Prediction System for Prostate Cancer Recurrence Using Machine Learning

Lee, Sun Jung; Yu, Sung Hye; Kim, Yejin; Kim, Jae Kwon; Hong, Jun Hyuk; Kim, Choung Soo; Seo, Seong Il; Byun, Seok Soo; Jeong, Chang Wook; Lee, Ji Youl; Choi, In Young

doi:10.3390/app10041333

Cited by 7 publications

(6 citation statements)

References 17 publications

(16 reference statements)

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“…Prior studies suggested several prediction models using various combinations of variables including initial PSA, greatest percentage of biopsy cores, number of positive biopsy cores, GS, and cancer stage for successful risk stratification, as well as oncologic outcomes including metastatic progression and prostate cancer-specific mortality following treatment [1,[16][17][18][19][20][21][22]. In our study, variables related to early BCR were in accordance with those in prior studies.…”

Section: Discussionsupporting

confidence: 86%

“…In order to predict patients with early BCR after RP, supervised learning algorithms, which mean classification methods, are used in a previous study of Lee et al [16]. Supervised learning algorithms analyze the characteristics of patients in two groups statistically, and based on these statistical differences, it can provide a prediction model that classifies patients with prostate cancer that are likely to experience BCR after RP.…”

Section: Prediction Of Early Bcr Using Classification Methodsmentioning

confidence: 99%

“…As explained in Section 2.4.1, prediction of early BCR can be done by supervised learning with classification capabilities [16]. However, in some special cases, supervised learning may show its limitations.…”

Section: Prediction Of Early Bcr Using Auto-encoder Methodsmentioning

confidence: 99%

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A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

2020

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Background: Approximately 20–50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20–30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. The sensitivity, specificity, and accuracy for detection of early BCR of each algorithm were evaluated. Area under the receiver operating characteristics (AUROC) analyses were conducted. Results: A prediction model using an auto-encoder showed the highest prediction ability of early BCR after RP using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by MLP (AUC = 0.607 and 0.598), KNN (AUC = 0.596 and 0.571), and DT (AUC = 0.534 and 0.495). Conclusion: The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP.

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Section: Discussionsupporting

confidence: 86%

Section: Prediction Of Early Bcr Using Classification Methodsmentioning

confidence: 99%

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A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

2020

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“…To the best of our knowledge, this is the first study to develop a postirradiation recurrence prediction model using ML algorithms in PC carcinoma patients. This approach was previously used in order to predict the three-and five-year biochemical recurrence in PC patients initially treated surgically [27]. Also, one of the previous studies developed a model for the prediction of the presence of bone metastases, but this study had mixed early-stage cases with those who had advanced stage of the disease, and also both newly diagnosed patients and the ones who were previously treated [28].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients

Marinkovic

Popović

Stojanović-Rundić

et al. 2022

BioMed Research International

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After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naïve Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason’s score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models’ performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected.

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“…Some of these systems, namely clinical decision support systems (CDSS), are helpful tools for medical personnel to automate decision making and diagnose various health disorders with more precision. CDSS is widely used in certain medical fields, including the diagnoses of heart diseases [7][8][9][10], kidney diseases [11,12], diabetes [13,14], cancer [15,16], Alzheimer's disease [17], etc. CDSS are also applied in the field of geriatrics, where they are used successfully to assess signs of depression with an average accuracy of 95% [18] and diagnose dementia with a balanced accuracy of 82.3% [19], with an accuracy of 95.2% [20], and with an accuracy varying from 73% to 92% for various cases as presented in [21].…”

mentioning

confidence: 99%

Explainable Artificial Intelligence-Based Decision Support System for Assessing the Nutrition-Related Geriatric Syndromes

et al. 2021

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The use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis of a geriatric patient is a complex, experience-based, and time-consuming process that involves a variety of questionnaires and subjective and inaccurate patient responses. This paper proposes the explainable artificial intelligence-based (XAI) clinical decision support system (CDSS) to assess nutrition-related factors (symptoms) and to determine the likelihood of geriatric patient health risks associated with four syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. The proposed system’s prototype was tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The subjects of this study were 83 geriatric patients with various health conditions. The assessments of the nutritional status and syndromes of the patients provided by the CDSS were compared with the diagnoses of the physicians obtained using standard assessment methods. The results show that proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. The research confirms that the proposed XAI-based CDSS is an effective tool, able to assess nutrition-related health risk factors and their dependencies and, in some cases, makes even a more accurate decision than a less experienced physician.

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Prediction System for Prostate Cancer Recurrence Using Machine Learning

Cited by 7 publications

References 17 publications

A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients

Explainable Artificial Intelligence-Based Decision Support System for Assessing the Nutrition-Related Geriatric Syndromes

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