PD-L1 expression in resected lung adenocarcinoma: prevalence and prognostic significance in relation to the IASLC grading system.

AIMS: Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for adjuvant immunotherapy and has been linked to poor differentiation in lung adenocarcinoma. However, its prevalence and prognostic role in the context of the novel histologic grade has not been evaluated. METHODS: We analysed a cohort of 1233 patients with resected lung adenocarcinoma where PD-L1 immunohistochemistry (22C3 assay) was reflexively tested. Tumour PD-L1 expression was correlated with the new standardized International Association for the Study of Lung Cancer (IASLC) histologic grading system (G1, G2, and G3). Clinicopathologic features including patient outcome were analysed. RESULTS: PD-L1 was positive (≥1%) in 7.0%, 23.5%, and 63.0% of G1, G2, and G3 tumours, respectively. PD-L1 positivity was significantly associated with male sex, smoking, and less sublobar resection among patients with G2 tumours, but this association was less pronounced in those with G3 tumours. PD-L1 was an independent risk factor for recurrence (adjusted hazard ratio [HR] = 3.25, 95% confidence intervals [CI] = 1.93-5.48, P < 0.001) and death (adjusted HR = 2.69, 95% CI = 1.13-6.40, P = 0.026) in the G2 group, but not in the G3 group (adjusted HR for recurrence = 0.94, 95% CI = 0.64-1.40, P = 0.778). CONCLUSION: PD-L1 expression differs substantially across IASLC grades and identifies aggressive tumours within the G2 subgroup. This knowledge may be used for both prognostication and designing future studies on adjuvant immunotherapy.

An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer.

BACKGROUND: Accurate pathological diagnosis of invasion depth and histologic grade is key for clinical management in patients with bladder cancer (BCa), but it is labour-intensive, experience-dependent and subject to interobserver variability. Here, we aimed to develop a pathological artificial intelligence diagnostic model (PAIDM) for BCa diagnosis. METHODS: A total of 854 whole slide images (WSIs) from 692 patients were included and divided into training and validation sets. The PAIDM was developed using the training set based on the deep learning algorithm ScanNet, and the performance was verified at the patch level in validation set 1 and at the WSI level in validation set 2. An independent validation cohort (validation set 3) was employed to compare the PAIDM and pathologists. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUCs of the PAIDM were 0.878 (95% CI 0.875-0.881) at the patch level in validation set 1 and 0.870 (95% CI 0.805-0.923) at the WSI level in validation set 2. In comparing the PAIDM and pathologists, the PAIDM achieved an AUC of 0.847 (95% CI 0.779-0.905), which was non-inferior to the average diagnostic level of pathologists. There was high consistency between the model-predicted and manually annotated areas, improving the PAIDM's interpretability. CONCLUSIONS: We reported an artificial intelligence-based diagnostic model for BCa that performed well in identifying invasion depth and histologic grade. Importantly, the PAIDM performed admirably in patch-level recognition, with a promising application for transurethral resection specimens.

Breast Amide Proton Transfer Imaging at 3 T: Diagnostic Performance and Association With Pathologic Characteristics.

BACKGROUND: Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined. PURPOSE: To assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics. STUDY TYPE: Prospective. SUBJECTS: Eighty-four patients with breast lesions. FIELD STRENGTH/SEQUENCE: A 3.0 T/single-shot fast spin echo APT imaging. ASSESSMENT: APTw signal in breast lesion was quantified. Lesion malignancy, T stage, grades, Ki-67 index, molecular biomarkers (estrogen receptor [ER] expression, progesterone receptor [PR] expression, human epidermal growth factor receptor [HER-2] expression), molecular subtypes (luminal A, luminal B, triple negative, and HER-2 enriched) were determined. STATISTICAL TESTS: Student t-test, one-way analysis of variance, receiver operating characteristic analysis, and Pearson's correlation with P < 0.05 as statistical significance. RESULTS: APTw signal was significantly higher in malignant lesions (1.55% ± 1.24%) than in benign lesions (0.54% ± 1.13%), and in grade III lesions than in grade II lesions (1.65% ± 0.84% vs. 0.96% ± 0.96%), and in T2- (1.57% ± 0.64%) and T3-stage lesions (1.54% ± 0.63%) than in T1-stage lesions (0.81% ± 0.64%) for invasive breast carcinoma of no special type. APTw signal significantly correlated with Ki-67 index (r = 0.364) but showed no significant difference in groups of ER (P = 0.069), PR (P = 0.069), HER-2 (P = 0.961), and among molecular subtypes (P = 0.073). DATA CONCLUSION: APT imaging shows potential in differentiating breast lesion malignancy and associates with prognosis-related tumor grade, T stage, and proliferative activity. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Analysis of prognostic factors in different grades of histologic differentiation in colorectal cancer patients receiving preoperative neoadjuvant chemotherapy and establishment of prognostic nomograms for moderately differentiated grade.

OBJECTIVE: The aim of this study is to analyze the differences in independent prognostic factors of cancer-specific survival (CSS) and overall survival (OS) in patients with different grades of histologic differentiation of colorectal cancer (CRC) who received preoperative neoadjuvant chemotherapy (NAC) and to establish a nomogram for predicting postoperative survival based on moderately differentiated CRC. METHODS: We analyzed CRC patients from the SEER database who received NAC before operation between 2010 and 2015. The Kaplan-Meier curves were drawn to describe the differences in CSS and OS of CRC patients with different histologic grades of differentiation. Cox regression analysis was used to determine the independent prognostic factors. Nomograms were established to predict CSS and OS at 3 and 5 years by integrating independent prognostic factors. The calibration curve, receiver operating characteristic (ROC) curve, and C-index were used to verify nomograms. RESULTS: A total of 6481 patients with CRC who received preoperative NAC were included in this study. Patients with different grades of histologic differentiation had significant differences in CSS and OS (P < 0.001), and the independent prognostic factors of different grades of histologic differentiation showed heterogeneity. In patients with moderately differentiated grade CRC, the independent prognostic factors for CSS and OS were age, race, marital status, serum carcinoembryonic antigen (CEA) level before treatment, site of primary tumor, histologic type, pT stage, pN stage, liver metastasis, and lung metastasis. Nomograms were established based on the independent prognostic factors of moderately differentiated grade CRC, and its calibration curves, area under the curve (AUC), and C-index showed good prediction accuracy. CONCLUSIONS: The independent prognostic factors of CSS and OS are different in patients with different grades of histologic differentiation of CRC who received NAC before the operation. Nomograms can be used to predict the survival of patients with moderately differentiated grade CRC who received preoperative NAC and to assist clinicians in making clinical decisions.

Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study.

PURPOSE: Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC. METHODS: One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed. RESULTS: The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05). CONCLUSION: ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.

Favorable locoregional control in clinically node-negative hormone-receptor positive breast cancer with low 21-gene recurrence scores: a single-institution study with 10-year follow-up.

BACKGROUND: Recent studies have shown a lower likelihood of locoregional recurrences in patients with a low 21-gene recurrence score (RS). In this single-institution study, we investigated whether there are any associations between different cutoff values of 21-gene RS, histopathological factors, and outcome in patients with long-term follow-up. METHODS: The study included 61 patients who had early-stage (I-II) clinically node-negative hormone receptor-positive and HER2-negative breast cancer and were tested with the 21-gene RS assay between February 2010 and February 2013. Demographic, clinicopathological, treatment, and outcome characteristics were analyzed. RESULTS: The median age was 48 years (range, 29-72 years). Patients with high histologic grade (HG), Ki-67 ≥ 25%, or Ki-67 ≥ 30% were more likely to have intermediate/high RS (≥ 18). Based on the 21-gene RS assay, only 19 patients (31%) received adjuvant chemotherapy. At a median follow-up of 112 months, 3 patients developed locoregional recurrences (4.9%), which were treated with endocrine therapy alone. Among patients treated with endocrine treatment alone (n = 42), the following clinicopathological characteristics were not found to be significantly associated with 10-year locoregional recurrence free survival (LRRFS): age < 40 years, age < 50 years, high histological or nuclear grade, high Ki-67-scores (≥ 15%, ≥ 20%, ≥ 25%, ≥ 30%), presence of lymphovascular invasion, luminal-A type, multifocality, lymph node positivity, tumor size more than 2 cm, RS ≥ 18, and RS > 11. However, patients with RS ≥ 16 had significantly poorer 10-year LRRFS compared to those with RS < 16 (75% vs. 100%, respectively; p = 0.039). CONCLUSIONS: The results suggest that patients with clinically node-negative disease and RS ≥ 16 are more likely to benefit from adjuvant chemotherapies. However, those with RS < 16 have an excellent outcome and local control in long-term follow-up with endocrine treatment alone.

The effect of histologic grade on neoadjuvant treatment outcomes in esophageal cancer.

BACKGROUND AND OBJECTIVES: The gold standard for locoregional esophageal cancer (LEC) treatment includes preoperative chemoradiation and surgical resection, with possible perioperative or adjuvant systemic therapy. With few data associating histologic grade and prognosis in LEC patients receiving neoadjuvant chemoradiation followed by resection, we seek to evaluate this association. METHODS: Our institutional esophagectomy database between 1999 and 2019 was queried, selecting esophageal adenocarcinoma patients who completed neoadjuvant therapy (NAT), followed by esophagectomy. Propensity-score matching of low- and high-histologic grade groups was performed to assess survival metrics using initial clinical grade (cG) and final pathologic grade (pG). We performed a multivariable logistic regression to study predictors of pathologic complete response as a secondary objective. RESULTS: A total of 518 patients met the inclusion criteria. Kaplan-Meier analysis of the matched dataset showed no difference in initial or 5-year recurrence-free survival or overall survival (OS) between cG1 and cG2 versus cG3 based on original grade. When matched according to pG, cG1-2 had improved median survival parameters compared to cG3, with 5-year OS for cG1-2 of 45% versus 27% (p = 0.001). Higher pG, pathologic N stage, and poor response to NAT are predictors of poor survival. CONCLUSION: Patients with post-NAT pG1-2 demonstrated improved survival. Integrating histologic grade into postneoadjuvant staging may be warranted.

Quantitative parameters derived from 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging can accurately estimate the histologic grade of hypopharyngeal squamous cell carcinoma preoperatively.

PURPOSE: Among head and neck cancers, hypopharyngeal squamous cell carcinoma (HSCC) shows the highest malignancy, which is associated with histologic grading. This study was designed to investigate whether quantitative parameters derived from 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) can preoperatively estimate the histologic grade of HSCC. METHODS: 18F-FDG PET/MRI of neck was successfully performed in 21 patients with histologically proven HSCC including poorly differentiated group (ten patients) and well-moderately differentiated group (eleven patients). Quantitative parameters derived from FDG-PET, diffusion-weighted imaging (DWI), and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) were calculated based on volume of interest drawn on the tumor and compared between two groups. The efficacy of quantitative parameters for the estimation of histologic grades of HSCC was evaluated. RESULTS: There were statistically significant differences in mean value of standard uptake value (SUV), apparent diffusion coefficient (ADC), and Ktrans derived from 18F-FDG PET/MRI of HSCC between two groups (p < 0.05). There was no statistically significant difference in other quantitative parameters derived from 18F-FDG PET/MRI of HSCC between two groups. The area under the curve (AUC) of the combination of SUVmean, ADCmean, and Ktrans in the estimation of histologic grade of HSCC was 0.936 with sensitivity of 90.0% and specificity of 81.8%. CONCLUSION: The combination of SUVmean, ADCmean, and Ktrans derived from 18F-FDG PET/MRI can accurately predict the histologic grade of HSCC preoperatively.

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer.

BACKGROUND: The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades. METHODS: A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP-IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP-IMD and clinical features. The performance of these models was evaluated and compared. RESULTS: Model-CP-IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP-IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP-IMD and model-Combine outperformed model-CP according to decision curve analysis. CONCLUSION: DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

The increasing importance of histologic grading in tailoring adjuvant systemic therapy in 30,843 breast cancer patients.

PURPOSE: The large variation in histologic grading of invasive breast cancer (IBC) that has been reported likely influences tailoring adjuvant therapy. The role of grading in therapeutic decision-making in daily practice, was evaluated using the Dutch national guidelines for IBC-management. METHODS: Synoptic reports of IBC resection-specimens, obtained between 2013 and 2016, were extracted from the nationwide Dutch Pathology Registry, and linked to treatment-data from the Netherlands Cancer Registry. The relevance of grading for adjuvant chemotherapy (aCT) was quantified by identifying patients for whom grade was the determinative factor. In addition, the relation between grade and aCT-administration was evaluated by multivariate logistic regression for patients with a guideline-aCT-indication. RESULTS: 30,843 patients were included. Applying the guideline that was valid between 2013 and 2016, grade was the determinative factor for the aCT-indication in 7744 (25.1%) patients, a percentage that even increased according to the current guideline where grade would be decisive for aCT in 10,869 (35.2%) patients. Also in current practice, the indication for adjuvant endocrine therapy (aET) would be based on grade in 9173 (29.7%) patients. Finally, as patients with lower-grade tumors receive aCT significantly less often, grade was also decisive in tailoring aCT de-escalation. CONCLUSIONS: In the largest study published so far we illustrate the increasing importance of histologic grade in tailoring adjuvant systemic breast cancer therapy. Next to playing a key-role in aCT-indication and de-escalation, the role of grading has expanded to the indication for aET. Optimizing histologic grading by pathologists is urgently needed to diminish the risk of worse patient outcome due to non-optimal treatment.

Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the oral tongue and floor of mouth: a preliminary study.

BACKGROUND: Histologic grade assessment plays an important part in the clinical decision making and prognostic evaluation of squamous cell carcinoma (SCC) of the oral tongue and floor of mouth (FOM). PURPOSE: To assess the value of apparent diffusion coefficient (ADC)-based radiomics in discriminating between low- and high-grade SCC of the oral tongue and FOM. MATERIAL AND METHODS: We included data from 88 patients (training cohort: n = 59; testing cohort: n = 29) who underwent diffusion-weighted imaging with a 3.0-T magnetic resonance imaging scanner before treatment. A total of 526 radiomics features were extracted from ADC maps to construct a radiomics signature with least absolute shrinkage and selection operator logistic regression. Receiver operating characteristic curves and areas under the curve (AUCs) were used to evaluate the performance of radiomic signature. RESULTS: Five features were selected to construct the radiomics signature for predicting histologic grade. The ADC-based radiomics signature performed well for discriminating between low- and high-grade tumors, with AUCs of 0.83 in both cohorts. Based on the cut-off value of the training cohort, the radiomics signature achieved accuracies of 0.78 and 0.79, sensitivities of 0.65 and 0.71, and specificities of 0.85 and 0.82 in the training and testing cohorts, respectively. CONCLUSION: ADC-based radiomics can be a useful and promising non-invasive method for predicting histologic grade of SCC of the oral tongue and FOM.

Preoperative prediction of histologic grade in invasive breast cancer by using contrast-enhanced spectral mammography-based radiomics.

OBJECTIVE: To develop and validate a radiomics model based on contrast-enhanced spectral mammography (CESM), and preoperatively discriminate low-grade (grade I/II) and high-grade (grade III) invasive breast cancer. METHOD: A total of 205 patients with CESM examination and pathologically confirmed invasive breast cancer were retrospectively enrolled. We randomly divided patients into two independent sets namely, training set (164 patients) and test set (41 patients) with a ratio of 8:2. Radiomics features were extracted from the low-energy and subtracted images. The least absolute shrinkage and selection operator (LASSO) logistic regression were established for feature selection, which were then utilized to construct three classification models namely, low energy, subtracted images and their combined model to discriminate high- and low-grade invasive breast cancer. Receiver operator characteristic (ROC) curves were used to confirm performance of three models in training set. The clinical usefulness was evaluated by using decision curve analysis (DCA). An independent test set was used to confirm the discriminatory power of the models. To test robustness of the result, we used 100 times LGOCV (leave group out cross validation) to validate three models. RESULTS: From initial radiomics feature pool, 17 and 11 features were selected for low-energy image and subtracted image, respectively. The combined model using 28 features showed the best performance for preoperatively evaluating the histologic grade of invasive breast cancer, with an area under the curve, AUC = 0.88, and 95%confidence interval [CI] 0.85 to 0.92 in the training set and AUC = 0.80 (95%CI 0.67 to 0.92) in the test set. The mean AUC of LGOCV is 0.82. CONCLUSIONS: CESM-based radiomics model is a non-invasive predictive tool that demonstrates good application prospects in preoperatively predicting histological grade of invasive breast cancer.

Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma.

OBJECTIVES: To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA). METHODS: This retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450) and validation (n=142) sets according to the time of computed tomography (CT) examination. Radiomic features were extracted from the portal venous phase CT images. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection, data dimension reduction and radiomics signature construction. Multivariable logistic regression analysis was applied to develop the prediction model. The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration and discrimination. RESULTS: A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA (P<0.001 for both training and validation sets). A nomogram including the radiomics signature and tumor location as predictors was developed. The model showed both good calibration and good discrimination, in which C-index in the training set, 0.752 [95% confidence interval (95% CI): 0.701-0.803]; C-index in the validation set, 0.793 (95% CI: 0.711-0.874). CONCLUSIONS: This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures, which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.

Significant inter- and intra-laboratory variation in grading of invasive breast cancer: A nationwide study of 33,043 patients in the Netherlands.

Accurate, consistent and reproducible grading by pathologists is of key-importance for identification of individual patients with invasive breast cancer (IBC) that will or will not benefit from adjuvant systemic treatment. We studied the laboratory-specific grading variation using nationwide real-life data to create insight and awareness in grading variation. Synoptic pathology reports of all IBC resection-specimens, obtained between 2013 and 2016, were retrieved from the nationwide Dutch Pathology Registry (PALGA). Absolute differences in laboratory-proportions of Grades I-III were compared to the national reference. Multivariable logistic regression provided laboratory-specific odds ratios (ORs) for high- vs. low-grade IBC. 33,792 IBC pathology reports of 33,043 patients from 39 laboratories were included, of which 28.1% were reported as Grade I (range between laboratories 16.3-43.3%), 47.6% as Grade II (38.4-57.8%), and 24.3% as Grade III (15.5-34.3%). Based on national guidelines, the indication for adjuvant chemotherapy was dependent on histologic grade in 29.9% of patients. After case-mix correction, 20 laboratories (51.3%) showed a significantly deviant OR. Significant grading differences were also observed among pathologists within laboratories. In this cohort of 33,043 breast cancer patients, we observed substantial inter- and intra-laboratory variation in histologic grading. It can be anticipated that this has influenced outcome including exposure to unnecessary toxicity, since choice of adjuvant chemotherapy was dependent on grade in nearly a third of patients. Better standardization and training seems warranted.

The apparent diffusion coefficient (ADC) on 3-T MRI differentiates myometrial invasion depth and histological grade in patients with endometrial cancer.

BACKGROUND: Diffusion-weighted magnetic resonance imaging (DW-MRI) with apparent diffusion coefficient (ADC) measurement provides additional information about tumor microstructure with potential relevance for staging and predicting aggressive disease in patients with endometrial cancer (EC). PURPOSE: To determine whether ADC values in EC diverge according to the tumor's histologic grade and myometrial invasion depth. MATERIAL AND METHODS: A sample of 48 pathologically confirmed cases of EC were reviewed retrospectively. The sample was distributed as follows: G1 (n = 9); G2 (n = 18); G3 (n = 21); with myometrial invasion <50% (n = 31); and with myometrial invasion ≥50% (n = 17). DW images were performed at 3.0T with b factors of 0-1000/mm2. The region of interest (ROI) was defined within the tumor with T1-weighted and T2-weighted imaging and copied manually to an ADC map. The tumor's grade and myometrial invasion's depth were determined by postoperative histopathological tests. RESULTS: The means of ADCmin and ADCmean values were significantly lower for patients with G2 and G3 endometrial tumors than G1. The same tendency was observed in myometrial invasion, as both ADCmin and ADCmean values were lower for patients with deep than for those with superficial myometrial invasion. The cut-off values of the ADCmin and ADCmean that predicted high-grade tumors were 0.69 × 10-3 mm2/s and 0.82 × 10-3 mm2/s, respectively, while those for myometrial infiltration were 0.70 × 10-3 mm2/s (ADCmin) and 0.88 × 10-3 mm2/s (ADCmean). CONCLUSION: ADCmin and ADCmean values correlated with histologic tumor grade and myometrial invasion depth; therefore, it is suggested that ADC on MRI may be a useful indicator to predict malignancy of ECs.

Histological Aspects and Quantitative Assessment of Ki67 as Prognostic Factors in Breast Cancer Patients: Result from a Single-Center, Cross Sectional Study.

Background and objectives: Our aim is to explore the relationship between the levels of protein encoded by Ki67 and the histopathological aspects regarding the overall survival and progression-free survival in a single university center. A secondary objective was to examine other factors that can influence these endpoints. New approaches to the prognostic assessment of breast cancer have come from molecular profiling studies. Ki67 is a nuclear protein associated with cell proliferation. Together with the histological type and tumor grade, it is used to appreciate the aggressiveness of the breast tumors. Materials and Methods: We conducted a retrospective single-institution study, at Elias University Emergency Hospital, Bucharest, Romania, in which we enrolled women with stage I to III breast cancer. The protocol was amended to include the immunohistochemistry determination of Ki67 and the histological aspects. The methodology consisted in using a Kaplan-Meier analysis for the entire sample and restricted mean survival time up to 36 months. Results: Both lower Ki67 and low tumor grade are associated with better prognosis in terms of overall survival (OS) and progression-free survival (PFS) for our patients' cohort. In our group, the histological type did not impact the time to progression or survival. Conclusions: Both overall survival and progression-free survival may be influenced by the higher value of Ki67 and less differentiated tumors. Further studies are needed in order to establish if the histologic type may impact breast cancer prognostic, probably together with other histologic and molecular markers.

Four novel biomarkers for bladder cancer identified by weighted gene coexpression network analysis.

Bladder cancer (BC) is one of the most malignancies in terms of incidence and recurrence worldwide. The aim of this study is to find out novel and prognostic biomarkers for patients with BC. First, we identified 258 differentially expressed genes by using GSE19915 from Gene Expression Omnibus database. Second, a total of 33 modules were identified by constructing a coexpression network by using weighted gene coexpression network analysis and yellow module was regarded as the key module. Furthermore, by constructing protein-protein interaction networks, we preliminarily picked out 13 genes. Among them, four hub genes (CCNB1, KIF4A, TPX2, and TRIP13) were eventually identified by using five different methods (survival analysis, one-way analysis of variance, the Spearman correlation analysis, receiver operating characteristic curve, and expression value comparison), which were significantly correlated with the prognosis of BC. The validation of transcriptional and translational levels made sense (based on Oncomine and The Human Protein Atlas database). Moreover, functional enrichment analysis suggested that all the hub genes played crucial roles in chromosome segregation, sister chromatid segregation, nuclear chromosome segregation, mitotic nuclear division, nuclear division, and organelle fission during cell mitosis. In addition, three of the hub genes (KIF4A, TPX2, and TRIP13) might be potential targets of cancer drugs according to the results of the genetical alteration. In conclusion, this study indicates that four hub genes have great predictive value for the prognosis of BC, and may contribute to the exploration of the further and more in-depth research of BC.

Significant inter- and intra-laboratory variation in grading of ductal carcinoma in situ of the breast: a nationwide study of 4901 patients in the Netherlands.

PURPOSE: A considerable part of ductal carcinoma in situ (DCIS) lesions may never progress into invasive breast cancer. However, standard treatment consists of surgical excision. Trials aim to identify a subgroup of low-risk DCIS patients that can safely forgo surgical treatment based on histologic grade, which highlights the importance of accurate grading. Using real-life nationwide data, we aimed to create insight and awareness in grading variation of DCIS in daily clinical practice. METHODS: All synoptic pathology reports of pure DCIS resection specimens between 2013 and 2016 were retrieved from PALGA, the nationwide Dutch Pathology Registry. Absolute differences in proportions of grade I-III were visualized using funnel plots. Multivariable analysis was performed by logistic regression to correct for case-mix, providing odds ratios and 95% confidence intervals for high-grade (III) versus low-grade (I-II) DCIS. RESULTS: 4952 DCIS reports from 36 laboratories were included, of which 12.5% were reported as grade I (range 6.1-24.4%), 39.5% as grade II (18.2-57.6%), and 48.0% as grade III (30.2-72.7%). After correction for case-mix, 14 laboratories (38.9%) reported a significantly lower (n = 4) or higher (n = 10) proportion of high-grade DCIS than the reference laboratory. Adjusted ORs (95%CI) ranged from 0.52 (0.31-0.87) to 3.83 (1.42-10.39). Significant grading differences were also observed among pathologists within laboratories. CONCLUSION: In this cohort of 4901 patients, we observed substantial inter- and intra-laboratory variation in DCIS grading, not explained by differences in case-mix. Therefore, there is an urgent need for nationwide standardization of grading practices, especially since the future management of DCIS may alter significantly depending on histologic grade.

Association of Glioma Grading With Inflow-Based Vascular-Space-Occupancy MRI: A Preliminary Study at 3T.

BACKGROUND: Inflow-based vascular-space-occupancy (iVASO) MRI is a noninvasive perfusion technique that does not require administration of exogenous contrast agents. Arteriolar cerebral blood volume (CBVa) obtained from iVASO MRI is hypothesized to be an indicator of tumor microvasculature. PURPOSE: To assess the diagnostic performance of iVASO MRI implemented at 3T in predicting histologic grades of cerebral gliomas. STUDY TYPE: Retrospective. SUBJECTS: Forty-five patients (31 males) consisting of 14 WHO grade IV glioblastoma multiformes, 14 grade III, and 17 grade II gliomas. FIELD STRENGTH/SEQUENCE: At 3T we acquired CBVa data using an iVASO sequence. ASSESSMENT: The maximum and mean CBVa (CBVa_max and CBVa_mean) values were calculated in the tumor and normalized to the contralateral thalamus (nCBVa_max and nCBVa_mean). STATISTICAL TESTS: Kruskal-Wallis test, Mann-Whitney test, and receiver operating characteristics (ROC) curve were used for statistical analysis. RESULTS: Both CBVa_max and nCBVa_max increased with tumor grade (P < 0.001). Grade II gliomas showed CBVa_max <0.78 ml / 100 ml in 10/17 cases and nCBVa_max <1.20 in 11/17 cases. Grade III gliomas showed both CBVa_max >0.78 ml / 100 ml and nCBVa_max >1.20 in 13/14 cases, and CBVa_max <2.06 ml / 100 ml in 13/14 cases and nCBVa_max <2.33 in 11/14 cases. Grade IV gliomas showed CBVa_max >2.06 ml / 100 ml in 9/14 cases and nCBVa_max >2.33 in 13/14 cases. The areas under the ROC curve, sensitivity, and specificity were 0.839 (P < 0.001), 92.9% (26/28), and 64.7% (11/17) for CBVa_max, and 0.883 (P < 0.001), 92.9% (26/28), and 70.6% (12/17) for nCBVa_max in the discrimination between grade II and high-grade (grade III and grade IV) tumors, respectively. DATA CONCLUSION: iVASO MRI might be used to help determine and predict glioma grade. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1817-1823.

Feline Invasive Mammary Carcinomas: Prognostic Value of Histological Grading.

Feline mammary carcinomas are highly malignant tumors usually associated with poor outcome. Nevertheless, survival times can differ significantly according to various prognostic factors. The Elston and Ellis (EE) histologic grading system, originally developed for human breast cancer, is commonly used to grade feline mammary carcinomas, although it is not really adapted for this species, hence the need of a more relevant grading system. Although few veterinary studies attempted to validate previously published results in an independent cohort, the aim of our study was to evaluate the prognostic value of different histologic grading systems in feline invasive mammary carcinomas, including the EE grading system applicable to human breast cancers and the modified and newly designed histologic grading systems recently proposed by Mills et al. Survey data and histologic features of 342 feline invasive mammary carcinomas were analyzed with respect to overall and cancer-specific survival. The histological grading system with best prognostic value was the mitotic-modified Elston and Ellis (MMEE) grading system: grade III carcinomas (P = .04, hazard ratio [HR] = 1.46, 95% CI, 1.01-2.11), grade II (P = .03, HR = 1.39, 95% CI, 1.03-1.88), and grade I carcinomas (HR = 1.00, reference), with decreasing hazard ratios significantly were associated with a worse overall survival, independently from the pathologic tumor size (pT ≥ 20 mm: P = .002, HR = 1.45, 95% CI, 1.15-1.83) and positive nodal stage (P = .001, HR = 1.51, 95% CI, 1.18-1.94). This retrospective study validates Mills et al's proposal to adapt the thresholds for mitotic counts to better assess the histological grade of the highly proliferative mammary carcinomas encountered in the cat.