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Molecular subtyping of breast cancer may provide additional prognostic information regarding patient outcome. However, its clinical significance remains to be established. In this study, the main aims were to discover whether reclassification of breast cancer into molecular subtypes provides more precise information regarding outcome compared to conventional histopathological grading and to study breast cancer-specific survival in the different molecular subtypes. Cases of breast cancer occurring in a cohort of women born between 1886 and 1928 with long-term follow-up were included in the study. Tissue microarrays were constructed from archival formalin-fixed, paraffin-embedded tissue from 909 cases. Using immunohistochemistry and in situ hybridisation as surrogates for gene expression analyses, all cases were reclassified into the following molecular subtypes: Luminal A; Luminal B (HER2−); Luminal B (HER2+); HER2 subtype; Basal phenotype; and five negative phenotype. Kaplan–Meier survival curves and Cox proportional hazards models were used in the analyses. During the first 5 years after diagnosis, there were significant differences in prognosis according to molecular subtypes with the best survival for the Luminal A subtype and the worst for HER2 and five negative phenotype. In this historic cohort of women with breast cancer, differences in breast cancer-specific survival according to subtype occur almost exclusively amongst the histopathological grade 2 tumours. From 5 years after time of diagnosis until the end of follow-up, there appears to be no difference in survival according to molecular subtype or histopathological grade.

Breast cancer is the most common cancer in women and one of the leading causes of morbidity and mortality in women worldwide. In Indonesia there are approximately 65,000 new cases, in 2020. Expression of HER-2 and Ki-67 were used as markers to determine the prognosis and predict the response of a certain therapeutic modality in breast cancer. This study aims to determine the relationship between the expression of HER-2 and Ki-67 with the histopathological grade in breast cancer. This research was a cross-sectional analytical study using secondary data obtained from 50 samples of breast cancer at Abdoel Wahab Sjahranie Hospital Samarinda during 2019 using purposive sampling technique. The Chi Square test was used to assess all of the data. The relationship between the expression of HER-2 and Ki-67 with the histopathological grade resulted in p-values of 0.334 and 0.115, respectively. There was no significant relationship between the expression of HER-2 and Ki-67 with the histopathological grade of breast cancer in Abdoel Wahab Sjahranie Hospital, Samarinda, East Borneo Indonesia.

Background Prostate cancer (CaP) is the leading non-cutaneous cancer in men of African descent, with the higher mortality rates found in sub-Saharan Africa. Early diagnosis, staging, and management of prostate cancer could help curb its mortality rate in SSA. However, access to precise radiological imaging for staging purposes is limited in our setting. We sought to evaluate the correlation between total prostate specific antigen (tPSA) and histological grading of CaP in our resource-limited setting. Method We conducted a retrospective review of records of patients treated for biopsy-proven CaP at the AICKH diagnosed between January 2018 and December 2022. We excluded patients who were already on any sort of treatment of bladder outlet obstruction and incomplete charts. We used Spearman correlation coefficients, and ANOVA to evaluate the relationship between tPSA and various grading parameters. A P-value less than 0.05 was considered significant. Results We included 327 medical records. The mean tPSA was 112 ± 4.5ng/ml. The most common Gleason score and grade group were 8 (33.8%) and 4 (33.8%) respectively. Perineural involvement was present in 33% of our population. The tPSA had a positive correlation with Gleason score (rho = 0.253, p  < 0.001), grade group (rho = 0.296, p  < 0.001), perineural involvement (rho = 0.241, p  = 0.001) and proportion of sample invasion (rho = 0.171, p  = 0.005). A linear and homogenous variance existed in mean tPSA across increasing Gleason score ( p  < 0.001). Conclusion tPSA is a good predictor of the severity of CaP in resource-limited settings and can be used to inform management decisions.

To assess the performance of combining computed tomography (CT) texture analysis with machine learning for discriminating different histopathological grades of pancreatic ductal adenocarcinoma (PDAC). From July 2012 to August 2017, this retrospective study comprised 56 patients with confirmed histopathological PDAC (32 men, 24 women, mean age 64.04±7.82 years) who had undergone preoperative contrast-enhanced CT imaging within 1 month before surgery. Two radiologists blinded to the histopathological outcome independently segmented lesions for quantitative texture analysis. Histogram features, co-occurrence, and run-length texture were calculated. A support-vector machine was constructed to predict the pathological grade of PDAC based on preoperative texture features. Pathological analysis confirmed 37 low-grade PDAC (five well-differentiated/grade I and 32 moderately differentiated/grade II) and 19 high-grade PDAC (19 poorly differentiated/grade III) tumors. There were no significant differences in clinical or biological characteristics between patients with high-grade and low-grade tumors ( >0.05). There were significant differences between low-grade PDAC and high-grade PDAC on nine histogram features, seven run-length features, and two co-occurrence features. Cluster shade was the most important predictor (sensitivity 0.315). Using these texture features, the support-vector machine achieved 86% accuracy, 78% sensitivity, 95% and specificity. Machine learning-based CT texture analysis accurately predicted histopathological differentiation grade of PDAC based on preoperative texture features, leading to maximization patient survival and achievement of personalized precision treatment.

Zhongqiu WangDepartment of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong Road, Nanjing 210029, Jiangsu Province, People's Republic of ChinaTel +86 25 8661 8472Fax +86 25 8661 8139Email zhq200105@sina.comPurpose: To assess the performance of combining computed tomography (CT) texture analysis with machine learning for discriminating different histopathological grades of pancreatic ductal adenocarcinoma (PDAC).Methods: From July 2012 to August 2017, this retrospective study comprised 56 patients with confirmed histopathological PDAC (32 men, 24 women, mean age 64.04±7.82 years) who had undergone preoperative contrast-enhanced CT imaging within 1 month before surgery. Two radiologists blinded to the histopathological outcome independently segmented lesions for quantitative texture analysis. Histogram features, co-occurrence, and run-length texture were calculated. A support-vector machine was constructed to predict the pathological grade of PDAC based on preoperative texture features.Results: Pathological analysis confirmed 37 low-grade PDAC (five well-differentiated/grade I and 32 moderately differentiated/grade II) and 19 high-grade PDAC (19 poorly differentiated/grade III) tumors. There were no significant differences in clinical or biological characteristics between patients with high-grade and low-grade tumors (P>0.05). There were significant differences between low-grade PDAC and high-grade PDAC on nine histogram features, seven run-length features, and two co-occurrence features. Cluster shade was the most important predictor (sensitivity 0.315). Using these texture features, the support-vector machine achieved 86% accuracy, 78% sensitivity, 95% and specificity.Conclusion: Machine learning-based CT texture analysis accurately predicted histopathological differentiation grade of PDAC based on preoperative texture features, leading to maximization patient survival and achievement of personalized precision treatment.

Background: DNA methyltransferase (DNMT) enzymes, encoded by DNMT1, DNMT3A andDNMT3B genes, play a major role in the development of cancers through aberrant promotermethylation. Due to little information about the biological and clinical significance of expressionchanges of these genes in Laryngeal Squamous Cell carcinoma (LSCC), the current study wasdesigned to evaluate the contribution of DNMTs expression as potential diagnostic biomarkersin progression of LSCC. Methods: DNMT1, DNMT3A and DNMT3B expressions in tumoral and normal tissues fromthirty-three LSCC patients were evaluated by relative comparative real-time PCR, prior toany therapeutic intervention. Relationship between genes expression and clinicopathologicalfeatures were also analyzed. Results: The mRNA expression levels of all three DNMTs (DNMT1, DNMT3A and DNMT3B)were significantly elevated in LSCC tumor specimens compared to that of non-tumor tissues(P<0.0001, P=0.011 and P<0.0001, respectively). The expression of DNMT1 and DNMT3Bwas strongly associated with histopathological tumor grade. Moreover, the mRNA expressionlevels of DNMT3A were significantly correlated with laryngopharyngeal reflux. No significantrelationships existed with other clinicopathological parameters. Conclusion: Data showed that the expression levels of DNMT1, DNMT3A and DNMT3Bmarkedly increased in LSCC tissues. DNMT1 and DNMT3B were mainly overexpressed in highgrade LSCC tumors, therefore, they may have a role in LSCC progression. It seems that thesegenes may serve as diagnostic biomarkers in development of LSCC.

Background: The correlation of diffusion-weighted MRI and tumor aggressiveness has been established for different tumor types, which leads to the question if it could also apply for neuroendocrine tumors (NET).Purpose: To investigate the possible correlation between apparent diffusion coefficient (ADC) value on magnetic resonce imaging (MRI) and histopathologic WHO-grades of NET.Material and Methods: Electronic patient records from patients presented at the multidiscipliry neuro-endocrine tumor board between November 2017 and April 2019 were retrospectively reviewed. Patients with both available MR imaging (primary tumor or metastasis) and known WHO tumor grade were included (n = 47). Average and minimum ADC values (avgADC; miDC) were measured by drawing a freehand ROI excluding only the outermost border of the lesion. The largest axial size (primary tumor) or most clearly delineated lesion (metastasis) was used.Results: Forty seven patients met the inclusion criteria (mean age 59 ± 12 SD; 24F/23M). Twenty one patients (45%) were diagnosed with WHO G1 tumor, 17 seventeen with G2 (36%) and nine with G3 (19%) tumor. Twenty eight primary tumors and 19 metastases were measured. A significant difference was found between low-grade (G1+G2) and high-grade (G3) tumors (Mann-Whitney; avgADC: p < 0,001; miDC: p = 0,001). There was a moderate negative correlation between WHO-grade and avgADC/miDC (Spearman; avgADC: –0,606; 95% CI [–0,773; –0,384]; miDC: –0,581; 95% CI [–0.759; –0.353]).Conclusion: Our data show a significant difference in both average and minimum ADC values on MRI between low and high grade NET. A moderate negative correlation was found between histopathologic WHO grade and ADC value.

Objective: To investigate whether the dynamic contrast-enhanced ultrasound (DCE-US) analysis and quantitative parameters could be helpful for predicting histopathologic grades of pancreatic neuroendocrine tumors (pNETs). Methods: This retrospective study conducted a comprehensive review of the CEUS database between March 2017 and November 2021 in Zhongshan Hospital, Fudan University. Ultrasound examinations were performed by an ACUSON Sequioa unit equipped with a 3.5 MHz 6C−1 convex array transducer, and an ACUSON OXANA2 unit equipped with a 3.5 MHz 5C−1 convex array transducer. SonoVue® (Bracco Inc., Milan, Italy) was used for all CEUS examinations. Time intensity curves (TICs) and quantitative parameters of DCE-US were created by Vuebox® software (Bracco, Italy). Inclusion criteria were: patients with histopathologically proved pNETs, patients who underwent pancreatic B-mode ultrasounds (BMUS) and CEUS scans one week before surgery or biopsy and had DCE-US imaging documented for more than 2 min, patients with solid or predominantly solid lesions and patients with definite diagnosis of histopathological grades of pNETs. Based on their prognosis, patients were categorized into two groups: pNETs G1/G2 group and pNETs G3/pNECs group. Results: A total of 42 patients who underwent surgery (n = 38) or biopsy (n = 4) and had histopathologically confirmed pNETs were included. According to the WHO 2019 criteria, all pNETs were classified into grade 1 (G1, n = 10), grade 2 (G2, n = 21), or grade 3 (G3)/pancreatic neuroendocrine carcinomas (pNECs) (n = 11), based on the Ki−67 proliferation index and the mitotic activity. The majority of the TICs (27/31) of pNETs G1/G2 were above or equal to those of pancreatic parenchyma in the arterial phase, but most (7/11) pNETs G3/pNECs had TICs below those of pancreatic parenchyma from arterial phase to late phase (p < 0.05). Among all the CEUS quantitative parameters of DCE-US, values of relative rise time (rPE), relative mean transit time (rmTT) and relative area under the curve (rAUC) were significantly higher in pNETs G1/G2 group than those in pNETs G3/pNECs group (p < 0.05). Taking an rPE below 1.09 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [58.70% to 99.80%], 67.64% [48.61% to 83.32%] and 85.78% [74.14% to 97.42%], respectively. Taking rAUC below 0.855 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [66.26% to 99.53%], 83.87% [67.37% to 92.91%] and 94.72% [88.30% to 100.00%], respectively. Conclusions: Dynamic contrast-enhanced ultrasound analysis might be helpful for predicting the pathological grades of pNETs. Among all quantitative parameters, rPE, rmTT and rAUC are potentially useful parameters for predicting G3/pNECs with aggressive behavior.

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) constitutes one of the most common malignancies in the world. The geographic location influences the etiologic factors and site of tumor. Aims and Objectives: The present study was carried out to illustrate the clinicopathologic profiles of HNSCC patients since data regarding these tumors from eastern region of India are scarce. Materials and Methods: A prospective study was undertaken for 2 years in which patients with histologically proven HNSCC were included. The clinicopathologic features of each case were analyzed. Results: A total of 108 cases were included in the study, among which 79 (73.15%) were male and 29 (26.85%) were female. Mean age of the patients was 53.21 (±12.17) years. The most common risk factor was smoking (63 cases, 58.33%) followed by tobacco or betel nut chewing (41 cases, 37.96%). The common patterns of presentation included ulcerated lesion (51 cases, 47.22%), whitish lesion (28 cases, 25.93%), and hoarseness of voice (11 cases, 10.19%). The most common sites involved were buccal mucosa (36 cases, 33.33%) and dorsal surface of the tongue (26 cases, 24.07%). The most common site for exophytic tumors was buccal mucosa (9 out of 23, 39.13%) and that for ulceroproliferative lesions was tongue (9 out of 17, 52.04%). Microscopically, well-differentiated (Grade I) tumors were most common (67 cases, 62.04%) followed by moderately differentiated (Grade II) tumors (38 cases, 35.19%). A statistically significant correlation was obtained between anatomic site and grade of the tumor. Conclusion: Patients of HNSCC from the eastern region of India have distinctive features with regard to macroscopic appearance and microscopic grade of their tumors.

Polo-like kinase 1 (Plk1) is an interesting molecule both as a biomarker and as a target for highly specific cancer therapy for several reasons. However, the functional significance of Plk1 in renal cell carcinoma (RCC) has not been reported. To explore whether Plk1 plays a general role in renal carcinoma, we examined the expression of Plk1 protein in renal urothelial carcinoma and cell lines, and analyzed the relationship between Plk1 protein expression and development, proliferation, and invasion of renal carcinoma. Immunohistochemisty was used to detect the expression of Plk1 in 100 renal carcinoma tissues. Moreover, the expression of Plk1 was analyzed by western blot and real-time polymerase chain reaction (PCR) in 80 renal carcinoma tissues and 20 normal renal tissues. CCK-8 assay, colony formation assay, and Transwell assay were used to examine proliferation and invasion ability of renal cancer cells with treatment of scytonemin (the specific inhibitor of Plk1). Statistical analysis was used to discuss the association between Plk1 expression and clinicopathologic parameters, and proliferation and invasion ability of renal cancer cells. Plk1 expressions were greater in cancerous tissues than in normal tissues ( P  < 0.05). With an increase in tumor grade and stage, tumor metastasis, and recurrence, the level of Plk1 increased significantly in renal cancerous tissues. Moreover, there was a significantly higher expression of Plk1 in higher degree of malignant renal adenocarcinoma cell ACHN than that in renal adenocarcinoma cell 769-P. With increasing concentration of scytonemin, we found that cell proliferation and invasion activity decreased significantly. Plk1 expression status was closely correlated with important histopathologic characteristics (grades, stages, metastasis, and recurrence) of renal carcinomas. Furthermore, Plk1 played an important function on renal cancer cells' proliferation and invasion.