Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches

Recent Advances in Artificial Intelligence for Precision Diagnosis and Treatment of Bladder Cancer: A Review

BACKGROUND

Bladder cancer is one of the top ten cancers globally, with its incidence steadily rising in China. Early detection and prognosis risk assessment play a crucial role in guiding subsequent treatment decisions for bladder cancer. However, traditional diagnostic methods such as bladder endoscopy, imaging, or pathology examinations heavily rely on the clinical expertise and experience of clinicians, exhibiting subjectivity and poor reproducibility.

MATERIALS AND METHODS

With the rise of artificial intelligence, novel approaches, particularly those employing deep learning technology, have shown significant advancements in clinical tasks related to bladder cancer, including tumor detection, molecular subtyping identification, tumor staging and grading, prognosis prediction, and recurrence assessment.

RESULTS

Artificial intelligence, with its robust data mining capabilities, enhances diagnostic efficiency and reproducibility when assisting clinicians in decision-making, thereby reducing the risks of misdiagnosis and underdiagnosis. This not only helps alleviate the current challenges of talent shortages and uneven distribution of medical resources but also fosters the development of precision medicine.

CONCLUSIONS

This study provides a comprehensive review of the latest research advances and prospects of artificial intelligence technology in the precise diagnosis and treatment of bladder cancer.

Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer

BACKGROUND

Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subtypes having different prognostic and therapeutic outcomes. Traditional methods for molecular subtyping are often time-consuming and resource-intensive. This study aims to develop machine learning models using deep learning features from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) to predict basal and luminal subtypes in BLCA.

METHODS

RNA sequencing data and clinical outcomes were downloaded from seven public BLCA databases, including TCGA, GEO datasets, and the IMvigor210C cohort, to assess the prognostic value of BLCA molecular subtypes. WSIs from TCGA were used to construct and validate the machine learning models, while WSIs from Shanghai Tenth People's Hospital (STPH) and The Affiliated Guangdong Second Provincial General Hospital of Jinan University (GD2H) were used as external validations. Deep learning models were trained to obtained tumor patches within WSIs. WSI level deep learning features were extracted from tumor patches based on the RetCCL model. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were developed using these features to classify basal and luminal subtypes.

RESULTS

Kaplan-Meier survival and prognostic meta-analyses showed that basal BLCA patients had significantly worse overall survival compared to luminal BLCA patients (hazard ratio = 1.47, 95% confidence interval: 1.25-1.73, P < 0.001). The LR model based on tumor patch features selected by Resnet50 model demonstrated superior performance, achieving an area under the curve (AUC) of 0.88 in the internal validation set, and 0.81 and 0.64 in the external validation sets from STPH and GD2H, respectively. This model outperformed both junior and senior pathologists in the differentiation of basal and luminal subtypes (AUC: 0.85, accuracy: 74%, sensitivity: 66%, specificity: 82%).

CONCLUSIONS

This study showed the efficacy of machine learning models in predicting the basal and luminal subtypes of BLCA based on the extraction of deep learning features from tumor patches in H&E-stained WSIs. The performance of the LR model suggests that the integration of AI tools into the diagnostic process could significantly enhance the accuracy of molecular subtyping, thereby potentially informing personalized treatment strategies for BLCA patients.

Digital and Computational Pathology Applications in Bladder Cancer: Novel Tools Addressing Clinically Pressing Needs

Bladder cancer (BC) remains a major disease burden in terms of incidence, morbidity, mortality, and economic cost. Deciphering the intrinsic molecular subtypes and identification of key drivers of BC has yielded successful novel therapeutic strategies. Advances in computational and digital pathology are reshaping the field of anatomical pathology. This review offers an update on the most relevant computational algorithms in digital pathology that have been proposed to enhance BC management. These tools promise to enhance diagnostics, staging, and grading accuracy and streamline efficiency while advancing practice consistency. Computational applications that enable intrinsic molecular classification, predict response to neoadjuvant therapy, and identify targets of therapy are also reviewed.

Targeting ketone body metabolism in mitigating gemcitabine resistance

Chemotherapy is often combined with surgery for muscle invasive and nonmuscle invasive bladder cancer (BCa). However, 70% of the patients recur within 5 years. Metabolic reprogramming is an emerging hallmark in cancer chemoresistance. Here, we report a gemcitabine resistance mechanism that promotes cancer reprogramming via the metabolic enzyme OXCT1. This mitochondrial enzyme, responsible for the rate-limiting step in β-hydroxybutyrate (βHB) catabolism, was elevated in muscle invasive disease and in patients with chemoresistant BCa. Resistant orthotopic tumors presented an OXCT1-dependent rise in mitochondrial oxygen consumption rate, ATP, and nucleotide biosynthesis. In resistant BCa, knocking out OXCT1 restored gemcitabine sensitivity, and administering the nonmetabolizable βHB enantiomer (S-βHB) only partially restored gemcitabine sensitivity. Suggesting an extrametabolic role for OXCT1, multi-omics analysis of gemcitabine sensitive and resistant cells revealed an OXCT1-dependent signature with the transcriptional repressor OVOL1 as a master regulator of epithelial differentiation. The elevation of OVOL1 target genes was associated with its cytoplasmic translocation and poor prognosis in a cohort of patients with BCa who have been treated with chemotherapy. The KO of OXCT1 restored OVOL1 transcriptional repressive activity by its nuclear translocation. Orthotopic mouse models of BCa supported OXCT1 as a mediator of gemcitabine sensitivity through ketone metabolism and regulating cancer stem cell differentiation.

Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology

Recent advances in spatial omics technologies have enabled new approaches for analyzing tissue morphology, cell composition, and biomolecule expression patterns in situ. These advances are promoting the development of new computational tools and quantitative techniques in the emerging field of digital pathology. In this review, we survey current trends in the development of computational methods for spatially mapped omics data analysis using digitized histopathology slides and supplementary materials, with an emphasis on tools and applications relevant to genitourinary oncological research. The review contains three sections: 1) an overview of image processing approaches for histopathology slide analysis; 2) machine learning integration with spatially resolved omics data analysis; 3) a discussion of current limitations and future directions for integration of machine learning in the clinical decision-making process.

Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities

Bladder cancer (BC) is a serious and common malignant tumor of the urinary system. Accurate and convenient diagnosis and treatment of BC is a major challenge for the medical community. Due to the limited medical resources, the existing diagnosis and treatment protocols for BC without the assistance of artificial intelligence (AI) still have certain shortcomings. In recent years, with the development of AI technologies such as deep learning and machine learning, the maturity of AI has made it more and more applied to the medical field, including improving the speed and accuracy of BC diagnosis and providing more powerful treatment options and recommendations related to prognosis. Advances in medical imaging technology and molecular-level research have also contributed to the further development of such AI applications. However, due to differences in the sources of training information and algorithm design issues, there is still room for improvement in terms of accuracy and transparency for the broader use of AI in clinical practice. With the popularization of digitization of clinical information and the proposal of new algorithms, artificial intelligence is expected to learn more effectively and analyze similar cases more accurately and reliably, promoting the development of precision medicine, reducing resource consumption, and speeding up diagnosis and treatment. This review focuses on the application of artificial intelligence in the diagnosis and treatment of BC, points out some of the challenges it faces, and looks forward to its future development.

Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN

The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial intelligence (AI) system aimed at objectively grading NMIBC. This system uses a novel convolutional neural network (CNN) architecture called the multi-scale pyramidal pretrained CNN to analyze both local and global pathology markers extracted from digital pathology images. The proposed CNN structure takes as input three levels of patches, ranging from small patches (e.g., 128 × 128 ) to the largest size patches ( 512 × 512 ). These levels are then fused by random forest (RF) to estimate the severity grade of NMIBC. The optimal patch sizes and other model hyperparameters are determined using a grid search algorithm. For each patch size, the proposed system has been trained on 32K patches (comprising 16K low-grade and 16K high-grade samples) and subsequently tested on 8K patches (consisting of 4K low-grade and 4K high-grade samples), all annotated by two pathologists. Incorporating light and efficient processing, defining new benchmarks in the application of AI to histopathology, the ShuffleNet-based AI system achieved notable metrics on the testing data, including 94.25% ± 0.70% accuracy, 94.47% ± 0.93% sensitivity, 94.03% ± 0.95% specificity, and a 94.29% ± 0.70% F1-score. These results highlight its superior performance over traditional models like ResNet-18. The proposed system's robustness in accurately grading pathology demonstrates its potential as an advanced AI tool for diagnosing human diseases in the domain of digital pathology.

Artificial Intelligence in Uropathology

The global population is currently at unprecedented levels, with an estimated 7.8 billion people inhabiting the planet. We are witnessing a rise in cancer cases, attributed to improved control of cardiovascular diseases and a growing elderly population. While this has resulted in an increased workload for pathologists, it also presents an opportunity for advancement. The accurate classification of tumors and identification of prognostic and predictive factors demand specialized expertise and attention. Fortunately, the rapid progression of artificial intelligence (AI) offers new prospects in medicine, particularly in diagnostics such as image and surgical pathology. This article explores the transformative impact of AI in the field of uropathology, with a particular focus on its application in diagnosing, grading, and prognosticating various urological cancers. AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. This comprehensive review is dedicated to providing an insightful overview of the primary data concerning the utilization of AI in diagnosing, predicting prognosis, and determining drug responses for tumors of the urinary tract. By embracing these advancements, we can look forward to improved outcomes and better patient care.

Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image

We aimed to build a deep learning-based pathomics model to predict the early recurrence of non-muscle-infiltrating bladder cancer (NMIBC) in this work. A total of 147 patients from Xuzhou Central Hospital were enrolled as the training cohort, and 63 patients from Suqian Affiliated Hospital of Xuzhou Medical University were enrolled as the test cohort. Based on two consecutive phases of patch level prediction and WSI-level predictione, we built a pathomics model, with the initial model developed in the training cohort and subjected to transfer learning, and then the test cohort was validated for generalization. The features extracted from the visualization model were used for model interpretation. After migration learning, the area under the receiver operating characteristic curve for the deep learning-based pathomics model in the test cohort was 0.860 (95% CI 0.752-0.969), with good agreement between the migration training cohort and the test cohort in predicting recurrence, and the predicted values matched well with the observed values, with p values of 0.667766 and 0.140233 for the Hosmer-Lemeshow test, respectively. The good clinical application was observed using a decision curve analysis method. We developed a deep learning-based pathomics model showed promising performance in predicting recurrence within one year in NMIBC patients. Including 10 state prediction NMIBC recurrence group pathology features be visualized, which may be used to facilitate personalized management of NMIBC patients to avoid ineffective or unnecessary treatment for the benefit of patients.

A New Method of Artificial-Intelligence-Based Automatic Identification of Lymphovascular Invasion in Urothelial Carcinomas

The presence of lymphovascular invasion (LVI) in urothelial carcinoma (UC) is a poor prognostic finding. This is difficult to identify on routine hematoxylin-eosin (H&E)-stained slides, but considering the costs and time required for examination, immunohistochemical stains for the endothelium are not the recommended diagnostic protocol. We developed an AI-based automated method for LVI identification on H&E-stained slides. We selected two separate groups of UC patients with transurethral resection specimens. Group A had 105 patients (100 with UC; 5 with cystitis); group B had 55 patients (all with high-grade UC; D2-40 and CD34 immunohistochemical stains performed on each block). All the group A slides and 52 H&E cases from group B showing LVI using immunohistochemistry were scanned using an Aperio GT450 automatic scanner. We performed a pixel-per-pixel semantic segmentation of selected areas, and we trained InternImage to identify several classes. The DiceCoefficient and Intersection-over-Union scores for LVI detection using our method were 0.77 and 0.52, respectively. The pathologists' H&E-based evaluation in group B revealed 89.65% specificity, 42.30% sensitivity, 67.27% accuracy, and an F1 score of 0.55, which is much lower than the algorithm's DCC of 0.77. Our model outlines LVI on H&E-stained-slides more effectively than human examiners; thus, it proves a valuable tool for pathologists.

A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients

In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate between donor-inherited lesions and those acquired post-transplantation. However, many centers do not perform such biopsies since they are invasive, costly and may delay the transplant procedure. We aim to generate a non-invasive virtual biopsy system using routinely collected donor parameters. Using 14,032 day-zero kidney biopsies from 17 international centers, we develop a virtual biopsy system. 11 basic donor parameters are used to predict four Banff kidney lesions: arteriosclerosis, arteriolar hyalinosis, interstitial fibrosis and tubular atrophy, and the percentage of renal sclerotic glomeruli. Six machine learning models are aggregated into an ensemble model. The virtual biopsy system shows good performance in the internal and external validation sets. We confirm the generalizability of the system in various scenarios. This system could assist physicians in assessing organ quality, optimizing allograft allocation together with discriminating between donor derived and acquired lesions post-transplantation.

The classification of the bladder cancer based on Vision Transformers (ViT)

Bladder cancer is a prevalent malignancy with diverse subtypes, including invasive and non-invasive tissue. Accurate classification of these subtypes is crucial for personalized treatment and prognosis. In this paper, we present a comprehensive study on the classification of bladder cancer into into three classes, two of them are the malignant set as non invasive type and invasive type and one set is the normal bladder mucosa to be used as stander measurement for computer deep learning. We utilized a dataset containing histopathological images of bladder tissue samples, split into a training set (70%), a validation set (15%), and a test set (15%). Four different deep-learning architectures were evaluated for their performance in classifying bladder cancer, EfficientNetB2, InceptionResNetV2, InceptionV3, and ResNet50V2. Additionally, we explored the potential of Vision Transformers with two different configurations, ViT_B32 and ViT_B16, for this classification task. Our experimental results revealed significant variations in the models' accuracies for classifying bladder cancer. The highest accuracy was achieved using the InceptionResNetV2 model, with an impressive accuracy of 98.73%. Vision Transformers also showed promising results, with ViT_B32 achieving an accuracy of 99.49%, and ViT_B16 achieving an accuracy of 99.23%. EfficientNetB2 and ResNet50V2 also exhibited competitive performances, achieving accuracies of 95.43% and 93%, respectively. In conclusion, our study demonstrates that deep learning models, particularly Vision Transformers (ViT_B32 and ViT_B16), can effectively classify bladder cancer into its three classes with high accuracy. These findings have potential implications for aiding clinical decision-making and improving patient outcomes in the field of oncology.

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.

Pesticides and Bladder Cancer: Mechanisms Leading to Anti-Cancer Drug Chemoresistance and New Chemosensitization Strategies

Multiple risk factors have been associated with bladder cancer. This review focuses on pesticide exposure, as it is not currently known whether agricultural products have a direct or indirect effect on bladder cancer, despite recent reports demonstrating a strong correlation. While it is known that pesticide exposure is associated with an increased risk of bladder cancer in humans and dogs, the mechanism(s) by which specific pesticides cause bladder cancer initiation or progression is unknown. In this narrative review, we discuss what is currently known about pesticide exposure and the link to bladder cancer. This review highlights multiple pathways modulated by pesticide exposure with direct links to bladder cancer oncogenesis/metastasis (MMP-2, TGF-β, STAT3) and chemoresistance (drug efflux, DNA repair, and apoptosis resistance) and potential therapeutic tactics to counter these pesticide-induced affects.

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach

Accurate clinical staging of bladder cancer aids in optimizing the process of clinical decision-making, thereby tailoring the effective treatment and management of patients. While several radiomics approaches have been developed to facilitate the process of clinical diagnosis and staging of bladder cancer using grayscale computed tomography (CT) scans, the performances of these models have been low, with little validation and no clear consensus on specific imaging signatures. We propose a hybrid framework comprising pre-trained deep neural networks for feature extraction, in combination with statistical machine learning techniques for classification, which is capable of performing the following classification tasks: (1) bladder cancer tissue vs. normal tissue, (2) muscle-invasive bladder cancer (MIBC) vs. non-muscle-invasive bladder cancer (NMIBC), and (3) post-treatment changes (PTC) vs. MIBC.

Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images

BACKGROUND

Applying deep learning to digital histopathology is hindered by the scarcity of manually annotated datasets. While data augmentation can ameliorate this obstacle, its methods are far from standardized. Our aim was to systematically explore the effects of skipping data augmentation; applying data augmentation to different subsets of the whole dataset (training set, validation set, test set, two of them, or all of them); and applying data augmentation at different time points (before, during, or after dividing the dataset into three subsets). Different combinations of the above possibilities resulted in 11 ways to apply augmentation. The literature contains no such comprehensive systematic comparison of these augmentation ways.

RESULTS

Non-overlapping photographs of all tissues on 90 hematoxylin-and-eosin-stained urinary bladder slides were obtained. Then, they were manually classified as either inflammation (5948 images), urothelial cell carcinoma (5811 images), or invalid (3132 images; excluded). If done, augmentation was eight-fold by flipping and rotation. Four convolutional neural networks (Inception-v3, ResNet-101, GoogLeNet, and SqueezeNet), pre-trained on the ImageNet dataset, were fine-tuned to binary classify images of our dataset. This task was the benchmark for our experiments. Model testing performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Model validation accuracy was also estimated. The best testing performance was achieved when augmentation was done to the remaining data after test-set separation, but before division into training and validation sets. This leaked information between the training and the validation sets, as evidenced by the optimistic validation accuracy. However, this leakage did not cause the validation set to malfunction. Augmentation before test-set separation led to optimistic results. Test-set augmentation yielded more accurate evaluation metrics with less uncertainty. Inception-v3 had the best overall testing performance.

CONCLUSIONS

In digital histopathology, augmentation should include both the test set (after its allocation), and the remaining combined training/validation set (before being split into separate training and validation sets). Future research should try to generalize our results.

Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images

Colorectal cancer (CRC) is now the third most common malignancy to cause mortality worldwide, and its prognosis is of great importance. Recent CRC prognostic prediction studies mainly focused on biomarkers, radiometric images, and end-to-end deep learning methods, while only a few works paid attention to exploring the relationship between the quantitative morphological features of patients' tissue slides and their prognosis. However, existing few works in this area suffered from the drawback of choosing the cells randomly from the whole slides, which contain the non-tumor region that lakes information about prognosis. In addition, the existing works, which tried to demonstrate their biological interpretability using patients' transcriptome data, failed to show the biological meaning closely related to cancer. In this study, we proposed and evaluated a prognostic model using morphological features of cells in the tumor region. The features were first extracted by the software CellProfiler from the tumor region selected by Eff-Unet deep learning model. Features from different regions were then averaged for each patient as their representative, and the Lasso-Cox model was used to select the prognosis-related features. The prognostic prediction model was at last constructed using the selected prognosis-related features and was evaluated through KM estimate and cross-validation. In terms of biological meaning, Gene Ontology (GO) enrichment analysis of the expressed genes that correlated with the prognostically significant features was performed to show the biological interpretability of our model.With the help of tumor segmentation, our model achieved better statistical significance and better biological interpretability compared to the results without tumor segmentation. Statistically, the Kaplan Meier (KM) estimate of our model showed that the model using features in the tumor region has a higher C-index, a lower p-value, and a better performance on cross-validation than the model without tumor segmentation. In addition, revealing the pathway of the immune escape and the spread of the tumor, the model with tumor segmentation demonstrated a biological meaning much more related to cancer immunobiology than the model without tumor segmentation. Our prognostic prediction model using quantitive morphological features from tumor regions was almost as good as the TNM tumor staging system as they had a close C-index, and our model can be combined with the TNM tumor stage system to make a better prognostic prediction. And to the best of our knowledge, the biological mechanisms in our study were the most relevant to the immune mechanism of cancer compared to the previous studies.

An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer

BACKGROUND

Accurate pathological diagnosis of invasion depth and histologic grade is key for clinical management in patients with bladder cancer (BCa), but it is labour-intensive, experience-dependent and subject to interobserver variability. Here, we aimed to develop a pathological artificial intelligence diagnostic model (PAIDM) for BCa diagnosis.

METHODS

A total of 854 whole slide images (WSIs) from 692 patients were included and divided into training and validation sets. The PAIDM was developed using the training set based on the deep learning algorithm ScanNet, and the performance was verified at the patch level in validation set 1 and at the WSI level in validation set 2. An independent validation cohort (validation set 3) was employed to compare the PAIDM and pathologists. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value.

RESULTS

The AUCs of the PAIDM were 0.878 (95% CI 0.875-0.881) at the patch level in validation set 1 and 0.870 (95% CI 0.805-0.923) at the WSI level in validation set 2. In comparing the PAIDM and pathologists, the PAIDM achieved an AUC of 0.847 (95% CI 0.779-0.905), which was non-inferior to the average diagnostic level of pathologists. There was high consistency between the model-predicted and manually annotated areas, improving the PAIDM's interpretability.

CONCLUSIONS

We reported an artificial intelligence-based diagnostic model for BCa that performed well in identifying invasion depth and histologic grade. Importantly, the PAIDM performed admirably in patch-level recognition, with a promising application for transurethral resection specimens.

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.

Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence

Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.

Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends

Bladder cancer (BC) is the 10th most common cancer globally and has a high mortality rate if not detected early and treated promptly. Non-muscle-invasive BC (NMIBC) is a subclassification of BC associated with high rates of recurrence and progression. Current tools for predicting recurrence and progression on NMIBC use scoring systems based on clinical and histopathological markers. These exclude other potentially useful biomarkers which could provide a more accurate personalized risk assessment. Future trends are likely to use artificial intelligence (AI) to enhance the prediction of recurrence in patients with NMIBC and decrease the use of standard clinical protocols such as cystoscopy and cytology. Here, we provide a comprehensive survey of the most recent studies from the last decade (N = 70 studies), focused on the prediction of patient outcomes in NMIBC, particularly recurrence, using biomarkers such as radiomics, histopathology, clinical, and genomics. The value of individual and combined biomarkers is discussed in detail with the goal of identifying future trends that will lead to the personalized management of NMIBC.

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.

Methods

We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).

Results

There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.

Conclusions

These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

BACKGROUND

Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first clinically approved targeted therapy in bladder cancer. However, it requires previous molecular testing of each patient, which is costly and not ubiquitously available.

OBJECTIVE

To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer.

DESIGN, SETTING, AND PARTICIPANTS

We trained a deep learning network to detect FGFR3 mutations on digitized slides of muscle-invasive bladder cancers stained with hematoxylin and eosin from the Cancer Genome Atlas (TCGA) cohort (n = 327) and validated the algorithm on the "Aachen" cohort (n = 182; n = 121 pT2-4, n = 34 stroma-invasive pT1, and n = 27 noninvasive pTa tumors).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary endpoint was the area under the receiver operating curve (AUROC) for mutation detection. Performance of the deep learning system was compared with visual scoring by an uropathologist.

RESULTS AND LIMITATIONS

In the TCGA cohort, FGFR3 mutations were detected with an AUROC of 0.701 (p < 0.0001). In the Aachen cohort, FGFR3 mutants were found with an AUROC of 0.725 (p < 0.0001). When trained on TCGA, the network generalized to the Aachen cohort, and detected FGFR3 mutants with an AUROC of 0.625 (p = 0.0112). A subgroup analysis and histological evaluation found highest accuracy in papillary growth, luminal gene expression subtypes, females, and American Joint Committee on Cancer (AJCC) stage II tumors. In a head-to-head comparison, the deep learning system outperformed the uropathologist in detecting FGFR3 mutants.

CONCLUSIONS

Our computer-based artificial intelligence system was able to detect genetic alterations of the FGFR3 gene of bladder cancer patients directly from histological slides. In the future, this system could be used to preselect patients for further molecular testing. However, analyses of larger, multicenter, muscle-invasive bladder cancer cohorts are now needed in order to validate and extend our findings.

PATIENT SUMMARY

In this report, a computer-based artificial intelligence (AI) system was applied to histological slides to predict genetic alterations of the FGFR3 gene in bladder cancer. We found that the AI system was able to find the alteration with high accuracy. In the future, this system could be used to preselect patients for further molecular testing.