Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images

Background Tongue squamous cell carcinoma (TSCC) is one of the most difficult malignancies to control. It displays particular and aggressive behaviour even at an early stage. The purpose of this paper is to explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of TSCC. Methods Retrospective analysis of 127 patients with TSCC who were randomly divided into a primary cohort and a test cohort, including well-differentiated, moderately differentiated and poorly differentiated. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to all data and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74. Conclusions The MRI-based radiomics signature could discriminate between well-differentiated, moderately differentiated and poorly differentiated TSCC and might be used as a biomarker for preoperative grading.

Quantitative Evaluation of the Relationship between Magnetic Resonance Elastic Value and Pathological Grade of Hepatocellular Carcinoma by Magnetic Resonance Elastography

Objectives: To try another non-invasive method to evaluate the relationship between Magnetic Resonance (MR) elastic value and pathological grade of Hepatocellular Carcinoma (HCC) using Magnetic Resonance Elastography (MRE). Methods: Forty-seven HCC patients underwent MR Imaging (MRI), elastography in the upper abdomen. The elastic value of the lesion area was measured, and that of the normal liver tissue was measured adjacent to the lesion area at the same level. The Mann-Whitney U test was used to compare the difference in elasticity between the lesion area and normal area, and the difference between the low and middle-high differentiation groups. The Receiver Operating Characteristic Curve (ROC) of the lesion area and normal area in the complete case group and different differentiation groups were used to determine the diagnostic cut-off value to distinguish the lesion area from the normal area in each group. Results: (1) There was a significant difference in elasticity between the normal area and HCC area (p<0.001). The diagnostic cut-off value was 4842 Pa. (2) There was a significant difference in elasticity between the low-and middle-high differentiation groups (p<0.001). The diagnostic cut-off value was 10456 Pa. (3) There was a statistically significant correlation between the elastic value on MRE and degree of pathological differentiation of HCC in the two groups (p<0.001). Conclusion: The elastic value of HCC measured using MRE can be used to evaluate the degree of pathological differentiation of HCC. MRE may be a non-invasive and reliable method for evaluating the pathological grade of HCC.

Comparison of Diagnostic Performance between Perfusion-Related Intravoxel Incoherent Motion DWI and Dynamic Contrast-Enhanced MRI in Rectal Cancer

This study was aimed to determine the diagnostic performance of perfusion-related parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) by comparing them with quantitative parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on differentiation grades of rectal cancer. We retrospectively analyzed 98 patients with rectal cancer. Perfusion-related IVIM parameters ( D ∗ , f , and f · D ∗ ) and quantitative DCE parameters ( K trans , K ep , V e , and V p ) were obtained by plotting the volume-of-interest on in-house software. Furthermore, we compared the difference and diagnostic performance of all well-moderately and poorly differentiated rectal cancer parameters. Finally, we analyzed the correlation between those DCE and IVIM parameters and pathological differentiation grade. The values of f , K trans , and K ep significantly differentiated poor and well-moderate rectal cancers. K trans achieved the highest area under the curve (AUC) value compared to perfusion-related IVIM and DCE parameters. Furthermore, K trans showed a better correlation with pathological differentiation grade than f . The diagnostic efficiency of DCE-MRI was greater than perfusion-related IVIM parameters. The f value derived from perfusion-related IVIM offered a diagnostic performance similar to DCE-MRI for patients with renal insufficiency.

Analysis of the Related Risk Factors of Inguinal Lymph Node Metastasis in Patients with Penile Cancer: A Cross-sectional Study

Background: To determine independent predictors of inguinal lymph node(ILN) metastasis in patients with penile-cancer.Patients and methods: We retrospectively analyzed all patients with penile-cancer undergoing surgery at our medical center in ten years(N=157). Using univariate and multivariate logistic-regression models, we assessed associations between the following factors: age, medical-history, phimosis, onset-time, number and maximum diameter of involved ILNs, pathological T stage, degree of tumor differentiation and/or cornification, lymphatic vascular infiltration(LVI), nerve infiltration, and ILN metastases. Interaction and stratified analyses were then used to assess age, phimosis, onset-time, number of ILNs, cornification, and nerve infiltration.Results: Ultimately, 110 patients were included. Multiple logistic-regression analysis showed that the following factors were significantly correlated with ILN metastasis: maximum diameter of enlarged ILNs, T stage, pathological differentiation, and LVI. Among patients with a maximum ILN diameter of ≥1.5 cm, 50%(19/38) had LNM(HR=2.3, 95%CI: 1.0–5.1), whereas only 30.6%(22/72) of patients with a maximum ILN diameter <1.5 cm showed LNM. Among 44 patients with stage Ta/T1, 10(22.7%) showed ILN metastases, while 31 of 66(47.0%) patients with stage T2 showed ILN metastases(HR=3.0, 95%CI: 1.3–7.1). Among 40 patients with highly differentiated penile-cancer, eight(20%) showed ILN metastasis, while 33 of 70(47.1%) patients with low-to-middle differentiation showed ILN metastases(HR=3.6, 95%CI: 1.4–8.8). In the LVI-free group, the rate of LNM was 33.3%(32/96), whereas it was 64.3%(9/14) in the LVI group(HR=3.6, 95%CI: 1.1–11.6). Conclusion: Our single-center results suggested that maximum ILN diameter, pathological T stage, pathological differentiation, and LVI were independent risk factors for ILN metastases.

Clinical Characteristics and Prognosis of Patients With Pulmonary Mucoepidermoid Carcinoma: A SEER-Based Analysis

BackgroundPrimary pulmonary mucoepidermoid carcinoma (PMEC) is an extremely rare malignancy. Its clinical characteristics and prognosis are not fully understood. This study evaluated clinical characteristics and prognostic factors of PMEC and established a nomogram to predict its 1-, 3-, 5- and 10-year cancer-specific survival (CSS) rates.MethodsIn the Surveillance, Epidemiology, and End Results database from January 1, 2016 to December 31, 2016, patients pathologically diagnosed with PMEC were identified. Kaplan–Meier analysis and Cox regression were performed to evaluate the CSS stratified by different covariates. A predictive nomogram model was built and validated by the concordance index (C-index) and calibration curves.ResultsA total of 585 PMEC patients were identified. A total of 408 (70%) of patients were placed into the training cohort, and 177 (30%) patients were placed into the validation cohort. The 5- and 10-year CSS rates of stage I–II PMEC patients were 91.4 and 88.9, respectively. The 1-, 3- and 5-year CSS rates of stage III–IV PMEC were 56.5, 39.45, and 32.1%, respectively. Survival curves showed that older age, large tumor size, poor differentiation, and high TNM stage were associated with a significantly worse prognosis. CSS outcomes were significantly better in patients who received surgical treatments (surgical alone, surgery plus radiation and/or chemotherapy). Patients who received radiation and/or chemotherapy had the worst prognosis. Multivariate Cox results revealed that covariates, including age, tumor laterality, tumor sizes, pathological differentiation, lymph node metastasis, distant metastasis, TNM stage and therapy, were independent prognostic factors for PMEC. These factors were used to construct a nomogram. The C-index of the nomogram was 0.921. The calibration curve presented favorable consistency between the predicted CSS and actual observations. This nomogram was validated by the validation cohort. The C-index of the validation cohort was 0.968.ConclusionAge, bilateral tumors, tumor size, pathological differentiation grade, lymph node metastasis, distant metastasis, TNM stage and therapy were independent prognostic factors of PMEC patients. The first nomogram for predicting the CSS of PMEC was built and validated, showing its potential value in practice.

Evaluation of Risk Factors for Laryngeal Squamous Cell Carcinoma: A Single-Center Retrospective Study

BackgroundThe survival rate of patients with laryngeal squamous cell carcinoma (LSCC) is correlated with several factors. However, the independent prognostic factors of patients with LSCC remain unclear. Thus, we sought to identify prognostic factors affecting LSCC outcomes in the Chinese population.MethodsThe survival and potential prognostic factors of 211 patients with LSCC between April 2011 and July 2019 were retrospectively analyzed. Overall survival (OS) and progression free survival (PFS) were estimated by the Kaplan Meier method, and a log-rank test was used to compare the possible prognostic factors between different groups. The Cox proportional hazard model was used to perform multivariable analysis of significant covariants.ResultsA total of 211 LSCC patients were included, of which 164 (77.7%) were male and 47 (22.3%) were female. Mean age was 62.19 ± 8.328 years. A univariate analysis showed that seven factors including pathological differentiation, clinical stage, tobacco consumption, alcohol consumption, T stage, N stage, and concurrent chemoradiotherapy were correlated with survival (P&lt;0.05). Cox proportional hazards regression analyses revealed that clinic stage (hazard ratio=3.100, p=0.048), pathological differentiation (hazard ratio = 2.538, p=0.015), alcohol consumption (hazard ratio = 8.456, p =0.004) were associated with OS in LSCC. Pathological differentiation (hazard ratio =5.677, p=0.000), alcohol consumption (hazard ratio =6.766, p=0.000) were associated with PFS in LSCC.ConclusionsPathological differentiation, alcohol consumption, are independent prognostic factors and predictors of recurrence in LSCC. These factors could help inform guidelines for clinical treatment and prognosis.

Expression of caveolin-2 in patients with oral cancer and correlations with clinicopathological parameters

We aimed to analyze the expression of caveolin-2 (CAV2) in patients with oral cancer and its correlations with clinicopathological parameters.The expression of CAV2 in oral cancer and its influence on the survival curves of oral cancer patients were inquired through the Human Protein Atlas Database. The cancer tissue specimens and normal paracancerous tissue specimens (?2 cm away from cancer tissues) were collected from 173 patients with oral cancer confirmed by pathology. Moreover, real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry were performed to detect the messenger ribonucleic acid (mRNA) and protein expressions of CAV2 in oral cancer tissues and corresponding paracancerous tissues, respectively, and their associations with the clinicopathological characteristics and survival conditions of oral cancer patients were analyzed.It was shown in the Human Protein Atlas Database that the expression of CAV2 was increased significantly in oral cancer tissues compared with that in normal tissues (P<0.05), and patients with a low expression of CAV2 had a longer survival time than those with a high expression of CAV2 (P<0.05). The results of qRT-PCR and immunohistochemistry manifested that the mRNA expression level of CAV2 and the percentage of CAV2-positive cells were significantly higher in oral cancer tissues than those in paracancerous tissues (P<0.05). The CAV2 expression was correlated with clinical stage and pathological differentiation degree (P<0.05). In comparison with those in patients with a low CAV2 expression, the overall survival (OS) curve, relapse-free survival (RFS) curve and survival rate declined significantly in patients with a high CAV2 expression (P=0.001). Besides, the CAV2 expression, clinical stage and pathological differentiation degree were independent influencing factors for the postoperative OS and RFS of patients. The expression of CAV2 had relatively high predictive value for the OS and RFS of patients with oral cancer within 5 years after operation, of which the area under curve was 0.827 and 0.874, and the optimal cut-off value was 27.97% and 32.84%, respectively.CAV2 is highly expressed in oral cancer. With rising CAV2 expression level, the survival time of patients is shortened and the relapse risk is elevated, suggesting a poor prognosis.

Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images

Background: To explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of tongue squamous cell carcinoma (TSCC).Methods: Retrospective analysis of 87 TSCC patients who were randomly divided into a primary cohort and a test cohort. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI) and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to the set and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results: In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74.Conclusion: In this model, there was a significant relationship between radiomics characteristics and the degree of pathological differentiation, and the degree can be predicted from MRI features using machine learning. Advances in knowledge: Texture analysis and prediction of the differentiation degree of TSCC by MRI are not only a breakthrough and innovation in the diagnosis of TSCC but are also of significance in clinical diagnosis and treatment.

MiR-1301-3p Inhibits Epithelial to Mesenchymal Transition via Targeting RhoA in Pancreatic Cancer

Background: Micro(mi)RNAs play an essential role in the epithelial-mesenchymal transition (EMT) process in human cancers. This study aimed to uncover the regulatory mechanism of miR-1301-3p on EMT in pancreatic cancer (PC).Methods: GEO database (GSE31568, GSE41372, and GSE32688) and the PC cohort of The Cancer Genome Atlas were applied to discover the expression and prognostic role of miR-1301-3p. In the validation cohort, qRT-PCR was performed in 72 paired PC tissue samples. CCK-8, wound healing, and transwell migration assays were used to detect miR-1301-3p function on PC cells. Luciferase reporter assays and western blotting were performed to discover the potential target of miR-1301-3p on EMT.Results: Our study revealed that miR-1301-3p was downregulated in PC tissues compared with normal samples. A low level of miR-1301-3p was associated with malignant pathological differentiation, lymphatic metastasis, tumor residual, and unsatisfactory overall survival. Gene Ontology analyses indicated that miR-1301-3p possibly regulated cell cycle and adheren junction. In vitro assays showed that miR-1301-3p suppressed proliferation, migration, and invasion ability of PC cells. Mechanically, miR-1301-3p inhibits RhoA expression, and knockdown of RhoA upregulated E-cadherin; however, downregulated N-cadherin and vimentin level.Conclusions: MiR-1301-3p acts as a prognostic biomarker for PC and inhibits PC progression by targeting RhoA induced EMT process.