PSA and new biomarkers within multivariate models to improve early detection of prostate cancer

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Detection of Single Cancer Cells in Blood with Artificially Intelligent Nanoarray

Detection and monitoring of single cancer cells (SCCs), such as circulating tumor cells (CTCs), would be of aid in an efficient early detection of cancer, a tailored (personalized) therapy, and in a fast bedside assessment of treatment efficacy. Nevertheless, currently available techniques, which mostly rely on the isolation of SCCs based on their physical or biological properties, suffer from low sensitivity, complicated technical procedures, low cost-effectiveness, and being unsuitable for continuous monitoring. We report here on the design and use of an artificially intelligent nanoarray based on a heterogeneous set of chemisensitive nanostructured films for the detection of SCCs using volatile organic compounds emanating in the air trapped above blood samples containing SCCs. For demonstration purposes, we have focused on samples containing A549 lung cancer cells (hereafter, SCC^A549). The nanoarray developed to detect SCC^A549 has >90% accuracy, >85% sensitivity, and >95% specificity. Detection works irrespective of the medium and/or the environment. These results were validated by complementary mass spectrometry. The ability to continuously record, store, and preprocess the signals increases the chances that this nanotechnology might also be useful in the early detection of cancer cells in the blood and continuous monitoring of their possible progression.

A Systematic Review of Artificial Intelligence in Prostate Cancer

The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

A chemiluminescent dual-aptasensor capable of simultaneously quantifying prostate specific antigen and vascular endothelial growth factor

An aptasensor with guanine chemiluminescence detection was developed for the early diagnosis of prostate cancer with the simultaneous quantifications of prostate specific antigen (PSA) and vascular endothelial growth factor (VEGF). A PSA DNA aptamer conjugated with Texas Red emits red light in guanine chemiluminescence reaction, whereas a VEGF DNA aptamer conjugated with 6-FAM emits green light. The PSA and VEGF aptamers immobilized on the surface of paramagnetic beads mixed with a sample were incubated for 30 min. After the incubation, PSA and VEGF bound with PSA and VEGF aptamers were simultaneously quantified for 20 s using guanine chemiluminescence detections operated with two photomultiplier tubes and two optical filters capable of selectively collecting green or red light. With the increase of PSA concentration, the strength of the red light emitted from Texas Red dropped exponentially, whereas with the increase of VEGF concentration, the green light emitted from 6-FAM was enhanced. The limits of detection were as low as 0.6 ng/ml for PSA and 0.4 ng/ml for VEGF. We confirmed that the dual-aptasensor can be applied as an advanced and new medical device capable of simultaneously quantifying PSA and VEGF to early diagnose prostate cancer with good accuracy, precision, and reproducibility.

Electrochemical aptamer-based biosensor developed to monitor PSA and VEGF released by prostate cancer cells

Early prostate cancer (PCa) diagnostic is crucial to enhance patient survival rates; besides, non-invasive platforms have been developed worldwide in order to precisely detect PCa biomarkers. Therefore, the aim of the present study is to develop a new aptamer-based biosensor through the self-assembling of thiolated aptamers for PSA and VEGF on the top of gold electrodes. This biosensor was tested in three prostate cell lines (RWPE-1, LNCaP and PC3). The results evidenced a stable and sensitive sensor presenting wide linear detection ranges (0.08-100 ng/mL for PSA and 0.15 ng-100 ng/mL for VEGF). Therefore, the aptasensor was able to detect the patterns of PSA and VEGF released in vitro by PCa cells, which gave new insights about the prostate cancer protein dynamics. Thus, it could be used as a non-invasive PCa clinical diagnosis instrument in the near future. Graphical Abstract Overview of the experimental design applied to the aptamer-based electrochemical sensor self-assembled on the thiolated hairpin structure. A filter membrane was added on top of working electrode to provide the cell-attachment surface after aptamer incubation, without compromising the aptamer layer. The pore membrane allowed target proteins to pass to the aptamer surface; the MCH backfilling avoided unspecific protein binding to the gold electrode surface.

Circulating tumor cells in prostate cancer: Precision diagnosis and therapy

The primary cause of tumor-associated mortality in prostate cancer (PCa) remains distant metastasis. The dissemination of tumor cells from the primary tumor to distant sites through the bloodstream cannot be detected early by standard imaging methods. Circulating tumor cells (CTCs) represent an effective prognostic and predictive biomarker, which are able to monitor efficacy of adjuvant therapies, detect early development of metastases, and finally, assess therapeutic responses of advanced disease earlier than traditional diagnostic methods. In addition, since repeated tissue biopsies are invasive, costly and not always feasible, the assessment of tumor characteristics on CTCs, by a peripheral blood sample as a liquid biopsy, represents an attractive opportunity. The implementation of molecular and genomic characterization of CTCs may contribute to improve the treatment selection and thus, to move toward more precise diagnosis and therapy in PCa. The present study summarizes the current advances in CTC enrichment and detection strategies and reviews how CTCs may contribute to significant insights in the metastatic process, as well as how they may be utilized in clinical application in PCa. Although it is proposed that CTCs may offer insights into the prognosis and management of PCa, there are a number of challenges in the study of circulating tumor cells, and their clinical utility remains under investigation.

pH-Responsive fluorescent graphene quantum dots for fluorescence-guided cancer surgery and diagnosis

Cancer remains a major cause of morbidity and mortality around the world. Improved cancer treatment requires enhancement of cancer diagnosis and detection. To achieve this goal, here we report a novel imaging probe, pH-responsive fluorescent graphene quantum dots (pRF-GQDs). pRF-GQDs were prepared by electrolysis of graphite rods in sodium p-toluenesulfonate acetonitrile solution. The resulting pRF-GQDs, which have minimal toxicity, display a sharp fluorescence transition between green and blue at pH 6.8, a pH matching the acidic extracellular microenvironment in solid tumors. We found that this unique fluorescence switch property allows tumors to be distinguished from normal tissues. In addition to fluorescence, pRF-GQDs also exhibit upconversion photoluminescence (UCPL). We demonstrate that the combination of UCPL and fluorescence switch enables detection of solid tumors of different origin at an early developmental stage. Therefore, pRF-GQDs have great potential to be used as a universal probe for fluorescence-guided cancer surgery and cancer diagnosis.

The Quantified Level of Circulating Prostate Stem Cell Antigen mRNA relative to GAPDH Level Is a Clinically Significant Indictor for Predicting Biochemical Recurrence in Prostate Cancer Patients after Radical Prostatectomy

The study quantified the relative absolute PSCA level in relation to the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) level in the peripheral blood of 478 hormone-naive prostate cancer (PC) patients who underwent radical prostatectomy from 2005 to 2012 and evaluated its prognostic significance as a risk factor for predicting biochemical recurrence (BCR), compared to known parameters. Nested real-time polymerase chain reaction (RT-PCR) and gel electrophoresis detected PSCA levels and measured the PSCA/GAPDH ratio. Clinicopathological data from the institutional database were examined to determine the adequate cut-off level to predict postoperative BCR. A total of 110 patients had a positive PSCA result (23.0%) via RT-PCR (mean blood ratio 1.1 ± 0.4). The BCR was significantly higher in the PSCA-positive detection group (p = 0.009). A multivariate model was created to show that a PSCA/GAPDH ratio between 1.0 and 1.5 (HR 12.722), clinical T2c stage (HR 0.104), preoperative PSA (HR 1.225), extraprostatic capsule extension (HR 0.006), lymph node dissection (HR 16.437), and positive resection margin (HR 27.453) were significant predictive factors for BCR (p < 0.05). The study showed successful quantification of PSCA with its significance for BCR-related risk factor; however, further studies are needed to confirm its clinical predictive value.

Current biomarkers for diagnosing of prostate cancer

Prostate cancer (PCa) is mostly detected by prostate-specific antigen (PSA) as one of the most widely used tumor markers. But PSA is limited with its low specificity. The prostate health index (phi) can improve specificity over percent free and total PSA and correlates with aggressive cancer. The urinary PCA3 also shows its utility to detect PCa but its correlation with aggressiveness and the low sensitivity at high values are limitations. While the detection of alterations of the androgen-regulated TMPRSS2 and ETS transcription factor genes in tissue of ˜50% of all PCa patients was one research milestone, the urinary assay should only be used in combination with PCA3. Both US FDA-approved markers phi and PCA3 perform equally.

KLK11 mRNA expression predicts poor disease-free and overall survival in colorectal adenocarcinoma patients

BACKGROUND

Dysregulated expression of several KLK family members has been observed in colorectal adenocarcinoma. In the present study, the prognostic value of KLK11 mRNA expression as a molecular tissue biomarker in colorectal adenocarcinoma was examined.

MATERIALS & METHODS

Using quantitative real-time PCR, KLK11 mRNA expression was studied in 120 cancerous and 41 paired noncancerous colorectal specimens obtained from 120 patients with primary colorectal adenocarcinoma.

RESULTS

A significant upregulation of KLK11 transcripts in colorectal tumors was observed. KLK11 mRNA expression was associated with the depth of tumor invasion and the histological grade. Furthermore, KLK11 mRNA expression predicted poor disease-free and overall survival, independently of patient gender, age, tumor size, location, histological subtype, grade, venous invasion, lymphatic invasion, TNM stage, radiotherapy and chemotherapy treatment.

CONCLUSION

KLK11 mRNA expression could be considered as a new molecular prognostic biomarker in colorectal adenocarcinoma, with additional prognostic value in patients with highly invasive tumors and/or positive lymph nodes.

Prostate-specific antigen and other serum and urine markers in prostate cancer

Prostate-specific antigen (PSA) is one of the most widely used tumor markers, and strongly correlates with the risk of harboring from prostate cancer (PCa). This risk is visible already several years in advance but PSA has severe limitations for PCa detection with its low specificity and low negative predictive value. There is an urgent need for new biomarkers especially to detect clinically significant and aggressive PCa. From all PSA-based markers, the FDA-approved Prostate Health Index (phi) shows improved specificity over percent free and total PSA. Other serum kallikreins or sarcosine in serum or urine show more diverging data. In urine, the FDA-approved prostate cancer gene 3 (PCA3) has also proven its utility in the detection and management of early PCa. However, some aspects on its correlation with aggressiveness and the low sensitivity at very high values have to be re-examined. The detection of a fusion of the androgen regulated TMPRSS2 gene with the ERG oncogene (from the ETS family), which acts as transcription factor gene, in tissue of ~50% of all PCa patients was one milestone in PCa research. When combining the urinary assays for TMPRSS2:ERG and PCA3, an improved accuracy for PCa detection is visible. PCA3 and phi as the best available PCa biomarkers show an equal performance in direct comparisons.

Fully automated prostate segmentation on MRI: comparison with manual segmentation methods and specimen volumes

OBJECTIVE

The objective of our study was to compare calculated prostate volumes derived from tridimensional MR measurements (ellipsoid formula), manual segmentation, and a fully automated segmentation system as validated by actual prostatectomy specimens.

MATERIALS AND METHODS

Ninety-eight consecutive patients (median age, 60.6 years; median prostate-specific antigen [PSA] value, 6.85 ng/mL) underwent triplane T2-weighted MRI on a 3-T magnet with an endorectal coil while undergoing diagnostic workup for prostate cancer. Prostate volume estimates were determined using the formula for ellipsoid volume based on tridimensional measurements, manual segmentation of triplane MRI, and automated segmentation based on normalized gradient fields cross-correlation and graph-search refinement. Estimates of prostate volume based on ellipsoid volume, manual segmentation, and automated segmentation were compared with prostatectomy specimen volumes. Prostate volume estimates were compared using the Pearson correlation coefficient and linear regression analysis. The Dice similarity coefficient was used to quantify spatial agreement between manual segmentation and automated segmentation.

RESULTS

The Pearson correlation coefficient revealed strong positive correlation between prostatectomy specimen volume and prostate volume estimates derived from manual segmentation (R = 0.89-0.91, p < 0.0001) and automated segmentation (R = 0.88-0.91, p < 0.0001). No difference was observed between manual segmentation and automated segmentation. Mean partial and full Dice similarity coefficients of 0.92 and 0.89, respectively, were achieved for axial automated segmentation.

CONCLUSION

Prostate volume estimates obtained with a fully automated 3D segmentation tool based on normalized gradient fields cross-correlation and graph-search refinement can yield highly accurate prostate volume estimates in a clinically relevant time of 10 seconds. This tool will assist in developing a broad range of applications including routine prostate volume estimations, image registration, biopsy guidance, and decision support systems.

Kallikrein-related peptidase 4 (KLK4) mRNA predicts short-term relapse in colorectal adenocarcinoma patients

The members of the kallikrein-related peptidase (KLK) family are aberrantly expressed in cancer, including colorectal adenocarcinoma. KLK4 is an endogenous activator of protease-activated receptor 1 (PAR1) in HT-29 colorectal adenocarcinoma cells, inducing PAR1 signaling and subsequent ERK1/2 activation. The aim of this study was to analyze KLK4 mRNA expression in colorectal adenocarcinoma and to examine its prognostic value as a novel molecular tissue biomarker in this malignancy. Therefore, total RNA was isolated from primary tumors of 81 colorectal adenocarcinoma patients, cDNA was prepared, and KLK4 mRNA expression analysis was performed using quantitative real-time PCR. KLK4 mRNA was significantly associated with the Dukes stage, tumor invasion, size, and histological grade. Survival analysis demonstrated that KLK4 mRNA expression constitutes an unfavorable prognostic biomarker in colorectal adenocarcinoma, predicting poor disease-free survival (DFS), independently of the nodal status and tumor size. Furthermore, KLK4 mRNA predicts short-term relapse of lymph node-negative patients or those with tumors of early Dukes stage. In conclusion, KLK4 mRNA expression can be regarded as a novel potential tissue biomarker in colorectal adenocarcinoma.

External validation of an artificial neural network and two nomograms for prostate cancer detection

Background. Multivariate models are used to increase prostate cancer (PCa) detection rate and to reduce unnecessary biopsies. An external validation of the artificial neural network (ANN) "ProstataClass" (ANN-Charité) was performed with daily routine data. Materials and Methods. The individual ANN predictions were generated with the use of the ANN application for PSA and free PSA assays, which rely on age, tPSA, %fPSA, prostate volume, and DRE (ANN-Charité). Diagnostic validity of tPSA, %fPSA, and the ANN was evaluated by ROC curve analysis and comparisons of observed versus predicted probabilities. Results. Overall, 101 (35.8%) PCa were detected. The areas under the ROC curve (AUCs) were 0.501 for tPSA, 0.669 for %fPSA, 0.694 for ANN-Charité, 0.713 for nomogram I, and 0.742 for nomogram II, showing a significant advantage for nomogram II (P = 0.009) compared with %fPSA while the other model did not differ from %fPSA (P = 0.15 and P = 0.41). All models overestimated the predicted PCa probability. Conclusions. Beside ROC analysis, calibration is an important tool to determine the true value of using a model in clinical practice. The worth of multivariate models is limited when external validations were performed without knowledge of the circumstances of the model's development.

Automated computer-derived prostate volumes from MR imaging data: comparison with radiologist-derived MR imaging and pathologic specimen volumes

PURPOSE

To compare prostate gland volume (PV) estimation of automated computer-generated multifeature active shape models (MFAs) performed with 3-T magnetic resonance (MR) imaging with that of other methods of PV assessment, with pathologic specimens as the reference standard.

MATERIALS AND METHODS

All subjects provided written informed consent for this HIPAA-compliant and institutional review board-approved study. Freshly weighed prostatectomy specimens from 91 patients (mean age, 59 years; range, 42-84 years) served as the reference standard. PVs were manually calculated by two independent readers from MR images by using the standard ellipsoid formula. Planimetry PV was calculated from gland areas generated by two independent investigators by using manually drawn regions of interest. Computer-automated assessment of PV with an MFA was determined by the aggregate computer-calculated prostate area over the range of axial T2-weighted prostate MR images. Linear regression, linear mixed-effects models, concordance correlation coefficients, and Bland-Altman limits of agreement were used to compare volume estimation methods.

RESULTS

MFA-derived PVs had the best correlation with pathologic specimen PVs (slope, 0.888). Planimetry derived volumes produced slopes of 0.864 and 0.804 for two independent readers when compared with specimen PVs. Ellipsoid formula-derived PVs had slopes closest to one when compared with planimetry PVs. Manual MR imaging and MFA PV estimates had high concordance correlation coefficients with pathologic specimens.

CONCLUSION

MFAs with axial T2-weighted MR imaging provided an automated and efficient tool with which to assess PV. Both MFAs and MR imaging planimetry require adjustments for optimized PV accuracy when compared with prostatectomy specimens.

Functionalized radioactive gold nanoparticles in tumor therapy

The development of new treatment modalities that offer clinicians the ability to reduce sizes of tumor prior to surgical resection or to achieve complete ablation without surgery would be a significant medical breakthrough in the overall care and treatment of prostate cancer patients. The goal of our investigation is aimed at validating the hypothesis that Gum Arabic-functionalized radioactive gold nanoparticles (GA-(198) AuNP) have high affinity toward tumor vasculature. We hypothesized further that intratumoral delivery of the GA-(198) AuNP agent within prostate tumor will allow optimal therapeutic payload that will significantly or completely ablate tumor without side effects, in patients with hormone refractory prostate cancer. In order to evaluate the therapeutic efficacy of this new nanoceutical, GA-(198) AuNP was produced by stabilization of radioactive gold nanoparticles ((198) Au) with the FDA-approved glycoprotein, GA. This review will describe basic and clinical translation studies toward realization of the therapeutic potential and myriad of clinical applications of GA-(198) AuNP agent in treating prostate and various solid tumors in human cancer patients.

A novel indium-111-labeled gonadotropin-releasing hormone peptide for human prostate cancer imaging

The purpose of this study was to evaluate the tumor targeting and imaging properties of a novel (111)In-labeled gonadotropin-releasing hormone (GnRH) peptide {1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-Ahx-(D-Lys(6)-GnRH1)} for human prostate cancer. The biodistribution and tumor imaging properties of (111)In-DOTA-Ahx-(D-Lys(6)-GnRH1) were determined in DU145 human prostate cancer-xenografted nude mice. (111)In-DOTA-Ahx-(d-Lys(6)-GnRH1) exhibited rapid tumor uptake (1.27 ± 0.40% ID/g at 0.5h post-injection) coupled with fast whole-body clearance through the urinary system. The DU145 human prostate cancer-xenografted tumor lesions were clearly visualized by single photon emission computed tomography (SPECT)/CT at 0.5h post-injection of (111)In-DOTA-Ahx-(D-Lys(6)-GnRH1). The successful imaging of DU145 human prostate cancer-xenografted tumor lesions using (111)In-DOTA-Ahx-(d-Lys(6)-GnRH1) highlighted its potential as a novel imaging probe for human prostate cancer imaging.

Between-method differences in prostate-specific antigen assays affect prostate cancer risk prediction by nomograms

BACKGROUND

To date, no published nomogram for prostate cancer (PCa) risk prediction has considered the between-method differences associated with estimating concentrations of prostate-specific antigen (PSA).

METHODS

Total PSA (tPSA) and free PSA were measured in 780 biopsy-referred men with 5 different assays. These data, together with other clinical parameters, were applied to 5 published nomograms that are used for PCa detection. Discrimination and calibration criteria were used to characterize the accuracy of the nomogram models under these conditions.

RESULTS

PCa was found in 455 men (58.3%), and 325 men had no evidence of malignancy. Median tPSA concentrations ranged from 5.5 μg/L to 7.04 μg/L, whereas the median percentage of free PSA ranged from 10.6% to 16.4%. Both the calibration and discrimination of the nomograms varied significantly across different types of PSA assays. Median PCa probabilities, which indicate PCa risk, ranged from 0.59 to 0.76 when different PSA assays were used within the same nomogram. On the other hand, various nomograms produced different PCa probabilities when the same PSA assay was used. Although the ROC curves had comparable areas under the ROC curve, considerable differences were observed among the 5 assays when the sensitivities and specificities at various PCa probability cutoffs were analyzed.

CONCLUSIONS

The accuracy of the PCa probabilities predicted according to different nomograms is limited by the lack of agreement between the different PSA assays. This difference between methods may lead to unacceptable variation in PCa risk prediction. A more cautious application of nomograms is recommended.

Diabetes mellitus in pancreatic cancer and the need for diagnosis of asymptomatic disease

Pancreatic cancer is strongly associated with the development of hyperglycemia, peripheral insulin resistance and diabetes mellitus, especially when presented as new-onset diabetes mellitus. Peripheral insulin resistance and hyperinsulinemia have been suggested to promote growth of pancreatic cancer cells, and therefore a relation between long-standing diabetes mellitus type 2 and pancreatic cancer has been implied. Epidemiological studies, though, give incongruent results to this problem. There are data supporting a tumor-derived influence on glucose metabolism, insulin secretion and eventually the development of diabetes mellitus in early stages of pancreatic cancer. The only possibility for curative intent in pancreatic cancer is to diagnose the disease before symptoms occur. Patients with newly diagnosed diabetes mellitus type 2 or hyperglycemia as a risk group have been recommended for primary screening for pancreatic cancer. To date, there is no specific biomarker to identify patients with an asymptomatic pancreatic cancer. The review discuss the relationship between pancreatic cancer and diabetes mellitus and the possibility of secondary screening of patients with newly diagnosed diabetes mellitus type 2 or hyperglycemia in an artificial neural network. PubMed was searched for articles using the Mesh term 'pancreatic neoplasms' combined with 'insulin resistance' and 'glucose metabolism disorders'. Additional articles were retrieved through hyperlinks and by manually searching reference lists in original published articles. In total 36 articles were systematically reviewed. and IAP.

A prospective, multicenter, National Cancer Institute Early Detection Research Network study of [-2]proPSA: improving prostate cancer detection and correlating with cancer aggressiveness

BACKGROUND

The free prostate-specific antigen (PSA) isoform, [-2]proPSA, has been shown to be associated with prostate cancer. The study objective was to characterize the clinical utility of serum [-2]proPSA for prostate cancer detection and assess its association with aggressive disease.

METHODS

From among 669 subjects in a prospective prostate cancer detection study at four National Cancer Institute Early Detection Research Network clinical validation centers, 566 were eligible. Serum PSA, free PSA, and [-2]proPSA were measured (Beckman Coulter Access 2 Analyzer).

RESULTS

Two hundred and forty-five (43%) of the 566 participants had prostate cancer on biopsy. At 70% specificity, the sensitivity of %[-2]proPSA ([-2]proPSA/fPSA) was 54% [95% confidence interval (CI), 48-61%; null hypothesis, 40%]. Including %[-2]proPSA in a multivariate prediction model incorporating PSA and %fPSA improved the performance (P<0.01). In the 2 to 4 ng/mL PSA range, %[-2]proPSA outperformed %fPSA (receiver operator characteristic-areas under the curve, 0.73 versus 0.61; P=0.01). At 80% sensitivity, %[-2]proPSA had significantly higher specificity (51.6%; 95% CI, 41.2-61.8%) than PSA (29.9%; 95% CI, 21.0-40.0%) and %fPSA (28.9%; 95% CI, 20.1-39.0%). In the 2 to 10 ng/mL PSA range, a multivariate model had significant improvement (area under the curve, 0.76) over individual PSA forms (P<0.01 to <0.0001). At 80% sensitivity, the specificity of %[-2]proPSA (44.9%; 95% CI, 38.4-51.5%) was significantly higher than PSA (30.8%; 95% CI, 24.9-37.1%) and relatively higher than %fPSA (34.6%; 95% CI, 28.5-41.4%). %[-2]proPSA increased with increasing Gleason score (P<0.001) and was higher in aggressive cancers (P=0.03).

CONCLUSIONS

In this prospective study, %[-2]proPSA showed potential clinical utility for improving prostate cancer detection and was related to the risk of aggressive disease.

IMPACT

The addition of %[-2]proPSA could affect the early detection of prostate cancer.

Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine

BACKGROUND

We evaluated the use of the artificial neural network (ANN) program "ProstataClass" of the Department of Urology and the Institute of Medical Informatics at the Charité-Universitätsmedizin Berlin in daily routine to increase prostate cancer (CaP) detection rate and to reduce unnecessary biopsies.

MATERIALS AND METHODS

From May 2005 to April 2007, a total of 204 patients were included in the study. The Beckman Access PSA assay was used, and pretreatment prostate specific antigen (PSA) was measured prior to digital rectal examination (DRE) and 12 core systematic transrectal ultrasound (TRUS) guided biopsies. The individual ANN predictions were generated with the use of the ANN application for the Beckman Access PSA and free PSA assays, which relies on age, PSA, percent free prostate specific antigen (%fPSA), prostate volume, and DRE. Diagnostic validity of total prostate specific antigen (tPSA), %fPSA, and the ANN was evaluated by ROC curve analysis.

RESULTS

PSA and %fPSA ranged from 4.01 to 9.91 ng/ml (median: 6.65) and 5% to 48% (median: 15%), respectively. Of all men, 46 (22.5%) demonstrated suspicious DRE findings. Total prostate volume ranged from 7.1 to 119.2 cc (median: 35). Overall, 71 (34.8%) CaP were detected. Of men with suspicious DRE, 28 (60.9%) had CaP on initial biopsy. The ANN was 78% accurate in the original report. The AUC of ROC curve analysis was 0.51 for PSA, 0.66 for %PSA, and 0.72 for the ANN-Output, respectively.

CONCLUSIONS

Our results in this independent cohort show that ANN is a very helpful parameter in daily routine to increase the CaP detection rate and reduce unnecessary biopsies.

Automatic decision using dirty databases: application to prostate cancer diagnosis

Currently, the best way to reduce the mortality of cancer is to detect and treat it in its early stages. Automatic decision support systems, such as automatic diagnosis systems, are very helpful in this task but their performance is constrained by the integrity of the clinical input data. This could be a problem since clinical databases, in which these systems are based on, are commonly built up containing dirty data (empty fields, non-standard or normalized values, etc). This article presents a study of the performance of a clinical decision support system, based on an artificial neural networks, using sets of clean and dirty prostate cancer data. The study shows that is possible to obtain an implementation that allow us to avoid the problems associated to the database's lack of integrity and reach a similar performance using either clean or dirty data.

Internal validation of an artificial neural network for prostate biopsy outcome

OBJECTIVES

To carry out an internal validation of the retrospectively trained artificial neural network (ANN) 'ProstataClass'.

METHODS

A prospectively collected database of 393 patients undergoing 8-12 core prostate biopsy was analyzed. Data of these patients were applied to the online available ANN 'ProstataClass' using the Elecsys total prostate-specific antigen (tPSA) and free PSA (fPSA) assays. Beside the internal validation of the ANN 'ProstataClass' an additional ANN (named as ANN internal validation: ANNiv) only using the 393 prospective patient data was evaluated. The new ANN model was constructed with the MATLAB Neural Network Toolbox. Diagnostic accuracy was evaluated by receiver operator characteristic (ROC) curves comparing the areas under the ROC curves (AUC) and specificities at 90% and 95% sensitivity.

RESULTS

Within a tPSA range of 1.0-22.8 ng/mL, 229 men (58.3%) had prostate cancer (PCa). tPSA, %fPSA and the number of positive digital rectal examinations (DRE) differed significantly from the cohort of patients of the ANN 'ProstataClass', whereas age and prostate volume were comparable. AUCs for tPSA, %fPSA and the ANN 'ProstataClass' were 0.527, 0.726 and 0.747 (P = 0.085 between %fPSA and ANN). The AUC of the ANNiv (0.754) was significantly better compared with %fPSA (P = 0.021), whereas the AUC of two ANN models built on external cohorts (0.726 and 0.729) showed no differences to %fPSA and the other ANN models.

CONCLUSIONS

Significant differences of DRE status and %fPSA medians decrease the power of the 'ProstataClass' ANN in the internal validation cohort. The effect of retrospective data evaluation the 'ProstataClass' cohort and prospective fPSA measurement may be responsible for %fPSA differences. All ANN models built with different PSA and fPSA assays performed equally if applied to the two cohorts.

The value of an artificial neural network in the decision-making for prostate biopsies

PURPOSE

In majority of patients who are subjected to prostate biopsies, no prostate cancer (PCa) is found. It is important to prevent unnecessary biopsies since serious complications may occur. An artificial neural network (ANN) may be able to predict the risk of the presence of PCa.

METHODS

Included were all patients, who underwent transrectal ultrasound-guided prostate biopsies between June 2006 and June 2007 with a total PSA (tPSA) level between 2 and 20 microg/l. The patients were divided into two groups according to their tPSA level (2-10 microg/l and 10-20 microg/l). The ANN Prostataclass of the Universitätsklinikum Charité in Berlin was used. The predictions of the ANN were compared to the pathology results of the biopsies.

RESULTS

Overall 165 patients were included. PCa was diagnosed in 53 patients, whereas the ANN predicted "no risk" in 19 of these patients (36%). The ANN output receiver operator characteristic (ROC) plots for the range of tPSA 2-10 microg/l and tPSA 10-20 microg/l showed an area under the curve (AUC) of 63 and 88% for the initial biopsy group, versus 69 and 57%, respectively, for the repeat biopsy group.

CONCLUSIONS

The ANN resulted in a false negative rate of 36%, missing PCa in 19 patients. For use in an outpatient-clinical setting, this ANN is insufficient to predict the risk of presence of PCa reliably.

A [-2]proPSA-based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases

BACKGROUND

The aim of this study was to combine the new automated Access [-2]proPSA (p2PSA) assay with a percent free PSA (%fPSA) based artificial neural network (ANN) or logistic regression (LR) model to enhance discrimination between patients with prostate cancer (PCa) and with no evidence of malignancy (NEM) and to detect aggressive PCa.

METHODS

Sera from 311 PCa patients and 275 NEM patients were measured with the p2PSA, total PSA (tPSA) and free PSA (fPSA) assays on Access immunoassay technology (Beckman Coulter, Fullerton, CA) within the 0-30 ng/ml tPSA range. Four hundred seventy-five patients (264 PCa, 211 NEM) had a tPSA of 2-10 ng/ml. LR models and leave-one-out (LOO) ANN models with Bayesian regularization by using tPSA, %fPSA, p2PSA/fPSA (%p2PSA), age and prostate volume were constructed and compared by receiver-operating characteristic (ROC) curve analysis.

RESULTS

The ANN and LR model each utilizing %p2PSA, %fPSA, tPSA and age, but without prostate volume, reached the highest AUCs (0.85 and 0.84) and best specificities (ANN: 62.1% and 45.5%; LR: 53.1% and 41.2%) compared with tPSA (22.7% and 11.4%) and %fPSA (45.5% and 26.1%) at 90% and 95% sensitivity. The %p2PSA furthermore distinguished better than tPSA and %fPSA between pT2 and pT3, and Gleason sum <7 and >or=7 PCa.

CONCLUSIONS

The automated p2PSA assay offers a new tool to improve PCa detection, and especially aggressive PCa detection. Incorporation of %p2PSA into an ANN and LR model further enhances the diagnostic accuracy to differentiate between malignant and non-malignant prostate diseases.

Diagnostic strategies and the incidence of prostate cancer: reasons for the low reported incidence of prostate cancer in China

We have analysed the reasons for the low reported incidence of prostate cancer in China and argue for early diagnosis and treatment of this disease. According to the 2002 database of the International Agency for Research on Cancer (IARC), the age-standardized incidence of prostate cancer in China is 1.6/10(5) person years (PY), with a mortality rate of 1.0/10(5) PY and mortality-to-incidence rate ratio (MR/IR) = 0.63. The MR/IR ratio of prostate cancer in China was found to be higher than the average in Asia (MR/IR = 0.57) and much higher than that in North America (MR/IR = 0.13). These data indicate that in China most prostate cancers were in the advanced stages at the time of diagnosis, and that patients had a short survival time thereafter. In 2004, Stamey et al. reported a retrospective American study of prostate cancer for the years 1983-2003. It was shown that most cases of prostate cancer detected by prostate-specific antigen (PSA) screening were in the advanced stage at the start of this 20-year period. These early follow-up data are quite similar to the results obtained from mass PSA screening of elderly men in Changchun, China. However, after the American programmes for early diagnosis and treatment of prostate cancer were accepted, tumours were diagnosed at earlier stages. On the basis of these findings, mass screening should be performed in the whole of China using serum PSA to facilitate early diagnosis and treatment of prostate cancer.

An artificial neural network for five different assay systems of prostate-specific antigen in prostate cancer diagnostics

OBJECTIVE

To compare separate prostate-specific antigen (PSA) assay-specific artificial neural networks (ANN) for discrimination between patients with prostate cancer (PCa) and no evidence of malignancy (NEM).

PATIENTS AND METHODS

In 780 patients (455 with PCa, 325 with NEM) we measured total PSA (tPSA) and free PSA (fPSA) with five different assays: from Abbott (AxSYM), Beckman Coulter (Access), DPC (Immulite 2000), and Roche (Elecsys 2010) and with tPSA and complexed PSA (cPSA) assays from Bayer (ADVIA Centaur). ANN models were developed with five input factors: tPSA, percentage free/total PSA (%fPSA), age, prostate volume and digital rectal examination status for each assay separately to examine two tPSA ranges of 0-10 and 10-27 ng/mL.

RESULTS

Compared with the median tPSA concentrations (range from 4.9 [Bayer] to 6.11 ng/mL [DPC]) and especially the median %fPSA values (range from 11.2 [DPC] to 17.4%[Abbott], for tPSA 0-10 ng/mL), the areas under the receiver operating characteristic curves (AUC) for all calculated ANN models did not significantly differ from each other. The AUC were: 0.894 (Abbott), 0.89 (Bayer), 0.895 (Beckman), 0.882 (DPC) and 0.892 (Roche). At 95% sensitivity the specificities were without significant differences, whereas the individual absolute ANN outputs differed markedly.

CONCLUSIONS

Despite only slight differences, PSA assay-specific ANN models should be used to optimize the ANN outcome to reduce the number of unnecessary prostate biopsies. We further developed the ANN named 'ProstataClass' to provide clinicians with an easy to use tool in making their decision about follow-up testing.

Serum markers for prostate cancer: a rational approach to the literature

INTRODUCTION

Due to its universal applicability for early detection and prediction of cancer stage and disease recurrence, widespread implementation of serum-based prostate-specific antigen (PSA) measurements has a significant influence on current treatment strategies for men with prostate cancer (PCa). However, over-detection and the resultant over-treatment of indolent cancers have been strongly implicated to occur. Using current recommended guidelines, the PSA test suffers from both limited sensitivity and specificity to enable efficacious population-based cancer detection. Therefore, novel biomarkers are much needed to complement PSA by enhancing its diagnostic and prognostic performance.

METHODS

The present literature on serum markers for PCa was reviewed. PSA derivatives, molecular PSA isoforms, and novel molecular targets in blood were summarized and weighted against their potential to improve decision-making of men with PCa.

RESULTS

Current evidence suggests that no single analyte is likely to achieve the desired level of diagnostic and prognostic accuracy for PCa. However, the combination of biomarkers with clinical and demographic data, for example, using established standard nomograms, has produced progress toward the goal of both optimal screening and risk assessment. Furthermore, potential candidate molecular markers for PCa can be derived from high-throughput technologies. Current studies demonstrate that understanding dynamic PSA changes over time may offer diagnostic and prognostic information.

CONCLUSIONS

Bridging the gap between basic science and clinical practice represents the main goal in the near future to enable physicians to tailor risk-adjusted screening and treatment strategies for current patients with PCa.