Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine

Lee, Hak Jong; Hwang, Sung Il; Han, Song; Kim, Seung Hyup; Cho, Jeong Yeon; Seong, Chang Gyu; Choe, Gheeyoung

doi:10.1007/s00330-009-1686-x

Cited by 27 publications

(36 citation statements)

References 49 publications

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“…19 Commonly used techniques include logistic regression, artifi cial neural networks and support vector machines. These complex methods have been applied to a wide range of clinical decision-making problems, including diagnosis of prostate cancer, 20 screening for obstructive sleep apnoea in people with ischaemic heart disease 21 and identifi cation of psychiatric problems. 22 In systems of simple or mid-level complexity, the decision computed by the CDSS can usually be easily explained to the clinician (eg by showing a trace of the rules used to compute the outcomes).…”

Section: Brief Taxonomy Of Clinical Decision Support Systemsmentioning

confidence: 99%

Decision time for clinical decision support systems

et al. 2014

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KEYWORDS: Clinical decision support systems, decision-making, diagnosis ContextInformation technology (IT) is now commonplace in almost every branch of healthcare. Electronic health records, eprescribing and digital medical imaging are well known to clinicians and have been implemented with varying degrees of success. 1 In addition, clinicians increasingly make use of online repositories such as PubMed and Google Scholar, 2 and specialised search engines such as FindZebra 3 to help answer clinical questions. One often overlooked set of IT tools are clinical decision support systems (CDSSs), which have been defi ned as systems that 'provide clinicians or patients with computer-generated clinical knowledge and patient-related information, intelligently fi ltered or presented at appropriate times, to enhance patient care'. 4 Such CDSSs have been the subject of academic computer science research for more than ABSTRACT 50 years 5 and offer the potential for better supported decision making by clinicians, improved compliance with medical standards and improved clinical effi ciency and safety. 6,7 Nonetheless, utilisation of CDSSs remains limited, and most healthcare IT systems do not include robust CDSS functions that can be widely employed across organisations, clinical presentations and domains. 8 Some of the challenges to implementation of CDSSs relate to the volume of high-quality data required for state-of-the-art systems, the translation of such data to machine-readable states and the mapping of CDSS processes to fi t with existing clinical workfl ows. As a result, successful implementation of CDSSs has tended to be site and domain specifi c, with major diffi culties replicating these successes more extensively throughout healthcare systems. 9 This is in contrast to commercial fi elds such as fi nance, where decision support technologies have been widely deployed. For example, risk-profi ling tools for fi nancial experts have been developed as easy-to-use programs that can assimilate information and guide users through complex fi nancial information and associated decisions tailored to individual customer needs. Healthcare decision making is signifi cantly more complex than fi nancial planning; however, some of the challenges in both domains are similar: large quantities of data need to be linked, integrated and translated to machine readable formats, and expert knowledge is required to contextualise and apply the data in a meaningful way. We discuss some reasons for the limited dissemination and adoption of CDSSs to date and refl ect on the major barriers that need to be overcome for these useful tools to be adopted more widely.

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Section: Brief Taxonomy Of Clinical Decision Support Systemsmentioning

confidence: 99%

Decision time for clinical decision support systems

et al. 2014

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“…Recently, automated cancer detecting and classification based on preoperative mp-MRI through a novel support vector machine (SVM) analysis has been an ongoing interest. SVM is one of the best-known classification techniques and usually provides the best classification for computer-aided detection in radiology [20]. Recent technical developments in SVM by expending advanced algorithms (e.g., principal component analysis [21, 22], particle swarm optimization [23] and cross-validation schemes) have produced encouraging results regarding solving or classifying pattern recognition problems [24].…”

Section: Introductionmentioning

confidence: 99%

“…Recent technical developments in SVM by expending advanced algorithms (e.g., principal component analysis [21, 22], particle swarm optimization [23] and cross-validation schemes) have produced encouraging results regarding solving or classifying pattern recognition problems [24]. The SVM analysis has the ability of reducing false positives in the determination of abnormal lesions in brain [24, 25], breast [26] and prostate [20], showing high accuracy, elegant mathematical tractability and direct geometric interpretation. Although the application of SVM to the diagnosis of PCa has validity in research, a more meaningful clinical application of it is in helping the identification of predictors of outcome [9, 27, 28].…”

Section: Introductionmentioning

confidence: 99%

An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Zhang

Wang

et al. 2016

Oncotarget

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Preoperatively predict the probability of Prostate cancer (PCa) biochemical recurrence (BCR) is of definite clinical relevance. The purpose of this study was to develop an imaging-based approach in the prediction of 3-years BCR through a novel support vector machine (SVM) classification. We collected clinicopathologic and MR imaging datasets in 205 patients pathologically confirmed PCa after radical prostatectomy. Univariable and multivariable analyses were used to assess the association between MR findings and 3-years BCR, and modeled the imaging variables and follow-up data to predict 3-year PCa BCR using SVM analysis. The performance of SVM was compared with conventional Logistic regression (LR) and D'Amico risk stratification scheme by area under the receiver operating characteristic curve (Az) analysis. We found that SVM had significantly higher Az (0.959 vs. 0.886; p = 0.007), sensitivity (93.3% vs. 83.3%; p = 0.025), specificity (91.7% vs. 77.2%; p = 0.009) and accuracy (92.2% vs. 79.0%; p = 0.006) than LR analysis. Performance of popularized D'Amico scheme was effectively improved by adding MRI-derived variables (Az: 0.970 vs. 0.859, p < 0.001; sensitivity: 91.7% vs. 86.7%, p = 0.031; specificity: 94.5% vs. 78.6%, p = 0.001; and accuracy: 93.7% vs. 81.0%, p = 0.007). Additionally, beside pathological Gleason score (hazard ratio [HR] = 1.560, p = 0.008), surgical-T3b (HR = 4.525, p < 0.001) and positive surgical margin (HR = 1.314, p = 0.007), apparent diffusion coefficient (HR = 0.149, p = 0.035) was the only independent imaging predictor of time to PSA failure. Therefore, We concluded that imaging-based approach using SVM was superior to LR analysis in predicting PCa outcome. Adding MR variables improved the performance of D'Amico scheme.

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“…One might argue that our simulation method might soon become outdated because these models are very simple, but more sophisticated risk models do not evidently show higher predictive ability. Risk models based on neural networks, decision trees, support vector machines (Forberg et al, 2009; Gulkesen et al, 2010; Lee et al, 2010; Muniz et al, 2010; Wu et al, 2010; Kim et al, 2011; Van Der Ploeg et al, 2011) often show higher predictive ability than logistic regression models in data that were used to develop the models (Lee et al, 2010; Muniz et al, 2010; Kim et al, 2011), but are frequently outperformed by logistic regression analyses in validation studies (Forberg et al, 2009; Gulkesen et al, 2010; Wu et al, 2010; Van Der Ploeg et al, 2011). This suggests that logistic regression is expected to remain relevant for constructing genetic risk models in the future.…”

Section: Discussionmentioning

confidence: 99%

Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies

et al. 2014

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Background: There is increasing interest in investigating genetic risk models in empirical studies, but such studies are premature when the expected predictive ability of the risk model is low. We assessed how accurately the predictive ability of genetic risk models can be estimated in simulated data that are created based on the odds ratios (ORs) and frequencies of single-nucleotide polymorphisms (SNPs) obtained from genome-wide association studies (GWASs).Methods: We aimed to replicate published prediction studies that reported the area under the receiver operating characteristic curve (AUC) as a measure of predictive ability. We searched GWAS articles for all SNPs included in these models and extracted ORs and risk allele frequencies to construct genotypes and disease status for a hypothetical population. Using these hypothetical data, we reconstructed the published genetic risk models and compared their AUC values to those reported in the original articles.Results: The accuracy of the AUC values varied with the method used for the construction of the risk models. When logistic regression analysis was used to construct the genetic risk model, AUC values estimated by the simulation method were similar to the published values with a median absolute difference of 0.02 [range: 0.00, 0.04]. This difference was 0.03 [range: 0.01, 0.06] and 0.05 [range: 0.01, 0.08] for unweighted and weighted risk scores.Conclusions: The predictive ability of genetic risk models can be estimated using simulated data based on results from GWASs. Simulation methods can be useful to estimate the predictive ability in the absence of empirical data and to decide whether empirical investigation of genetic risk models is warranted.

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Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine

Abstract: Image-based clinical decision support models allow patients to be informed of the actual probability of having a prostate cancer.

Cited by 27 publications

References 49 publications

Decision time for clinical decision support systems

Decision time for clinical decision support systems

An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies

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