Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients

Ten challenges and opportunities in computational immuno-oncology

Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary collaborations amid the constantly evolving interplay between clinical needs and technological innovation.

A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges

People diagnosed with cancer and their formal and informal caregivers are increasingly faced with a deluge of complex information, thanks to rapid advancements in the type and volume of diagnostic, prognostic, and treatment data. This commentary discusses the opportunities and challenges that the society faces as we integrate large volumes of data into regular cancer care.

Towards equitable AI in oncology

Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.

From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images

Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.

Disrupting prostate cancer research: Challenge accepted; report from the 2023 Coffey-Holden Prostate Cancer Academy Meeting

INTRODUCTION

The 2023 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, themed "Disrupting Prostate Cancer Research: Challenge Accepted," was convened at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA, from June 22 to 25, 2023.

METHODS

The 2023 marked the 10th Annual CHPCA Meeting, a discussion-oriented scientific think-tank conference convened annually by the Prostate Cancer Foundation, which centers on innovative and emerging research topics deemed pivotal for advancing critical unmet needs in prostate cancer research and clinical care. The 2023 CHPCA Meeting was attended by 81 academic investigators and included 40 talks across 8 sessions.

RESULTS

The central topic areas covered at the meeting included: targeting transcription factor neo-enhancesomes in cancer, AR as a pro-differentiation and oncogenic transcription factor, why few are cured with androgen deprivation therapy and how to change dogma to cure metastatic prostate cancer without castration, reducing prostate cancer morbidity and mortality with genetics, opportunities for radiation to enhance therapeutic benefit in oligometastatic prostate cancer, novel immunotherapeutic approaches, and the new era of artificial intelligence-driven precision medicine.

DISCUSSION

This article provides an overview of the scientific presentations delivered at the 2023 CHPCA Meeting, such that this knowledge can help in facilitating the advancement of prostate cancer research worldwide.

PatchSorter: a high throughput deep learning digital pathology tool for object labeling

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Harnessing artificial intelligence for prostate cancer management

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Demographic bias in misdiagnosis by computational pathology models

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.

Artificial intelligence applications in prostate cancer

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview

The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.

Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer. Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs). Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78. Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.

Computational Image and Molecular Analysis Reveal Unique Prognostic Features of Immune Architecture in African Versus European American Women with Endometrial Cancer

Endometrial cancer (EC) disproportionately affects African American (AA) women in terms of progression and death. In our study, we sought to employ computerized image and bioinformatic analysis to tease out morphologic and molecular differences in EC between AA and European-American (EA) populations. We identified the differences in immune cell spatial patterns between AA and EA populations with markers of tumor biology, including histologic and molecular subtypes. The models performed best when they were trained and validated using data from the same population. Unsupervised clustering revealed a distinct association between immune cell features and known molecular subtypes of endometrial cancer that varied between AA and EA populations. Our genomic analysis revealed two distinct and novel gene sets with mutations associated with improved prognosis in AA and EA patients. Our study findings suggest the need for population-specific risk prediction models for women with endometrial cancer.

The prognostic value of tumor-stroma ratio and a newly developed computer-aided quantitative analysis of routine H&E slides in high-grade serous ovarian cancer

INTRODUCTION

Tumor-stroma ratio (TSR) is prognostic in multiple cancers, while its role in high-grade serous ovarian cancer (HGSOC) remains unclear. Despite the prognostic insight gained from genetic profiles and tumor-infiltrating lymphocytes (TILs), the prognostic use of histology slides remains limited, while it enables the identification of tumor characteristics via computational pathology reducing scoring time and costs. To address this, this study aimed to assess TSR's prognostic role in HGSOC and its association with TILs. We additionally developed an algorithm, Ovarian-TSR (OTSR), using deep learning for TSR scoring, comparing it to manual scoring. Methods : 340 patients with advanced-stage who underwent primary debulking surgery (PDS) or neo-adjuvant chemotherapy (NACT) with interval debulking (IDS). TSR was assessed in both the most invasive (MI) and whole tumor (WT) regions through manual scoring by pathologists and quantification using OTSR. Patients were categorized as stroma-rich (≥ 50% stroma) or stroma-poor (< 50%). TILs were evaluated via immunohistochemical staining. Result s: In PDS, stroma-rich tumors were significantly associated with a more frequent papillary growth pattern (60% vs 34%), while In NACT stroma-rich tumors had a lower Tumor Regression Grading (TRG 4&5, 21% vs 57%) and increased pleural metastasis (25% vs 16%). Stroma-rich patients had significantly shorter overall and progression-free survival compared to stroma-poor (31 versus 45 months; P < 0.0001, and 15 versus 17 months; P = 0.0008, respectively). Combining stromal percentage and TILs led to three distinct survival groups with good (stroma-poor, high TIL), medium (stroma-rich, high TIL, or; stroma-poor, Low TIL), and poor(stroma-rich, low TIL) survival. These survival groups remained significant in CD8 and CD103 in multivariable analysis (Hazard ratio (HR) = 1.42, 95% Confidence-interval (CI) = 1.02-1.99; HR = 1.49, 95% CI = 1.01-2.18, and HR = 1.48, 95% CI = 1.05-2.08; HR = 2.24, 95% CI = 1.55-3.23, respectively). OTSR was able to recapitulate these results and demonstrated high concordance with expert pathologists (correlation = 0.83). Conclusions : TSR is an independent prognostic factor for survival assessment in HGSOC. Stroma-rich tumors have a worse prognosis and, in the case of NACT, a higher likelihood of pleural metastasis. OTSR provides a cost and time-efficient way of determining TSR with high reproducibility and reduced inter-observer variability.

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

BACKGROUND

Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival.

METHODS

The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics.

RESULTS

We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox.

CONCLUSIONS

A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.

Identification and validation of cancer-associated fibroblast-related subtypes and the prognosis model of biochemical recurrence in prostate cancer based on single-cell and bulk RNA sequencing

BACKGROUND

Cancer-associated fibroblasts (CAFs) are an essential component of the tumor immune microenvironment that are involved in extracellular matrix (ECM) remodeling. We aim to investigate the characteristics of CAFs in prostate cancer and develop a biochemical recurrence (BCR)-related CAF signature for predicting the prognosis of PCa patients.

METHODS

The bulk RNA-seq and relevant clinical information were obtained from the TCGA and GEO databases, respectively. The infiltration scores of CAFs in prostate cancer patients were calculated using the MCP counter and EPIC algorithms. The single-cell RNA sequencing (scRNA-seq) was downloaded from the GEO database. Subsequently, univariate Cox regression analysis was employed to identify prognostic genes associated with CAFs. We identified two subtypes (C1 and C2) of prostate cancer that were associated with CAFs via non-negative matrix factorization (NMF) clustering. In addition, the BCR-related CAF signatures were constructed using Lasso regression analysis. Finally, a nomogram model was established based on the risk score and clinical characteristics of the patients.

RESULTS

Initially, we found that patients with high CAF infiltration scores had shorter biochemical recurrence-free survival (BCRFS) times. Subsequently, CAFs in four pairs of tumors and paracancerous tissues were identified. We discovered 253 significantly differentially expressed genes, of which 13 had prognostic significance. Using NMF clustering, we divided PCa patients into C1 and C2 subgroups, with the C1 subgroup having a worse prognosis and substantially enriched cell cycle, homologous recombination, and mismatch repair pathways. Furthermore, a BCR-related CAFs signature was established. Multivariate COX regression analysis confirmed that the BCR-related CAFs signature was an independent prognostic factor for BCR in PCa. In addition, the nomogram was based on the clinical characteristics and risk scores of the patient and demonstrated high accuracy and reliability for predicting BCR. Lastly, our findings indicate that the risk score may be a useful tool for predicting PCa patients' sensitivity to immunotherapy and drug treatment.

CONCLUSION

NMF clustering based on CAF-related genes revealed distinct TME immune characteristics between groups. The BCR-related CAF signature accurately predicted prognosis and immunotherapy response in prostate cancer patients, offering a promising new approach to cancer treatment.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

Algorithmic fairness in artificial intelligence for medicine and healthcare

In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also review emerging technology for mitigating biases via disentanglement, federated learning and model explainability, and their role in the development of AI-based software as a medical device.

GNL3 and PA2G4 as Prognostic Biomarkers in Prostate Cancer

Prostate cancer is a multifocal and heterogeneous disease common in males and remains the fifth leading cause of cancer-related deaths worldwide. The prognosis of prostate cancer is variable and based on the degree of cancer and its stage at the time of diagnosis. Existing biomarkers for the prognosis of prostate cancer are unreliable and lacks specificity and sensitivity in guiding clinical decision. There is need to search for novel biomarkers having prognostic and predictive capabilities in guiding clinical outcomes. Using a bioinformatics approach, we predicted GNL3 and PA2G4 as biomarkers of prognostic significance in prostate cancer. A progressive increase in the expression of GNL3 and PA2G4 was observed during cancer progression having significant association with poor survival in prostate cancer patients. The Receiver Operating Characteristics of both genes showed improved area under the curve against sensitivity versus specificity in the pooled samples from three different GSE datasets. Overall, our analysis predicted GNL3 and PA2G4 as prognostic biomarkers of clinical significance in prostate cancer.

Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer

PURPOSE OF REVIEW

This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management.

RECENT FINDINGS

Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.

Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even obtain worse outcomes after therapy. Hence, predictors of treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potential of breast cancer stromal histology via a deep learning (DL)-based approach and proposed the tumor-associated stroma score (TS-score) for predicting pathological complete response (pCR) to NAC with a multicenter dataset. The TS-score was demonstrated to be an independent predictor of pCR, and it not only outperformed the baseline variables and stromal tumor-infiltrating lymphocytes (sTILs) but also significantly improved the prediction performance of the baseline variable-based model. Furthermore, we discovered that unlike lymphocytes, collagen and fibroblasts in the stroma were likely associated with a poor response to NAC. The TS-score has the potential to better stratify breast cancer patients in NAC settings.

Image analysis reveals differences in tumor multinucleations in Black and White patients with human papillomavirus-associated oropharyngeal squamous cell carcinoma

BACKGROUND

Understanding biological differences between different racial groups of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) patients, who have differences in terms of incidence, survival, and tumor morphology, can facilitate accurate prognostic biomarkers, which can help develop personalized treatment strategies.

METHODS

This study evaluated whether there were morphologic differences between HPV-associated tumors from Black and White patients in terms of multinucleation index (MuNI), an image analysis-derived metric that measures density of multinucleated tumor cells within epithelial regions on hematoxylin-eosin images and previously has been prognostic in HPV-associated OPSCC patients. In this study, the authors specifically evaluated whether the same MuNI cutoff that was prognostic of overall survival (OS) and disease-free survival in their previous study, T_TR , is valid for Black and White patients, separately. We also evaluated population-specific cutoffs, T_B for Blacks and T_W for Whites, for risk stratification.

RESULTS

MuNI was statistically significantly different between Black (mean, 3.88e-4; median, 3.67e-04) and White patients (mean, 3.36e-04; median, 2.99e-04), with p = .0078. Using T_TR , MuNI was prognostic of OS in the entire population with hazard ratio (HR) of 1.71 (p = .002; 95% confidence interval [CI], 1.21-2.43) and in White patients with HR of 1.72 (p = .005; 95% CI, 1.18-2.51). Population-specific cutoff, T_W , yielded improved HR of 1.77 (p = .003; 95% CI, 1.21-2.58) for White patients, whereas T_B did not improve risk-stratification in Black patients with HR of 0.6 (p = .3; HR, 0.6; 95% CI, 0.2-1.80).

CONCLUSIONS

Histological difference between White and Black patient tumors in terms of multinucleated tumor cells suggests the need for considering population-specific prognostic biomarkers for personalized risk stratification strategies for HPV-associated OPSCC patients.

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

PURPOSE

The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy.

EXPERIMENTAL DESIGN

We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187).

RESULTS

Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response.

CONCLUSIONS

Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.

Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning

Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the prognostic power of computational histopathology and genomics, this paper constructs a multi-modality prognostic model for survival prediction. We collected 346 patients diagnosed with hepatocellular carcinoma (HCC) from The Cancer Genome Atlas (TCGA), each patient has 1-3 whole slide images (WSIs) and an mRNA expression file. WSIs were processed by a multi-instance deep learning model to obtain the patient-level survival risk scores; mRNA expression data were processed by weighted gene co-expression network analysis (WGCNA), and the top hub genes of each module were extracted as risk factors. Information from two modalities was integrated by Cox proportional hazard model to predict patient outcomes. The overall survival predictions of the multi-modality model (Concordance index (C-index): 0.746, 95% confidence interval (CI): ±0.077) outperformed these based on histopathology risk score or hub genes, respectively. Furthermore, in the prediction of 1-year and 3-year survival, the area under curve of the model achieved 0.816 and 0.810. In conclusion, this paper provides an effective workflow for multi-modality prognosis of HCC, the integration of histopathology and genomic information has the potential to assist clinical prognosis management.

Multi-modality artificial intelligence in digital pathology

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.

Prostate Cancer Tumor Stroma: Responsibility in Tumor Biology, Diagnosis and Treatment

Simple Summary

The crosstalk between prostate stroma and its epithelium is essential to tissue homeostasis. Likewise, reciprocal signaling between tumor cells and the stromal compartment is required in tumor progression to facilitate or stimulate key processes such as cell proliferation and invasion. The aim of the present work was to review the current state of knowledge on the significance of tumor stroma in the genesis, progression and therapeutic response of prostate carcinoma. Additionally, we addressed the future therapeutic opportunities.

Abstract

Prostate cancer (PCa) is a common cancer among males globally, and its occurrence is growing worldwide. Clinical decisions about the combination of therapies are becoming highly relevant. However, this is a heterogeneous disease, ranging widely in prognosis. Therefore, new approaches are needed based on tumor biology, from which further prognostic assessments can be established and complementary strategies can be identified. The knowledge of both the morphological structure and functional biology of the PCa stroma compartment can provide new diagnostic, prognostic or therapeutic possibilities. In the present review, we analyzed the aspects related to the tumor stromal component (both acellular and cellular) in PCa, their influence on tumor behavior and the therapeutic response and their consideration as a new therapeutic target.

The state of the art for artificial intelligence in lung digital pathology

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Risk subtyping and prognostic assessment of prostate cancer based on consensus genes

Prostate cancer (PCa) is the most frequent malignancy in male urogenital system around worldwide. We performed molecular subtyping and prognostic assessment based on consensus genes in patients with PCa. Five cohorts containing 1,046 PCa patients with RNA expression profiles and recorded clinical follow-up information were included. Univariate, multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were used to select prognostic genes and establish the signature. Immunohistochemistry staining, cell proliferation, migration and invasion assays were used to assess the biological functions of key genes. Thirty-nine intersecting consensus prognostic genes from five independent cohorts were identified. Subsequently, an eleven-consensus-gene classifier was established. In addition, multivariate Cox regression analyses showed that the classifier served as an independent indicator of recurrence-free survival in three of the five cohorts. Combined receiver operating characteristic (ROC) analysis achieved synthesized effects by combining the classifier with clinicopathological features in four of five cohorts. SRD5A2 inhibits cell proliferation, while ITGA11 promotes cell migration and invasion, possibly through the PI3K/AKT signaling pathway. To conclude, we established and validated an eleven-consensus-gene classifier, which may add prognostic value to the currently available staging system.

By analysis of gene expression profiles of prostate cancer patients from multiple platforms, an eleven-consensus-gene classifier is constructed to provide a robust tool for the prediction of recurrence-free survival.

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. SIGNIFICANCE: An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Low Tumor-to-Stroma Ratio Reflects Protective Role of Stroma against Prostate Cancer Progression

Tumor-to-stroma ratio (TSR) is a prognostic factor that expresses the relative amounts of tumor and intratumoral stroma. In this study, its clinical and molecular relevance was evaluated in prostate cancer (PCa). The feasibility of automated quantification was tested in digital scans of tissue microarrays containing 128 primary tumors from 72 PCa patients stained immunohistochemically for epithelial cell adhesion molecule (EpCAM), followed by validation in a cohort of 310 primary tumors from 209 PCa patients. In order to investigate the gene expression differences between tumors with low and high TSR, we applied multigene expression analysis (nCounter® PanCancer Progression Panel, NanoString) of 42 tissue samples. TSR scores were categorized into low (<1 TSR) and high (≥1 TSR). In the pilot cohort, 31 patients (43.1%) were categorized as low and 41 (56.9%) as high TSR score, whereas 48 (23.0%) patients from the validation cohort were classified as low TSR and 161 (77.0%) as high. In both cohorts, high TSR appeared to indicate the shorter time to biochemical recurrence in PCa patients (Log-rank test, p = 0.04 and p = 0.01 for the pilot and validation cohort, respectively). Additionally, in the multivariate analysis of the validation cohort, TSR predicted BR independent of other factors, i.e., pT, pN, and age (p = 0.04, HR 2.75, 95%CI 1.07-7.03). Our data revealed that tumors categorized into low and high TSR score show differential expression of various genes; the genes upregulated in tumors with low TSR score were mostly associated with extracellular matrix and cell adhesion regulation. Taken together, this study shows that high stroma content can play a protective role in PCa. Automatic EpCAM-based quantification of TSR might improve prognostication in personalized medicine for PCa.

Pathomics in urology

PURPOSE OF REVIEW

Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summarize its most relevant applications in urology.

RECENT FINDINGS

There is a steady rise in the number of studies employing Pathomics, and especially deep learning, in urology. In prostate cancer, several algorithms have been developed for the automatic differentiation between benign and malignant lesions and to differentiate Gleason scores. Furthermore, several applications have been developed for the automatic cancer cell detection in urine and for tumor assessment in renal cancer. Despite the explosion in research, Pathomics is not fully ready yet for widespread clinical application.

SUMMARY

In prostate cancer and other urologic pathologies, Pathomics is avidly being researched with commercial applications on the close horizon. Pathomics is set to improve the accuracy, speed, reliability, cost-effectiveness and generalizability of pathology, especially in uro-oncology.

Artificial Intelligence in Cancer Research and Precision Medicine

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.

Harnessing non-destructive 3D pathology

High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.

Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology

Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction with radiographic imaging techniques, have been linked to outcome, progression, and survival for a number of cancers. The gold standard of staging in oncology has been the TNM (tumor-node-metastasis) staging system. Though histopathological grading has shown prognostic significance, it is subjective and limited by interobserver variability even among experienced surgical pathologists. Recently, artificial intelligence (AI) approaches have been applied to pathology images toward diagnostic-, prognostic-, and treatment prediction-related tasks in cancer. AI approaches have the potential to overcome the limitations of conventional TNM staging and tumor grading approaches, providing a direct prognostic prediction of disease outcome independent of tumor stage and grade. Broadly speaking, these AI approaches involve extracting patterns from images that are then compared against previously defined disease signatures. These patterns are typically categorized as either (1) handcrafted, which involve domain-inspired attributes, such as nuclear shape, or (2) deep learning (DL)-based representations, which tend to be more abstract. DL approaches have particularly gained considerable popularity because of the minimal domain knowledge needed for training, mostly only requiring annotated examples corresponding to the categories of interest. In this article, we discuss AI approaches for digital pathology, especially as they relate to disease prognosis, prediction of genomic and molecular alterations in the tumor, and prediction of treatment response in oncology. We also discuss some of the potential challenges with validation, interpretability, and reimbursement that must be addressed before widespread clinical deployment. The article concludes with a brief discussion of potential future opportunities in the field of AI for digital pathology and oncology.

The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers

The learning health system is a conceptual model for continuous learning and knowledge generation rooted in the daily practice of medicine. While companies such as Google and Amazon use dynamic learning systems that learn iteratively through every customer interaction, this efficiency has not materialized on a comparable scale in health systems. An ideal learning health system would learn from every patient interaction to benefit the care for the next patient. Notable advances include the greater use of data generated in the course of clinical care, Common Data Models, and advanced analytics. However, many remaining barriers limit the most effective use of large and growing health care data assets. In this review, we explore the accomplishments, opportunities, and barriers to realizing the learning health system.