Abstract
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.
Highlights
Prostate carcinoma (PC) is the second most frequently diagnosed carcinoma and the fifth leading cause of carcinomarelated deaths in the male population worldwide [1]
This study is aimed at developing and comparing two machine learning (ML) classifiers based on artificial neural networks (ANN) and naïve Bayes (NB) in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during the follow-up of PC patients treated with radical radiotherapy with/or without androgen deprivation therapy (ADT)
We excluded patients with metastatic disease, those with performed salvage or postoperative RT and patients who did not have bone scan (BS) during follow-up. (Figure 1) The final analysis was conducted on 109 patients, which has been shown to meet the criteria of a minimum number of necessary samples of PC in Serbia according to incidence and population size using the 95% confidence level [19]
Summary
Prostate carcinoma (PC) is the second most frequently diagnosed carcinoma and the fifth leading cause of carcinomarelated deaths in the male population worldwide [1]. 1.4 million new PC cases occurred in 2020 worldwide, with an incidence rate of 4.34 per 100.000 in Serbia [1]. 10% of newly diagnosed PC patients is presented with bone metastases and it is increasing to 80% at advanced stages of the disease [2]. Metastases are related to poor prognosis, bone pain, and indicate the incurability of disease in most cases with a 5-year survival rate of 25% and median survival of approximately 40 months [3, 4]. The decision of optimal treatment for localized PC is based on numerous factors, but the most important are the following: T stage, Gleason’s score (GS), and initial serum prostate-specific antigen (PSA) level [6]. Treatment options for localized PC are the external beam radiation therapy (EBRT) alone or with the addition of androgen deprivation therapy (ADT) based on clinical indications, brachytherapy, radical prostatectomy, or active surveillance
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