Mobile application-based Seoul National University Prostate Cancer Risk Calculator: development, validation, and comparative analysis with two Western risk calculators in Korean men

Prostate cancer nomograms and their application in Asian men: a review

Nomograms help to predict outcomes in individual patients rather than whole populations and are an important part of evaluation and treatment decision making. Various nomograms have been developed in malignancies to predict and prognosticate clinical outcomes such as severity of disease, overall survival, and recurrence-free survival. In prostate cancer, nomograms were developed for determining need for biopsy, disease course, need for adjuvant therapy, and outcomes. Most of these predictive nomograms were based on Caucasian populations. Prostate cancer is significantly affected by race, and Asian men have a significantly different racial and genetic susceptibility compared to Caucasians, raising the concern in generalizability of these nomograms. We reviewed the existing literature for nomograms in prostate cancer and their application in Asian men. There are very few studies that have evaluated the applicability and validity of the existing nomograms in these men. Most have found significant differences in the performance in this population. Thus, more studies evaluating the existing nomograms in Asian men or suggesting modifications for this population are required.

Natural history of incidentally diagnosed prostate cancer after holmium laser enucleation of the prostate

OBJECTIVES

There is no consensus on the management plan for incidental prostate cancer (IPCa) after holmium laser enucleation of the prostate (HoLEP). This study aims to investigate the natural course of this disease and suggest appropriate treatment in real clinical practice.

METHODS

The medical records of a prospective cohort of patients with LUTS/BPH who underwent HoLEP between July 2008 and December 2020 at Seoul National University Hospital were retrospectively reviewed. Patients who underwent HoLEP for palliative purpose of prostate cancer control were excluded. The natural history of IPCa was assessed by the clinician in a descriptive manner for each treatment option.

RESULTS

Among 2630 patients, 141 (5.4%) were diagnosed with IPCa after HoLEP. Pathologic T stage and magnetic resonance imaging results were highly associated with the physician's primary treatment decision-making for IPCa. Active surveillance (AS) was performed in 80% of patients, of whom 90% underwent follow-up without intervention, while the remaining 10% underwent deferred active treatment with a median follow-up of 46.3 months due to International Society of Urological Pathology grade group upgrading or increasing core involvement percentage. Meanwhile, 20% of patients underwent immediate active treatment. With a median follow-up period of 88.3 months after treatment, only one of 25 patients had biochemical recurrence.

CONCLUSIONS

The incidence of IPCa after HoLEP was 5.4%, and among these, approximately 20% proceeded with immediate definitive therapy and an additional 6% ultimately received definitive therapy within a median of 4 years of AS but showed excellent oncological outcomes.

Nomogram predicting the risk of three-year chronic kidney disease adverse outcomes among East Asian patients with CKD

Background

Chronic kidney disease (CKD) is a common health challenge. There are some risk models predicting CKD adverse outcomes, but seldom focus on the Mongoloid population in East Asian. So, we developed a simple but intuitive nomogram model to predict 3-year CKD adverse outcomes for East Asian patients with CKD.

Methods

The development and internal validation of prediction models used data from the CKD-ROUTE study in Japan, while the external validation set used data collected at the First People’s Hospital of Foshan in southern China from January 2013 to December 2018. Models were developed using the cox proportional hazards model and nomogram with SPSS and R software. Finally, the model discrimination, calibration and clinical value were tested by R software.

Results

The development and internal validation data-sets included 797 patients (191 with progression [23.96%]) and 341 patients (89 with progression [26.10%]), respectively, while 297 patients (108 with progression [36.36%]) were included in the external validation data set. The nomogram model was developed with age, eGFR, haemoglobin, blood albumin and dipstick proteinuria to predict three-year adverse-outcome-free probability. The C-statistics of this nomogram were 0.90(95% CI, 0.89–0.92) for the development data set, 0.91(95% CI, 0.89–0.94) for the internal validation data set and 0.83(95% CI, 0.78–0.88) for the external validation data-set. The calibration and decision curve analyses were good in this model.

Conclusion

This visualized predictive nomogram model could accurately predict CKD three-year adverse outcomes for East Asian patients with CKD, providing an easy-to-use and widely applicable tool for clinical practitioners.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02496-7.

Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review

Background

Screening for prostate cancer has long been a debated, complex topic. The use of risk calculators for prostate cancer is recommended for determining patients’ individual risk of cancer and the subsequent need for a prostate biopsy. These tools could lead to better discrimination of patients in need of invasive diagnostic procedures and optimized allocation of health care resources

Objective

The goal of the research was to systematically review available literature on the performance of current prostate cancer risk calculators in healthy populations by comparing the relative impact of individual items on different cohorts and on the models’ overall performance.

Methods

We performed a systematic review of available prostate cancer risk calculators targeted at healthy populations. We included studies published from January 2000 to March 2021 in English, Spanish, French, Portuguese, or German. Two reviewers independently decided for or against inclusion based on abstracts. A third reviewer intervened in case of disagreements. From the selected titles, we extracted information regarding the purpose of the manuscript, analyzed calculators, population for which it was calibrated, included risk factors, and the model’s overall accuracy.

Results

We included a total of 18 calculators from 53 different manuscripts. The most commonly analyzed ones were the Prostate Cancer Prevention Trial (PCPT) and European Randomized Study on Prostate Cancer (ERSPC) risk calculators developed from North American and European cohorts, respectively. Both calculators provided high diagnostic ability of aggressive prostate cancer (AUC as high as 0.798 for PCPT and 0.91 for ERSPC). We found 9 calculators developed from scratch for specific populations that reached a diagnostic ability as high as 0.938. The most commonly included risk factors in the calculators were age, prostate specific antigen levels, and digital rectal examination findings. Additional calculators included race and detailed personal and family history.

Conclusions

Both the PCPR and ERSPC risk calculators have been successfully adapted for cohorts other than the ones they were originally created for with no loss of diagnostic ability. Furthermore, designing calculators from scratch considering each population’s sociocultural differences has resulted in risk tools that can be well adapted to be valid in more patients. The best risk calculator for prostate cancer will be that which has been calibrated for its intended population and can be easily reproduced and implemented.

Trial Registration

PROSPERO CRD42021242110; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=242110

Mobile applications in oncology: A systematic review of health science databases

INTRODUCTION

In recent years there has been an exponential growth in the number of mobile applications (apps) relating to the early diagnosis of cancer and prevention of side effects during cancer treatment. For health care professionals and users, it can thus be difficult to determine the most appropriate app for given needs and assess the level of scientific evidence supporting their use. Therefore, this review aims to examine the research studies that deal with this issue and determine the characteristics of the apps involved.

METHODOLOGY

This study involved a systematic review of the scientific literature on randomized clinical trials that use apps to improve cancer management among patients, using the Pubmed (Medline), Latin America and the Caribbean in Health Sciences (LILACS), and Cochrane databases. The search was limited to articles written in English and Spanish published in the last 10 years. A search of the App Store for iOS devices and Google Play for Android devices was performed to find the apps identified in the included research articles.

RESULTS

In total, 54 articles were found to analyze the development of an application in the field of oncology. These articles were most frequently related to the use of apps for the early detection of cancer (n = 28), particularly melanoma (n = 9). In total, 21 studies reflected the application used. The apps featured in nine articles were located using the App Store and Google Play (n = 9), of which five were created to manage cancer-related issues. The rest of the apps were designed for use in the general population (n = 4).

CONCLUSIONS

There is an increasing number of research articles that study the use of apps in the field of oncology; however, these mobile applications tend to disappear from app stores after the studies are completed.

Nomograms for predicting the survival rate for cervical cancer patients who undergo radiation therapy: a SEER analysis

Aim: To integrate multiple independent risk factors to establish prognostic nomograms for better predicting overall survival and disease-specific survival in patients with cervical cancer receiving radiation therapy. Materials & methods: Cox analysis used to construct nomograms. The C-index, time-dependent receiver operating characteristic and calibration plots were used to evaluate the performance. The discrimination abilities were compared using the decision curve analysis, net reclassification improvement and integrated discrimination improvement. Results: After randomization, 2869 and 1230 cervical cancer patients were included in the training and validation sets, respectively. Nomograms that incorporated all of the significant independent factors for predicting the 3- and 5-year overall survival and disease-specific survival in the training cohort were established. Conclusion: Compared with the International Federation of Gynecology and Obstetrics staging system, the proposed nomograms exhibit superior prognostic discrimination and survival prediction.

Prostate cancer screening in Europe and Asia

Prostate cancer (PCa) is the second most common cancer among men worldwide and even ranks first in Europe. Although Asia is known as the region with the lowest PCa incidence, it has been rising rapidly over the last 20 years mostly due to the introduction of prostate-specific antigen (PSA) testing. Randomized PCa screening studies in Europe show a mortality reduction in favor of PSA-based screening but coincide with high proportions of unnecessary biopsies, overdiagnosis and subsequent overtreatment. Conclusive data on the value of PSA-based screening and hence the balance between harms and benefits in Asia is still lacking. Because of known racial variations, Asian countries should not directly apply the European screening models. Like in the western world also in Asia, new predictive markers, tools and risk stratification strategies hold great potential to improve the early detection of PCa and to reduce the worldwide existing negative aspects of PSA-based PCa screening.

Rotterdam Prostate Cancer Risk Calculator: Development and Usability Testing of the Mobile Phone App

Background

The use of prostate cancer screening tools that take into account relevant prebiopsy information (ie, risk calculators) is recommended as a way of determining the risk of cancer and the subsequent need for a prostate biopsy. This has the potential to limit prostate cancer overdiagnosis and subsequent overtreatment. mHealth apps are gaining traction in urological practice and are used by both practitioners and patients for a variety of purposes.

Objective

The impetus of the study was to design, develop, and assess a smartphone app for prostate cancer screening, based on the Rotterdam Prostate Cancer Risk Calculator (RPCRC).

Methods

The results of the Rotterdam arm of the European Randomized Study of Screening for Prostate Cancer (ERSPC) study were used to elaborate several algorithms that allowed the risk of prostate cancer to be estimated. A step-by-step workflow was established to ensure that depending on the available clinical information the most complete risk model of the RPCRC was used. The user interface was designed and then the app was developed as a native app for iOS. The usability of the app was assessed using the Post-Study System Usability Questionnaire (PSSUQ) developed by IBM, in a group of 92 participants comprising urologists, general practitioners, and medical students.

Results

A total of 11 questions were built into the app, and, depending on the answers, one of the different algorithms of the RPCRC could be used to predict the risk of prostate cancer and of clinically significant prostate cancer (Gleason score ≥7 and clinical stage >T2b). The system usefulness, information quality, and interface quality scores were high—92% (27.7/30), 87% (26.2/30), and 89% (13.4/15), respectively. No usability problems were identified.

Conclusions

The RPCRC app is helpful in predicting the risk of prostate cancer and, even more importantly, clinically significant prostate cancer. Its algorithms have been externally validated before and the usability score shows the app’s interface is well designed. Further usability testing is required in different populations to verify these results and ensure that it is easy to use, to warrant a broad appeal, and to provide better patient care.

Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort

PURPOSE

We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort.

MATERIALS AND METHODS

Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.

RESULTS

PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed.

CONCLUSIONS

KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in clinical settings.

Clinical Nomograms to Predict Stone-Free Rates after Shock-Wave Lithotripsy: Development and Internal-Validation

PURPOSE

Shock-wave lithotripsy (SWL) is accepted as the first line treatment modality for uncomplicated upper urinary tract stones; however, validated prediction models with regards to stone-free rates (SFRs) are still needed. We aimed to develop nomograms predicting SFRs after the first and within the third session of SWL. Computed tomography (CT) information was also modeled for constructing nomograms.

MATERIALS AND METHODS

From March 2006 to December 2013, 3028 patients were treated with SWL for ureter and renal stones at our three tertiary institutions. Four cohorts were constructed: Total-development, Total-validation, CT-development, and CT-validation cohorts. The nomograms were developed using multivariate logistic regression models with selected significant variables in a univariate logistic regression model. A C-index was used to assess the discrimination accuracy of nomograms and calibration plots were used to analyze the consistency of prediction.

RESULTS

The SFR, after the first and within the third session, was 48.3% and 68.8%, respectively. Significant variables were sex, stone location, stone number, and maximal stone diameter in the Total-development cohort, and mean Hounsfield unit (HU) and grade of hydronephrosis (HN) were additional parameters in the CT-development cohort. The C-indices were 0.712 and 0.723 for after the first and within the third session of SWL in the Total-development cohort, and 0.755 and 0.756, in the CT-development cohort, respectively. The calibration plots showed good correspondences.

CONCLUSIONS

We constructed and validated nomograms to predict SFR after SWL. To the best of our knowledge, these are the first graphical nomograms to be modeled with CT information. These may be useful for patient counseling and treatment decision-making.

The role of 3-tesla diffusion-weighted magnetic resonance imaging in selecting prostate cancer patients for active surveillance

Purpose

Differentiating significant cancer from insignificant cancer is a major challenge in active surveillance (AS) for prostate cancer. We evaluated whether the apparent diffusion coefficient (ADC) grade from 3-T diffusion-weighted magnetic resonance imaging (DW-MRI) is useful to exclude men with unfavorable pathological features from men meeting current AS eligibility criteria.

Methods

Among patients who underwent radical prostatectomy, 117 potential AS candidates defined according to 2013 European Association of Urology guidelines who had undergone preoperative 3-T DW-MRI were included. A blinded uro-radiologist graded the level of suspicion from the ADC map using the Likert scale from 1 to 5. The rate of unfavorable pathological features was evaluated according to ADC grade. Unfavorable pathological features were defined as non–organ-confined disease or pathological Gleason score≥7 (4+3). The associations between unfavorable pathological features and clinical variables including ADC grade (>3 vs. ≤3) were evaluated using logistic regression analysis.

Results

The rates of unfavorable pathological features were 0.0% (0/14), 2.9% (1/34), 5.4% (2/37), 25.0% (6/24), and 37.5% (3/8) from grades 1 to 5 (P=0.002). The predictive accuracy was as high as 0.804. The rates were significantly different between low (≤3, 3.5%) and high (>3, 28.1%, P<0.001) grades. The sensitivity, specificity, and positive and negative predictive values were 75.0%, 78.1%, 28.1%, and 96.5%. ADC grade (odds ratio [OR], 10.696; 95% confidence interval [CI], 2.675–42.773) was significantly associated with unfavorable pathological features, even after adjusting for other variables (OR, 11.274; 95% CI, 2.622–48.471).

Conclusions

ADC grade from 3-T DW-MRI is useful to predict men with unfavorable pathologic features from AS candidates.