Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support

Application of artificial intelligence in the diagnosis and treatment of urinary tumors

Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery.

Machine learning-based CT radiomics enhances bladder cancer staging predictions: A comparative study of clinical, radiomics, and combined models

BACKGROUND

Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, especially machine learning-based computed tomography (CT) image-based radiomics, hold promise in improving stage prediction accuracy in various tumors. However, the comparative performance and clinical utility of models for BLCA are under investigation.

PURPOSE

We aimed to investigate the application value of machine learning-based CT radiomics in preoperative staging prediction by comparing the performance of clinical, radiomics, and clinical-radiomics combined models.

METHODS

A retrospective cohort of 105 patients with initial BLCA was randomized into training (70%) and testing (30%) cohorts. Radiomics features were extracted from CT images using the optimal feature filter, followed by the application of the least absolute shrinkage and selection operator algorithm for optimum feature selection. Furthermore, machine learning algorithms were used to establish a radiomics model within the training cohort. Independent risk factors for muscle-invasive BLCA (MIBC) obtained by multivariate logistic regression (LR) analysis were separately used to construct a clinical model. For a clinical-radiomics fusion model, radiomics features were combined with clinical parameters. Performance was evaluated based on receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and standard performance metrics.

RESULTS

Patients exhibited a significantly higher age (p = 0.029), larger tumor size (p = 0.01), and an increased neutrophil-to-lymphocyte ratio (NLR; p = 0.045) in the MIBC group than in the NMIBC group. LR analysis revealed age (p = 0.026), tumor size (p = 0.007), and NLR (p = 0.019) as significant predictors for constructing the clinical model. In the testing cohort, the radiomics model, which used an Support Vector Machine classifier, achieved the highest area under the curve (AUC) value of 0.857. The clinical-radiomics model outperformed the remaining two models, with AUC values of 0.958 and 0.893 in the training and testing cohorts, respectively. DeLong's test indicated significant differences between the three models. Calibration curves showed good agreement, and DCA confirmed the superior clinical utility of the clinical-radiomics model.

CONCLUSIONS

Machine learning-based CT radiomics combined with clinical parameters was a promising approach in staging BLCA accurately, which outperformed the individual models. Integrating radiomics features with clinical information holds the potential to improve personalized treatment planning and patient outcomes in BLCA.

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria

OBJECTIVE

To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria.

METHODS

Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method.

RESULTS

The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%).

CONCLUSIONS

We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

Applications of artificial intelligence in urologic oncology

PURPOSE

With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology.

MATERIALS AND METHODS

We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery."

RESULTS

A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods.

CONCLUSIONS

AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma

OBJECTIVES

It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC.

METHODS

A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model.

RESULTS

This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000).

CONCLUSION

The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC.

CLINICAL RELEVANCE STATEMENT

This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making.

KEY POINTS

• It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

Personalization of thoracoabdominal CT examinations using scanner integrated clinical decision support systems - Impact on the acquisition technique, scan range, and reconstruction type

OBJECTIVES

This study evaluates the impact of a scanner-integrated, customized clinical decision support system (CDSS) on the acquisition technique, scan range, and reconstruction in thoracoabdominal CT.

MATERIALS AND METHODS

We applied CDSS in contrast-enhanced examinations of the trunk with various clinical indications on a recent scanner with the capability of dual-energy CT (DECT), anatomic landmark detection (ALD), and iterative metal-artifact reduction (MAR). Simple and comprehensive questions about the patient's breath hold capability, the anatomical region of interest, and metal implants can be answered after the localizer. The acquisition technique (single energy, SECT, or dual energy), scan range (chest-abdomen-pelvis or chest-abdomen), and reconstruction technique (with or without MAR) were then automatically adapted in the examination protocols in coherence with these selections. Retrospectively, we compared the usage rates for these techniques in 624 examinations on the study scanner with 740 examinations on a comparable scanner without CDSS. Subgroup analysis of effective dose (ED), scan duration, and image quality (IQ) was performed in the study group.

RESULTS

CDSS leads to an increased usage rate of DECT (64.4% vs. 2.8%) and MAR (75.4% vs. 44.0%). All scan range adaptations by ALD were successful. The resulting subjective IQ between single energy and DECT acquisitions was comparable (all p > 0.05). Scan duration was significantly longer in DECT than in SECT (16.9 s vs. 6.5 s; p < 0.001). However, the objective IQ was significantly higher in DECT (CNRD 2.1 vs. 1.8; p < 0.01), and the ED significantly lower (6.7 mSv vs. 7.6 mSv; p = 0.004).

CONCLUSION

CDSS for thoracoabdominal CT leads to a substantially increased usage rate of innovative techniques during acquisition and reconstruction. Patients with adapted protocols benefit from improved image quality and increased post-processing options at lower radiation doses.

Predictive Value of Computed Tomography Following Neoadjuvant Chemotherapy for Muscle Invasive Bladder Cancer

BACKGROUND

Neoadjuvant chemotherapy (NAC) prior to radical cystectomy (RC) remains standard treatment for select patients with muscle-invasive bladder cancer (MIBC). Although computed tomography (CT) is often obtained prior to RC, its ability to predict pathologic response is poorly characterized.

OBJECTIVE

The purpose of this study is to evaluate the predictive value of CT in assessing disease burden after NAC.

METHODS

Patients with MIBC having received NAC prior to RC were identified. Pre- and post-NAC CT scans were reviewed by an abdominal radiologist. The correlation between pathologic complete response (PCR) and radiologic complete response (RCR) was determined as the primary aim. As a secondary aim, the correlation between pathologic partial response (PPR) and radiologic partial response (RPR) was determined. Logistic regression analysis was utilized to determine the predictive value of CT in determining disease burden at RC.

RESULTS

A total of 141 patients were identified for analysis. PCR and PPR was achieved in 34% and 16% of patients, respectively. The positive predictive value of post-NAC CT was 53.5% for PCR and 28.8% for PPR. The negative predictive value of post-NAC CT was 73.5% for PCR and 46.2% for PPR. There was no significant association between RCR and PCR (OR 1.13, p = 0.67). Similarly, there was no meaningful association between RPR and PPR, lymph node involvement, or presence of extravesical disease.

CONCLUSIONS

CT findings correlate poorly with final pathology at RC and should not be used to evaluate local disease burden.

Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system

Background

For the adoption of machine learning clinical decision support systems (ML-CDSS) it is critical to understand the performance aid of the ML-CDSS. However, it is not trivial, how the performance aid should be evaluated. To design reliable performance evaluation study, both the knowledge from the practical framework of experimental study design and the understanding of domain specific design factors are required.

Objective

The aim of this review study was to form a practical framework and identify key design factors for experimental design in evaluating the performance of clinicians with or without the aid of ML-CDSS.

Methods

The study was based on published ML-CDSS performance evaluation studies. We systematically searched articles published between January 2016 and December 2022. From the articles we collected a set of design factors. Only the articles comparing the performance of clinicians with or without the aid of ML-CDSS using experimental study methods were considered.

Results

The identified key design factors for the practical framework of ML-CDSS experimental study design were performance measures, user interface, ground truth data and the selection of samples and participants. In addition, we identified the importance of randomization, crossover design and training and practice rounds. Previous studies had shortcomings in the rationale and documentation of choices regarding the number of participants and the duration of the experiment.

Conclusion

The design factors of ML-CDSS experimental study are interdependent and all factors must be considered in individual choices.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12553-023-00763-1.

PET Imaging in Bladder Cancer: An Update and Future Direction

Molecular imaging with positron emission tomography is a powerful tool in bladder cancer management. In this review, we aim to address the current place of the PET imaging in bladder cancer care and offer perspectives on potential future radiopharmaceutical and technological advancements. A special focus is given to the following: the role of [¹⁸F] 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography in the clinical management of bladder cancer patients, especially for staging and follow-up; treatment guided by [¹⁸F]FDG PET/CT; the role of [¹⁸F]FDG PET/MRI, the other PET radiopharmaceuticals beyond [¹⁸F]FDG, such as [⁶⁸Ga]- or [¹⁸F]-labeled fibroblast activation protein inhibitor; and the application of artificial intelligence.

Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer

BACKGROUND

Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited.

OBJECTIVE

To evaluate the performance of quantitative radiomics signatures for detection of LN metastases in BC.

DESIGN, SETTING, AND PARTICIPANTS

Of 1354 patients with BC who underwent radical cystectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automatically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detection of LN metastases in BC. Statistical analysis was performed using the receiver operating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC).

RESULTS AND LIMITATIONS

In total, 1845 LNs were manually segmented. Automated segmentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63).

CONCLUSIONS

A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach.

PATIENT SUMMARY

For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options.

Predicting Complete Response to Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer

Muscle-invasive bladder cancer is a life-threatening disease best managed with multimodal therapy. Neoadjuvant chemotherapy prior to cystectomy significantly improves survival with the greatest benefit noted in patients with a complete pathologic response noted at cystectomy. While radical cystectomy is currently an important part of the treatment plan, surgical morbidity remains high. Accurate prediction of complete responses to chemotherapy would enable avoiding the morbidity of radical cystectomy. Multiple clinical, pathologic, molecular, and radiographic predictors have been evaluated. Clinical and standard pathologic findings have not been found to be accurate predictors of complete response. To date, tumor genomic findings have been the most promising and have led to multiple clinical trials to evaluate if bladder preservation is possible in select patients. Radiomics has shown initial promise with larger validation series needed. These predictors can be further characterized as treatment specific and non-treatment specific. With the potential changing landscape of neoadjuvant therapy prior to radical cystectomy and the limitations of individual predictors of a complete response, a panel of several biomarkers may enhance patient selection for bladder preservation. The aim of this review is to summarize predictors of complete response to neoadjuvant chemotherapy.

Deep learning in bladder cancer imaging: A review

Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images. Bladder cancer (BCa) is the tenth most common cancer in the world. Imaging, as a safe, noninvasive, and relatively inexpensive technique, is a powerful tool to aid in the diagnosis and treatment of bladder cancer. In this review, we provide an overview of the latest progress in the application of deep learning to the imaging assessment of bladder cancer. First, we review the current deep learning approaches used for bladder segmentation. We then provide examples of how deep learning helps in the diagnosis, staging, and treatment management of bladder cancer using medical images. Finally, we summarize the current limitations of deep learning and provide suggestions for future improvements.

Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study

This observer study investigates the effect of computerized artificial intelligence (AI)-based decision support system (CDSS-T) on physicians’ diagnostic accuracy in assessing bladder cancer treatment response. The performance of 17 observers was evaluated when assessing bladder cancer treatment response without and with CDSS-T using pre- and post-chemotherapy CTU scans in 123 patients having 157 pre- and post-treatment cancer pairs. The impact of cancer case difficulty, observers’ clinical experience, institution affiliation, specialty, and the assessment times on the observers’ diagnostic performance with and without using CDSS-T were analyzed. It was found that the average performance of the 17 observers was significantly improved (p = 0.002) when aided by the CDSS-T. The cancer case difficulty, institution affiliation, specialty, and the assessment times influenced the observers’ performance without CDSS-T. The AI-based decision support system has the potential to improve the diagnostic accuracy in assessing bladder cancer treatment response and result in more consistent performance among all physicians.

Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas

OBJECTIVE

To assess the impact of using a computer-assisted reporting and decision support (CAR/DS) tool at the radiologist point-of-care on ordering provider compliance with recommendations for adrenal incidentaloma workup.

METHOD

Abdominal CT reports describing adrenal incidentalomas (2014 - 2016) were retrospectively extracted from the radiology database. Exclusion criteria were history of cancer, suspected functioning adrenal tumor, dominant nodule size < 1 cm or ≥ 4 cm, myelolipomas, cysts, and hematomas. Multivariable logistic regression models were employed to predict follow-up imaging (FUI) and hormonal screening orders as a function of patient age and sex, nodule size, and CAR/DS use. CAR/DS reports were compared to conventional reports regarding ordering provider compliance with, frequency, and completeness of, guideline-warranted recommendations for FUI and hormonal screening of adrenal incidentalomas using Chi-square test.

RESULT

Of 174 patients (mean age 62.4; 51.1% women) with adrenal incidentalomas, 62% (108/174) received CAR/DS-based recommendations versus 38% (66/174) unassisted recommendations. CAR/DS use was an independent predictor of provider compliance both with FUI (Odds Ratio [OR]=2.47, p = 0.02) and hormonal screening (OR=2.38, p = 0.04). CAR/DS reports recommended FUI (97.2%,105/108) and hormonal screening (87.0%,94/108) more often than conventional reports (respectively, 69.7% [46/66], 3.0% [2/66], both p <0.0001). CAR/DS recommendations more frequently included instructions for FUI time, protocol, and modality than conventional reports (all p <0.001).

CONCLUSION

Ordering providers were at least twice as likely to comply with report recommendations for FUI and hormonal evaluation of adrenal incidentalomas generated using CAR/DS versus unassisted reporting. CAR/DS-directed recommendations were more adherent to guidelines than those generated without.

A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study

Background

Despite the unprecedented performance of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces hinders the adoption of these AI systems in practice.

Objective

This study aims to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered manner.

Methods

Our study is based on a user-centered design framework for developing explanations in a CDSS that identifies why explanations are needed, what information should be contained in explanations, and how explanations can be provided in the CDSS. We conducted user interviews, user observation sessions, and an iterative design process to identify three key aspects for designing explanations in the CDSS. After constructing the CDSS, the tool was evaluated to investigate how the CDSS explanations helped technicians. We measured the accuracy of sleep staging and interrater reliability with macro-F1 and Cohen κ scores to assess quantitative improvements after our tool was adopted. We assessed qualitative improvements through participant interviews that established how participants perceived and used the tool.

Results

The user study revealed that technicians desire explanations that are relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of AI predictions. Here, technicians wanted explanations that could be used to evaluate whether the AI models properly locate and use these patterns during prediction. On the basis of this, information that is closely related to sleep EEG patterns was formulated for the AI models. In the iterative design phase, we developed a different visualization strategy for each pattern based on how technicians interpreted the EEG recordings with these patterns during their workflows. Our evaluation study on 9 polysomnographic technicians quantitatively and qualitatively investigated the helpfulness of the tool. For technicians with <5 years of work experience, their quantitative sleep staging performance improved significantly from 56.75 to 60.59 with a P value of .05. Qualitatively, participants reported that the information provided effectively supported them, and they could develop notable adoption strategies for the tool.

Conclusions

Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model

Many significant efforts have so far been made to classify malignant tumors by using various machine learning methods. Most of the studies have considered a particular tumor genre categorized according to its originating organ. This has enriched the domain-specific knowledge of malignant tumor prediction, we are devoid of an efficient model that may predict the stages of tumors irrespective of their origin. Thus, there is ample opportunity to study if a heterogeneous collection of tumor images can be classified according to their respective stages. The present research work has prepared a heterogeneous tumor dataset comprising eight different datasets from The Cancer Imaging Archives and classified them according to their respective stages, as suggested by the American Joint Committee on Cancer. The proposed model has been used for classifying 717 subjects comprising different imaging modalities and varied Tumor-Node-Metastasis stages. A new non-sequential deep hybrid model ensemble has been developed by exploiting branched and re-injected layers, followed by bidirectional recurrent layers to classify tumor images. Results have been compared with standard sequential deep learning models and notable recent studies. The training and validation accuracy along with the ROC-AUC scores have been found satisfactory over the existing models. No model or method in the literature could ever classify such a diversified mix of tumor images with such high accuracy. The proposed model may help radiologists by acting as an auxiliary decision support system and speed up the tumor diagnosis process.

Artificial intelligence in bladder cancer prognosis: a pathway for personalized medicine

PURPOSE OF REVIEW

This review aims to provide an update of the results of studies published in the last 2 years involving the use of artificial intelligence in bladder cancer (BCa) prognosis.

RECENT FINDINGS

Recently, many studies evaluated various artificial intelligence models to predict BCa evolution using either deep learning or machine learning. Many trials evidenced a better prediction of recurrence-free survival and overall survival for muscle invasive BCa (MIBC) for deep learning-based models compared with clinical stages. Improvements in imaging associated with the development of deep learning neural networks and radiomics seem to improve post neo-adjuvant chemotherapy response. One study showed that digitalized histology could predict nonmuscle invasive BCa recurrence.

SUMMARY

BCa prognosis could be better assessed using artificial intelligence models not only in the case of MIBC but also NMIBC. Many studies evaluated its role for the prediction of overall survival and recurrence-free survival but there is still little data in the case of NMIBC. Recent findings showed that artificial intelligence could lead to a better assessment of BCa prognosis before treatment and to personalized medicine.

Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support

We evaluated the intraobserver variability of physicians aided by a computerized decision-support system for treatment response assessment (CDSS-T) to identify patients who show complete response to neoadjuvant chemotherapy for bladder cancer, and the effects of the intraobserver variability on physicians' assessment accuracy. A CDSS-T tool was developed that uses a combination of deep learning neural network and radiomic features from computed tomography (CT) scans to detect bladder cancers that have fully responded to neoadjuvant treatment. Pre- and postchemotherapy CT scans of 157 bladder cancers from 123 patients were collected. In a multireader, multicase observer study, physician-observers estimated the likelihood of pathologic T0 disease by viewing paired pre/posttreatment CT scans placed side by side on an in-house-developed graphical user interface. Five abdominal radiologists, 4 diagnostic radiology residents, 2 oncologists, and 1 urologist participated as observers. They first provided an estimate without CDSS-T and then with CDSS-T. A subset of cases was evaluated twice to study the intraobserver variability and its effects on observer consistency. The mean areas under the curves for assessment of pathologic T0 disease were 0.85 for CDSS-T alone, 0.76 for physicians without CDSS-T and improved to 0.80 for physicians with CDSS-T (P = .001) in the original evaluation, and 0.78 for physicians without CDSS-T and improved to 0.81 for physicians with CDSS-T (P = .010) in the repeated evaluation. The intraobserver variability was significantly reduced with CDSS-T (P < .0001). The CDSS-T can significantly reduce physicians' variability and improve their accuracy for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.

Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Simple Summary

This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine.

Abstract

Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.

Artificial intelligence in urological oncology: An update and future applications

There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.

Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review

IMPORTANCE

An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer).

OBJECTIVES

To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation.

EVIDENCE REVIEW

A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed.

FINDINGS

A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I.

CONCLUSIONS AND RELEVANCE

This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.

The Role of 18F-FDG PET/CT in Guiding Precision Medicine for Invasive Bladder Carcinoma

Bladder cancer (BC) is the 10th most common cancer worldwide. Approximately one quarter of patients with BC have muscle-invasive disease (MIBC). Muscle-invasive disease carries a poor prognosis and choosing the optimal treatment option is critical to improve patients’ outcomes. Ongoing research supports the role of 2-deoxy-2-(18F)fluoro-D-glucose positron emission tomography (18F-FDG PET) in guiding patient-specific management decisions throughout the course of MIBC. As an imaging modality, 18F-FDG PET is acquired simultaneously with either computed tomography (CT) or MRI to offer a hybrid approach combining anatomical and metabolic information that complement each other. At initial staging, 18F-FDG PET/CT enhances the detection of extravesical disease, particularly in patients classified as oligometastatic by conventional imaging. 18F-FDG PET/CT has value in monitoring response to neoadjuvant and systemic chemotherapy, as well as in localizing relapse after treatment. In the new era of immunotherapy, 18F-FDG PET/CT may also be useful to monitor treatment efficacy as well as to detect immune-related adverse events. With the advent of artificial intelligence techniques such as radiomics and deep learning, these hybrid medical images can be mined for quantitative data, providing incremental value over current standard-of-care clinical and biological data. This approach has the potential to produce a major paradigm shift toward data-driven precision medicine with the ultimate goal of personalized medicine. In this review, we highlight current literature reporting the role of 18F-FDG PET in supporting personalized management decisions for patients with MIBC. Specific topics reviewed include the incremental value of 18F-FDG PET in prognostication, pre-operative planning, response assessment, prediction of recurrence, and diagnosing drug toxicity.

A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea

BACKGROUND

Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning.

METHODS

We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center's Colorectal Cancer Treatment Protocol (GCCTP).

RESULTS

For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy.

CONCLUSIONS

This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.

The Role of Imaging in Bladder Cancer Diagnosis and Staging

Bladder cancer (BC) is the most common cancer of the urinary tract in the United States. Imaging plays a significant role in the management of patients with BC, including the locoregional staging and evaluation for distant metastatic disease, which cannot be assessed at the time of cystoscopy and biopsy/resection. We aim to review the current role of cross-sectional and molecular imaging modalities for the staging and restaging of BC and the potential advantages and limitations of each imaging modality. CT is the most widely available and frequently utilized imaging modality for BC and demonstrates good performance for the detection of nodal and visceral metastatic disease. MRI offers potential value for the locoregional staging and evaluation of muscular invasion of BC, which is critically important for prognostication and treatment decision-making. FDG-PET/MRI is a novel hybrid imaging modality combining the advantages of both MRI and FDG-PET/CT in a single-setting comprehensive staging examination and may represent the future of BC imaging evaluation.

CT-based radiomics to predict the pathological grade of bladder cancer

OBJECTIVE

To build a CT-based radiomics model to predict the pathological grade of bladder cancer (BCa) preliminarily.

METHODS

Patients with surgically resected and pathologically confirmed BCa and who received CT urography (CTU) in our institution from October 2014 to September 2017 were retrospectively enrolled and randomly divided into training and validation groups. After feature extraction, we calculated the linear dependent coefficient between features to eliminate the collinearity. F-test was then used to identify the best features related to pathological grade. The logistic regression method was used to build the prediction model, and diagnostic performance was analyzed by plotting receiver operating characteristic (ROC) curve and calculating area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

Out of 145 included patients, 108 constituted the training group and 37 the validation group. The AUC value of the radiomics prediction model to diagnose the pathological grade of BCa was 0.950 (95% confidence interval [CI] 0.912-0.988) in the training group and 0.860 (95% CI 0.742-0.979) in the validation group, respectively. In the validation group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 83.8%, 88.5%, 72.7%, 88.5%, and 72.7%, respectively.

CONCLUSIONS

CT-based radiomics model can differentiate high-grade from low-grade BCa with a fairly good diagnostic performance.

KEY POINTS

•CT-based radiomics model can predict the pathological grade of bladder cancer. •This model has good diagnostic performance to differentiate high-grade and low-grade bladder cancer. •This preoperative and non-invasive prediction method might become an important addition to biopsy.

Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management

Bladder cancer is a fatal cancer that happens in the genitourinary tract with quite high morbidity and mortality annually. The high level of recurrence rate ranging from 50 to 80% makes bladder cancer one of the most challenging and costly diseases to manage. Faced with various problems in existing methods, a recently emerging concept for the measurement of imaging biomarkers and extraction of quantitative features called "radiomics" shows great potential in the application of detection, grading, and follow-up management of bladder cancer. Furthermore, machine-learning (ML) algorithms on the basis of "big data" are fueling the powers of radiomics for bladder cancer monitoring in the era of precision medicine. Currently, the usefulness of the novel combination of radiomics and ML has been demonstrated by a large number of successful cases. It possesses outstanding strengths including non-invasiveness, low cost, and high efficiency, which may serve as a revolution to tumor assessment and emancipate workforce. However, for the extensive clinical application in the future, more efforts should be made to break down the limitations caused by technology deficiencies, inherent problems during the process of radiomic analysis, as well as the quality of present studies.

Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer

PURPOSE

The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date.

METHODS

A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa).

RESULTS

In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods.

CONCLUSIONS

The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

BACKGROUND

Bladder cancer (BCa) can be divided into muscle-invasive BCa (MIBC) and non-muscle-invasive BCa (NMIBC). Whether the tumor infiltrates the detrusor muscle is a critical determinant of disease management in patients with BCa. However, the current preoperative diagnostic accuracy of muscular invasiveness is less than satisfactory. The authors report a radiomic-clinical nomogram for the individualized preoperative differentiation of MIBC from NMIBC.

METHODS

In total, 2602 radiomics features were extracted from whole bladder tumors and the basal part of the lesions on T2-weighted magnetic resonance imaging. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set (n = 130). Furthermore, a radiomic-clinical nomogram was developed incorporating the radiomics signature and selected clinical predictors based on a multivariable logistic regression analysis. The performance of the nomogram (discrimination, calibration, and clinical usefulness) was assessed and validated in an independent validation set (n = 69).

RESULTS

The radiomics signature, consisting of 23 selected features, showed good discrimination in the training and validation sets (area under the curve [AUC], 0.913 and 0.874, respectively). Incorporating the radiomics signature and magnetic resonance imaging-determined tumor size, the radiomic-clinical nomogram showed favorable calibration and discrimination in the training set with an AUC of 0.922, which was confirmed in the validation set (AUC, 0.876). Decision curve analysis and net reclassification improvement and integrated discrimination improvement indices (net reclassification improvement, 0.338, integrated discrimination improvement, 0.385) demonstrated the clinical usefulness of the nomogram.

CONCLUSIONS

The proposed noninvasive radiomic-clinical nomogram can increase the accuracy of preoperatively discriminating MIBC from NMIBC, which may aid in clinical decision making and improve patient prognosis.

A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma

OBJECTIVES

To determine the integrative value of contrast-enhanced computed tomography (CECT), transcriptomics data and clinicopathological data for predicting the survival of bladder urothelial carcinoma (BLCA) patients.

METHODS

RNA sequencing data, radiomics features and clinical parameters of 62 BLCA patients were included in the study. Then, prognostic signatures based on radiomics features and gene expression profile were constructed by using least absolute shrinkage and selection operator (LASSO) Cox analysis. A multi-omics nomogram was developed by integrating radiomics, transcriptomics and clinicopathological data. More importantly, radiomics risk score-related genes were identified via weighted correlation network analysis and submitted to functional enrichment analysis.

RESULTS

The radiomics and transcriptomics signatures significantly stratified BLCA patients into high- and low-risk groups in terms of the progression-free interval (PFI). The two risk models remained independent prognostic factors in multivariate analyses after adjusting for clinical parameters. A nomogram was developed and showed an excellent predictive ability for the PFI in BLCA patients. Functional enrichment analysis suggested that the radiomics signature we developed could reflect the angiogenesis status of BLCA patients.

CONCLUSIONS

The integrative nomogram incorporated CECT radiomics, transcriptomics and clinical features improved the PFI prediction in BLCA patients and is a feasible and practical reference for oncological precision medicine.

KEY POINTS

• Our radiomics and transcriptomics models are proved robust for survival prediction in bladder urothelial carcinoma patients. • A multi-omics nomogram model which integrates radiomics, transcriptomics and clinical features for prediction of progression-free interval in bladder urothelial carcinoma is established. • Molecular functional enrichment analysis is used to reveal the potential molecular function of radiomics signature.

The Bionic Radiologist: avoiding blurry pictures and providing greater insights

Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care. The Bionic Radiologist is a concept that combines humanity and digitalization for better health care integration of radiology. At a practical level, this concept will achieve critical goals: (1) testing decisions being made scientifically on the basis of disease probabilities and patient preferences; (2) image analysis done consistently at any time and at any site; and (3) treatment suggestions that are closely linked to imaging results and are seamlessly integrated with other information. The Bionic Radiologist will thus help avoiding missed care opportunities, will provide continuous learning in the work process, and will also allow more time for radiologists' primary roles: interacting with patients and referring physicians. To achieve that potential, one has to cope with many implementation barriers at both the individual and institutional levels. These include: reluctance to delegate decision making, a possible decrease in image interpretation knowledge and the perception that patient safety and trust are at stake. To facilitate implementation of the Bionic Radiologist the following will be helpful: uncertainty quantifications for suggestions, shared decision making, changes in organizational culture and leadership style, maintained expertise through continuous learning systems for training, and role development of the involved experts. With the support of the Bionic Radiologist, disparities are reduced and the delivery of care is provided in a humane and personalized fashion.