Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

A Multi-Omics Prognostic Model Capturing Tumor Stemness and the Immune Microenvironment in Clear Cell Renal Cell Carcinoma

Background: Cancer stem-like cells (CSCs), a distinct subset recognized for their stem cell-like abilities, are intimately linked to the resistance to radiotherapy, metastatic behaviors, and self-renewal capacities in tumors. Despite their relevance, the definitive traits and importance of CSCs in the realm of oncology are still not fully comprehended, particularly in the context of clear cell renal cell carcinoma (ccRCC). A comprehensive understanding of these CSCs' properties in relation to stemness, and their impact on the efficacy of treatment and resistance to medication, is of paramount importance. Methods: In a meticulous research effort, we have identified new molecular categories designated as CRCS1 and CRCS2 through the application of an unsupervised clustering algorithm. The analysis of these subtypes included a comprehensive examination of the tumor immune environment, patterns of metabolic activity, progression of the disease, and its response to immunotherapy. In addition, we have delved into understanding these subtypes' distinctive clinical presentations, the landscape of their genomic alterations, and the likelihood of their response to various pharmacological interventions. Proceeding from these insights, prognostic models were developed that could potentially forecast the outcomes for patients with ccRCC, as well as inform strategies for the surveillance of recurrence after treatment and the handling of drug-resistant scenarios. Results: Compared with CRCS1, CRCS2 patients had a lower clinical stage/grading and a better prognosis. The CRCS2 subtype was in a hypoxic state and was characterized by suppression and exclusion of immune function, which was sensitive to gefitinib, erlotinib, and saracatinib. The constructed prognostic risk model performed well in both training and validation cohorts, helping to identify patients who may benefit from specific treatments or who are at risk of recurrence and drug resistance. A novel therapeutic target, SAA2, regulating neutrophil and fibroblast infiltration, and, thus promoting ccRCC progression, was identified. Conclusions: Our findings highlight the key role of CSCs in shaping the ccRCC tumor microenvironment, crucial for therapy research and clinical guidance. Recognizing tumor stemness helps to predict treatment efficacy, recurrence, and drug resistance, informing treatment strategies and enhancing ccRCC patient outcomes.

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

Comprehensive molecular characterization of TFE3-rearranged renal cell carcinoma

TFE3-rearranged renal cell cancer (tRCC) is a rare form of RCC that involves chromosomal translocation of the Xp11.2 TFE3 gene. Despite its early onset and poor prognosis, the molecular mechanisms of the pathogenesis of tRCC remain elusive. This study aimed to identify novel therapeutic targets for patients with primary and recurrent tRCC. We collected 19 TFE3-positive RCC tissues that were diagnosed by immunohistochemistry and subjected them to genetic characterization to examine their genomic and transcriptomic features. Tumor-specific signatures were extracted using whole exome sequencing (WES) and RNA sequencing (RNA-seq) data, and the functional consequences were analyzed in a cell line with TFE3 translocation. Both a low burden of somatic single nucleotide variants (SNVs) and a positive correlation between the number of somatic variants and age of onset were observed. Transcriptome analysis revealed that four samples (21.1%) lacked the expected fusion event and clustered with the genomic profiles of clear cell RCC (ccRCC) tissues. The fusion event also demonstrated an enrichment of upregulated genes associated with mitochondrial respiration compared with ccRCC expression profiles. Comparison of the RNA expression profile with the TFE3 ChIP-seq pattern data indicated that PPARGC1A is a metabolic regulator of the oncogenic process. Cell proliferation was reduced when PPARGC1A and its related metabolic pathways were repressed by its inhibitor SR-18292. In conclusion, we demonstrate that PPARGC1A-mediated mitochondrial respiration can be considered a potential therapeutic target in tRCC. This study identifies an uncharacterized genetic profile of an RCC subtype with unique clinical features and provides therapeutic options specific to tRCC.

Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review

CONTEXT

Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases.

OBJECTIVE

To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC).

EVIDENCE ACQUISITION

A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool.

EVIDENCE SYNTHESIS

In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported.

CONCLUSIONS

This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice.

PATIENT SUMMARY

Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma

The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.

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.

Digital Spatial Profiling Identifies the Tumor Periphery as a Highly Active Biological Niche in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by a high degree of intratumoral heterogeneity (ITH). Besides genomic ITH, there is considerable functional ITH, which encompasses spatial niches with distinct proliferative and signaling activities. The full extent of functional spatial heterogeneity in ccRCC is incompletely understood. In the present study, a total of 17 ccRCC tissue specimens from different sites (primary tumor, n = 11; local recurrence, n = 1; distant metastasis, n = 5) were analyzed using digital spatial profiling (DSP) of protein expression. A total of 128 regions of interest from the tumor periphery and tumor center were analyzed for the expression of 46 proteins, comprising three major signaling pathways as well as immune cell markers. Results were correlated to clinico-pathological variables. The differential expression of granzyme B was validated using conventional immunohistochemistry and was correlated to the cancer-specific patient survival. We found that a total of 37 proteins were differentially expressed between the tumor periphery and tumor center. Thirty-five of the proteins were upregulated in the tumor periphery compared to the center. These included proteins involved in cell proliferation, MAPK and PI3K/AKT signaling, apoptosis regulation, epithelial-to-mesenchymal transition, as well as immune cell markers. Among the most significantly upregulated proteins in the tumor periphery was granzyme B. Granzyme B upregulation in the tumor periphery correlated with a significantly reduced cancer-specific patient survival. In conclusion, this study highlights the unique cellular contexture of the tumor periphery in ccRCC. The correlation between granzyme B upregulation in the tumor periphery and patient survival suggests local selection pressure for aggressive tumor growth and disease progression. Our results underscore the potential of spatial biology for biomarker discovery in ccRCC and cancer in general.

Comprehensive Systematic Review of Biomarkers in Metastatic Renal Cell Carcinoma: Predictors, Prognostics, and Therapeutic Monitoring

BACKGROUND

Challenges remain in determining the most effective treatment strategies and identifying patients who would benefit from adjuvant or neoadjuvant therapy in renal cell carcinoma. The objective of this review is to provide a comprehensive overview of biomarkers in metastatic renal cell carcinoma (mRCC) and their utility in prediction of treatment response, prognosis, and therapeutic monitoring in patients receiving systemic therapy for metastatic disease.

METHODS

A systematic literature search was conducted using the PubMed database for relevant studies published between January 2017 and December 2022. The search focused on biomarkers associated with mRCC and their relationship to immune checkpoint inhibitors, targeted therapy, and VEGF inhibitors in the adjuvant, neoadjuvant, and metastatic settings.

RESULTS

The review identified various biomarkers with predictive, prognostic, and therapeutic monitoring potential in mRCC. The review also discussed the challenges associated with anti-angiogenic and immune-checkpoint monotherapy trials and highlighted the need for personalized therapy based on molecular signatures.

CONCLUSION

This comprehensive review provides valuable insights into the landscape of biomarkers in mRCC and their potential applications in prediction of treatment response, prognosis, and therapeutic monitoring. The findings underscore the importance of incorporating biomarker assessment into clinical practice to guide treatment decisions and improve patient outcomes in mRCC.

Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance

The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.

Improving CNNs classification with pathologist-based expertise: the renal cell carcinoma case study

The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist's expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT's superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases.

Computational pathology to improve biomarker testing in breast cancer: how close are we?

The recent advancements in breast cancer precision medicine have highlighted the urgency for the precise and reproducible characterization of clinically actionable biomarkers. Despite numerous standardization efforts, biomarker testing by conventional methodologies is challenged by several issues such as high inter-observer variabilities, the spatial heterogeneity of biomarkers expression, and technological heterogeneity. In this respect, artificial intelligence-based digital pathology approaches are being increasingly recognized as promising methods for biomarker testing and subsequently improved clinical management. Here, we provide an overview on the most recent advances for artificial intelligence-assisted biomarkers testing in breast cancer, with a particular focus on tumor-infiltrating lymphocytes, programmed death-ligand 1, phosphatidylinositol-3 kinase catalytic alpha, and estrogen receptor 1. Challenges and solutions for this integrative analysis in pathology laboratories are also provided.

A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network

Histopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolutional neural networks. Specifically, the proposed model consists of a spatial branch and a channel branch. In the spatial branch, residual structural blocks are used to extract deep spatial features. In the channel branch, a multi-scale feature extraction module and some multi-level feature extraction modules are designed to extract channel features in order to increase the representational ability of the model. The experimental results of the Multidimensional Choledoch Database show that the proposed method performs better than other classical CNN classification methods.

TFE3 gene rearrangement and protein expression contribute to a poor prognosis of renal cell carcinoma

Background

TFE3-rearranged renal cell carcinoma (TFE3-rearranged RCC) is a type of kidney cancer with a low incidence, with no consensus about whether it has a worse prognosis than clear cell renal cell carcinoma (ccRCC). This study attempted to elucidate the impact of TFE3-rearranged RCC by analyzing its clinical features and prognosis.

Methods

Patients treated in Sun Yat-sen Memorial Hospital (SYSMH) who were suspected to be diagnosed with TFE3-rearranged RCC were divided into two groups, TFE3-rearranged RCC and ccRCC with positive TFE3 protein expression on immunohistochemistry [TFE3(+) ccRCC], by dual-color, break-apart fluorescence in situ hybridization (FISH). After balancing the baseline characteristics with TFE3(+) ccRCC using the propensity score matching (PSM) method in a ratio of 2, we selected patients diagnosed with ccRCC with negative TFE3 protein expression on immunohistochemistry [TFE3(-) ccRCC]. The impact of TFE3 gene rearrangement and protein expression on renal cell carcinoma was determined by feature comparison with a nonparametric test and survival analysis with the Kaplan‒Meier method.

Results

Among 37 patients suspected of having TFE3-rearranged RCC, 13 patients were diagnosed with TFE3-rearranged RCC, and 24 patients had TFE3(+) ccRCC. The recurrence and new metastasis of TFE3-rearranged RCC was relatively common, even if the tumor stage was early at the first diagnosis. Through feature comparison and survival analysis, we found that TFE3-rearranged RCC was quite similar to TFE3(+) ccRCC. Compared with TFE3(-) ccRCC, TFE3(+) ccRCC tended to have a larger tumor diameter (P = 0.011), higher neutrophil/lymphocyte ratio (NLR) (P = 0.017) and metastatic potential (P = 0.022), and worse overall survival (OS) (P = 0.043) and PFS (P = 0.016). The survival analysis showed that TFE3-rearranged RCC had a worse PFS than ccRCC (P = 0.002), and TFE3(+) RCC had a worse PFS than TFE3(-) RCC (P = 0.001). According to the stratification system based on the combination of TFE3 and lymphovascular invasion (LVI), we further found that the prognosis from good to poor was TFE3(-) LVI(-), TFE3(+) LVI(-), TFE3(+) LVI(+) and TFE3(-) LVI(+), with statistically significant differences in both OS (P = 0.001) and PFS (P < 0.001). In addition, we also reported two cases with poor prognosis, of which one was TFE3-rearranged RCC and the other was TFE3(+) ccRCC.

Conclusions

This is a novel finding that both FISH confirmed TFE3 gene rearrangement-mediated TFE3-rearranged RCC and IHC confirmed positive TFE3 protein expression [TFE3(+)] contribute to a poor prognosis in RCC, suggesting more active treatment and careful follow-up for TFE3(+) RCC patients. The combination of TFE3 and LVI may be a new risk stratification system for RCC.

Identification of basement membrane-related prognostic signature for predicting prognosis, immune response and potential drug prediction in papillary renal cell carcinoma

Papillary renal cell carcinoma (PRCC) is a malignant neoplasm of the kidney and is highly interesting due to its increasing incidence. Many studies have shown that the basement membrane (BM) plays an important role in the development of cancer, and structural and functional changes in the BM can be observed in most renal lesions. However, the role of BM in the malignant progression of PRCC and its impact on prognosis has not been fully studied. Therefore, this study aimed to explore the functional and prognostic value of basement membrane-associated genes (BMs) in PRCC patients. We identified differentially expressed BMs between PRCC tumor samples and normal tissue and systematically explored the relevance of BMs to immune infiltration. Moreover, we constructed a risk signature based on these differentially expressed genes (DEGs) using Lasso regression analysis and demonstrated their independence using Cox regression analysis. Finally, we predicted 9 small molecule drugs with the potential to treat PRCC and compared the differences in sensitivity to commonly used chemotherapeutic agents between high and low-risk groups to better target patients for more precise treatment planning. Taken together, our study suggested that BMs might play a crucial role in the development of PRCC, and these results might provide new insights into the treatment of PRCC.

A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue

The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes.

Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models

Introduction

Melanoma is the fifth most common cancer in US, and the incidence is increasing 1.4% annually. The overall survival rate for early-stage disease is 99.4%. However, melanoma can recur years later (in the same region of the body or as distant metastasis), and results in a dramatically lower survival rate. Currently there is no reliable method to predict tumor recurrence and metastasis on early primary tumor histological images.

Methods

To identify rapid, accurate, and cost-effective predictors of metastasis and survival, in this work, we applied various interpretable machine learning approaches to analyze melanoma histopathological H&E images. The result is a set of image features that can help clinicians identify high-risk-of-metastasis patients for increased clinical follow-up and precision treatment. We use simple models (i.e., logarithmic classification and KNN) and "human-interpretable" measures of cell morphology and tissue architecture (e.g., cell size, staining intensity, and cell density) to predict the melanoma survival on public and local Stage I-III cohorts as well as the metastasis risk on a local cohort.

Results

We use penalized survival regression to limit features available to downstream classifiers and investigate the utility of convolutional neural networks in isolating tumor regions to focus morphology extraction on only the tumor region. This approach allows us to predict survival and metastasis with a maximum F1 score of 0.72 and 0.73, respectively, and to visualize several high-risk cell morphologies.

Discussion

This lays the foundation for future work, which will focus on using our interpretable pipeline to predict metastasis in Stage I & II melanoma.

Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information

Triple-negative breast cancer (TNBC) is one of the more aggressive subtypes of breast cancer. The prognosis of TNBC patients remains low. Therefore, there is still a need to continue identifying novel biomarkers to improve the prognosis and treatment of TNBC patients. Research in recent years has shown that the effective use and integration of information in genomic data and image data will contribute to the prediction and prognosis of diseases. Considering that imaging genetics can deeply study the influence of microscopic genetic variation on disease phenotype, this paper proposes a sample prior information-induced multidimensional combined non-negative matrix factorization (SPID-MDJNMF) algorithm to integrate the Whole-slide image (WSI), mRNAs expression data, and miRNAs expression data. The algorithm effectively fuses high-dimensional data of three modalities through various constraints. In addition, this paper constructs an undirected graph between samples, uses an adjacency matrix to constrain the similarity, and embeds the clinical stage information of patients in the algorithm so that the algorithm can identify the co-expression patterns of samples with different labels. We performed univariate and multivariate Cox regression analysis on the mRNAs and miRNAs in the screened co-expression modules to construct a TNBC-related prognostic model. Finally, we constructed prognostic models for 2-mRNAs (IL12RB2 and CNIH2) and 2-miRNAs (miR-203a-3p and miR-148b-3p), respectively. The prognostic model can predict the survival time of TNBC patients with high accuracy. In conclusion, our proposed SPID-MDJNMF algorithm can efficiently integrate image and genomic data. Furthermore, we evaluated the prognostic value of mRNAs and miRNAs screened by the SPID-MDJNMF algorithm in TNBC, which may provide promising targets for the prognosis of TNBC patients.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms

Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Introduction

Gastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. Therefore, this paper compares the performance of multiple algorithms in anticipation of applying ensemble learning to a practical gastric cancer classification problem.

Methods

The complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier.

Results

The experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity.

Discussion

Suitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.

Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics

As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, limitations stymying clinical artificial intelligence (AI) will not be extinguished without thorough validation and interrogation of ML tools by pathologists and regulatory bodies alike. ML solutions deployed in clinical settings for applications in prostate pathology yield promising results. Recent breakthroughs in clinical artificial intelligence for genitourinary pathology demonstrate unprecedented generalizability, heralding prospects for a future in which AI-driven assistive solutions may be seen as laboratory faculty, rather than novelty.

A novel pipeline for computerized mouse spermatogenesis staging

MOTIVATION

Differentiating 12 stages of the mouse seminiferous epithelial cycle is vital towards understanding the dynamic spermatogenesis process. However, it is challenging since two adjacent spermatogenic stages are morphologically similar. Distinguishing Stages I-III from Stages IV-V is important for histologists to understand sperm development in wildtype mice and spermatogenic defects in infertile mice. To achieve this, we propose a novel pipeline for computerized spermatogenesis staging (CSS).

RESULTS

The CSS pipeline comprises four parts: (i) A seminiferous tubule segmentation model is developed to extract every single tubule; (ii) A multi-scale learning (MSL) model is developed to integrate local and global information of a seminiferous tubule to distinguish Stages I-V from Stages VI-XII; (iii) a multi-task learning (MTL) model is developed to segment the multiple testicular cells for Stages I-V without an exhaustive requirement for manual annotation; (iv) A set of 204D image-derived features is developed to discriminate Stages I-III from Stages IV-V by capturing cell-level and image-level representation. Experimental results suggest that the proposed MSL and MTL models outperform classic single-scale and single-task models when manual annotation is limited. In addition, the proposed image-derived features are discriminative between Stages I-III and Stages IV-V. In conclusion, the CSS pipeline can not only provide histologists with a solution to facilitate quantitative analysis for spermatogenesis stage identification but also help them to uncover novel computerized image-derived biomarkers.

AVAILABILITY AND IMPLEMENTATION

https://github.com/jydada/CSS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers

BACKGROUND

Cancer is the leading cause of death worldwide with breast and prostate cancer the most common among women and men, respectively. Gene expression and image features are independently prognostic of patient survival; but until the advent of spatial transcriptomics (ST), it was not possible to determine how gene expression of cells was tied to their spatial relationships (i.e., topology).

METHODS

We identify topology-associated genes (TAGs) that correlate with 700 image topological features (ITFs) in breast and prostate cancer ST samples. Genes and image topological features are independently clustered and correlated with each other. Themes among genes correlated with ITFs are investigated by functional enrichment analysis.

RESULTS

Overall, topology-associated genes (TAG) corresponding to extracellular matrix (ECM) and Collagen Type I Trimer gene ontology terms are common to both prostate and breast cancer. In breast cancer specifically, we identify the ZAG-PIP Complex as a TAG. In prostate cancer, we identify distinct TAGs that are enriched for GI dysmotility and the IgA immunoglobulin complex. We identified TAGs in every ST slide regardless of cancer type.

CONCLUSIONS

These TAGs are enriched for ontology terms, illustrating the biological relevance to our image topology features and their potential utility in diagnostic and prognostic models.

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability

Background

Microsatellite instability (MSI) is associated with several tumor types and has become increasingly vital in guiding patient treatment decisions; however, reasonably distinguishing MSI from its counterpart is challenging in clinical practice.

Methods

In this study, interpretable pathological image analysis strategies are established to help medical experts to identify MSI. The strategies only require ubiquitous hematoxylin and eosin–stained whole-slide images and perform well in the three cohorts collected from The Cancer Genome Atlas. Equipped with machine learning and image processing technique, intelligent models are established to diagnose MSI based on pathological images, providing the rationale of the decision in both image level and pathological feature level.

Findings

The strategies achieve two levels of interpretability. First, the image-level interpretability is achieved by generating localization heat maps of important regions based on deep learning. Second, the feature-level interpretability is attained through feature importance and pathological feature interaction analysis. Interestingly, from both the image-level and feature-level interpretability, color and texture characteristics, as well as their interaction, are shown to be mostly contributed to the MSI prediction.

Interpretation

The developed transparent machine learning pipeline is able to detect MSI efficiently and provide comprehensive clinical insights to pathologists. The comprehensible heat maps and features in the intelligent pipeline reflect extra- and intra-cellular acid–base balance shift in MSI tumor.

Exosomes in urological diseases - Biological functions and clinical applications

Exosomes are extracellular vesicles with a variety of biological functions that exist in various biological body fluids and exert their functions through proteins, nucleic acids, lipids, and metabolites. Recent discoveries have revealed the functional and biomarker roles of miRNAs in urological diseases, including benign diseases and malignancies. Exosomes have several uses in the diagnosis, treatment, and monitoring of urological diseases, especially cancer. Proteins and nucleic acids can be used as alternative biomarkers for detecting urological diseases. Additionally, exosomes can be detected in most body fluids, thereby avoiding pathogenesis. More importantly, for urological tumors, exosomes display a higher sensitivity than circulating tumor cells and tumor-derived DNA in body fluid biopsies because of their low immunogenicity and high stability. These advantages have made it a research hotspot in recent years. In this review, we focus on the biological characteristics and functions of exosomes and summarize their advantages and the latest progress in the diagnosis and treatment of urological diseases.

Cross Analysis of Genomic-Pathologic Features on Multiple Primary Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy cancer worldwide with a poor prognosis. Hepatic resection is indicated as a potentially curative option for HCC patients in the early stage. However, due to multiple nodules, it leads to clinical challenges for surgical management. Approximately 41%–75% of HCC cases are multifocal at initial diagnosis, which may arise from multicentric occurrence (MO-HCC) or intrahepatic metastasis (IM-HCC) pattern with significantly different clinical outcomes. Effectively differentiating the two mechanisms is crucial to prioritize the allocation of surgery for multifocal HCC. In this study, we collected a multifocal hepatocellular carcinoma cohort of 17 patients with a total of 34 samples. We performed whole-exome sequencing and staining of pathological HE sections for each lesion. Reconstruction of the clonal evolutionary pattern using genome mutations showed that the intrahepatic metastogenesis pattern had a poorer survival performance than independent origins, with variants in the TP53, ARID1A, and higher CNV variants occurring more significantly in the metastatic pattern. Cross-modality analysis with pathology showed that molecular classification results were consistent with pathology results in 70.6% of patients, and we found that pathology results could further complement the classification for undefined patterns of occurrence. Based on these results, we propose a model to differentiate the pattern of multifocal hepatocellular carcinoma based on the pathological results and genome mutations information, which can provide guidelines for diagnosing and treating multifocal hepatocellular carcinoma.

Artificial Intelligence in Kidney Cancer

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.

Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer

Background

TMPRSS2-ERG gene rearrangement, the most common E26 transformation specific (ETS) gene fusion within prostate cancer, is known to contribute to the pathogenesis of this disease and carries diagnostic annotations for prostate cancer patients clinically. The ERG rearrangement status in prostatic adenocarcinoma currently cannot be reliably identified from histologic features on H&E-stained slides alone and hence requires ancillary studies such as immunohistochemistry (IHC), fluorescent in situ hybridization (FISH) or next generation sequencing (NGS) for identification.

Methods

Objective

We accordingly sought to develop a deep learning-based algorithm to identify ERG rearrangement status in prostatic adenocarcinoma based on digitized slides of H&E morphology alone.

Design

Setting, and Participants: Whole slide images from 392 in-house and TCGA cases were employed and annotated using QuPath. Image patches of 224 × 224 pixel were exported at 10 ×, 20 ×, and 40 × for input into a deep learning model based on MobileNetV2 convolutional neural network architecture pre-trained on ImageNet. A separate model was trained for each magnification. Training and test datasets consisted of 261 cases and 131 cases, respectively. The output of the model included a prediction of ERG-positive (ERG rearranged) or ERG-negative (ERG not rearranged) status for each input patch.

Outcome measurements and statistical analysis: Various accuracy measurements including area under the curve (AUC) of the receiver operating characteristic (ROC) curves were used to evaluate the deep learning model.

Results and Limitations

All models showed similar ROC curves with AUC results ranging between 0.82 and 0.85. The sensitivity and specificity of these models were 75.0% and 83.1% (20 × model), respectively.

Conclusions

A deep learning-based model can successfully predict ERG rearrangement status in the majority of prostatic adenocarcinomas utilizing only H&E-stained digital slides. Such an artificial intelligence-based model can eliminate the need for using extra tumor tissue to perform ancillary studies in order to assess for ERG gene rearrangement in prostatic adenocarcinoma.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

Effective prediction model for preventing postoperative deep vein thrombosis during bladder cancer treatment

Objective

To begin to understand how to prevent deep vein thrombosis (DVT) after an innovative operation termed intracorporeal laparoscopic reconstruction of detenial sigmoid neobladder, we explored the factors that influence DVT following surgery, with the aim of constructing a model for predicting DVT occurrence.

Methods

This retrospective study included 151 bladder cancer patients who underwent intracorporeal laparoscopic reconstruction of detenial sigmoid neobladder. Data describing general clinical characteristics and other common parameters were collected and analyzed. Thereafter, we generated model evaluation curves and finally cross-validated their extrapolations.

Results

Age and body mass index were risk factors for DVT, whereas postoperative use of hemostatic agents and postoperative passive muscle massage were significant protective factors. Model evaluation curves showed that the model had high accuracy and little bias. Cross-validation affirmed the accuracy of our model.

Conclusion

The prediction model constructed herein was highly accurate and had little bias; thus, it can be used to predict the likelihood of developing DVT after surgery.

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients

Background: Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. We aimed to develop an artificial intelligence algorithm that can predict sepsis early.

Methods: This was a secondary analysis of an observational cohort study from the Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University. A total of 4,449 infected patients were randomly assigned to the development and validation data set at a ratio of 4:1. After extracting electronic medical record data, a set of 55 features (variables) was calculated and passed to the random forest algorithm to predict the onset of sepsis.

Results: The pre-procedure clinical variables were used to build a prediction model from the training data set using the random forest machine learning method; a 5-fold cross-validation was used to evaluate the prediction accuracy of the model. Finally, we tested the model using the validation data set. The area obtained by the model under the receiver operating characteristic (ROC) curve (AUC) was 0.91, the sensitivity was 87%, and the specificity was 89%.

Conclusions: This newly established machine learning-based model has shown good predictive ability in Chinese sepsis patients. External validation studies are necessary to confirm the universality of our method in the population and treatment practice.

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture

Characterizing likelihood of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is an important yet unmet challenge. In this study, a machine-learning framework is developed using imaging of biopsy pathology specimens to generate models of likelihood of NAC response. Developed using cross-validation (evaluable N = 66) and an independent validation cohort (evaluable N = 56), our models achieve promising results (65%-73% accuracy). Interestingly, one model-using features derived from hematoxylin and eosin (H&E)-stained tissues in conjunction with clinico-demographic features-is able to stratify the cohort into likely responders in cross-validation and the validation cohort (response rate of 65% for predicted responder compared with the 41% baseline response rate in the validation cohort). The results suggest that computational approaches applied to routine pathology specimens of MIBC can capture differences between responders and non-responders to NAC and should therefore be considered in the future design of precision oncology for MIBC.

Kidney cancer management 3.0: can artificial intelligence make us better?

PURPOSE OF REVIEW

Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space.

RECENT FINDINGS

Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses.

SUMMARY

Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.

Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies

CONTEXT.—

Large-cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established.

OBJECTIVE.—

To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis.

DESIGN.—

We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient's probability of transformation.

RESULTS.—

Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and ≥30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine generated quantification demonstrated better reproducibility and stronger correlation with final outcome data.

CONCLUSIONS.—

These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist-augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice.

Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images

BACKGROUND AND OBJECTIVE

Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better risk stratification, prognosis prediction, and therapy management. In this paper, we focused on the automatic evaluation of TME in giga-pixel digital histopathology whole-slide images.

METHODS

A convolutional neural network is used to recognize nine different content presented in colon cancer whole-slide images. Several implementation details, including the foreground filtering and stain normalization are discussed. Based on the whole-slide segmentation, several TME descriptors are quantified and correlated with the clinical outcome by Kaplan-Meier analysis and Cox regression. Specifically, the stroma, tumor, necrosis, and lymphocyte components are discussed.

RESULTS

We validated the method on colon adenocarcinoma cases from The Cancer Genome Atlas project. The result shows that the stroma is an independent predictor of progression-free interval (PFI) after corrected by age and pathological stage, with a hazard ratio of 1.665 (95%CI: 1.110~2.495, p = 0.014). High-level necrosis component and lymphocytes component tend to be correlated with poor PFI, with a hazard ratio of 1.552 (95%CI: 0.943~2.554, p = 0.084) and 1.512 (95%CI: 0.979~2.336, p = 0.062), respectively.

CONCLUSIONS

The result reveals the complex role of the tumor microenvironment in colon adenocarcinoma, and the quantified descriptors are potential predictors of disease progression. The method could be considered for risk stratification and targeted therapy and extend to other types of cancer, leading to a better understanding of the tumor microenvironment.

Nuclear translocation of ASPL-TFE3 fusion protein creates favorable metabolism by mediating autophagy in translocation renal cell carcinoma

The ASPL-TFE3 fusion gene, resulting from t(X;17)(p11.2;q25.3), is one of the most commonly identified fusion genes in Xp11 translocation renal cell carcinoma (tRCC). However, its roles and underlying mechanism in RCC development are not yet clear. Here, we identified ASPL-TFE3 fusion as the most common tRCC subtype in a Chinese population (29/126, 23.03%). This fusion protein translocated into the nucleus and promoted RCC cell proliferation both in vitro and in vivo. Mechanistically, the fusion protein transcriptionally activated the lysosome-autophagy pathway by binding to the promoters of lysosome-related genes. Autophagy, activated by ASPL-TFE3, enabled RCC cells to escape energy stress by promoting the utilization of proteins and lipids. Moreover, we found that the ASPL-TFE3 fusion escaped regulation by the classic mTOR-TFE3 signal and instead activated phospho-mTOR and its downstream targets. Finally, targeting both autophagy and the mTOR axis resulted in a greater antiproliferative effect than single pathway inhibition. In summary, these results confirmed the ASPL-TFE3 fusion as a master regulator of metabolic adaptation mediated by autophagy in tRCC. The simultaneous manipulation of autophagy and the mTOR axis may represent a novel treatment strategy for ASPL-TFE3 fusion RCC.

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.

Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection

With the arrival of the internet of things, smart environments are becoming increasingly ubiquitous in our everyday lives. Sensor data collected from smart home environments can provide unobtrusive, longitudinal time series data that are representative of the smart home resident's routine behavior and how this behavior changes over time. When longitudinal behavioral data are available from multiple smart home residents, differences between groups of subjects can be investigated. Group-level discrepancies may help isolate behaviors that manifest in daily routines due to a health concern or major lifestyle change. To acquire such insights, we propose an algorithmic framework based on change point detection called Behavior Change Detection for Groups (BCD-G). We hypothesize that, using BCD-G, we can quantify and characterize differences in behavior between groups of individual smart home residents. We evaluate our BCD-G framework using one month of continuous sensor data for each of fourteen smart home residents, divided into two groups. All subjects in the first group are diagnosed with cognitive impairment. The second group consists of cognitively healthy, age-matched controls. Using BCD-G, we identify differences between these two groups, such as how impairment affects patterns of performing activities of daily living and how clinically-relevant behavioral features, such as in-home walking speed, differ for cognitively-impaired individuals. With the unobtrusive monitoring of smart home environments, clinicians can use BCD-G for remote identification of behavior changes that are early indicators of health concerns.