Identification of Immunological Features Enables Survival Prediction of Muscle-Invasive Bladder Cancer Patients Using Machine Learning

Gavriel, Christos G.; Dimitriou, Neofytos; Brieu, Nicolas; Nearchou, Ines P.; Arandjelović, Ognjen; Schmidt, Günter; Harrison, David J.; Caie, Peter D.

doi:10.1101/2020.02.24.963181

Cited by 3 publications

(2 citation statements)

References 72 publications

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“…[4]. To predict the prognosis over the next 5 years using various combinations of image, clinical, and spatial features, Gavriel et al [84] proposed an ensemble system that consists of ML-based algorithms. The method demonstrated a 71.4% accuracy in correctly identifying patients who experienced unfavorable outcomes and succumbed to muscle-invasive bladder cancer (MIBC) within a 5-year timeframe.…”

Section: Prediction and Prognosismentioning

confidence: 99%

Artificial Intelligence in Urooncology: What We Have and What We Expect

Froń,

Semianiuk,

Lazuk

et al. 2023

Cancers

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Introduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. Methodology: We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. Results: Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. Conclusions: AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.

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Section: Prediction and Prognosismentioning

confidence: 99%

Artificial Intelligence in Urooncology: What We Have and What We Expect

Froń,

Semianiuk,

Lazuk

et al. 2023

Cancers

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show abstract

“…Survival prediction in bladder cancer can be performed based on data sources such as clinicopathological information [7], histological slides [8,9], gene expression, or molecular markers [10]. The analysis methods include machine-learning models like support vector machines, logistic regression, and random forest [11,12], as well as nomogram [13] and risk stratification [14]. Nomogram models, often developed based on large-size cohorts, can offer high-accuracy prediction capabilities.…”

Section: Introductionmentioning

confidence: 99%

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

Sun,

Hadjiiski,

Gormley

et al. 2023

Cancers

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

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Emerging Applications of Artificial Intelligence (AI) and Machine Learning (ML) in Modern Urology

Barua

Das

Mondal

2022

Advances in Medical Technologies and Clinical Practice

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AI (artificial intelligence) and ML (machine learning) are the technical controlling implements for clarifying difficult difficulties in modern urology. AI may also progress in surgical training and exercise due to its computerized performance. Presently, the ML technique has been recognized to be a respectable method to attain difficult configuration recognition and regression analysis dispossessed of a clear need to idea and crack the essential physical prototypes. ML is the best comprehensively applied model because of the great dataset that is currently available, robust computational power, and refined algorithm architecture. AI may also be engaged in progressive strategies. This chapter aims to offer a synopsis of the current state of AI and ML tools in either analysis, conclusion, treatment potentials, or diagnosis consequence estimate in practical urology.

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Identification of Immunological Features Enables Survival Prediction of Muscle-Invasive Bladder Cancer Patients Using Machine Learning

Cited by 3 publications

References 72 publications

Artificial Intelligence in Urooncology: What We Have and What We Expect

Artificial Intelligence in Urooncology: What We Have and What We Expect

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

Emerging Applications of Artificial Intelligence (AI) and Machine Learning (ML) in Modern Urology

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