkers were quantified in primary breast cancers, skin lesions and visceral metastatic lesions. Differences in biomarkers distribution between matched tissues were statistically analyzed using the Wilcoxon signed-rank test and Kruskal–Wallis one-way ANOVA. Results: A total of 71 female breast cancer patients were reviewed in this study. Our study found that the expression levels of various lymphocyte immune markers in primary tumor specimens were higher than those in skin recurrences. The expression of CD8, CD57 and CD31 in primary breast cancer was higher than those in the skin. Compared to visceral metastatic lesions, D2-40 was highly expressed in the skin, while CD8 tended to decrease. In the skin specimens, the expression of CD8 (P < 0.001), FOXP3 (P = 0.006) and CD68 (P < 0.001) in the intratumoral area was higher, while the expression of CD57 (P < 0.001) was higher in the peritumoral area. Analyzing specimens from the same patient at different time points of skin progression, it was found that the expression of peritumoral CD4 decreased (P = 0.044) as the disease progressed. The low expression of D2-40 and CD163 in the skin lesions suggested a decrease in DFS. Conclusion: The immune microenvironment of breast cancer skin recurrence may be in a state of suppression, and this suppression may intensify with disease progression. The pattern of skin recurrence may be more inclined toward lymphatic invasion. Our study provides new insights into the biological behaviors of this disease and its response to immunotherapy. [ABSTRACT FROM AUTHOR]

Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC. In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05. GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 −0.959), 0.770 (0.689 −0.852), and 0.843 (0.776 −0.910) in the training set and 0.931 (0.863 −0.999), 0.747 (0.613 −0.880), and 0.849 (0.759 −0.938) in the validation set. The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC. [ABSTRACT FROM AUTHOR]

Synovial myxoma, a rare joint tumor in dogs, has traditionally been considered benign, acknowledging that local invasion into regional tissues including bone may be present. Given the diagnostic challenges in distinguishing synovial myxoma from other joint lesions through clinical features and diagnostic imaging, definitive diagnosis relies on characteristic gross and histologic features. Within the inner surface of the joint capsule, synovial myxomas form nodules of stellate-to-spindle cells within abundant myxomatous matrix. We present here 2 cases of synovial myxoma with metastasis to regional lymph nodes and compare these 2 cases to 3 cases without evidence of lymph node metastasis. Aside from lymphovascular invasion in one case with metastasis, there were no overt histologic features of the primary tumor to suggest aggressive biologic behavior. The finding of lymph node metastasis warrants reconsideration of the term "synovial myxoma" for this neoplasm. We suggest the term "synovial myxosarcoma," considering that histologic features of the primary tumor do not predict biologic behavior. Our case series highlights the importance of lymph node sampling in suspected synovial myxosarcoma cases as well as thorough histologic examination, emphasizing careful evaluation for lymphovascular invasion. [ABSTRACT FROM AUTHOR]

Tumor budding (TB) has been associated with poor survival in a variety of cancers including intrahepatic cholangiocarcinoma (iCCA). As tumor histomorphological features are significantly altered after neoadjuvant therapy (NAT), our study aims to assess the prognostic significance of TB in iCCA patients before and after NAT, by the modified International Tumor Budding Consensus Conference (ITBCC) criteria. 147 NAT-treated iCCA cases were included in this study. In biopsy specimens obtained before NAT, the TB-positive subgroup had lower overall survival (OS) in univariate analysis (P = 0.010). In resection specimens obtained after NAT, the TB-positive subgroup had reduced OS (P = 0.002) and recurrence-free survival (RFS) (P = 0.013) in univariate analysis. In multivariate analysis including TNM stage, lymphovascular invasion and perineural invasion, TB-positive in post-NAT resection was also found as an independent prognostic factor for both OS and RFS (OS, HR, 3.005; 95% CI, 1.333–6.775, P = 0.008; RFS, HR, 1.748; 95% CI, 1.085–2.816, P = 0.022). In conclusion, assessing the presence of TB by modified ITBCC criteria provides robust prognostic information in the NAT setting of iCCA patients and can be considered to be included in routine pathological reporting. [ABSTRACT FROM AUTHOR]

Opinion Statement: Rectal neuroendocrine neoplasms (NENs) are increasing in incidence. Most lesions are low grade, well-differentiated neuroendocrine tumours with good long term outcomes. However there is metastatic potential and resection offers the only option for a cure and in most cases should be offered to reduce the risk of metastases. Careful staging of rectal NENs should be performed prior to consideration of resection in order to ensure the appropriate technique is chosen, and reduce the risk of incomplete resection. Resection can be endoscopic or surgical, and selecting the appropriate resection technique relies on tumour characteristics such as size, grade, invasion into the muscularis propria, presence of lymph node involvement or of distal metastases. Some patients may require systemic therapies which may involve somatostatin analogues (SSAs), everolimus, tyrosine kinase inhibitors (TKIs), chemotherapy or peptide receptor radionuclide therapy (PRRT). Due the rarity of these tumours, much of the evidence is based on retrospective reviews or smaller cohort studies. This article is an update of the current evidence available to guide management. [ABSTRACT FROM AUTHOR]

To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839–0.898) and 0.847 (95% CI: 0.787–0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90–0.95), 0.91 (95% CI 0.87–0.95), and 0.91 (95% CI 0.86–0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions. [ABSTRACT FROM AUTHOR]

Background: The significance of lymphovascular invasion (LVI) in esophageal adenocarcinoma (EAC) has not yet been described. Potential utility as an adjunct to current staging guidelines remains unknown. Methods: The National Cancer Database was queried from 2006 to 2020. Univariate and multivariable models, Kaplan Meier method, and log-rank test were used. Subgroup analyses by pN stage were conducted. Results: Of 9,689 patients, 23.2% had LVI. LVI was an independent prognostic factor (hazard ratio [HR] 1.401, 95% confidence interval [CI] 1.307–1.502, p < 0.0001) with reduction in median survival to 20.0 months (95% CI 18.9–21.4) from 39.7 months (95% CI 37.8–42.3, p < 0.0001). Multivariable survival analysis adjusted on pN and pT stage found that patients with LVI had decreased survival in a given pN stage (p < 0.001). pN0/LVI+ patients had a similar prognosis to the higher staged pN1/LVI− (28.6 months), although pN1/LVI− patients did slightly worse (p = 0.0135). Additionally, patients with pN1/LVI+ had equivalent survival compared with pN2/LVI− (p = 0.178) as did pN2/LVI+ patients compared with pN3/LVI− (p = 0.995). Conclusions: In these data, LVI is an independent negative prognostic factor in EAC. LVI was associated with a survival reduction similar to an upstaged nodal status irrespective of T stage. Patients with LVI may be better classified at a higher pN stage. [ABSTRACT FROM AUTHOR]

Background: Colon cancer is the third most common type of cancer. High lymphovascular levels are linked to a number of cancers, including colon cancer, while lymph vascular invasion levels as a predictor of outcome are not well understood. Objective: Determine the influence of lymphovascular invasion on the recurrence of colon cancer. Patients and Methods: it was collected 126 colon cancer patients who had surgery and additional chemotherapy. The patients attended in the Baquba teaching hospital oncology centre and the Al Jawad oncology centre of Alkadhemiya hospital. Results: it was shown in this study that the high percentage ages of colon cancer patients was between 55-60 years, and there were more in women (57 %) than in men (43 %). The most common stage of the tumours was stage 3 (42.9 %), and in grade 2 (76.2 %). In terms of return, in this study, 71.4 % of people who had lymphovascular invasion positive and had a return of cancer in a percentage 73.3% of colon cancer patients. Conclusion: LVI plays an essential role for increasing recurrence of colon cancer, and there were a strong link between them. [ABSTRACT FROM AUTHOR]

Background: The presence of lymphovascular invasion (LVI) in cases with breast cancer is considered a bad prognostic sign. The purpose of this study is to compare the efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) versus unenhanced magnetic resonance imaging (UE-MRI + DBT) in predicting LVI in women with pathologically confirmed breast cancer. Methods: This prospective self-controlled study enrolled a total of 70 cases of pathologically proven breast cancer. All the patients underwent tomosynthesis, non-contrast, and post-contrast MRI. Depending on the broken halo sign seen in tomosynthesis, peritumoral edema, dark rim diffusion at diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) values evaluated in MRI. Results: The accuracy of LVI detection by tomosynthesis was 58%; unenhanced and enhanced MRI had the same results at 60%. The accuracy of detecting LVI was raised to 64% by combining the tomosynthesis results with unenhanced MRI. Conclusions: Tomosynthesis parameters are promising tools in detecting LVI in breast cancer with better diagnostic accuracy in combination with unenhanced MRI. [ABSTRACT FROM AUTHOR]

Background: Lymphovascular invasion (LVI) is a significant risk factor for lymph node metastasis in gastric cancer (GC) and is closely related to the prognosis and recurrence of GC. This study aimed to establish clinical models, radiomics models and combination models for the diagnosis of GC vascular invasion. Methods: This study enrolled 146 patients with GC proved by pathology and who underwent radical resection of GC. The patients were assigned to the training and validation cohorts. A total of 1,702 radiomic features were extracted from contrast-enhanced computed tomography images of GC. Logistic regression analyses were performed to establish a clinical model, a radiomics model and a combined model. The performance of the predictive models was measured by the receiver operating characteristic (ROC) curve. Results: In the training cohort, the age of LVI negative (-) patients and LVI positive (+) patients were 62.41 ± 8.41 and 63.76 ± 10.08 years, respectively, and there were more male (n = 63) than female (n = 19) patients in the LVI (+) group. Diameter and differentiation were the independent risk factors for determining LVI (-) and (+). A combined model was found to be relatively highly discriminative based on the area under the ROC curve for both the training (0.853, 95% CI: 0.784-0.920, sensitivity: 0.650 and specificity: 0.907) and the validation cohorts (0.742, 95% CI: 0.559-0.925, sensitivity: 0.736 and specificity: 0.700). Conclusions: The combined model had the highest diagnostic effectiveness, and the nomogram established by this model had good performance. It can provide a reliable prediction method for individual treatment of LVI in GC before surgery. [ABSTRACT FROM AUTHOR]

Nodal status is well known to be the most important prognostic factor for esophageal cancer patients, even if they are treated with neoadjuvant therapy. To establish an optimal postoperative adjuvant strategy for patients, we aimed to more accurately predict the prognosis of patients and systemic recurrence by using clinicopathological factors, including nodal status, in patients with esophageal cancer who received neoadjuvant chemotherapy. The clinicopathological factors associated with survival and systemic recurrence were investigated in 488 patients with esophageal squamous cell carcinoma who received neoadjuvant chemotherapy. Overall survival differed according to tumor depth, nodal status, tumor regression, and lymphovascular (LV) invasion. In the multivariate analysis, nodal status and LV invasion were identified as independent prognostic factors (P  < 0.0001, P  = 0.0008). Nodal status was also identified as an independent factor associated with systemic recurrence, although LV invasion was a borderline factor (P  = 0.066). In each pN stage, patients with LV invasion showed significantly worse overall survival than those without LV invasion (pN0: P  = 0.036, pN1: P  = 0.0044, pN2: P  = 0.0194, pN3: P  = 0.0054). Patients with LV invasion were also more likely to have systemic, and any recurrence than those without LV invasion in each pN stage. Pathological nodal status and LV invasion were the most important predictors of survival and systemic recurrence in patients with esophageal cancer who underwent neoadjuvant chemotherapy followed by surgery. This finding could provide useful information about selecting candidates for adjuvant therapy among these patients. Our analysis showed that LV invasion was an independent prognostic factor in patients with esophageal cancer who underwent neoadjuvant chemotherapy and that combining LV invasion with pathological nodal status makes it possible to stratify the prognosis in those patients. [ABSTRACT FROM AUTHOR]

Objective: Differentiated thyroid cancer (DTC) is the most common pediatric endocrine cancer but studies are scarce. Latest recommendations advocate for an individualized risk-based approach to select patients for additional therapy. Lymphovascular invasion is not considered, despite being a well-known risk factor in the adult population. The aim of this study was to describe the outcomes of a cohort of DTC patients diagnosed at pediatric age and to evaluate the impact of lymphovascular invasion on the risk of persistence/ recurrence. Methods: A retrospective study of patients diagnosed with DTC at pediatric age from 2010 to 2022 at a single center was performed. All patients had total thyroidectomy. Radioactive iodine therapy (RAI) was used in selected patients. The response to therapy and occurrence of persistent/recurrent disease were evaluated. Results: A total of 21 DTC were diagnosed, mostly papillary thyroid carcinoma (PTC) (81.0%, n=17). Six patients (28.6%) had nodal involvement and one (4.8%) had lung metastasis at the time of the diagnosis. Lymphovascular invasion was present in 11 patients (52.4%). After surgery, 13 patients (61.9%) underwent RAI. The mean follow-up time was 5.7±3.1 years. In total, 6 patients (31.6%) experienced persistent/recurrent disease during the follow-up time. Among PTC patients, persistent/recurrent disease was more frequent in the presence of lymphovascular invasion [55.6% (5/9) vs. 0.0% (0/6), p=0.031]. Conclusion: An individualized risk-based approach is recommended. Our study suggests that lymphovascular invasion may be associated with a higher risk of persistence/recurrence and should therefore be considered for decision making in children and adolescents with PTC. [ABSTRACT FROM AUTHOR]

BackgroundPelvic lymph node metastasis is a critical factor influencing prognosis and treatment strategies in cervical cancer patients. This study aimed to identify significant clinical and pathological risk factors associated with pelvic lymph node metastasis in patients with cervical cancer.MethodsWe conducted a retrospective analysis of 186 cervical cancer patients treated at the Affiliated Hospital, Southwest Medical University from January 2010 to December 2020. Patients were divided into two groups: those with pelvic lymph node metastasis (n=40) and those without (n=146). Data on demographics, clinical characteristics, pathological features, and treatment modalities were collected. Statistical analysis included t-tests, chi-square tests, and logistic regression to evaluate potential risk factors for lymph node metastasis.ResultsPatients with pelvic lymph node metastasis were significantly older (mean age 52.5 ± 8.3 years) than those without metastasis (mean age 48.7 ± 10.2 years; p=0.023). High-risk HPV positivity was significantly associated with lymph node metastasis (75% vs. 41%, p=0.001). Lymphovascular invasion was observed in 75% of the metastatic group compared to 24.7% in the non-metastatic group (p4 cm was more frequent in patients with metastasis (50% vs. 12.3%, p4 cm (OR 6.24, 95% CI: 3.24-12.02, p

Lymphovascular invasion (LVI) in lung cancer is a significant prognostic factor that influences treatment and outcomes, yet its reliable detection is challenging due to interobserver variability. This study aims to develop a deep learning model for LVI detection using whole slide images (WSIs) and evaluate its effectiveness within a pathologist's information system. Experienced pathologists annotated blood vessels and invading tumor cells in 162 WSIs of non-mucinous lung adenocarcinoma sourced from two external and one internal datasets. Two models were trained to segment vessels and identify images with LVI features. DeepLabV3+ model achieved an Intersection-over-Union of 0.8840 and an area under the receiver operating characteristic curve (AUC-ROC) of 0.9869 in vessel segmentation. For LVI classification, the ensemble model achieved a F1-score of 0.9683 and an AUC-ROC of 0.9987. The model demonstrated robustness and was unaffected by variations in staining and image quality. The pilot study showed that pathologists' evaluation time for LVI detecting decreased by an average of 16.95%, and by 21.5% in “hard cases”. The model facilitated consistent diagnostic assessments, suggesting potential for broader applications in detecting pathological changes in blood vessels and other lung pathologies.

Abstract To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.