Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

Background: Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides.

Pathologists' first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study.

Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators.

Deep learning based tumor-stroma ratio scoring in colon cancer correlates with microscopic assessment.

Background: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor-stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation.

A whole-slide imaging based workflow reduces the reading time of pathologists.

Even though entirely digitized microscopic tissue sections (whole slide images, WSIs) are increasingly being used in histopathology diagnostics, little data is still available on the effect of this technique on pathologists' reading time. This study aimed to compare the time required to perform the microscopic assessment by pathologists between a conventional workflow (an optical microscope) and digitized WSIs. WSI was used in primary diagnostics at the Laboratory for Pathology Eastern Netherlands for several years (LabPON, Hengelo, The Netherlands).

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies.

In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive.

Evaluation Criteria for Chromosome Instability Detection by FISH to Predict Malignant Progression in Premalignant Glottic Laryngeal Lesions.

Background: The definition of objective, clinically applicable evaluation criteria for FISH 1c/7c in laryngeal precursor lesions for the detection of chromosome instability (CI). Copy Number Variations (CNV) for chromosomes 1 and 7 reflect the general ploidy status of premalignant head and neck lesions and can therefore be used as a marker for CI. Methods: We performed dual-target FISH for chromosomes 1 and 7 centromeres on 4 µm formalin-fixed, paraffin-embedded tissue sections of 87 laryngeal premalignancies to detect CNVs.

Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm.

An increasing number of pathology laboratories are now fully digitised, using whole slide imaging (WSI) for routine diagnostics. WSI paves the road to use artificial intelligence (AI) that will play an increasing role in computer-aided diagnosis (CAD). In melanocytic skin lesions, the presence of a dermal mitosis may be an important clue for an intermediate or a malignant lesion and may indicate worse prognosis.

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.

Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management.

Recommendations for diagnosing STIC: a systematic review and meta-analysis.

Our understanding of the oncogenesis of high-grade serous cancer of the ovary and its precursor lesions, such as serous tubal intraepithelial carcinoma (STIC), has significantly increased over the last decades. Adequate and reproducible diagnosis of these precursor lesions is important. Diagnosing STIC can have prognostic consequences and is an absolute requirement for safely offering alternative risk reducing strategies, such as risk reducing salpingectomy with delayed oophorectomy.

Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach.

Background: Histopathological classification of Wilms tumors determines treatment regimen. Machine learning has been shown to contribute to histopathological classification in various malignancies but requires large numbers of manually annotated images and thus specific pathological knowledge. This study aimed to assess whether trained, inexperienced observers could contribute to reliable annotation of Wilms tumor components for classification performed by machine learning.

Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning.

Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics.

Value of Public Challenges for the Development of Pathology Deep Learning Algorithms.

The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these tools is being facilitated by public challenges related to specific diagnostic tasks within anatomic pathology.

Histological subtypes in triple negative breast cancer are associated with specific information on survival.

Much research has focused on finding novel prognostic biomarkers for triple negative breast cancer (TNBC), whereas only scattered information about the relation between histopathological features and survival in TNBC is available. This study aims to explore the prognostic value of histological subtypes in TNBC. A multicenter retrospective TNBC cohort was established from five Dutch hospitals.

Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

Background: The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS).

Methods: We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets.

HER2, chromosome 17 polysomy and DNA ploidy status in breast cancer; a translational study.

Breast cancer treatment depends on human epidermal growth factor receptor-2 (HER2) status, which is often determined using dual probe fluorescence in situ hybridisation (FISH). Hereby, also loss and gain of the centromere of chromosome 17 (CEP17) can be observed (HER2 is located on chromosome 17). CEP17 gain can lead to difficulty in interpretation of HER2 status, since this might represent true polysomy.

Deep learning assisted mitotic counting for breast cancer.

As part of routine histological grading, for every invasive breast cancer the mitotic count is assessed by counting mitoses in the (visually selected) region with the highest proliferative activity. Because this procedure is prone to subjectivity, the present study compares visual mitotic counting with deep learning based automated mitotic counting and fully automated hotspot selection. Two cohorts were used in this study.

A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole-Slide Images.

Background: Accurate assessment of hepatic steatosis is a key to grade disease severity in non-alcoholic fatty liver disease (NAFLD).

Methods: We developed a digital automated quantification of steatosis on whole-slide images (WSIs) of liver tissue and performed a validation study. Hematoxylin-eosin stained liver tissue slides were digitally scanned, and steatotic areas were manually annotated.

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

Purpose: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we assessed the prognostic value of absolute mitotic counts for TNBC, using both deep learning and manual procedures.

Validation of Whole-slide Digitally Imaged Melanocytic Lesions: Does Z-Stack Scanning Improve Diagnostic Accuracy?

Background: Accurate diagnosis of melanocytic lesions is challenging, even for expert pathologists. Nowadays, whole-slide imaging (WSI) is used for routine clinical pathology diagnosis in several laboratories. One of the limitations of WSI, as it is most often used, is the lack of a multiplanar focusing option.

Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer.

Purpose: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images.

Quantification of Leydig cells and stromal hyperplasia in the postmenopausal ovary of women with endometrial carcinoma.

Endometrioid endometrial carcinomas (EECs) are correlated with high serum levels of androgens and estrogen. We hypothesized that Leydig cells and ovarian stromal hyperplasia contribute to postmenopausal ovarian androgen production and are observed more frequently in EEC patients. Ovaries of postmenopausal women with EEC (n = 36) or non-endometrioid endometrial carcinoma (NEEC; n = 19) were examined for the presence of hilar Leydig cells and compared with ovaries resected for benign conditions (n = 22).

Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.

The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40-65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project.