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Utilizing digital pathology to quantify stromal caveolin-1 expression in malignant and benign ovarian tumors: Associations with clinicopathological parameters and clinical outcomes

ABSTRACT: Loss of stromal caveolin-1 (Cav-1) is a biomarker of a cancer-associated fibroblast (CAF) phenotype and is related to progression, metastasis, and poor outcomes in several cancers. The objective of this study was to evaluate the clinical significance of Cav-1 expression in invasive epithelial ovarian cancer (OvCa). Epithelial and stromal Cav-1 expression were quantified in serous OvCa and benign ovarian tissue in two, independent cohorts–one quantified expression using immunohistochemistry (IHC) and the other using multiplex immunofluorescence (IF) with digital image analysis designed to target CAF-specific expression. Cav-1 expression was significantly downregulated in OvCa stroma compared to non-neoplastic stroma using both the IHC (p = 0.002) and IF (p = 1.8x10-13) assays. OvCa stroma showed Cav-1 downregulation compared to tumor epithelium with IHC (p = 1.2x10-24). Conversely, Cav-1 expression was higher in OvCa stroma compared to tumor epithelium with IF (p = 0.002). There was moderate correlation between IHC and IF methods for stromal Cav-1 expression (r2 = 0.69, p = 0.006) whereas there was no correlation for epithelial expression (r2 = 0.006, p = 0.98). Irrespective of the staining assay, neither response to therapy or overall survival correlated with the expression level of Cav-1 in the stroma or tumor epithelium. Our findings demonstrate a loss of stromal Cav-1 expression in ovarian serous carcinomas. Studies are needed to replicate these findings and explore therapeutic implications, particularly for immunotherapy response.

SUBMITTER: Saeed-Vafa D

PROVIDER: S-EPMC8610269 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.

Project description:Background:The role of splenectomy to diagnose and treat hematologic disease continues to evolve. In this single-center retrospective review, we describe modern morbidity, mortality, and long-term outcomes associated with splenectomy for benign and malignant hematologic disorders. Methods:We analyzed all nontrauma splenectomies performed for benign or malignant hematologic disorders from January 2009 to September 2018. Variables collected included demographics, preexisting comorbidities, laboratory results, intra- and postoperative features, and long-term follow-up. Outcomes of interest included postoperative complications, 30-day mortality, and overall mortality. Results:We identified 161 patients who underwent splenectomy for hematologic disorders. Median age was 54?years (range 19-94), and 83 (52%) were female. Splenectomy was performed for 95 (59%) patients with benign hematologic disorders and for 66 (41%) with malignant conditions. Most splenectomies were laparoscopic (76%), followed by laparoscopic hand assisted (11%), open (8%), and laparoscopic converted to open (6%). Median follow-up was 761?days (interquartile range: 179-2025?days). Major complications occurred in 21 (13%) patients. Three (2%) patients died within 30?days; 16 (9%) died more than 30?days after operation, none from surgical complications, with median time to death of 438?days (interquartile range: 231-1497?days). Among malignant cases, only preoperative thrombocytopenia predicted death (odds ratio?=?5.8, 95% confidence interval?=?1.1-31.8, P?=?.04). For benign cases, increasing age was associated with inferior survival (odds ratio?=?2.3, 95% confidence interval?=?1.0-5.1, P?=?.05). Conclusion:Splenectomy remains an important diagnostic and therapeutic option for patients with benign and malignant hematologic disorders and can be performed with a low complication rate. Despite considerable burden of comorbid disease in these patients, early postoperative mortality was uncommon.

Project description:PurposeComputed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment.MethodsLung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign).ResultsThe inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures.ConclusionsRadiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

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Utilizing digital pathology to quantify stromal caveolin-1 expression in malignant and benign ovarian tumors: Associations with clinicopathological parameters and clinical outcomes

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