Meyke Hermsen

Comparative genomic hybridisation.

Comparative genomic hybridisation (CGH) is a technique that permits the detection of chromosomal copy number changes without the need for cell culturing. It provides a global overview of chromosomal gains and losses throughout the whole genome of a tumour. Tumour DNA is labelled with a green fluorochrome, which is subsequently mixed (1:1) with red labelled normal DNA and hybridised to normal human metaphase preparations. The green and red labelled DNA fragments compete for hybridisation to their locus of origin on the chromosomes. The green to red fluorescence ratio measured along the chromosomal axis represents loss or gain of genetic material in the tumour at that specific locus. In addition to a fluorescence microscope, the technique requires a computer with dedicated image analysis software to perform the analysis. This review aims to provide a detailed discussion of the CGH technique, and to provide a protocol with an emphasis on crucial steps.

Progression from colorectal adenoma to carcinoma is associated with non-random chromosomal gains as detected by comparative genomic hybridisation.

AIMS: Chromosomal gains and losses were surveyed by comparative genomic hybridisation (CGH) in a series of colorectal adenomas and carcinomas, in search of high risk genomic changes involved in colorectal carcinogenesis. METHODS: Nine colorectal adenomas and 14 carcinomas were analysed by CGH, and DNA ploidy was assessed with both flow and image cytometry. RESULTS: In the nine adenomas analysed, an average of 6.6 (range 1 to 11) chromosomal aberrations were identified. In the 14 carcinomas an average of 11.9 (range 5 to 17) events were found per tumour. In the adenomas the number of gains and losses was in balance (3.6 v 3.0) while in carcinomas gains occurred more often than losses (8.2 v 3.7). Frequent gains involved 13q, 7p, 8q, and 20q, whereas losses most often occurred at 18q, 4q, and 8p. Gains of 13q, 8q, and 20q, and loss of 18q occurred more often in carcinomas than in adenomas (p = 0.005, p = 0.05, p = 0.05, and p = 0.02, respectively). Aneuploid tumours showed more gains than losses (mean 9.3 v 4.9, p = 0.02), in contrast to diploid tumours where gains and losses were nearly balanced (mean 3.1 v 4.1, p = 0.5). CONCLUSIONS: The most striking difference between chromosomal aberrations in colorectal adenomas and carcinomas, as detected by CGH, is an increased number of chromosomal gains that show a nonrandom distribution. Gains of 13q and also of 20q and 8q seem especially to be involved in the progression of adenomas to carcinomas, possibly owing to low level overexpression of oncogenes at these loci.

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Segundos tumores primarios en el cáncer escamoso de cabeza y cuello

Resumen Introduccion El desarrollo de un segundo tumor primario (STP) en el cancer escamoso de cabeza y cuello (CECC) presenta elevada mortalidad y condiciona la decision terapeutica. Objetivo Describir las caracteristicas clinicas de los STP y determinar su implicacion en la supervivencia. Material y metodo Revision de 633 pacientes con CECC entre 1984-2004 describiendo las principales caracteristicas de los STP. Resultados Se observan en el 11% de los CECC. Los tumores indice que se asocian mas a un STP son los de laringe supraglotica (21%) y cavidad oral (16%). Los STP ocurren sobre todo en el area de cabeza y cuello (47%), pulmon (32%) y esofago (11%). Tienen gran impacto en la supervivencia de los pacientes con CECC, reduciendola en un 30% (23% versus 53% en el grupo control). Conclusiones Debido a la alta incidencia de STP es necesario profundizar en su estudio para realizar una prevencion adecuada y un tratamiento eficaz.

Metástasis a distancia en el cáncer de cabeza y cuello

INTRODUCTION: The presence of distant metastasis (DM) after the initial treatment of head and neck squamous cell carcinoma (HNSCC) is not considered a common event and it is associated to a poor outcome. PURPOSE: To investigate the prevalence and risk factors associated with the diagnosis of distant metastasis in SCC. METHODS AND MATERIALS: A retrospective study of 633 patients with HNSCC to describe the clinical characteristics of the DM. RESULTS: During the follow-up period after the initial treatment, 6.2% of the patients were diagnosed of having distant metastasis. The site of primary tumor was hypopharynx in 14.4%, unknown origin in 11.8% and oropharynx in 8.5%. The most common sites of DM were the lungs (58%) and the bone (22%). Three year overall survival in patients with DM was 2.5% (versus 49,5% in the control group). CONCLUSIONS: This study confirms that DM have an adverse impact in survival. There is a need of guidelines for screening of distant metastases in patients with HNSCC in order to get an early diagnosis and a more effective treatment. Because of the poor prognosis of DM, protocols including adjuvant chemotherapy should be investigated.

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Abstract. Currently, histopathological tissue examination by a pathologist represents the gold standard for breast lesion diagnostics. Automated classification of histopathological whole-slide images (WSIs) is challenging owing to the wide range of appearances of benign lesions and the visual similarity of ductal carcinoma in-situ (DCIS) to invasive lesions at the cellular level. Consequently, analysis of tissue at high resolutions with a large contextual area is necessary. We present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, DCIS, and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of hematoxylin and eosin stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of nonmalignant and malignant slides and obtains a three-class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potential for routine diagnostics.

A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images

This paper presents a new algorithm for automatic detection of regions of interest in whole slide histopathological images. The proposed algorithm generates and classifies superpixels at multiple resolutions to detect regions of interest. The algorithm emulates the way the pathologist examines the whole slide histopathology image by processing the image at low magnifications and performing more sophisticated analysis only on areas requiring more detailed information. However, instead of the traditional usage of fixed sized rectangular patches for the identification of relevant areas, we use superpixels as the visual primitives to detect regions of interest. Rectangular patches can span multiple distinct structures, thus degrade the classification performance. The proposed multi-scale superpixel classification approach yields superior performance for the identification of the regions of interest. For the evaluation, a set of 10 whole slide histopathology images of breast tissue were used. Empirical evaluation of the performance of our proposed algorithm relative to expert manual annotations shows that the algorithm achieves an area under the Receiver operating characteristic (ROC) curve of 0.958, demonstrating its efficacy for the detection of regions of interest.

Stunting correlates with high salivary and serum antibody levels after 13-valent pneumococcal conjugate vaccination of Venezuelan Amerindian children

OBJECTIVE To determine the impact of pre-vaccination nutritional status on vaccine responses in Venezuelan Warao Amerindian children vaccinated with the 13-valent pneumococcal conjugate vaccine (PCV13) and to investigate whether saliva can be used as read-out for these vaccine responses. METHODS A cross-sectional cohort of 504 Venezuelan Warao children aged 6 weeks - 59 months residing in nine geographically isolated Warao communities were vaccinated with a primary series of PCV13 according to Centers for Disease Control and Prevention (CDC)-recommended age-related schedules. Post-vaccination antibody concentrations in serum and saliva of 411 children were measured by multiplex immunoassay. The influence of malnutrition present upon vaccination on post-vaccination antibody levels was assessed by univariate and multivariable generalized estimating equations linear regression analysis. RESULTS In both stunted (38%) and non-stunted (62%) children, salivary antibody concentrations correlated well with serum levels for all serotypes with coefficients varying from 0.61 for serotype 3-0.80 for serotypes 5, 6A and 23F (all p < 0.01). Surprisingly, higher serum and salivary antibody levels were observed with increasing levels of stunting in children for all serotypes. This was statistically significant for 5/13 and 11/13 serotype-specific serum and saliva IgG concentrations respectively. CONCLUSION Stunted Amerindian children showed generally higher antibody concentrations than well-nourished children following PCV13 vaccination, indicating that chronic malnutrition influences vaccine response. Saliva samples might be useful to monitor serotype-specific antibody levels induced by PCV vaccination. This would greatly facilitate studies of vaccine efficacy in rural settings, since participant resistance generally hampers blood drawing.

Minimum Slice Spacing Required To Reconstruct 3D Shape For Serial Sections Of Breast Tissue For Comparison With Medical Imaging

There is currently an increasing interest in combining the information obtained from radiology and histology with the intent of gaining a better understanding of how different tumour morphologies can lead to distinctive radiological signs which might predict overall treatment outcome. Relating information at different resolution scales is challenging. Reconstructing 3D volumes from histology images could be the key to interpreting and relating the radiological image signal to tissue microstructure. The goal of this study is to determine the minimum sampling (maximum spacing between histological sections through a fixed surgical specimen) required to create a 3D reconstruction of the specimen to a specific tolerance. We present initial results for one lumpectomy specimen case where 33 consecutive histology slides were acquired.