Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning

Prognostic Impact and Spatial Interplay of Immune Cells in Urothelial Cancer

BACKGROUND AND OBJECTIVE

Quantity and the spatial relationship of specific immune cell types can provide prognostic information in bladder cancer. The objective of the study was to characterize the spatial interplay and prognostic role of different immune cell subpopulations in bladder cancer.

METHODS

A total of 2463 urothelial bladder carcinomas were immunostained with 21 antibodies using BLEACH&STAIN multiplex fluorescence immunohistochemistry in a tissue microarray format and analyzed using a framework of neuronal networks for an image analysis. Spatial immune parameters were compared with histopathological parameters and overall survival data.

KEY FINDINGS AND LIMITATIONS

The identification of > 300 different immune cell subpopulations and the characterization of their spatial relationship resulted in numerous spatial interaction patterns. Thirty-nine immune parameters showed prognostic significance in univariate analyses, of which 16 were independent from pT, pN, and histological grade in muscle-invasive bladder cancer. Among all these parameters, the strongest association with prolonged overall survival was identified for intraepithelial CD8+ cytotoxic T cells (time-dependent area under receiver operating characteristic curve [AUC]: 0.70), while stromal CD8+ T cells were less relevant (AUC: 0.65). A favorable prognosis of inflamed cancers with high levels of "exhaustion markers" suggests that TIM3, PD-L1, PD-1, and CTLA-4 on immune cells do not hinder antitumoral immune response in tumors rich of tumor infiltrating immune cells.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The density of intraepithelial CD8+ T cells was the strongest prognostic feature in muscle-invasive bladder cancer. Given that tumor cell killing by CD8+ cytotoxic T lymphocytes through direct cell-to-cell-contacts represents the "terminal end route" of antitumor immunity, the quantity of "tumor cell adjacent CD8+ T cells" may constitute a surrogate for the efficiency of cancer recognition by the immune system that can be measured straightaway in routine pathology as the CD8 labeling index.

PATIENT SUMMARY

Quantification of intraepithelial CD8+ T cells, the strongest prognosticfeature identified in muscle-invasive bladder cancer, can easily be assessed by brightfield immunohistochemistry and is therefore "ready to use" for routine pathology.

Prognostic significance of tumour budding in noncolorectal gastrointestinal tract and pancreatobiliary tract: a systematic review and meta-analysis

Tumour budding shows promise as a prognostic factor in various cancers, but its widespread application is hindered by the lack of large, validated studies and standardized criteria. This meta-analysis aims to review and examine the prognostic role of tumour budding specifically in noncolorectal gastrointestinal and pancreatobiliary tract cancers, broadening our perspective on its clinical relevance. The literature review was conducted through PubMed, Embase, and Web of Science from inception till 20 February 2023. Pooled odds ratio (OR) and hazard ratio (HR) with 95% confidence interval (CI) were calculated to assess the relation between tumour budding and clinicopathologic features, as well as overall survival. Each study was evaluated using the Newcastle-Ottawa Scale and both heterogeneity and publication bias were analysed. In this meta-analysis of 57 studies across various cancer types, multivariate HR revealed worse overall survival in oesophageal squamous cell carcinoma (HR 3.34 [95% CI 2.21-5.04]), gastric adenocarcinoma (2.03 [1.38-2.99]), pancreatic ductal adenocarcinoma (2.56 [2.02-3.25]), and biliary tract adenocarcinoma (3.11 [2.46-3.93]) with high-grade tumour budding. Additionally, high-grade tumour budding consistently correlated with adverse clinicopathological features, including lymph node metastasis, lymphovascular invasion, and distant metastasis without any observed inverse association. High heterogeneity was noted. Our study suggests that tumour budding is a valuable prognostic marker in various cancers. Nonetheless, standardized criteria tailored to specific organ types are necessary to enhance its clinical utility.

Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell's nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets-representative of clinical scenarios-are needed to address artificial intelligence's reliability, robustness, and black box challenge.

Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors

Accurate survival prediction for bladder cancer patients who have undergone radical cystectomy can improve their treatment management. However, the existing predictive models do not take advantage of both clinical and radiological imaging data. This study aimed to fill this gap by developing an approach that leverages the strengths of clinical (C), radiomics (R), and deep-learning (D) descriptors to improve survival prediction. The dataset comprised 163 patients, including clinical, histopathological information, and CT urography scans. The data were divided by patient into training, validation, and test sets. We analyzed the clinical data by a nomogram and the image data by radiomics and deep-learning models. The descriptors were input into a BPNN model for survival prediction. The AUCs on the test set were (C): 0.82 ± 0.06, (R): 0.73 ± 0.07, (D): 0.71 ± 0.07, (CR): 0.86 ± 0.05, (CD): 0.86 ± 0.05, and (CRD): 0.87 ± 0.05. The predictions based on D and CRD descriptors showed a significant difference (p = 0.007). For Kaplan-Meier survival analysis, the deceased and alive groups were stratified successfully by C (p < 0.001) and CRD (p < 0.001), with CRD predicting the alive group more accurately. The results highlight the potential of combining C, R, and D descriptors to accurately predict the survival of bladder cancer patients after cystectomy.

Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma

To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characterize spatial contexts of the TME at multiple scales, including close and long-distance spatial interactions between cell type pairs. We applied this framework to a total of 1,393 multiplex imaging data newly generated from 88 primary central nervous system lymphomas with complete follow-up data and identified significant prognostic subgroups mainly shaped by the spatial context. A supervised analysis confirmed a significant contribution of spatial context in predicting patient survival. In particular, we found an opposite prognostic value of macrophage infiltration depending on its proximity to specific cell types. Altogether, we provide a comprehensive framework to analyze spatial cellular interaction that can be broadly applied to other technologies and tumor contexts.

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.

Caveat Medicus: It's Time to Re-Think Stratification, You May Not Be Helping

Background

The focus of the present Letter is on the large and seemingly fertile body of work captured under the umbrella of 'patient stratification'.

Objectives

I identify and explain a fundamental methodological flaw underlying the manner in which the development of an increasingly large number of new stratification strategies is approached.

Design

I show an inherent conflict between the assumptions made, and the very purpose of stratification and its application in practice.

Methods

I analyse the methodological underpinnings of stratification as presently done and draw parallels with conceptually similarly flawed precedents which are now widely recognized.

Results

The highlighted flaw is shown to undermine the overarching ultimate goal of improved patient outcomes by undue fixation on an ill-founded proxy.

Conclusion

I issue a call for a re-think of the problem and the processes leading to the adoption of new stratification strategies in the clinic.

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy

(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P &lt; 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.

Proposal of an automated tumor-stromal ratio assessment algorithm and a nomogram for prognosis in early-stage invasive breast cancer

BACKGROUND

The tumor-stromal ratio (TSR) has been verified to be a prognostic factor in many solid tumors. In most studies, it was manually assessed on routinely stained H&E slides. This study aimed to assess the TSR using image analysis algorithms developed by the Qupath software, and integrate the TSR into a nomogram for prediction of the survival in invasive breast cancer (BC) patients.

METHODS

A modified TSR assessment algorithm based on the recognition of tumor and stroma tissues was developed using the Qupath software. The TSR of 234 invasive BC specimens in H&E-stained tissue microarrays (TMAs) were assessed with the algorithm and categorized as stroma-low or stroma-high. The consistency of TSR estimation between Qupath prediction and pathologist annotation was analyzed. Univariable and multivariable analyses were applied to select potential risk factors and a nomogram for predicting survival in invasive BC patients was constructed and validated. An extra TMA containing 110 specimens was obtained to validate the conclusion as an independent cohort.

RESULTS

In the discovery cohort, stroma-low and stroma-high were identified in 43.6% and 56.4% cases, respectively. Good concordance was observed between the pathologist annotated and Qupath predicted TSR. The Kaplan-Meier curve showed that stroma-high patients were associated with worse 5-DFS compared to stroma-low patients (p = 0.007). Multivariable analysis identified age, T stage, N status, histological grade, ER status, HER-2 gene, and TSR as potential risk predictors, which were included in the nomogram. The nomogram was well calibrated and showed a favorable predictive value for the recurrence of BC. Kaplan-Meier curves showed that the nomogram had a better risk stratification capability than the TNM staging system. In the external validation of the nomogram, the results were further validated.

CONCLUSIONS

Based on H&E-stained TMAs, this study successfully developed image analysis algorithms for TSR assessment and constructed a nomogram for predicting survival in invasive BC.

COVID-19 and Science Communication: The Recording and Reporting of Disease Mortality

The ongoing COVID-19 pandemic has brought science to the fore of public discourse and, considering the complexity of the issues involved, with it also the challenge of effective and informative science communication. This is a particularly contentious topic, in that it is both highly emotional in and of itself; sits at the nexus of the decision-making process regarding the handling of the pandemic, which has effected lockdowns, social behaviour measures, business closures, and others; and concerns the recording and reporting of disease mortality. To clarify a point that has caused much controversy and anger in the public debate, the first part of the present article discusses the very fundamentals underlying the issue of causative attribution with regards to mortality, lays out the foundations of the statistical means of mortality estimation, and concretizes these by analysing the recording and reporting practices adopted in England and their widespread misrepresentations. The second part of the article is empirical in nature. I present data and an analysis of how COVID-19 mortality has been reported in the mainstream media in the UK and the USA, including a comparative analysis both across the two countries as well as across different media outlets. The findings clearly demonstrate a uniform and worrying lack of understanding of the relevant technical subject matter by the media in both countries. Of particular interest is the finding that with a remarkable regularity (ρ>0.998), the greater the number of articles a media outlet has published on COVID-19 mortality, the greater the proportion of its articles misrepresented the disease mortality figures.

Nuances of Interpreting X-ray Analysis by Deep Learning and Lessons for Reporting Experimental Findings

With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age, gender, and the relative X-ray direction. We examined the diagnostic significance of different metrics used in the literature including those proposed by the International Medical Device Regulators Forum, and present the qualitative assessment of the spatial information learned by the model. We show that models that have very similar AUROCs can exhibit widely differing clinical applicability. As a result, our work demonstrates the importance of detailed reporting and analysis of the performance of machine-learning approaches in this field, which is crucial both for progress in the field and the adoption of such models in practice.

A Novel Methodology for Measuring the Abstraction Capabilities of Image Recognition Algorithms

Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.