Application of Artificial Intelligence to the Management of Urological Cancer

Abbod, Maysam F.; Catto, James W.F.; Linkens, D.A.; Hamdy, Freddie C.

doi:10.1016/j.juro.2007.05.122

Cited by 92 publications

(50 citation statements)

References 41 publications

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“…To evaluate this approach, rather than specific model designs, we used a Committee of models to merge gene rankings from individual models and structures. AI can identify complex relationships within non-linear data contaminated by variable noise and as such, can outperform statistical regression [8,24]. AI modeling is a generic process and these methods could be applied to re-interrogate microarray datasets for prognostic and functional data.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) is a machine learning approach without these prerequisites. Various AI techniques exist [8] and successful microarray analysis has been reported using artificial neural networks (ANN) [9] [10] and support vector machines (SVMs) [11,12] in non-urothelial malignancies. However, the hidden working layer of an ANN prevents model understanding and hinders its acceptance by the scientific community [13], whilst SVMs still use proximity to infer class-gene associations and function poorly with respect to interpretability [14].…”

Section: Introductionmentioning

confidence: 99%

“…This has a similar design to an ANN, but uses a transparent fuzzy logic internal structure [8]. This transparency allows model understanding, parameter interrogation and can facilitate the inclusion of priori qualitative knowledge.…”

Section: Introductionmentioning

confidence: 99%

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The Application of Artificial Intelligence to Microarray Data: Identification of a Novel Gene Signature to Identify Bladder Cancer Progression

et al. 2010

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Application of Artificial Intelligence to Microarray Data: Identification of a Novel Gene Signature to Identify Bladder Cancer Progression

et al. 2010

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“…Previous reports support the artificial intelligence approach to predictive modeling (reviewed in refs. 17,18) and suggest that its independence on reliance of data linearity can improve model development. The most common method of artificial intelligence modeling in medicine has been the artificial neural network.…”

Section: Discussionmentioning

confidence: 99%

“…Nomograms are easy to use and provide predictions with an accuracy of f70% to 80% using traditional clinical parameters (13 -16). An alternate predictive approach is with artificial intelligence modeling (17). Artificial intelligence techniques are not dependent on numerical linearity and do not infer relationships by statistical proximity.…”

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confidence: 99%

Neurofuzzy Modeling to Determine Recurrence Risk Following Radical Cystectomy for Nonmetastatic Urothelial Carcinoma of the Bladder

Catto¹,

Abbod

Linkens

et al. 2009

Clinical Cancer Research

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Purpose: Bladder cancer recurrence occurs in 40% of patients following radical cystectomy (RC) and pelvic lymphadenectomy (PLND). Although recurrence can be reduced with adjuvant chemotherapy, the toxicity and low response rates of this treatment restrict its use to patients at highest risk.We developed a neurofuzzy model (NFM) to predict disease recurrence following RC and PLND in patients who are not usually administered adjuvant chemotherapy. Experimental Design: The study comprised 1,034 patients treated with RC and PLND for bladder urothelial carcinoma. Four hundred twenty-five patients were excluded due to lymph node metastases and/or administration of chemotherapy. For the remaining 609 patients, we obtained complete clinicopathologic data relating to their tumor.We trained, tested, and validated two NFMs that predicted risk (Classifier) and timing (Predictor) of post-RC recurrence. We measured the accuracy of our model at various postoperative time points. Results: Cancer recurrence occurred in 172 (28 %) patients. With a median follow-up of 72.7 months, our Classifier NFM identified recurrence with an accuracy of 0.84 (concordance index 0.92, sensitivity 0.81, and specificity 0.85) and an excellent calibration.This was better than two predictive nomograms (0.72 and 0.74 accuracies). The Predictor NFMs identified the timing of tumor recurrence with a median error of 8.15 months. Conclusions: We have developed an accurate and well-calibrated model to identify disease recurrence following RC and PLND in patients with nonmetastatic bladder urothelial carcinoma. It seems superior to other available predictive methods and could be used to identify patients who would potentially benefit from adjuvant chemotherapy.Bladder cancer is the fifth commonest malignancy in the United States with an estimated incidence of 67,160 new cases and 13,750 deaths in 2007 (1). The majority of tumors are urothelial carcinoma and clinical evidence suggests that two distinct varieties exist. Whereas most tumors belong to the nonmuscle invasive pathway, around one third are muscle invasive. The prognosis of muscle invasive urothelial carcinoma is poor and strongly dependent on radical treatment and the stage of the disease. Currently, RC with bilateral pelvic lymphadenectomy (PLND) is the gold standard treatment for muscle invasive bladder cancer (2, 3). Following RC and PLND, tumor recurrence occurs in >40% of patients and the response to chemotherapy is poor (4, 5). Metastatic bladder urothelial carcinoma is a rapidly progressing disease with a median time of 13 months from RC to death (2, 6). Currently, the strongest predictors of recurrence risk following RC are pathologic stage and lymph node metastasis.For patients with advanced bladder urothelial carcinoma, improvements in survival have been made by the addition of extended PLND to RC (3,[6][7][8][9] and the use of chemotherapy in either an adjuvant or neoadjuvant setting (10). Both PLND and chemotherapy seem to have the best response rates in the patients with the ...

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Molecular subtyping of bladder cancer using Kohonen self‐organizing maps

et al. 2014

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Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6–5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3–11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering.

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Application of Artificial Intelligence to the Management of Urological Cancer

Cited by 92 publications

References 41 publications

The Application of Artificial Intelligence to Microarray Data: Identification of a Novel Gene Signature to Identify Bladder Cancer Progression

The Application of Artificial Intelligence to Microarray Data: Identification of a Novel Gene Signature to Identify Bladder Cancer Progression

Neurofuzzy Modeling to Determine Recurrence Risk Following Radical Cystectomy for Nonmetastatic Urothelial Carcinoma of the Bladder

Molecular subtyping of bladder cancer using Kohonen self‐organizing maps

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