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

Sarkar, Suryadipto; Kang, Min; Ikram, Waleed; Tatton, Ryan W.; Riaz, Irbaz Bin; Silva, Alvin C.; Bryce, Alan H.; Moore, Cassandra; Ho, Thai H.; Sonpavde, Guru; Abdul-Muhsin, Haidar; Singh, Parminder; Wu, Teresa

doi:10.3390/cancers15061673

Cited by 15 publications

(18 citation statements)

References 32 publications

(39 reference statements)

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“…Each study’s inclusion and exclusion criteria were highly heterogeneous, but they all focused on urological cancer. Finally, 58 studies on urological cancers (prostate cancer: 21 [ 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ], bladder cancer: 20 [ 5 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 ], kidney cancer: 17 [ 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 ]) were identified as shown in Fig. 1 .…”

Section: Methodsmentioning

confidence: 99%

Applications of artificial intelligence in urologic oncology

Pak,

Park,

Park

et al. 2024

Investig Clin Urol

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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.

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Section: Methodsmentioning

confidence: 99%

Applications of artificial intelligence in urologic oncology

Pak,

Park,

Park

et al. 2024

Investig Clin Urol

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“…This has been studied in 68 BCa patients and found that the accuracy of diagnosing muscle invasion using a cutoff of VI-RADS ≥ 4 was 94% (AUC 0.92) and DL reconstruction identified four further patients, initially misdiagnosed by VI-RADS score 3, and proper diagnosed was set by T2w imaging + denoising DL reconstruction. Sarkar et al [69] used a hybrid ML and DL model to automatically detect and stage BCa and discovered that their LDA classifier on the XceptionNet platform had the best performance (accuracy = 86.07%, sensitivity = 96.75%, specificity = 69.65%, precision = 83.07% and F1-score = 89.39%) for detecting normal lesions from BCa. For detecting invasiveness, the same hybrid approach achieved medium results (accuracy = 79.72%, sensitivity = 66.62%, specificity = 87.39%, precision = 75.58%, and F1-score = 70.81%).…”

Section: Bladder Cancer Imaging and Artificial Intelligencementioning

confidence: 99%

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

Ferro

Falagario

Barone³

et al. 2023

Diagnostics

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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.

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“…Zhang et al similarly developed models to differentiate between MIBC and NMIBC utilizing CT-based deep learning and radiomics models, however they did not reach an AUC higher than 0.8 in their evaluation of external validation datasets [53,56]. Additional studies were able to differentiate between MIBC and NMIBC on CT and MRI with superior diagnostic performance compared to inexperienced radiologists by mixing traditional machine learning and deep learning algorithms [57][58][59]. Although these studies have harnessed methodologies to differentiate MIBC, the diagnostic performance must be evaluated in a real-world clinical setting in a prospective manner to validate its widespread use.…”

Section: Predicting Muscle Invasive Bladder Cancermentioning

confidence: 99%

AI-powered radiomics: revolutionizing detection of urologic malignancies

Gelikman,

Rais-Bahrami,

Pinto

et al. 2023

Current Opinion in Urology

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Purpose of Review This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers. Recent Findings As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation. Summary Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.

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

Cited by 15 publications

References 32 publications

Applications of artificial intelligence in urologic oncology

Applications of artificial intelligence in urologic oncology

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

AI-powered radiomics: revolutionizing detection of urologic malignancies

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