Classification Of Prostate Cancer Research Articles

Trends in risk classification at diagnosis and choice of primary therapy for prostate cancer: An analysis of 10839 patients from the Nara Urological Research and Treatment Group registry between 2004 and 2015.

To evaluate trends in risk classification at diagnosis and choice of primary therapy in patients diagnosed with prostate cancer. This retrospective study included 10839 patients who were newly diagnosed with prostate cancer between 2004 and 2015 at 23 Japanese institutions. Risk classification and primary therapies between 2004 and 2015 were evaluated. The trends in risk classification and primary therapy were evaluated using chi-squared tests for trend during four periods (2004-2006; 2007-2009; 2010-2012; and 2013-2015). Binary logistic analysis was used to evaluate the extent to which factors such as age, risk classification, and institution influenced primary therapy choice in the 2013-2015 cohort. The number of patients with very-low or low-risk classification (P<0.001) and metastasis (P=0.04) decreased and the number with intermediate-risk classification (P<0.001) increased during the four periods. A tendency to choose radical prostatectomy as primary therapy for prostate cancer was not observed during the four periods (P=0.90). The number of patients who chose radiation therapy (P<0.001) and active surveillance/watchful waiting (P<0.001) as primary therapies increased during the four periods and the number of patients who chose androgen deprivation therapy (P<0.001) decreased. Age, institution, and risk classification significantly influenced primary therapy choice. We have shown the trends in risk classification of prostate cancer and primary therapy choices between 2004 and 2015 in Japan. Age, institution, and risk classification significantly influenced the decision on primary therapy for prostate cancer.

PO-1922 Classification of prostate cancer by hypoxia-related histological features in patient samples

PO-1922 Classification of prostate cancer by hypoxia-related histological features in patient samples

Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies.

Simple SummaryRadiologists interpret prostate multiparametric magnetic resonance imaging (mpMRI) to identify abnormalities that may correspond to prostate cancer, whose status is later confirmed by MR-guided targeted biopsy. Artificial intelligence algorithms may improve the diagnostic accuracy achievable by radiologists alone, as well as alleviate pressures on the prostate cancer diagnostic pathway caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. In this review article, we considered studies that compared the diagnostic accuracy of radiologists, artificial intelligence algorithms, and where possible, a combination of the two. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present, due to flaws in study designs and biases caused by performance comparisons using small, predominantly single-center patient cohorts. Several recommendations are made to ensure future studies bear greater clinical impact.Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.

Can high b-value 3.0 T biparametric MRI with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) be used in biopsy-naïve men?

ObjectiveTo analyze the clinical value of high b-value 3.0 T biparametric magnetic resonance with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) in biopsy-naïve men. MethodsA retrospective analysis of the data of 224 patients who underwent prostate biopsy (cognitive fusion targeted biopsy combined with systematic biopsy) after a high b-value 3.0 T magnetic resonance examination at Haikou Hospital from July 2018 to July 2020 was performed. Two radiologists performed multi-parameter magnetic resonance imaging (mp-MRI) with the prostate imaging report and data system version 2 (PI-RADS v2) and biparametric magnetic resonance imaging (bp-MRI) with the simplified prostate image reporting and data system (S-PI-RADS). The detection efficacy of the two regimens was evaluated by classifying prostate cancer (PCa) and clinically significant prostate cancer (csPCa) according to pathology, and the statistical significance of the differences between the two regimens was determined by Z-test. ResultsThe area under the receiver operating curve (AUC) values of mp-MRI based on PI-RADS v2 and bp-MRI based on S-PI-RADS to detect PCa were 0.905 and 0.892, respectively, while the AUC values for the detection of csPCa were 0.919 and 0.906, respectively. There was no statistically significant difference between the two tests (Z values were 0.909 and 1.145, p > 0.05). ConclusionThere was no significant difference in the detection efficacy of high b-value bp-MRI based on the S-PI-RADS score for prostate cancer and clinically significant prostate cancer compared with the standard PI-RADS v2 score with mp-MRI protocols, which can be applied clinically.

Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC

BackgroundCurrently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment.PurposeTo propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC.Methods and populationThe previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios.ResultsCompared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p < 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly.ConclusionDowngrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives.

P0931 - Improving intermediate risk classification of prostate cancer with radiomics features

P0931 - Improving intermediate risk classification of prostate cancer with radiomics features

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

Serum miRNAs Support the Indication for MRI-Ultrasound Fusion-Guided Biopsy of the Prostate in Patients with Low-PI-RADS Lesions

Multiparametric MRI (mpMRI) and targeted biopsy of the prostate enhance the tumor detection rate. However, the prediction of clinically significant prostate cancer (PCa) is still limited. Our study tested the additional value of serum levels of selected miRNAs in combination with clinical and mpMRI information for PCa prediction and classification. A total of 289 patients underwent targeted mpMRI-ultrasound fusion-guided prostate biopsy complemented by systematic biopsy. Serum miRNA levels of miRNAs (miR-141, miR-375, miR-21-5p, miR-320b, miR-210-3p, let-7c, and miR-486) were determined by quantitative PCR. Detection of any PCa and of significant PCa were the outcome variables. The patient age, pre-biopsy PSA level, previous biopsy procedure, PI-RADS score, and serum miRNA levels were covariates for regularized binary logistic regression models. The addition of miRNA expression of miR-486 and let-7c to the baseline model, containing only clinical parameters, increased the predictive accuracy. Particularly in patients with PI-RADS ≤3, we determined a sensitivity for detecting significant PCa (Gleason score ≥ 7a corresponding to Grade group ≥2) of 95.2%, and an NPV for absence of significant PCa of 97.1%. This accuracy could be useful to support patient counseling in selected cases.

DDV: A Taxonomy for Deep Learning Methods in Detecting Prostate Cancer

Deep learning is increasingly studied in the prediction of cancer yet few deep learning systems have been introduced for daily use for such purpose. The manual scanning, reading, and analysis by radiologists to detect cancer are very time-consuming processes due to their large volume. Although many types of research have been conducted in this area, the use of their results in the diagnosis of prostate cancer is yet to be properly carried out. In this paper, a Data, Detection, and View (DDV) taxonomy is introduced that defines each major component, which is required to implement a proper deep learning prostate cancer detection system. The proposed taxonomy is a step toward developing a way to assist the pathologists for early detecting prostate cancer and hence facilitating the patients to seek speedy counseling from the doctors. If the diagnosis of cancer can be performed in the early stages then it can be prevented from spreading to other cells. The components of the proposed taxonomy must be reviewed and used as an evaluation and validation criteria for approving the deep learning classification model to be used in real-world clinical practices. Through the study of 22 state-of-the-art research papers in the field of deep learning-based prostate cancer classification system, we proved the effectiveness and robustness of the proposed DDV taxonomy system.

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.

Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity. To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa. Retrospective. Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa. A 3 T, multishell diffusion-weighted and axial T2-weighted sequences. High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated. Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α=0.05. RSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05). RSI4 -C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning. 3 TECHNICAL EFFICACY: Stage 2.

Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering.

Simple SummaryArtificial intelligence techniques were used for the detection of prostate cancer through tissue feature engineering. A radiomic method was used to extract the important features or information from histopathology tissue images to perform binary classification (i.e., benign vs. malignant). This method can identify a histological pattern that is invisible to the human eye, which helps researchers to predict and detect prostate cancer. We used different performance metrics to evaluate the results of the classification. In the future, it is expected that a method like radiomic will provide a consistent contribution to analyze histopathology tissue images and differentiate between cancerous and noncancerous tumors.The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.

Identification of miRNA-Mediated Subpathways as Prostate Cancer Biomarkers Based on Topological Inference in a Machine Learning Process Using Integrated Gene and miRNA Expression Data.

Accurately identifying classification biomarkers for distinguishing between normal and cancer samples is challenging. Additionally, the reproducibility of single-molecule biomarkers is limited by the existence of heterogeneous patient subgroups and differences in the sequencing techniques used to collect patient data. In this study, we developed a method to identify robust biomarkers (i.e., miRNA-mediated subpathways) associated with prostate cancer based on normal prostate samples and cancer samples from a dataset from The Cancer Genome Atlas (TCGA; n = 546) and datasets from the Gene Expression Omnibus (GEO) database (n = 139 and n = 90, with the latter being a cell line dataset). We also obtained 10 other cancer datasets to evaluate the performance of the method. We propose a multi-omics data integration strategy for identifying classification biomarkers using a machine learning method that involves reassigning topological weights to the genes using a directed random walk (DRW)-based method. A global directed pathway network (GDPN) was constructed based on the significantly differentially expressed target genes of the significantly differentially expressed miRNAs, which allowed us to identify the robust biomarkers in the form of miRNA-mediated subpathways (miRNAs). The activity value of each miRNA-mediated subpathway was calculated by integrating multiple types of data, which included the expression of the miRNA and the miRNAs’ target genes and GDPN topological information. Finally, we identified the high-frequency miRNA-mediated subpathways involved in prostate cancer using a support vector machine (SVM) model. The results demonstrated that we obtained robust biomarkers of prostate cancer, which could classify prostate cancer and normal samples. Our method outperformed seven other methods, and many of the identified biomarkers were associated with known clinical treatments.

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.

BackgroundA definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications.PurposeTo develop an artificial intelligence (AI)‐based model (named AI‐biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information.Study TypeRetrospective.PopulationMagnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in‐house and 172 from external publicly available databases).Field Strength/Sequence1.5 to 3.0 Tesla, T2‐weighted image pulse sequences.AssessmentMR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high‐risk tumor from low‐risk tumor.Statistical TestsTo evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa.ResultsOur computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86–0.92]) and 0.78 (95% CI: [0.74–0.82]) to classify cancer vs. benign and high‐ vs. low‐risk of prostate disease, respectively.Data ConclusionAI‐biopsy provided a data‐driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI‐biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag‐and‐drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI‐assessed MR images in real time.Level of Evidence1Technical Efficacy Stage2

Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images.

BackgroundProstate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-the-art CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations.MethodsA dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multi-parametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion’s primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes.ResultsThe optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster.ConclusionsThe final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring.

Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.

A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade.

Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been sufficiently addressed. The aim of this study is to evaluate the generalizability of radiomics models for prostate cancer classification and to compare the performance of these models to the performance of radiologists. Multiparametric MRI, photographs and histology of radical prostatectomy specimens, and pathology reports of 107 patients were obtained from three healthcare centers in the Netherlands. By spatially correlating the MRI with histology, 204 lesions were identified. For each lesion, radiomics features were extracted from the MRI data. Radiomics models for discriminating high-grade (Gleason score ≥ 7) versus low-grade lesions were automatically generated using open-source machine learning software. The performance was tested both in a single-center setting through cross-validation and in a multi-center setting using the two unseen datasets as external validation. For comparison with clinical practice, a multi-center classifier was tested and compared with the Prostate Imaging Reporting and Data System version 2 (PIRADS v2) scoring performed by two expert radiologists. The three single-center models obtained a mean AUC of 0.75, which decreased to 0.54 when the model was applied to the external data, the radiologists obtained a mean AUC of 0.46. In the multi-center setting, the radiomics model obtained a mean AUC of 0.75 while the radiologists obtained a mean AUC of 0.47 on the same subset. While radiomics models have a decent performance when tested on data from the same center(s), they may show a significant drop in performance when applied to external data. On a multi-center dataset our radiomics model outperformed the radiologists, and thus, may represent a more accurate alternative for malignancy prediction.

A comparative study of PCS and PAM50 prostate cancer classification schemes

BackgroundTwo prostate cancer (PC) classification methods based on transcriptome profiles, a de novo method referred to as the “Prostate Cancer Classification System” (PCS) and a variation of the established PAM50 breast cancer algorithm, were recently proposed. Both studies concluded that most human PC can be assigned to one of three tumor subtypes, two categorized as luminal and one as basal, suggesting the two methods reflect consistency in underlying biology. Despite the similarity, differences and commonalities between the two classification methods have not yet been reported.MethodsHere, we describe a comparison of the PCS and PAM50 classification systems. PCS and PAM50 signatures consisting of 37 (PCS37) and 50 genes, respectively, were used to categorize 9,947 PC patients into PCS and PAM50 classes. Enrichment of hallmark gene sets and luminal and basal marker gene expression were assessed in the same datasets. Finally, survival analysis was performed to compare PCS and PAM50 subtypes in terms of clinical outcomes.ResultsPCS and PAM50 subtypes show clear differential expression of PCS37 and PAM50 genes. While only three genes are shared in common between the two systems, there is some consensus between three subtype pairs (PCS1 versus Luminal B, PCS2 versus Luminal A, and PCS3 versus Basal) with respect to gene expression, cellular processes, and clinical outcomes. PCS categories displayed better separation of cellular processes and luminal and basal marker gene expression compared to PAM50. Although both PCS1 and Luminal B tumors exhibited the worst clinical outcomes, outcomes between aggressive and less aggressive subtypes were better defined in the PCS system, based on larger hazard ratios observed.ConclusionThe PCS and PAM50 classification systems are similar in terms of molecular profiles and clinical outcomes. However, the PCS system exhibits greater separation in multiple clinical outcomes and provides better separation of prostate luminal and basal characteristics.

Prostate Cancer Classification Using Random Forest and Support Vector Machines

Nowadays, it gets more types of diseases in the medical sector. For this reason, the role of technology is very important in assisting medical staff to overcome the problem. This research discusses about Prostate Cancer. Prostate Cancer is suffered commonly by males. There are no exact causes how Prostate Cancer occurs in males, but there are several risk factors of a Prostate Cancer, such as age, ethnic group, family history, diet, smoking, and world area. In this research, the classification to diagnose Prostate Cancer is using two methods, those are Random Forest (RF) and Support Vector Machines (SVM). By comparing accuracy of those two methods, we will know which method is better with a dataset that we have from Al-Islam Bandung Hospital, Indonesia. The result is given that Random Forest has a better accuracy than Support Vector Machines. The accuracy shows 97.30% with 80% of data training.

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI.

One of the most common types of cancer in men is prostate cancer (PCa). Biopsies guided by bi-parametric magnetic resonance imaging (MRI) can aid PCa diagnosis. Previous works have mostly focused on either detection or classification of PCa from MRI. In this work, however, we present a neural network that simultaneously detects and grades cancer tissue in an end-to-end fashion. This is more clinically relevant than the classification goal of the ProstateX-2 challenge. We used the dataset of this challenge for training and testing. We use a 2D U-Net with MRI slices as input and lesion segmentation maps that encode the Gleason Grade Group (GGG), a measure for cancer aggressiveness, as output. We propose a method for encoding the GGG in the model target that takes advantage of the fact that the classes are ordinal. Furthermore, we evaluate methods for incorporating prostate zone segmentations as prior information, and ensembling techniques. The model scored a voxel-wise weighted kappa of 0.446 ±0.082 and a Dice similarity coefficient for segmenting clinically significant cancer of 0.370 ±0.046, obtained using 5-fold cross-validation. The lesion-wise weighted kappa on the ProstateX-2 challenge test set was 0.13 ±0.27. We show that our proposed model target outperforms standard multiclass classification and multi-label ordinal regression. Additionally, we present a comparison of methods for further improvement of the model performance.

Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification

BackgroundRecently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model. MethodIn this work, there are two main goals: i) present a fully automated pre-processing algorithm for a smart patch selection within histopathological images, and ii) evaluate the impact of the proposed strategy within a deep learning framework for the detection of prostate and breast cancer. The proposed algorithm is specifically designed to extract patches only on informative regions (i.e., high density of nuclei), most likely representative of where cancer can be detected. ResultsOur strategy was developed and tested on 1000 hematoxylin and eosin (H&E) stained images of prostate and breast tissue. By combining a stain normalization step and a segmentation-driven patch extraction, the proposed approach is capable of increasing the performance of a computer-aided diagnosis (CAD) system for the detection of prostate cancer (18.61% accuracy improvement) and breast cancer (17.72% accuracy improvement). ConclusionWe strongly believe that the integration of the proposed pre-processing steps within deep learning frameworks will allow the achievement of robust and reliable CAD systems. Being based on nuclei detection, this strategy can be easily extended to other glandular tissues (e.g., colon, thyroid, pancreas, etc.) or staining methods (e.g., PAS).