Grades of Poorly Differentiated Clusters are Associated with Lymph Node and the Tumour, Node and Metastasis Stages in Colorectal Carcinoma.

Background: Colorectal carcinoma (CRC) is the third most common cancer globally. In Malaysia, CRC is most prevalent among males and the second most common cancer among females. The CRC arises mainly from the adenocarcinoma sequence. Poorly differentiated clusters (PDCs) and tumour budding (TB) are believed to represent sequential steps in tumour growth. Therefore, this study analysed the association between PDC grades with clinicopathological and demographic characteristics of CRC. Methods: A total of 47 CRC cases previously diagnosed by histopathological examination were reviewed for the presence of PDCs and graded accordingly. The association between PDC grades with clinicopathological and demographic characteristics was statistically analysed. Results: Out of the 47 cases with PDCs, most of them were of grade 3 (G3) (n = 27, 57.4%), followed by grade 2 (G2) (n = 13, 27.7%) and grade 1 (G1) (n = 7, 14.9%). Higher PDC grades (G2 and G3) were mainly observed in higher tumour stage (T); T3 (n = 26, 83.9%), T4 (n = 12, 92.3%), N1 (n = 20, 86.9%), N2 (n = 15, 100%). In addition, there was a significant association between PDC grades with the nodal stage (N) (P = 0.013) and the tumour, node and metastasis (TNM) stages (P = 0.012). Conclusion: The PDC grades are useful for assessing the disease prognosis in CRC. A statistically significant association between PDC grades with N and TNM stages suggested that PDC grades are potential predictive parameters for invasive and metastatic risks in CRC.

Long-term outcome of surgical resection in patients with gastroenteropancreatic neuroendocrine neoplasia: results from a German nation-wide multi-centric registry.

BACKGROUND: Neuroendocrine neoplasia (NEN) are rare and heterogenous tumours. Few data exist on the impact of surgical therapy. MATERIALS AND METHODS: This is a retrospective analysis of prospectively collected data of gastroenteropancreatic NEN in the German NET-Registry (1999-2012). It focuses on patients without distant metastases (limited disease, LD, stage I-IIIB). RESULTS: Data of 2239 patients with NEN were recorded. Median age was 59 years, the gender ratio was 1:1.3 (f:m). A total of 986 patients (44%) had LD, and the 5-year survival rate (5 years) was 77% for all and 90% for patients with LD. A total of 1635 patients (73%) received a surgical therapy (1st to 6th line); the 5 and 10 ysr were 83/65% after and 59/35% without surgery for all patients (p < .001). The resection margins in the LD patients were 76%, 16%, and 3% for R0, R1 and R2, respectively. The 10 ysr was 84%, 59% and 42% for R0, R1 and R2 resections, respectively (p = .021 R0/R1, p < .001 R0/R2). The R0 resection rate was 75% for G1/G2 NET and 67% for G3 NEC. CONCLUSION: The rate of complete tumour resection (R0) in LD is independent of tumour grading, and R0 resection is the key determinant of long-term survival, as demonstrated by the 10 ysr. of 84%. All NEN patients with limited disease should be considered for operation, if possible, as the best 10-year survival is shown after an R0 resection.

Accuracy of grading pancreatic neuroendocrine neoplasms with Ki-67 index in fine-needle aspiration cellblock material.

OBJECTIVE: The aim of this study was to assess the preoperative tumour grade of pancreatic neuroendocrine neoplasms (panNENs) by determining the Ki-67 index in endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) material and to correlate the preoperative tumour grade with the postoperative tumour grade in surgical specimens. METHODS: We performed a retrospective review of the institutional pathology database over a 10-year period (2007-2017) to identify all cases of panNENs with corresponding preoperative EUS-FNA cytological material and surgical specimens. Fifteen cases with adequate EUS-FNA material (more than 400 tumour cells on cellblock) were identified. The cytological and histological samples were graded based on the mitotic rate and the Ki-67 index in accordance with the 2017 World Health Organisation grading system for panNENs. The tumour grades determined on EUS-FNA cellblock material were compared with the histological tumour grades. RESULTS: Mean age at diagnosis was 64.8 ± 12.7 years (range, 38-85 years). The grading scores assigned to the cytological and histological samples were concordant in all 15 (100%) cases. Of those, two (13%) cases were scored as grade 1, nine (60%) cases as grade 2 and four (27%) cases as grade 3 tumours. CONCLUSION: Our study shows that tumour grade in patients with PanNENs can be reliably determined by assessing the Ki-67 index in EUS-FNA specimens based on the 2017 World Health Organisation classification and grading system.

Prediction of the histopathologic findings of intrahepatic cholangiocarcinoma: qualitative and quantitative assessment of diffusion-weighted imaging.

OBJECTIVE: To correlate qualitative and quantitative diffusion weighted imaging (DWI) characteristics of intrahepatic cholangiocarcinoma (ICC) with histopathologic tumour grade and fibrosis content. METHODS: Fifty-one patients (21M/30F; mean age 61y) with ICC and MRI including DWI were included in this IRB-approved multicentre retrospective study. Qualitative tumour features were assessed. Tumour apparent diffusion coefficient (ADC) mean, minimum, and normalized (nADCliver) values were computed. Tumour grade [well(G1), moderately(G2), or poorly differentiated(G3)] and tumour fibrosis content [minimal(1), moderate(2), or abundant(3)] were categorized pathologically. Imaging findings and ADC values were compared with pathologic measures. Utility of ADC values for predicting tumour grade was assessed using ROC analysis. RESULTS: 51 ICCs (mean size 6.5±1.1 cm) were assessed. 33/51(64%) of ICCs demonstrated diffuse hyperintensity and 15/51(29%) demonstrated target appearance on DWI. Infiltrative morphology (p=0.02) and tumour size (p=0.04) were associated with G3. ADCmean and nADCmean of G3 (1.32±0.47x10-3 mm2/sec and 0.97±0.95) were lower than G1+G2 (1.57±0.39x10-3 mm2/sec and 1.24±0.49; p=0.03 and p=0.04). ADCmean and nADCmean were inversely correlated with tumour grade (p<0.025). No correlation was found between ADC and tumour fibrosis content. AUROC, sensitivity and specificity of nADCmean for G3 versus G1+G2 were 0.71, 89.5% and 55.5%. CONCLUSION: ADC quantification has reasonable accuracy for predicting ICC grade. KEY POINTS: • ADC quantification was useful for predicting ICC tumour grade. • Infiltrative tumour morphology and size were associated with poorly differentiated ICCs. • ADC values depended more on ICC tumour grade than fibrosis content. • Ability to predict ICC tumour grade non-invasively could impact patient management.

Amide proton transfer imaging for differentiation of benign and atypical meningiomas.

PURPOSE: To investigate the difference in amide proton transfer (APT)-weighted signals between benign and atypical meningiomas and determine the value of APT imaging for differentiating the two. METHODS: Fifty-seven patients with pathologically diagnosed meningiomas (benign, 44; atypical, 13), who underwent preoperative MRI with APT imaging between December 2014 and August 2016 were included. We compared normalised magnetisation transfer ratio asymmetry (nMTR asym ) values between benign and atypical meningiomas on APT-weighted images. Conventional MRI features were qualitatively assessed. Both imaging features were evaluated by multivariable logistic regression analysis. The discriminative value of MRI with and without nMTR asym was evaluated. RESULTS: The nMTR asym of atypical meningiomas was significantly greater than that of benign meningiomas (2.46% vs. 1.67%; P < 0.001). In conventional MR images, benign and atypical meningiomas exhibited significant differences in maximum tumour diameter, non-skull base location, and heterogeneous enhancement. On multivariable logistic regression analysis, high nMTR asym was an independent predictor of atypical meningiomas (adjusted OR, 11.227; P = 0.014). The diagnostic performance of MRI improved with nMTR asym for predicting atypical meningiomas. CONCLUSION: Atypical meningiomas exhibited significantly higher APT-weighted signal intensities than benign meningiomas. The discriminative value of conventional MRI improved significantly when combined with APT imaging for diagnosis of atypical meningioma. KEY POINTS: • APT imaging is useful for differentiating between atypical and benign meningiomas. • Atypical meningiomas exhibited high APT-weighted signal intensity than benign meningiomas. • The diagnostic performance of MRI improved with nMTR asym for predicting atypical meningiomas.

The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading.

OBJECTIVES: To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading. METHODS: Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform. RESULTS: There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2. CONCLUSIONS: HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice. KEY POINTS: • HFP shows positive correlation with neuroepithelial tumour grading. • HFP presents a good diagnostic efficacy for LGG and HGG. • HFP is helpful in the selection of brain tumour boundary.

Clear cell carcinoma of the ovary: evaluation of prognostic parameters based on a clinicopathological analysis of 100 cases.

AIMS: The aim of this study was to evaluate the clinicopathological features of ovarian clear cell carcinomas in order to identify which, if any, are prognostically significant, and to determine whether there is value in grading these tumours. METHODS AND RESULTS: One hundred tumours with clinical follow-up were reviewed. Features evaluated included age, preoperative/intraoperative rupture, size, architectural pattern(s), presence of oxyphilic cells, degree of cytological atypia, nucleolar grade, mitoses, background precursor and stage. Survival differences were analysed using the log-rank test and Kaplan-Meier estimator. Stage and lymph node status were the only parameters that were statistically significant (P < 0.0001). Patients with stage I disease (71%) had a 92% 5-year survival compared to a 31% 5-year survival in advanced stage disease (29%). Those with negative lymph nodes (92%) had an 80% 5-year survival compared to a 22% 5-year survival for those with positive nodes (8%). CONCLUSIONS: This study shows that stage and lymph node status are the only prognostically significant parameters in patients with ovarian clear cell carcinoma. It also confirms that most patients with clear cell carcinoma present with disease confined to the ovary, and have an excellent prognosis. Grading ovarian clear cell carcinomas based on morphological features is not recommended, as none are of prognostic significance.

Integrating single-cell and spatial transcriptomes reveals COL4A1/2 facilitates the spatial organisation of stromal cells differentiation in breast phyllodes tumours.

BACKGROUND: Breast phyllodes tumours (PTs) are a unique type of fibroepithelial neoplasms with metastatic potential and recurrence tendency. However, the precise nature of heterogeneity in breast PTs remains poorly understood. This study aimed to elucidate the cell subpopulations composition and spatial structure and investigate diagnostic markers in the pathogenesis of PTs. METHODS: We applied single-cell RNA sequencing and spatial transcriptomes on tumours and adjacent normal tissues for integration analysis. Immunofluorescence experiments were conducted to verify the tissue distribution of cells. Tumour cells from patients with PTs were cultured to validate the function of genes. To validate the heterogeneity, the epithelial and stromal components of tumour tissues were separated using laser capture microdissection, and microproteomics data were obtained using data-independent acquisition mass spectrometry. The diagnostic value of genes was assessed using immunohistochemistry staining. RESULTS: Tumour stromal cells harboured seven subpopulations. Among them, a population of widely distributed cancer-associated fibroblast-like stroma cells exhibited strong communications with epithelial progenitors which underwent a mesenchymal transition. We identified two stromal subpopulations sharing epithelial progenitors and mesenchymal markers. They were inferred to further differentiate into transcriptionally active stromal subpopulations continuously expressing COL4A1/2. The binding of COL4A1/2 with ITGA1/B1 facilitated a growth pattern from the stroma towards the surrounding glands. Furthermore, we found consistent transcriptional changes between intratumoural heterogeneity and inter-patient heterogeneity by performing microproteomics studies on 30 samples from 11 PTs. The immunohistochemical assessment of 97 independent cohorts identified that COL4A1/2 and CSRP1 could aid in accurate diagnosis and grading. CONCLUSIONS: Our study demonstrates that COL4A1/2 shapes the spatial structure of stromal cell differentiation and has important clinical implications for accurate diagnosis of breast PTs.

Association of Proliferative Activity in Invasive Ductal Carcinoma Breast in Pakistani Population.

Background: Personalized medicine has played very important role in management of breast cancer. Proliferative index is one among the prognostic and predictive factor but unfortunately due to varied reports , no definite consensus and routine medical practice has been approved for it. The objective of the study is to observe the association of Ki-67 index using St. Gallen Conference criteria in invasive ductal carcinoma breast in Pakistani Population. Methods: Eighty-three patients with confirmed light microscopic diagnosis of primary invasive ductal carcinoma were recruited in this prospective study . Expression of Ki67 was determined by classifying as low (<15%) and high (>15%) Ki67 in tumour. Statistical analysis was performed to observe the association of Ki-67 with clinicopathological parameters and molecular group (i.e., Luminal A, Luminal B, Her2 enriched and triple negative). Results: Out of 83 patients, 73.5% of patients showed >15% Ki67 (p value <0.001). High expression of Ki 67 (>15%) was observed in 3.6%, 21.7% and 48.2 % of Nottingham grade I, II and III (p value=0.017) respectively. Among molecular group, high expression of Ki67 was observed 20.5% in Luminal A, 9.6% in Luminal B, 15.7% in Her2 enriched and 27.7% in triple negative groups (p= 0.017). There was no significant association observed in expression of Ki 67 among lymph node stage, tumour stage and Nottingham prognostic index. Conclusion: Higher Ki-67 reactivity is usually associated with higher-grade morphology of tumour. It can act as an independent predictor in assessment of tumour behavior. However, larger validation clinical studies are still required for confirmation of its importance and for routine practice.

Combined DCE-MRI- and FDG-PET enable histopathological grading prediction in a rat model of hepatocellular carcinoma.

PURPOSE: To test combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-FDG positron emission tomography (FDG-PET)-derived parameters for prediction of histopathological grading in a rat Diethyl Nitrosamine (DEN)-induced hepatocellular carcinoma (HCC) model. METHODS: 15 male Wistar rats, aged 10 weeks were treated with oral DEN 0.01 % in drinking water and monitored until HCCs were detectable. DCE-MRI and PET were performed consecutively on small animal scanners. 38 tumors were identified and manually segmented based on HCC-specific contrast enhancement patterns. Grading (G2/3: 24 tumors, G1:14 tumors) alongside other histopathological parameters, tumor volume, contrast agent and 18F-FDG uptake metrics were noted. Class imbalance was addressed using SMOTE and collinearity was removed using hierarchical clustering and principal component analysis. A logistic regression model was fit separately to the individual parameter groups (DCE-MRI-derived, PET-derived, tumor volume) and the combined parameters. RESULTS: The combined model using all imaging-derived parameters achieved a mean ± STD sensitivity of 0.88 ± 0.16, specificity of 0.70 ± 0.20 and AUC of 0.90 ± 0.03. No correlation was found between tumor grading and tumor volume, morphology, necrosis, extracellular matrix, immune cell infiltration or underlying liver fibrosis. CONCLUSION: A combination of DCE-MRI- and 18F-FDG-PET-derived parameters provides high accuracy for histopathological grading of hepatocellular carcinoma in a relevant translational model system.

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

MOTIVATION: Brain or central nervous system cancer is the tenth leading cause of death in men and women. Even though brain tumour is not considered as the primary cause of mortality worldwide, 40% of other types of cancer (such as lung or breast cancers) are transformed into brain tumours due to metastasis. Although the biopsy is considered as the gold standard for cancer diagnosis, it poses several challenges such as low sensitivity/specificity, risk during the biopsy procedure, and relatively long waiting times for the biopsy results. Due to an increase in the sheer volume of patients with brain tumours, there is a need for a non-invasive, automatic computer-aided diagnosis tool that can automatically diagnose and estimate the grade of a tumour accurately within a few seconds. METHOD: Five clinically relevant multiclass datasets (two-, three-, four-, five-, and six-class) were designed. A transfer-learning-based Artificial Intelligence paradigm using a Convolutional Neural Network (CCN) was proposed and led to higher performance in brain tumour grading/classification using magnetic resonance imaging (MRI) data. We benchmarked the transfer-learning-based CNN model against six different machine learning (ML) classification methods, namely Decision Tree, Linear Discrimination, Naive Bayes, Support Vector Machine, K-nearest neighbour, and Ensemble. RESULTS: The CNN-based deep learning (DL) model outperforms the six types of ML models when considering five types of multiclass tumour datasets. These five types of data are two-, three-, four-, five, and six-class. The CNN-based AlexNet transfer learning system yielded mean accuracies derived from three kinds of cross-validation protocols (K2, K5, and K10) of 100, 95.97, 96.65, 87.14, and 93.74%, respectively. The mean areas under the curve of DL and ML were found to be 0.99 and 0.87, respectively, for p < 0.0001, and DL showed a 12.12% improvement over ML. Multiclass datasets were benchmarked against the TT protocol (where training and testing samples are the same). The optimal model was validated using a statistical method of a tumour separation index and verified on synthetic data consisting of eight classes. CONCLUSION: The transfer-learning-based AI system is useful in multiclass brain tumour grading and shows better performance than ML systems.

Intra- and inter-observer agreement in histological assessment of canine soft tissue sarcoma.

BACKGROUND: The diagnosis of canine soft tissue sarcoma (STS) is based on histological assessment. Assessment of criteria such as, degree of differentiation, necrosis score and mitotic score, gives rise to a final tumour grade, which is important in the recommendation of treatment and prognosis of patients. MATERIALS AND METHODS: Previously diagnosed cases of STS were independently assessed by three board-certified veterinary pathologists. Participating pathologists were blinded to the original results. For the intra-observer study, the cases were assessed by a single pathologist six months apart and slides were randomized between readings. For the inter-observer study, the whole case series was assessed by a single pathologist before being passed onto the next pathologist. Intraclass correlation coefficient (ICC) and Fleiss's Kappa (ƙ) for the intra- (single observer) and inter-observer agreement. RESULTS: Strong agreement was observed for the intra-observer assessment in necrosis score, mitotic score, total score and tumour grading (ICC between 0.78 to 0.91). The intra-observer agreement for differentiation score was rated perfect (ICC 1.00). The agreement between pathologists for the diagnosis and grading of canine STS was moderate (ƙ = 0.60 and 0.43 respectively). CONCLUSION: Histological assessment of canine STS had high reproducibility by an individual pathologist. The agreement of diagnosis and grading of canine STS was moderate between pathologists. Future studies are required to investigate further assessment criteria to improve the specificity of STS diagnosis and the accuracy of the STS grading in dogs.