Computerized morphometry as an aid in determining the grade of dysplasia and progression to adenocarcinoma in Barrett's esophagus

Stratification of Barrett's esophagus surveillance based on p53 immunohistochemistry: a cost-effectiveness analysis by an international collaborative group

BACKGROUND

Surveillance of nondysplastic Barrett's esophagus (NDBE) is recommended to identify progression to dysplasia; however, the most cost-effective strategy remains unclear. Mutation of TP53 or aberrant expression of p53 have been associated with the development of dysplasia in BE. We sought to determine if surveillance intervals for BE could be stratified based on p53 expression.

METHODS

A Markov model was developed for NDBE. Patients with NDBE underwent p53 immunohistochemistry (IHC) and those with abnormal p53 expression underwent surveillance endoscopy at 1 year, while patients with normal p53 expression underwent surveillance in 3 years. Patients with dysplasia underwent endoscopic therapy and surveillance.

RESULTS

On base-case analysis, the strategy of stratifying surveillance based on abnormal p53 IHC was cost-effective relative to conventional surveillance and a natural history model, with an incremental cost-effectiveness ratio (ICER) of $8258 for p53 IHC-based surveillance. Both the conventional and p53-stratified surveillance strategies dominated the natural history model. On probabilistic sensitivity analysis, the p53 IHC strategy ($28 652; 16.78 quality-adjusted life years [QALYs]) was more cost-effective than conventional surveillance ($25 679; 16.17 QALYs) with a net monetary benefit of $306 873 compared with conventional surveillance ($297 642), with an ICER <$50 000 in 96% of iterations. The p53-stratification strategy was associated with a 14% reduction in the overall endoscopy burden and a 59% increase in dysplasia detection.

CONCLUSION

A surveillance strategy for BE based on abnormal p53 IHC is cost-effective relative to a conventional surveillance strategy and is likely to be associated with higher rates of dysplasia diagnosis.

Evaluation of nuclear morphometry in exfoliative cytology of buccal mucosa in patients with high risk of oral cancer

BACKGROUND

Oral cancer poses a significant global health burden, with India having the highest prevalence. Effective detection is crucial in effective prevention. This study aimed to evaluate nuclear morphometric parameters (NMPs) in buccal mucosa cells of smokers, correlate NMPs with dysplasia, establish cut off values for grading dysplasia, and investigate the relationship between NMPs and smoking.

METHODS

After obtaining ethical approval and informed consent, patients were recruited from the outpatient department of our institution. A target sample size of 250 was calculated. The data included smoking exposure quantified in pack-years, nuclear morphometric analysis (NMA) of buccal mucosa cells obtained through oral cytology using Image J, and the severity of dysplasia of the slides assessed by pathologists. Statistical analysis assessed the impact of dysplasia and the association between nuclear characteristics and smoking exposure. Receiver operating characteristic (ROC) plots determined the potential of these parameters to distinguish dysplasia levels.

RESULTS

Significant differences in NMPs were observed among different smoking groups. Dysplasia severity had a significant correlation with NMPs, and strong correlations were found between NMPs and lifetime smoking exposure. ROC analysis established cut off values for NMPs with good sensitivity and specificity for classifying dysplasia severity.

CONCLUSIONS

This study highlights the potential of NMA as a tool for oral cancer screening. NMPs can distinguish dysplasia severity and correlate with tobacco (smoking). The efficiency of NMA in a non-invasive oral cytology offers promise for patient-centered screening Additionally, the findings suggest future applications in telepathology and the potential for AI integration in automated screening after conducting multicentric large-scale studies.

Fourier transformation based texture analysis for differentiating between hyperplastic polyps and sessile serrated adenomas

Colorectal cancer (CRC) is the third most common type of cancer. One major pathway involved in the development of CRC is the serrated pathway. Colorectal polyps can be divided in benign, like small hyperplastic polyps and premalignant polyps, like the sessile serrated adenomas (SSA) that has a significant potential of malignant transformation. The morphological similarity between these types of polyp, not-infrequently raises diagnostic difficulties. This study aimed to morphologically differentiate between hyperplastic polyps (HP) and SSAs by using automated computerized texture analysis of Fourier transformed histological images. Thirty images of HP and 58 images of SSA were analyzed by computerized texture analysis. A fast Fourier transformation was applied to the images. The Fourier frequency plots were further transformed into gray level co-occurrence matrices and four textural variables were extracted: entropy, correlation, contrast, and homogeneity. Our study is the first to combine this type of analysis for automated classification of colonic neoplasia. The results were analyzed using statistical and neural network (NNET) classification models. The predictive values of these classifiers were compared. The statistical regression algorithm presented a sensitivity of 95% to detect the SSA and a specificity of 80% to detect the HP. The NNET analysis was superior to the statistical analysis displaying a classification accuracy of 100%. The results of this study have confirmed the hypothesis that Fourier based texture image analysis is helpful in differentiating between HP and SSA. RESEARCH HIGHLIGHTS: Colorectal polyps can be divided in benign, like hyperplastic polyps (HP) and premalignant, like the sessile serrated adenomas (SSA). There is a high morphologic similarity between these two types of polyp that not-infrequently raises diagnostic difficulties. The results of our morphometric analysis that were used to build a neural network based model of prediction of the polyp types, have a great clinical importance of identifying SSA polyps which have significant potential of malignant progression as compared to HP.

Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology

Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.

Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques

PVL (proliferative verrucous leukoplakia) has distinct clinical characteristics. They have a proclivity for multifocality, a high recurrence rate after treatment, and malignant transformation, and they can progress to verrucous or squamous cell carcinoma. AI can aid in the diagnosis and prognosis of cancers and other diseases. Computational algorithms can spot tissue changes that a pathologist might overlook. This method is only used in a few studies to diagnose LB and PVL. To see if their cellular nuclei differed and if this cellular compartment could classify them, researchers used a computational system and a polynomial classifier to compare OLs and PVLs. 161 OL and 3 PVL specimens in the lab were grown, photographed, and used for training and computation. Exam orders revealed patients' sociodemographics and clinical pathologies. The nucleus was segmented using Mask R-CNN, and LB and PVL were classified using a polynomial classifier based on nucleus area, perimeter, eccentricity, orientation, solidity, entropies, and Moran Index (a measure of disorderliness). The majority of OL patients were male smokers; most PVL patients were female, with a third having malignant transformation. The neural network correctly identified cell nuclei 92.95% of the time. Except for solidity, 11 of the 13 nuclear characteristics compared between the PVL and the LB showed significant differences. The 97.6% under the curve of the polynomial classifier was used to classify the two lesions. These results demonstrate that computational methods can aid in diagnosing these two lesions.

Barrett's esophagus: The pathomorphological and molecular genetic keystones of neoplastic progression

Barrett's esophagus is a widespread chronically progressing disease of heterogeneous nature. A life threatening complication of this condition is neoplastic transformation, which is often overlooked due to lack of standardized approaches in diagnosis, preventative measures and treatment. In this essay, we aim to stratify existing data to show specific associations between neoplastic transformation and the underlying processes which predate cancerous transition. We discuss pathomorphological, genetic, epigenetic, molecular and immunohistochemical methods related to neoplasia detection on the basis of Barrett's esophagus. Our review sheds light on pathways of such neoplastic progression in the distal esophagus, providing valuable insight into progression assessment, preventative targets and treatment modalities. Our results suggest that molecular, genetic and epigenetic alterations in the esophagus arise earlier than cancerous transformation, meaning the discussed targets can help form preventative strategies in at-risk patient groups.

Computerized morphometry analysis of epithelial fimbriae nuclear symmetry in BRCA carriers may identify patients at risk for developing ovarian cancer

Serous ovarian tumors may originate in epithelial cells of the fallopian tubes. Computerized morphometry was able to find significant alterations in the fallopian tube epithelium of healthy BRCA carriers. The purpose of this study was to identify a subgroup of BRCA carriers that may be at risk of developing serous ovarian cancer by evaluation of the epithelial nuclear symmetry in the fallopian tubes. Four groups of patients were analyzed; healthy patients, ovarian cancer patients, BRCA carriers, and BRCA noncarriers. All fallopian tubes appeared normal by H&E examination. The ImageProPlus software was used to assess the nuclear symmetry of 65 fimbriae epithelium cells and an artificial neural network algorithm aided in detecting a subpopulation among fimbriae of healthy BRCA carriers at risk for ovarian cancer. Significant differences were found between healthy patients and ovarian cancer patients, and between BRCA carriers and noncarriers. The algorithm was able to accurately predict BRCA carriers with associated ovarian cancer based on fallopian tube nuclear symmetry characteristics. These results reinforce the hypothesis that fimbriae epithelial cells of BRCA carriers may undergo early-stage changes that could predict the risk of progression toward malignancy.

Computerized image analysis of blood vessels within mucosal defects for the prediction of delayed bleeding following colonic endoscopic mucosal resection: a pilot study

BACKGROUND : Clinically significant post-endoscopic bleeding (CSPEB) is a common complication following colonic endoscopic mucosal resection (EMR). Current prediction tools are clinical and do not use the appearance of the post-EMR mucosal defect. We aimed to predict CSPEB by analyzing blood vessel morphology within the post-EMR mucosal defect. METHODS : 43 patients with CSPEB were matched to 43 non-bleeders for clinical variables associated with CSPEB. Computerized image analysis quantified the morphologic characteristics of the blood vessels in the defect. Variables were measured in relation to the mucosal defect area. Multivariate analysis and a neural network (NNET) were used as prediction models. RESULTS : The CSPEB group vessels had larger maximum diameter (113.07 vs. 69.03; P < 0.001), larger minimum radius (5.09 vs. 3.28; P = 0.002), larger perimeter value (337.82 vs. 193.86; P < 0.001), larger vessel length-of-outline (351.83 vs. 220.68; P = 0.002), and larger fractal dimension (1.11 vs. 1.10; P = 0.005) compared with non-bleeders. Discriminant analysis yielded 86 % sensitivity and 76.7 % specificity and an NNET classifier yielded 100 % sensitivity and 76.9 % specificity for identifying patients at risk. CONCLUSIONS : Blood vessel morphology in the post-EMR defect can be used to predict bleeding following colonic EMR.

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

Morphometric analysis of nuclear symmetry in urothelial carcinoma for predicting tumor recurrence

Urothelial carcinoma is the ninth most common cancer in the world. Cytological analysis of the urine is used for screening, as well as for cases suspected for neoplasia of the urinary tract. However, the sensitivity of urine cytology examination is low. The golden standard for diagnosing bladder cancer relies upon cystoscopy followed by a biopsy, which is microscopically assessed by the pathologist. Treatment decisions are based on the histological grade and stage of the tumor. Posttreatment tumor recurrence is 50%. The purpose of this study is to predict recurrence of urothelial carcinoma using a novel morphometric method of nuclear symmetry analysis. This method may help tailor the appropriate treatment and may reduce the need of invasive surgical procedures in patients. Computerized morphometry was applied to develop multiple symmetry indices of the nuclei of the tumor cells as follows: each nucleus was physically divided along its digital axis in two segments that were separately analyzed for their shape, size, optical density, and texture. Subsequently, ratios were obtained by mathematically dividing between the morphometric values of the two nuclear segments where the denominator contained the largest value of the two. These ratios were named symmetry indices and were included as variables to predict the recurrence time of the tumors. The change in the symmetry indices (loss of symmetry) of the nuclear roundness, fractal dimension and margination were the only independent predictors of recurrence time. Computerized morphometry of nuclear symmetry indices may help to predict tumor recurrence in urothelial carcinomas.

Machine learning based tissue analysis reveals Brachyury has a diagnosis value in breast cancer

Background: The aim of the present study was to confirm the role of Brachyury in breast cancer and to verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.

Methods: We conducted a retrospective review of the medical records to obtain patient information, and made the patient’s paraffin tissue into tissue chips for staining analysis. We selected 303 patients for research and implemented four machine learning algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.

Results: The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (P=0.0335); patients with breast cancer with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (P=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of patients with breast cancer. The results showed that the decision tree model had the best performance (AUC = 0.781).

Conclusions: Brachyury is highly expressed in breast cancer and indicates that patients had a poor prognosis. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of patients with breast cancer.

Artificial intelligence technique in detection of early esophageal cancer

Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks (CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from in vitro image analysis for classification to real-time detection of early esophageal neoplasia. When AI technique comes to the pathological diagnosis, borderline lesions that are difficult to determine may become easier than before. In gene diagnosis, due to the lack of tissue specificity of gene diagnostic markers, they can only be used as supplementary measures at present. In predicting the risk of cancer, there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.

Artificial intelligence-assisted esophageal cancer management: Now and future

Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.

Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches

Background

One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a significantly higher risk of disease progression. Indeed, in a considerable number of cases, Ta and T1 tumors look very similar under microscope, making the distinction very difficult even for experienced pathologists. Thus, there is an urgent need for a favoring system based on machine learning (ML) to distinguish between the two stages of bladder cancer.

Methods

A total of 1177 images of bladder tumor tissues stained by hematoxylin and eosin were collected by pathologists at University of Rochester Medical Center, which included 460 non-invasive (stage Ta) and 717 invasive (stage T1) tumors. Automatic pipelines were developed to extract features for three invasive patterns characteristic to the T1 stage bladder cancer (i.e., desmoplastic reaction, retraction artifact, and abundant pinker cytoplasm), using imaging processing software ImageJ and CellProfiler. Features extracted from the images were analyzed by a suite of machine learning approaches.

Results

We extracted nearly 700 features from the Ta and T1 tumor images. Unsupervised clustering analysis failed to distinguish hematoxylin and eosin images of Ta vs. T1 tumors. With a reduced set of features, we successfully distinguished 1177 Ta or T1 images with an accuracy of 91–96% by six supervised learning methods. By contrast, convolutional neural network (CNN) models that automatically extract features from images produced an accuracy of 84%, indicating that feature extraction driven by domain knowledge outperforms CNN-based automatic feature extraction. Further analysis revealed that desmoplastic reaction was more important than the other two patterns, and the number and size of nuclei of tumor cells were the most predictive features.

Conclusions

We provide a ML-empowered, feature-centered, and interpretable diagnostic system to facilitate the accurate staging of Ta and T1 diseases, which has a potential to apply to other types of cancer.

A BAYESIAN MARK INTERACTION MODEL FOR ANALYSIS OF TUMOR PATHOLOGY IMAGES

With the advance of imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at large scale. Reliable statistical approaches to model the spatial pattern of cells can provide new insight into tumor progression and shed light on the biological mechanisms of cancer. We consider the problem of modeling spatial correlations among three commonly seen cells observed in tumor pathology images. A novel geostatistical marking model with interpretable underlying parameters is proposed in a Bayesian framework. We use auxiliary variable MCMC algorithms to sample from the posterior distribution with an intractable normalizing constant. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary exploratory analyses, by means of application to three benchmark datasets and a case study on the pathology images of 188 lung cancer patients. The case study shows that the spatial correlation between tumor and stromal cells predicts patient prognosis. This statistical methodology not only presents a new model for characterizing spatial correlations in a multitype spatial point pattern conditioning on the locations of the points, but also provides a new perspective for understanding the role of cell-cell interactions in cancer progression.

Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer

Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the "connectedness" between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling.

Can morphometric analysis of the fallopian tube fimbria predict the presence of uterine papillary serous carcinoma (UPSC)?

Uterine serous papillary carcinoma (UPSC) is an aggressive tumor, often diagnosed as a metastatic disease and characterized by a high recurrence rate and poor prognosis. UPSC represents a distinct subtype of endometrial cancer which is different in clinical and pathological behaviors from endometrioid endometrial carcinoma (EEC) and resembles more to serous ovarian carcinoma. Since tumors of serous papillary of the ovary are hypothesized to stem from cells of the fallopian tube's fimbria, we hypothesized that UPSC may also origin in the fallopian tubes. In our previous study, using a novel method of computerized morphometry of the fimbrial epithelium we have found significant differences between fimbriae of healthy women and serous ovarian cancer patients. In this study we showed the presence of morphologic differences between twenty-four fimbriae from healthy women, and twenty six fimbriae from uterus cancer (13 from UPSC patients and 13 from EEC patients). All fimbriae reported by the pathologist as "normal" were subjected to a computerized histomorphometric analysis. Two-step method of computerized histomorphometry, i.e. Fast Fourier transformation (FFT) followed by a co-occurrence matrix analysis and an additional analysis of the nuclear symmetry of the tubal fimbrial epithelium were applied. Using these novel methods, we were able to show differences in the morphometric characteristics of the fimbriae in UPSC patients compared to EEC and healthy patients. It is yet to be determined the clinical significance of this observation.

Evaluation of Microscopic Changes in Fallopian Tubes of BRCA Mutation Carriers by Morphometric Analysis of Histologic Slides: A Preliminary Pilot Study

Mutations in BRCA genes increase the risk of ovarian cancer, yet no method for early diagnosis is available. Some serous ovarian tumors are hypothesized to stem from cells of the fallopian tube fimbria. Using a novel method of computerized morphometry of the fimbrial epithelium, this study aimed to detect morphologic differences in noncancerous fimbriae between BRCA mutation carriers and noncarriers, and between healthy and serous ovarian cancer patients. Twenty-four fimbriae from healthy women (13 BRCA+, 11 BRCA-) and 21 fimbriae from women with serous ovarian cancer (10 BRCA+, 11 BRCA-), all reported as "normal" by hematoxylin and eosin examination, were subjected to computerized histomorphometric analysis. A Fast Fourier Transformation was applied to images of fimbrial epithelium and the Fast Fourier Transformation 2-dimensional frequency maps were subsequently quantified for nuclear orientation and planar distribution by a cooccurrence matrix analysis. Additional analysis of nuclear contour was applied to the fimbriae of the healthy women. Among the healthy women, significant differences were found in morphometric characteristics between the BRCA mutation carriers and noncarriers. Among the women with ovarian cancer, no significant differences were found between BRCA mutation carriers and noncarriers. Between healthy women and those with ovarian cancer, significant differences were detected, regardless of BRCA mutational status. A novel method, which combined Fast Fourier Transformation with cooccurrence matrix analysis, demonstrated differences in morphometric characteristics in the fimbriae between healthy and ovarian cancer patients, and between BRCA mutation carriers and noncarriers. The clinical significance of these observations should be investigated.

Large-scale DNA organization is a prognostic marker of breast cancer survival

Breast cancer is the leading cause of cancer-related deaths among women worldwide. We investigated whether changes in large-scale DNA organization (LDO) of tumor epithelial nuclei are an indicator of the aggressiveness of the tumor. We tested our algorithm on a set of 172 duplicates TMA cores samples coming from 95 breast cancer patients. Thirty-five patients died of breast cancer, and 60 were still alive 10 years after surgery. Duplicates cores were used to create training and test set. The TMA slides were stained with Feulgen-thionin and imaged using our in-house high-resolution Imaging system. Automated segmentation of cell nuclei followed by manual selection of intact, in-focus nuclei resulted in an average of 50 cell nuclei per sample available for analysis. Using forward stepwise linear discriminant analysis, a combination of six features that combined linearly gave the best discrimination between the two groups of cells: cells collected from 'deceased' patients TMA specimens and cells collected from "survivors" patients TMA specimens. Five of these features measure the spatial organization of DNA chromatin. The resulting canonical score is named cell LDO score. A patient LDO score, percentage of cell nuclei with a cell LDO score higher than a predefined cutoff value, was processed for the specimens in the test set, and a cutoff value was defined to classify patients with a low or a high LDO score. Using this binary test, 82.1% of patients were correctly classified are "deceased" or "survivors," with a specificity of 79% and a sensitivity of 88%. The relative risk of death of an individual with a high LDO score was nine times higher than for a patient with a low LDO score. When testing the combination of LDO score, node status, histological grade, and tumor grade to predict breast cancer survival, LDO was the most significant predictor. LDO classification was also highly associated with survival for only grade 1 and 2 patients as well as for only grade 3 patients. Our result confirms the potential of LDO to measure phenotypic changes associated with more aggressive disease and could be evaluated to identify patients more likely to benefit from adjuvant therapies.

Early histological findings may predict the clinical phenotype in Crohn's colitis

BACKGROUND AND AIMS

Predicting the clinical course of Crohn's disease (CD) is relevant for treatment selection. Currently, such diagnostic tools are lacking. In a previous pilot study, morphometric tissue image analysis showed promise in predicting the clinical phenotype and need for surgery. In this study, we aimed to validate our previous results on a larger cohort.

METHODS

Colonic biopsies from CD patients with colonic or ileocolonic disease and at least five years of post-biopsy clinical follow-up were analyzed. The results were used to predict post-biopsy clinical phenotypes and outcomes. Data analysis was performed using multivariate regression models, discriminant score (DS) computations and Neural Network (NNET).

RESULTS

Multivariate analysis of morphometric variables differentiated between B1 and B2 phenotypes (sensitivity 81%, specificity 74%, accuracy on cross-validation 75%; area under the curve (AUC) of 0.74 (CI 0.6-0.84; NNET model sensitivity 87%, specificity 67% on the testing population)). Differentiation between B1 and B3 phenotypes was also possible (sensitivity 69%, specificity 76%, accuracy 70.5% on cross-validation; AUC 0.78 (CI 0.68-0.89); NNET model sensitivity 78%, specificity 77% on the testing population)). Differentiating between B2 and B3 phenotypes was not possible using morphometric variables. Multivariate analysis predicted surgery (sensitivity 67%, specificity 72.5%, accuracy 69%; AUC 0.72 (CI 0.61-0.82); NNET model sensitivity 80%, specificity 91% on the testing population)).

CONCLUSIONS

This study validates previous results and suggests that morphometric image analysis of early biopsies from Crohn's colitis patients may contribute to the prediction of future outcomes such as clinical phenotype and surgery. Prospective validation on larger cohorts is still needed.

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.

Diagnosis of lung cancer through manual histopathology evaluation is insufficient to predict patient survival. Here, the authors use computerized image processing to identify diagnostically relevant image features and use these features to distinguish lung cancer patients with different prognoses.

Dysplasia discrimination in intestinal-type neoplasia of the esophagus and colon via digital image analysis

Determining gastrointestinal tract dysplasia level is clinically important but can be difficult, and given this challenge, we investigated colonic and esophageal dysplastic progression using digital image analysis (IA). Whole slide images were obtained for colonic normal mucosa (NCM), hyperplastic polyps (HP), conventional tubular adenomas (TA), and adenomas with high-grade dysplasia (HGD), and esophageal intestinal metaplasia negative for dysplasia (IM), indefinite for dysplasia (IFD), low-grade dysplasia (LGD), and HGD. Characteristic nuclei were circumscribed, and parameters discriminating groups included nuclear circumference (μm), area (μm(2)), and 15 positive pixel count (PPC) algorithm IA measurements. In colon polyps and esophageal lesions, average nuclear area and circumference ranged 30-108.6 μm(2) and 27.5-48.9 μm, respectively. Differences for average nuclear area and circumference met statistical significance (p < 0.05) between diagnostic groups in the esophagus and colon, except for IM versus IFD nuclear area. Pixel intensity (brightness) separated lesions within both groups with statistical significance except for colonic TAs versus HPs and esophageal LGD versus IM. HGD nuclei in both groups demonstrated more pixel staining heterogeneity than other lesions. Hierarchical clustering and principal component analysis demonstrated that lesions with similar diagnoses tended to cluster together on a low- to high-grade spectrum. Our results confirm that quantitative IA is an effective adjunct reflecting dysplasia in colon polyps and Barrett esophagus lesions. Nuclear area, circumference, and PPC algorithm findings distinguished lesions in a statistically significant manner. This suggests utility for future studies on similar methods, which may provide an adjunctive ancillary technique for pathologists and enhance patient care.

Differentiation of Diffuse Large B-cell Lymphoma From Follicular Lymphoma Using Texture Analysis on Conventional MR Images at 3.0 Tesla

RATIONAL AND OBJECTIVES

Diffuse large B-cell lymphoma (DLBCL) represents the most common type of aggressive non-Hodgkin lymphoma (NHL); follicular lymphoma (FL) is the most frequent indolent NHL. The aim of this study was to investigate whether texture-based analysis of conventional magnetic resonance imaging (MRI) allows discrimination of DLBCL from FL, and further, to correlate the MRI texture features with diffusion-weighted imaging apparent diffusion coefficient (ADC) value and tumor tissue cellularity.

MATERIALS AND METHODS

Forty-one patients with histologically proven NHL (30 DLBCL and 11 FL) underwent conventional MRI and diffusion-weighted imaging examination before treatment. Based on regions of interest, texture analysis was performed on T1-weighted images pre- and postcontrast enhancement and on T2-weighted images with and without fat suppression, and features derived from the run-length matrix- and co-occurrence matrix-based methods were analyzed. Receiver operating characteristic curves were performed for the three most discriminative texture features for the differentiation of the two most common types of lymphoma. The analyzed MRI texture features were correlated with the ADC value and the tumor tissue cellularity.

RESULTS

We found that on T1-weighted images postcontrast enhancement, run-length matrix-based texture analysis for lesion classification differentiated DLBCL from FL, with specificity and sensitivity of 76.6% and 76.5%, respectively. There was no correlation between the texture features and the ADC value or tumor tissue cellularity.

CONCLUSIONS

DLBCL and FL can be differentiated by means of texture analysis on T1-weighted MRI postcontrast enhancement. These results could serve as a basis for the use of the texture features on conventional MRI as adjunct to clinical examination to distinguish DLBCL from FL.

Application of the angle measure technique as image texture analysis method for the identification of uranium ore concentrate samples: New perspective in nuclear forensics

The identification of interdicted nuclear or radioactive materials requires the application of dedicated techniques. In this work, a new approach for characterizing powder of uranium ore concentrates (UOCs) is presented. It is based on image texture analysis and multivariate data modelling. 26 different UOCs samples were evaluated applying the Angle Measure Technique (AMT) algorithm to extract textural features on samples images acquired at 250× and 1000× magnification by Scanning Electron Microscope (SEM). At both magnifications, this method proved effective to classify the different types of UOC powder based on the surface characteristics that depend on particle size, homogeneity, and graininess and are related to the composition and processes used in the production facilities. Using the outcome data from the application of the AMT algorithm, the total explained variance was higher than 90% with Principal Component Analysis (PCA), while partial least square discriminant analysis (PLS-DA) applied only on the 14 black colour UOCs powder samples, allowed their classification only on the basis of their surface texture features (sensitivity>0.6; specificity>0.6). This preliminary study shows that this method was able to distinguish samples with similar composition, but obtained from different facilities. The mean angle spectral data obtained by the image texture analysis using the AMT algorithm can be considered as a specific fingerprint or signature of UOCs and could be used for nuclear forensic investigation.

BOB CAT: A Large-Scale Review and Delphi Consensus for Management of Barrett's Esophagus With No Dysplasia, Indefinite for, or Low-Grade Dysplasia

OBJECTIVES

Barrett's esophagus (BE) is a common premalignant lesion for which surveillance is recommended. This strategy is limited by considerable variations in clinical practice. We conducted an international, multidisciplinary, systematic search and evidence-based review of BE and provided consensus recommendations for clinical use in patients with nondysplastic, indefinite, and low-grade dysplasia (LGD).

METHODS

We defined the scope, proposed statements, and searched electronic databases, yielding 20,558 publications that were screened, selected online, and formed the evidence base. We used a Delphi consensus process, with an 80% agreement threshold, using GRADE (Grading of Recommendations Assessment, Development and Evaluation) to categorize the quality of evidence and strength of recommendations.

RESULTS

In total, 80% of respondents agreed with 55 of 127 statements in the final voting rounds. Population endoscopic screening is not recommended and screening should target only very high-risk cases of males aged over 60 years with chronic uncontrolled reflux. A new international definition of BE was agreed upon. For any degree of dysplasia, at least two specialist gastrointestinal (GI) pathologists are required. Risk factors for cancer include male gender, length of BE, and central obesity. Endoscopic resection should be used for visible, nodular areas. Surveillance is not recommended for <5 years of life expectancy. Management strategies for indefinite dysplasia (IND) and LGD were identified, including a de-escalation strategy for lower-risk patients and escalation to intervention with follow-up for higher-risk patients.

CONCLUSIONS

In this uniquely large consensus process in gastroenterology, we made key clinical recommendations for the escalation/de-escalation of BE in clinical practice. We made strong recommendations for the prioritization of future research.

Texture-based classification of different gastric tumors at contrast-enhanced CT

PURPOSE

To determine the feasibility of texture analysis for the classification of gastric adenocarcinoma, lymphoma, and gastrointestinal stromal tumors on contrast-enhanced hydrodynamic-MDCT images.

MATERIALS AND METHODS

The arterial phase scans of 47 patients with adenocarcinoma (AC) and a histologic tumor grade of [AC-G1, n=4, G1, n=4; AC-G2, n=7; AC-G3, n=16]; GIST, n=15; and lymphoma, n=5, and the venous phase scans of 48 patients with AC-G1, n=3; AC-G2, n=6; AC-G3, n=14; GIST, n=17; lymphoma, n=8, were retrospectively reviewed. Based on regions of interest, texture analysis was performed, and features derived from the gray-level histogram, run-length and co-occurrence matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher coefficients, probability of classification error, average correlation coefficients, and mutual information coefficients were used to create combinations of texture features that were optimized for tumor differentiation. Linear discriminant analysis in combination with a k-nearest neighbor classifier was used for tumor classification.

RESULTS

On arterial-phase scans, texture-based lesion classification was highly successful in differentiating between AC and lymphoma, and GIST and lymphoma, with misclassification rates of 3.1% and 0%, respectively. On venous-phase scans, texture-based classification was slightly less successful for AC vs. lymphoma (9.7% misclassification) and GIST vs. lymphoma (8% misclassification), but enabled the differentiation between AC and GIST (10% misclassification), and between the different grades of AC (4.4% misclassification). No texture feature combination was able to adequately distinguish between all three tumor types.

CONCLUSION

Classification of different gastric tumors based on textural information may aid radiologists in establishing the correct diagnosis, at least in cases where the differential diagnosis can be narrowed down to two histological subtypes.

Development of a computerized morphometry application for assessment of the tumor fraction in colon carcinoma tissue samples

Determining the fraction of tumor cells in colon carcinoma samples analyzed for KRAS mutation status is important for choosing the proper testing modality and accurately interpreting the results. However, when asked to determine the tumor cell fraction in tissue samples, different pathologists give considerably different estimations, possibly leading to erroneous interpretation of KRAS mutation analysis results and poor treatment choices. To address this issue, we developed a free, easy-to-use computer program that estimates the tumor cell fraction on colon carcinoma slides that are immune-stained with anti-cytokeratin antibody. The program differentiates between the tumor area and the surrounding stroma on the basis of the immunostaining. Sixty such samples were evaluated by the program. In addition, the actual tumor cell fraction in these samples was measured. The tumor cell fraction estimated by the computer program showed a highly significant correlation with the actual measurements (R=0.64, P<0.001). In addition, we found that a short calibration step before beginning the computer estimation increased the accuracy of the results. In 4 cases (7%), there was some discrepancy between the computer estimation and the actual measurements; however, this was attributed to lower-quality immunohistochemical staining, indicating the importance of this phase in the analysis. In conclusion, we believe that this program can be used for standardizing the evaluation of the tumor cell fraction in colon carcinoma and that its use might aid in making better diagnosis and treatment choices for these patients.

[Subjective grading of Barrett's neoplasia by pathologists: correlation with objective histomorphometric variables]

Even though pathologists are trained to recognize the same histological features for the diagnosis and grading of different histological images, not all pathologists are influenced to a similar level of intensity by the same morphological characteristics of the tissue when scoring Barrett's dysplasia/neoplasia. The variables which most pathologists have intuitively chosen to use for scoring of the severity of Barrett's changes are mainly those related to the general tissue architecture, such as nuclear crowding, orientation and stratification. Interestingly, nuclear size is not used by most pathologists but nuclear pleomorphism and symmetry does influence a significant number of pathologists. Maybe the most difficult variables for the human eye to recognize are variables of chromatin texture (such as margination or heterogeneity), the predictive importance of which has been demonstrated in a previously published work. Textural variables may therefore remain the subject of a computerized analysis. Nevertheless, the fact that a few pathologists do actually correlate with nuclear texture in scoring, argues in favor of making further attempts to train pathologists to also rely on texture, similar to cytologists, when scoring Barrett's dysplasia.

Human CD19(+)CD25(high) B regulatory cells suppress proliferation of CD4(+) T cells and enhance Foxp3 and CTLA-4 expression in T-regulatory cells

Studies in both animal models and humans have shown a subset of B cells behaving as immuno-regulatory cells, being a source of inhibitory cytokines such as IL-10 and TGF-β. Our aims were to establish the presence of human B regulatory (Breg) cells and to assess their ability to suppress proliferation of CD4(+) T cells and to mediate T regulatory (Treg) cells' properties. For this purpose, human Breg, CD4(+) T and Treg cells were purified using magnetic microbeads. CFSE-labeled CD4(+) T cells were stimulated and cultured alone or with Breg cells. Their proliferative response was determined 72 hours later based on the CFSE staining. In parallel, Treg cells were cultured alone or with Breg cells in different conditions for 24 hours, and then stained and analyzed for Foxp3 and CTLA-4 expression. We found that, the co-culture of Breg cells (defined as CD25(high) CD27(high) CD86(high) CD1d(high) IL-10(high) TGF-β(high)) with autologous stimulated CD4(+) T cells decreased significantly (in a dose-dependent way) the proliferative capacity of CD4(+) T cells. Furthermore, Foxp3 and CTLA-4 expression in Treg cells were enhanced by non-stimulated and further by ODN-CD40L stimulated Breg cells. The regulatory function of Breg cells on Treg cells was mainly dependent on a direct contact between Breg and Treg cells, but was also TGF-β but not IL-10 dependent. In conclusion, human Breg cells decrease the proliferation of CD4(+) T cells and also enhance the expression of Foxp3 and CTLA-4 in Treg cells by cell-to-cell contact.

Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival

AIM

To undertake a pilot study assessing whether tumour heterogeneity evaluated using computed tomography texture analysis (CTTA) has the potential to provide a marker of tumour aggression and prognosis in oesophageal cancer.

MATERIALS AND METHODS

In 21 patients, unenhanced CT images of the primary oesophageal lesion obtained using positron-emission tomography (PET)-CT examinations underwent CTTA. CTTA was carried out using a software algorithm that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features) with quantification as entropy and uniformity (measures image heterogeneity). Texture parameters were correlated with average tumour 2-[(18)F]-fluoro-2-deoxy-d-glucose (FDG) uptake [standardized uptake values (SUV(mean) and SUV(max))] and clinical staging as determined by endoscopic ultrasound (nodal involvement) and PET-CT (distant metastases). The relationship between tumour stage, FDG uptake, and texture with survival was assessed using Kaplan-Meier analysis.

RESULTS

Tumour heterogeneity correlated with SUV(max) and SUV(mean). The closest correlations were found for SUV(mean) measured as uniformity and entropy with coarse filtration (r=-0.754, p<0.0001; and r=0.748, p=0.0001 respectively). Heterogeneity was also significantly greater in patients with clinical stage III or IV for filter values between 1.0 and 2.0 (maximum difference at filter value 1.5: entropy: p=0.027; uniformity p=0.032). The median (range) survival was 21 (4-34) months. Tumour heterogeneity assessed by CTTA (coarse uniformity) was an independent predictor of survival [odds ratio (OR)=4.45 (95% CI: 1.08, 18.37); p=0.039].

CONCLUSION

CTTA assessment of tumour heterogeneity has the potential to identify oesophageal cancers with adverse biological features and provide a prognostic indicator of survival.

Corpus Callosum Shape Analysis with Application to Dyslexia

Morphometric studies of the corpus callosum suggest its involvement in a number of psychiatric conditions. In the present study we introduce a novel pattern recognition technique that offers a point-by-point shape descriptor of the corpus callosum. The method uses arc lengths of electric field lines in order to avoid discontinuities caused by folding anatomical contours. We tested this technique by comparing the shape of the corpus callosum in a series of dyslexic men (n = 16) and age-matched controls (n = 14). The results indicate a generalized increase in size of the corpus callosum in dyslexia with a concomitant diminution at its rostral and caudal poles. The reported shape analysis and 2D-reconstruction provide information of anatomical importance that would otherwise passed unnoticed when analyzing size information alone.

Endometrial cytology and computerized morphometric analysis of epithelial nuclei: a useful tool for reproductive diagnosis in the bitch

New diagnostic approaches are required to recognize early canine hypofertility or infertility. We suggest that the identification of different cytologic types, cellular aspects, and nuclear features of the endometrial epithelial cells may be suitable for this purpose. This study was performed on the bitch (Canis familiaris) during the physiologic reproductive cycle and in uterine diseases. We also applied computerized cytomorphometry to evaluate nuclear area, perimeter, diameter, density, aspect, and roundness of endometrial epithelial cells in healthy dogs (N=35) at different stages of the reproductive cycle (before puberty, during proestrus, estrus, diestrus, and anestrus) and in bitches affected by uterine disorders (N=10). The stage of the estrous cycle was determined by vaginal cytology and progesterone evaluation and also confirmed by clinical and histologic observations. Samples for endometrial cytology were collected in vivo by uterine flushing with transcervical uterine cannulation. After uterine sampling, each dog underwent OHE or uterine stump revision. Cytologic analyses were compared with histologic examinations to verify the uterine condition. The uterine cellular population was represented by endometrial epithelial cells, erythrocytes, neutrophils, lymphocytes, eosinophils, macrophages, plasma cells, and cervical or incidental vaginal cells. Bacteria and amorphous material were observed. The proportion of different cells and nuclear features in the cytologic samples varied throughout the stages of the reproductive cycle and between normal and pathologic uterine conditions. The computer-assisted nuclear morphometry, performed in cytologic specimens by means of the six nuclear parameters chosen to evaluate the endometrial epithelial cell population, proved to be useful for determining the stage of the reproductive cycle. Furthermore, this system was demonstrated to be a valid support to diagnose and distinguish uterine disorders.

Diagnostic accuracy of community pathologists in the interpretation of colorectal polyps

BACKGROUND AND AIMS

Management of patients with endoscopically removed colorectal polyps is generally dependent on pathological evaluation. The aim of this study was to assess the accuracy and clinical impact of pathologic interpretation of colorectal polyps by community pathologists.

METHODS

Two expert gastrointestinal pathologists reviewed the slides of 300 colorectal polyps initially examined by 14 general pathologists. Polyps had been detected by a fecal occult blood test colorectal cancer screening program in Haut-Rhin, a French administrative district.

RESULTS

Villous histology was overread in 24.8% of cases and high-grade dysplasia in 22.0%. The diagnosis of serrated adenoma was confirmed in 15.7% of cases. The diagnosis of T1 carcinoma was overestimated in seven cases (17.9%) and missed in four. In the screening program, the proportion of correct diagnoses of community pathologists was estimated at 45.3% of polyps, of misclassification without clinical impact at 27.5%, and of misclassification with a theoretical impact on management at 27.2%, leading to over-surveillance in 20.3% of polyps and to unnecessary surgical resection in three individuals. Overall, 37.5% of the pathology reports of malignant polyps were complete, presenting all criteria necessary for therapeutic decision-making.

CONCLUSION

Community pathologists exhibited moderate accuracy for interpreting colorectal polyps, with an impact on patient management for around one out of five individuals. Our results confirm the intrinsic poor reliability of the pathologic interpretation of villous histology and high-grade dysplasia and suggest that these advanced pathologic features should be abandoned for clinical use. They illustrate the need for a clarification of the nomenclature of serrated polyps.

The Society of Thoracic Surgeons practice guideline series: guidelines for the management of Barrett's esophagus with high-grade dysplasia

The management of Barrett's esophagus with high-grade dysplasia is controversial. The standard of care has traditionally been esophagectomy. However, a number of treatment options aimed at esophageal preservation are increasingly being utilized by many centers. These esophageal-sparing approaches include endoscopic surveillance, mucosal ablation, and endoscopic mucosal resection. In this guideline we review the best evidence supporting these commonly used strategies for high-grade dysplasia to better define management and guide future investigation.

Glycogen synthase kinase 3beta: a novel marker and modulator of inflammatory injury in chronic renal allograft disease

One key cell-signaling event central to inflammation in kidney diseases, including chronic renal allograft dysfunction or disease (CRAD), is the activation of NF-kappaB, which controls transcription of numerous proinflammatory mediators. Glycogen synthase kinase (GSK) 3beta is an indispensable element of NF-kappaB activation, however, the exact role of GSK3beta in the pathogenesis of inflammatory kidney diseases like CRAD is uncertain and was examined. Immunohistochemistry staining of GSK3beta was weak in normal kidneys, but was markedly induced in inflamed allograft kidneys, with prominent cytoplasmic staining of tubular cells in areas of inflammation. Net GSK3beta activity is regulated by inhibitory phosphorylation of its serine 9 residue, and this occurred in CRAD. Thus, the magnitude of GSK3beta inactivation was inversely correlated with the degree of injury as assessed by Banff criteria. In vitro in cultured human tubular epithelial cells, GSK3beta overexpression augmented, while GSK3beta silencing diminished proinflammatory cellular responses to TNF-alpha stimulation, including NF-kappaB activation and expression of chemokines MCP-1 and RANTES. These inflammatory responses were obliterated by GSK3beta inhibitors. Collectively, GSK3beta plays an important role in mediating proinflammatory NF-kappaB activation and renal inflammation. Suppression of GSK3beta activity might represent a novel therapeutic strategy to treat CRAD.

Validation of novel optical imaging technologies: the pathologists' view

Noninvasive optical imaging technology has the potential to improve the accuracy of disease detection and predict treatment response. Pathology provides the critical link between the biological basis of an image or spectral signature and clinical outcomes obtained through optical imaging. The validation of optical images and spectra requires both morphologic diagnosis from histopathology and parametric analysis of tissue features above and beyond the declared pathologic "diagnosis." Enhancement of optical imaging modalities with exogenously applied biomarkers also requires validation of the biological basis for molecular contrast. For an optical diagnostic or prognostic technology to be useful, it must be clinically important, independently informative, and of demonstrated beneficial value to patient care. Its usage must be standardized with regard to methods, interpretation, reproducibility, and reporting, in which the pathologist plays a key role. By providing insight into disease pathobiology, interpretive or quantitative analysis of tissue material, and expertise in molecular diagnosis, the pathologist should be an integral part of any team that is validating novel optical imaging modalities. This review will consider (1) the selection of validation biomarkers; (2) standardization in tissue processing, diagnosis, reporting, and quantitative analysis; (3) the role of the pathologist in study design; and (4) reference standards, controls, and interobserver variability.