Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality

Bibault, Jean‐Emmanuel; Hancock, Steven; Buyyounouski, Mark K.; Bagshaw, H.P.; Leppert, John T.; Liao, Joseph C.; Li, Xing

doi:10.3390/cancers13123064

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…The use of AI in predicting prostate cancer prognosis is also being actively studied. Bibault et al [ 17 ] used an AI algorithm to predict survival outcomes in prostate cancer patients. Using the prostate cancer dataset of 8,776 individuals from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, the training set included data from 7,021 individuals; the remaining 1,755 individuals made up the testing set.…”

Section: Resultsmentioning

confidence: 99%

“…Using the prostate cancer dataset of 8,776 individuals from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, the training set included data from 7,021 individuals; the remaining 1,755 individuals made up the testing set. The accuracy of the 10-year survival rate and 10-year cancer-specific survival rate were 87% and 98%, respectively [ 17 ]. One study demonstrated the use of an AI model to predict the recurrence rate and progression of prostate cancer based on biomarkers [ 12 ].…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Applications of artificial intelligence in urologic oncology

Pak,

Park,

Park

et al. 2024

Investig Clin Urol

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“…One difference in our study with some previous work 5 , 6 is we do not include treatment methods, e.g., hormone therapy and radiotherapy, as input variables in our models. Our focus is to provide an initial risk estimation before recommending any treatment.…”

Section: Discussionmentioning

confidence: 99%

Survival analysis of localized prostate cancer with deep learning

Dai

Park

Yoo

et al. 2022

Sci Rep

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In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics $$C_{\text {td}}$$ C td over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved $$C_{\text {td}}$$ C td 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained $$C_{\text {td}}$$ C td 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

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“…But this gets more challenging with other methods that are not directly interpretable and are often considered as "black boxes", especially for the case of Neural Networks. For such cases, we thus used the SHapley Additive exPlanations (SHAP) as done in other machine learning implementations for health purpose predictions 33,34 . SHAP is based on the Shapley value, a game theoretic approach that calculates the average marginal contribution of a feature value across all possible coalitions.…”

Section: Model Interpretabilitymentioning

confidence: 99%

Predicting antimicrobial resistance using historical bacterial resistance data with machine learning algorithms

Ureña

Camiade²,

Baalla³

et al. 2023

Preprint

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Antibiotic resistance of bacterial pathogens is considered by the World Health Organization as a major threat to global health aggravated by the misuse of antibiotics. In clinical practice results of bacterial cultures and antibiograms can take several days. In the meantime, prescribing an empirical antimicrobial treatment constitutes a challenge in which the practitioner has to strike a balance between antibiotics spectrum and expected susceptibility probability. In this contribution, we report the development and testing of a machine-learning-based system that early predicts the antimicrobial susceptibility probability and provides explanations of the contribution of the different cofactors at 4 different stages prior to the antibiogram (sampling, direct examination, culture, and species identification stages). A comparative analysis of different state of the art machine learning and probabilistic methods was carried out using 7 years of historical bacterial resistance data from the Hôpital Européen Marseille, France. Our results suggest that dense neural network-based models and Bayesian models are suitable to early predict antibiotics susceptibility (average AUC 0.91 at the species identification stage) even for the less frequent situations.

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Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality

Cited by 11 publications

References 24 publications

Applications of artificial intelligence in urologic oncology

Applications of artificial intelligence in urologic oncology

Survival analysis of localized prostate cancer with deep learning

Predicting antimicrobial resistance using historical bacterial resistance data with machine learning algorithms

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