Comparison of nuclear texture analysis and image cytometric DNA analysis for the assessment of dysplasia in Barrett's oesophagus. Br J Cancer. 2011;105:1218-1223. [PMID: 21934680 PMCID: PMC3208493 DOI: 10.1038/bjc.2011.353]

Exploring prognostic values of DNA ploidy, stroma-tumor fraction and nucleotyping in stage II colon cancer patients

PURPOSE

To assess the prognostic value of three novel biomarkers, DNA ploidy, stroma-tumor fraction, and nucleotyping, seeking for more accurate stratification in stage II colon cancer.

METHODS

A total of 417 patients with complete follow up information were enrolled in this study and divided into three clinical risk groups. IHC was performed to examine MSI status. DNA ploidy, stroma and nucleotyping were estimated using automated digital imaging system. Kaplan-Meier survival curves, Cox proportional hazards regression models, and correlation analyses were carried out to process our data.

RESULTS

In the whole cohort of stage II colon cancer, nucleotyping and DNA ploidy were significant prognostic factors on OS in univariate analyses. The combination of nucleotyping and DNA ploidy signified superior OS and DFS. Difference was not significant between low-stroma and high-stroma patients. In multivariable analyses, nucleotyping and the combination of nucleotyping and DNA ploidy were proven the dominant contributory factors for OS. In the low-risk group, we found the combination of nucleotyping and DNA ploidy as the independent prognostic factor statistically significant in both univariate and multivariable, while in the high-risk group, the nucleotyping.

CONCLUSIONS

Our study has proven nucleotyping and the combination of DNA ploidy and nucleotyping as independent prognostic indicators, thus expanding the application of nucleotyping as a predictor from high risk stage II colon cancer to whole risks.

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.

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.

Size and Shape Filtering of Malignant Cell Clusters within Breast Tumors Identifies Scattered Individual Epithelial Cells as the Most Valuable Histomorphological Clue in the Prognosis of Distant Metastasis Risk

Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy.

Association Between Proportion of Nuclei With High Chromatin Entropy and Prognosis in Gynecological Cancers

Background

Nuclear texture analysis measuring differences in chromatin structure has provided prognostic biomarkers in several cancers. There is a need for improved cell-by-cell chromatin analysis to detect nuclei with highly disorganized chromatin. The purpose of this study was to develop a method for detecting nuclei with high chromatin entropy and to evaluate the association between the presence of such deviating nuclei and prognosis.

Methods

A new texture-based biomarker that characterizes each cancer based on the proportion of high–chromatin entropy nuclei (<25% vs ≥25%) was developed on a discovery set of 175 uterine sarcomas. The prognostic impact of this biomarker was evaluated on a validation set of 179 uterine sarcomas, as well as on independent validation sets of 246 early-stage ovarian carcinomas and 791 endometrial carcinomas. More than 1 million images of nuclei stained for DNA were included in the study. All statistical tests were two-sided.

Results

An increased proportion of high–chromatin entropy nuclei was associated with poor clinical outcome. The biomarker predicted five-year overall survival for uterine sarcoma patients with a hazard ratio (HR) of 2.02 (95% confidence interval [CI] = 1.43 to 2.84), time to recurrence for ovarian cancer patients (HR = 2.91, 95% CI = 1.74 to 4.88), and cancer-specific survival for endometrial cancer patients (HR = 3.74, 95% CI = 2.24 to 6.24). Chromatin entropy was an independent prognostic marker in multivariable analyses with clinicopathological parameters (HR = 1.81, 95% CI = 1.21 to 2.70, for sarcoma; HR = 1.71, 95% CI = 1.01 to 2.90, for ovarian cancer; and HR = 2.03, 95% CI = 1.19 to 3.45, for endometrial cancer).

Conclusions

A novel method detected high–chromatin entropy nuclei, and an increased proportion of such nuclei was associated with poor prognosis. Chromatin entropy supplemented existing prognostic markers in multivariable analyses of three gynecological cancer cohorts.

The Pan-Cytokeratin Staining Intensity and Fractal Computational Analysis of Breast Tumor Malignant Growth Patterns Prognosticate the Occurrence of Distant Metastasis

Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors.

Prognostic biomarker value of binary and grayscale breast tumor histopathology images

AIM

Breast cancer prognosis is in the spotlight owing to its potentially major clinical importance in effective therapeutic management. Following our recent prognostic establishment of the fractal features calculated on binary breast tumor histopathology images, this study aimed to accomplish the first optimization of this methodology by direct comparison of monofractal, multifractal and co-occurrence algorithms in analysis of binary versus grayscale image formats.

PATIENTS & METHODS

The study included 93 patients with invasive breast cancer, without systemic treatment and a long median follow-up of 150 months.

RESULTS

Grayscale images provided a better prognostic source in comparison to binary, while monofractal, multifractal and co-occurrence image analysis algorithms exerted a comparable performance.

CONCLUSION

The critical prognostic importance of the grayscale texture is revealed.

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.

Chromatin changes predict recurrence after radical prostatectomy

Background:

Pathological evaluations give the best prognostic markers for prostate cancer patients after radical prostatectomy, but the observer variance is substantial. These risk assessments should be supported and supplemented by objective methods for identifying patients at increased risk of recurrence. Markers of epigenetic aberrations have shown promising results in several cancer types and can be assessed by automatic analysis of chromatin organisation in tumour cell nuclei.

Methods:

A consecutive series of 317 prostate cancer patients treated with radical prostatectomy at a national hospital between 1987 and 2005 were followed for a median of 10 years (interquartile range, 7–14). On average three tumour block samples from each patient were included to account for tumour heterogeneity. We developed a novel marker, termed Nucleotyping, based on automatic assessment of disordered chromatin organisation, and validated its ability to predict recurrence after radical prostatectomy.

Results:

Nucleotyping predicted recurrence with a hazard ratio (HR) of 3.3 (95% confidence interval (CI), 2.1–5.1). With adjustment for clinical and pathological characteristics, the HR was 2.5 (95% CI, 1.5–4.1). An updated stratification into three risk groups significantly improved the concordance with patient outcome compared with a state-of-the-art risk-stratification tool (P<0.001). The prognostic impact was most evident for the patients who were high-risk by clinical and pathological characteristics and for patients with Gleason score 7.

Conclusion:

A novel assessment of epigenetic aberrations was capable of improving risk stratification after radical prostatectomy.

Whole slide image cytometry: a novel method to detect abnormal DNA content in Barrett's esophagus

Barrett's esophagus (BE) is a precursor of esophageal adenocarcinoma (EAC). Both low-grade dysplasia (LGD) and high-grade dysplasia (HGD) are associated with an increased risk of progression to EAC. However, histological interpretation and grading of dysplasia (particularly LGD) is subjective and poorly reproducible. This study has combined whole slide imaging with DNA image cytometry to provide a novel method for the detection of abnormal DNA content through image analysis of tissue sections. A total of 20 cases were evaluated, including 8 negative for dysplasia (NFD), 6 LGD, and 6 HGD. Feulgen-stained esophageal sections were scanned in their entirety. Barrett's mucosa was interactively chosen for automatic nuclei segmentation where irrelevant cell types were ignored. The combined DNA content histogram for all nuclei within selected image regions was then obtained. In addition, three histogram measurements were computed, including xER-5C, 2cDI, and DNA-MG. Visual evaluation suggested the shape of DNA content histograms from NFD, LGD, and HGD cases exhibiting identifiable differences. The histogram measurements, xER-5C, 2cDI, and DNA-MG, were shown to be effective in differentiating metaplastic from dysplastic cases with statistical significance. Moreover, they also successfully separated NFD, LGD, and HGD patients with statistical significance. Whole slide image cytometry is a novel and effective method for the detection of abnormal DNA content in BE. Compared with histological review, it is more objective. Compared with flow cytometry and cytology-preparation image cytometry, it is low cost, simple to use, only requires a single 1 μm section, and facilitates selection of tissue and topographical correlation. Whole slide image cytometry can detect differences in DNA content between NFD, LGD, and HGD patients in this cross-sectional study. Abnormal DNA content detection by whole slide image cytometry is a promising biomarker of progression that could affect future diagnostics in BE.

Evaluation of Quantitative Digital Pathology in the Assessment of Barrett Esophagus-Associated Dysplasia

OBJECTIVES

Barrett esophagus (BE) is a precursor lesion that confers an increased risk of esophageal adenocarcinoma. Two issues confront the diagnosis of patients with BE: (1) sampling error at the time of endoscopy and (2) variability among pathologists in grading dysplasia. The purpose of our study was to evaluate quantitative digital pathology (QDP) as a marker of dysplasia and stratification from low-grade to high-grade dysplasia to intramucosal adenocarcinoma in BE.

METHODS

Sixty-one esophageal biopsy specimens with BE were selected and divided into six groups according to the dysplasia grade. QDP image analysis was carried out by an in-house automated quantitative system on sections. The values of 110 nuclear features that analyze the morphology and chromatin texture were generated for each nucleus.

RESULTS

A progressive correlation was found between nuclear morphometric features and chromatin features with BE dysplasia. The chromatin texture was the best discriminator of the class diagnosis. There was a significant difference between the chromatin features of isolated low-grade dysplasia vs low-grade dysplasia that was associated with higher grade lesions in other biopsy tissue fragments.

CONCLUSIONS

QDP is a promising tool in the new era of digital pathology. Pending clinical validation studies, analysis of chromatin texture could contribute to the differential diagnosis of BE class and the detection of concomitant high-grade lesions if not sampled.

Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk

Owing to exceptional heterogeneity in the outcome of invasive breast cancer it is essential to develop highly accurate prognostic tools for effective therapeutic management. Based on this pressing need, we aimed to improve breast cancer prognosis by exploring the prognostic value of tumor histology image analysis. Patient group (n=78) selection was based on invasive breast cancer diagnosis without systemic treatment with a median follow-up of 147 months. Gray-level co-occurrence matrix texture analysis was performed retrospectively on primary tumor tissue section digital images stained either nonspecifically with hematoxylin and eosin or specifically with a pan-cytokeratin antibody cocktail for epithelial malignant cells. Univariate analysis revealed stronger association with metastasis risk by texture analysis when compared with clinicopathological parameters. The combination of individual clinicopathological and texture variables into composite scores resulted in further powerful enhancement of prognostic performance, with an accuracy of up to 90%, discrimination efficiency by the area under the curve [95% confidence interval (CI)] of 0.94 (0.87-0.99) and hazard ratio (95% CI) of 20.1 (7.5-109.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the models are generalizable. Whereas further validation is needed on an external set of patients, this preliminary study indicates the potential use of primary breast tumor histology texture as a highly accurate, simple, and cost-effective prognostic indicator of distant metastasis risk.

Entropy-based adaptive nuclear texture features are independent prognostic markers in a total population of uterine sarcomas

Nuclear texture analysis measures the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image and is a promising quantitative tool for prognosis of cancer. The aim of this study was to evaluate the prognostic value of entropy-based adaptive nuclear texture features in a total population of 354 uterine sarcomas. Isolated nuclei (monolayers) were prepared from 50 µm tissue sections and stained with Feulgen-Schiff. Local gray level entropy was measured within small windows of each nuclear image and stored in gray level entropy matrices, and two superior adaptive texture features were calculated from each matrix. The 5-year crude survival was significantly higher (P < 0.001) for patients with high texture feature values (72%) than for patients with low feature values (36%). When combining DNA ploidy classification (diploid/nondiploid) and texture (high/low feature value), the patients could be stratified into three risk groups with 5-year crude survival of 77, 57, and 34% (Hazard Ratios (HR) of 1, 2.3, and 4.1, P < 0.001). Entropy-based adaptive nuclear texture was an independent prognostic marker for crude survival in multivariate analysis including relevant clinicopathological features (HR = 2.1, P = 0.001), and should therefore be considered as a potential prognostic marker in uterine sarcomas. © The Authors. Published 2014 International Society for Advancement of Cytometry

Prognostic impact of genomic instability in colorectal cancer

BACKGROUND

The prognostic impact of an indication of chromosomal instability (CIN) is evaluated in a consecutive series of 952 colorectal cancer patients treated at Aker University Hospital, Norway, during 1993-2003. Microsatellite instability (MSI) in this case series has recently been reported and made it possible to find the co-occurrence and compare the prognostic significance of CIN and MSI.

METHODS

Data sets for overall survival (OS; n=855) and time to recurrence (TTR; n=579) were studied. To reveal CIN we used automated image cytometry (ICM). Non-diploid histograms were taken as indicative of the presence of CIN. PCR-based measures of MSI in this material have already been described.

RESULTS

As with MSI, CIN was found to be an independent predictor of early relapse and death among stage II patients (TTR: n=278: HR 2.19 (95% CI: 1.35-3.55), P=0.002). Of the MSI tumours (16%), 71% were found to be DNA diploid, 21% were DNA tetraploid and 8% were DNA aneuploid. Among microsatellite stable tumours, 24% were DNA diploid, 15% were DNA tetraploid and 61% were DNA aneuploid.

CONCLUSION

For patients presenting with stage II disease, genomic instability as detected by DNA image cytometry has the potential to provide a useful biomarker for relapse and cancer-related death following surgery with curative intent.

Epigenetically induced changes in nuclear textural patterns and gelatinase expression in human fibrosarcoma cells

OBJECTIVE

Chromatin texture patterns of tumour cell nuclei can serve as cancer biomarkers, either to define diagnostic classifications or to obtain relevant prognostic information, in a large number of human tumours. Epigenetic mechanisms, mainly DNA methylation and histone post-translational modification, have been shown to influence chromatin packing states, and therefore nuclear texture. The aim of this study was to analyse effects of these two mechanisms on chromatin texture, and also on correlation with gelatinase expression, in human fibrosarcoma tumour cells.

MATERIALS AND METHODS

We investigated effects of DNA hypomethylating agent 5-aza-2'-deoxycytidine (5-azadC) and histone deacetylase inhibitor trichostatin A (TSA) on nuclear textural characteristics of human HT1080 fibrosarcoma cells, evaluated by image cytometry, and expression of gelatinases MMP-2 and MMP-9, two metalloproteinases implicated in cancer progression and metastasis.

RESULTS

5-azadC induced significant variation in chromatin higher order organization, particularly chromatin decondensation, associated with reduction in global DNA methylation, concomitantly with increase in MMP-9, and to a lesser extent, MMP-2 expression. TSA alone did not have any effect on HT1080 cells, but exhibited differential activity when added to cells treated with 5-azadC. When treated with both drugs, nuclei had higher texture abnormalities. In this setting, reduction in MMP-9 expression was observed, whereas MMP-2 expression remained unaffected.

CONCLUSIONS

These data show that hypomethylating drug 5-azadC and histone deacetylase inhibitor TSA were able to induce modulation of higher order chromatin organization and gelatinase expression in human HT1080 fibrosarcoma cells.