Automated Renal Cell Carcinoma Subtype Classification Using Morphological, Textural and Wavelets Based Features

Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis

Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital pathology tools emerged as a promising solution to enhance the diagnosis and management of renal cancer. This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. Through advanced image analysis algorithms, artificial intelligence (AI) technologies facilitate quantification of cellular and molecular markers, leading to improved accuracy and reproducibility in renal cancer diagnosis. Digital pathology platforms empower remote collaboration between pathologists and help with the creation of comprehensive databases for further research and machine learning applications. The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. In this article, we explored the digital pathology instruments available for clear cell, papillary and chromophobe renal cancers from pathologist and data analyst perspectives.

Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology

Purpose

Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides.

Design

This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing.

Results

Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area.

Conclusion

This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.

Histopathology Feature Mining and Association with Hyperspectral Imaging for the Detection of Squamous Neoplasia

Hyperspectral imaging (HSI) is a noninvasive optical modality that holds promise for early detection of tongue lesions. Spectral signatures generated by HSI contain important diagnostic information that can be used to predict the disease status of the examined biological tissue. However, the underlying pathophysiology for the spectral difference between normal and neoplastic tissue is not well understood. Here, we propose to leverage digital pathology and predictive modeling to select the most discriminative features from digitized histological images to differentiate tongue neoplasia from normal tissue, and then correlate these discriminative pathological features with corresponding spectral signatures of the neoplasia. We demonstrated the association between the histological features quantifying the architectural features of neoplasia on a microscopic scale, with the spectral signature of the corresponding tissue measured by HSI on a macroscopic level. This study may provide insight into the pathophysiology underlying the hyperspectral dataset.

High content analysis of phagocytic activity and cell morphology with PuntoMorph

BACKGROUND

Phagocytosis is essential for maintenance of normal homeostasis and healthy tissue. As such, it is a therapeutic target for a wide range of clinical applications. The development of phenotypic screens targeting phagocytosis has lagged behind, however, due to the difficulties associated with image-based quantification of phagocytic activity.

NEW METHOD

We present a robust algorithm and cell-based assay system for high content analysis of phagocytic activity. The method utilizes fluorescently labeled beads as a phagocytic substrate with defined physical properties. The algorithm employs statistical modeling to determine the mean fluorescence of individual beads within each image, and uses the information to conduct an accurate count of phagocytosed beads. In addition, the algorithm conducts detailed and sophisticated analysis of cellular morphology, making it a standalone tool for high content screening.

RESULTS

We tested our assay system using microglial cultures. Our results recapitulated previous findings on the effects of microglial stimulation on cell morphology and phagocytic activity. Moreover, our cell-level analysis revealed that the two phenotypes associated with microglial activation, specifically cell body hypertrophy and increased phagocytic activity, are not highly correlated. This novel finding suggests the two phenotypes may be under the control of distinct signaling pathways.

COMPARISON WITH EXISTING METHODS

We demonstrate that our assay system outperforms preexisting methods for quantifying phagocytic activity in multiple dimensions including speed, accuracy, and resolution.

CONCLUSIONS

We provide a framework to facilitate the development of high content assays suitable for drug screening. For convenience, we implemented our algorithm in a standalone software package, PuntoMorph.

Pathology imaging informatics for quantitative analysis of whole-slide images

Objectives

With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities.

Target audience

This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods.

Scope

First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

Histological image classification using biologically interpretable shape-based features

BACKGROUND

Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis.

METHODS

We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features.

RESULTS

The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors.

CONCLUSIONS

Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.

Recent advances in morphological cell image analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an "optimal" diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis.

Automated classification of renal cell carcinoma subtypes using bag-of-features

Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale invariant features for classification of renal cell carcinoma subtypes. Previous work shows that features from each subtype match those from expertly chosen template images. In this paper, we show that the performance of this match-based methodology greatly depends on the quality of the template images. To avoid this uncertainty, we propose a bag-of-features approach that does not require expert knowledge and instead learns a "vocabulary" of morphological characteristics from training data. We build a support vector machine using feature histograms and evaluate this method using 40 iterations of 3-fold cross validation. We achieve classification accuracy above 90% for a heterogeneous dataset labeled by an expert pathologist, showing its potential for future clinical applications.

Automated classification of renal cell carcinoma subtypes using scale invariant feature transform

The task of analyzing tissue biopsies performed by a pathologist is challenging and time consuming. It suffers from intra- and inter-user variability. Computer assisted diagnosis (CAD) helps to reduce such variations and speed up the diagnostic process. In this paper, we propose an automatic computer assisted diagnostic system for renal cell carcinoma subtype classification using scale invariant features. We capture the morphological distinctness of various subtypes and we have used them to classify a heterogeneous data set of renal cell carcinoma biopsy images. Our technique does not require color segmentation and minimizes human intervention. We circumvent user subjectivity using automated analysis and cater for intra-class heterogeneities using multiple class templates. We achieve a classification accuracy of 83% using a Bayesian classifier.

Improving Renal Cell Carcinoma Classification by Automatic Region of Interest Selection

In this paper, we present an improved automated system for classification of pathological image data of renal cell carcinoma. The task of analyzing tissue biopsies, generally performed manually by expert pathologists, is extremely challenging due to the variability in the tissue morphology, the preparation of tissue specimen, and the image acquisition process. Due to the complexity of this task and heterogeneity of patient tissue, this process suffers from inter-observer and intra-observer variability. In continuation of our previous work, which proposed a knowledge-based automated system, we observe that real life clinical biopsy images which contain necrotic regions and glands significantly degrade the classification process. Following the pathologist's technique of focusing on selected region of interest (ROI), we propose a simple ROI selection process which automatically rejects the glands and necrotic regions thereby improving the classification accuracy. We were able to improve the classification accuracy from 90% to 95% on a significantly heterogeneous image data set using our technique.