A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI

Prostate cancer prognosis using machine learning: A critical review of survival analysis methods

Prostate Cancer is a disease that affects the male reproductive system. The irregularity of the symptoms makes it hard for the clinicians to pinpoint the disease in the earlier stages. Techniques such as Machine Learning, Data Science, Deep Learning, etc. have been employed on the biomedical data to identify the symptoms of the patients and predict their stage and the chances of their survival. The survival analysis of prostate cancer is essential as it guides the clinicians to recommend the optimal treatment for the patient. Building an accurate model from electronic data using machine learning is quite difficult. This review article presents a systematic literature review focused on the area of prostate cancer survival analysis utilizing machine learning and other soft computing techniques. Through an extensive evaluation of the available research, we have identified and summarized key insights from the selected studies. A comprehensive comparison of various approaches for survival and treatment predictions in the literature has been conducted. Additionally, the gaps in previous research have been discussed, highlighting areas for further investigation and providing future recommendations. By synthesizing the current knowledge in prostate cancer survival analysis, this review contributes to the understanding of the field and lays the foundation for future advancements.

Optimizing Multiparametric MRI Protocols for Prostate Cancer Detection: A Comprehensive Assessment Aligned with PI-RADS Guidelines

Background and Aim

Multiparametric magnetic resonance imaging (mpMRI) is recognized as the most indicative method for diagnosing prostate cancer. The purpose of this narrative review is to provide a comprehensive evaluation aligned with the Prostate Imaging and Reporting Data System (PI-RADS) guidelines, offering an in-depth insight into the various MRI sequences used in a standard mpMRI protocol. Additionally, it outlines the critical technical requirements necessary to perform a standard mpMRI examination of the prostate, as defined by the PI-RADS specifications.

Methods

European Society of Urogenital Radiology has released PI-RADS guideline detailing its suggestions aimed at improving the standards of the procedure. The purpose of this guideline is to establish a standard strategy for MRI protocols and image interpretation, aiming to prevent variability in each of the imaging and interpretation stages.

Results

A standard mpMRI protocol comprises morphological sequences and functional sequences. Morphological sequences which encompass T1- and T2-weighted images, and various functional sequences include diffusion-weighted imaging, and dynamic contrast-enhanced MRI. The PI-RADS recommendations assert that having a standard and uniform protocol for all MRI centers is imperative. Furthermore, the existence of a standardized checklist for interpreting MRI images can foster greater consensus in the process of diagnosing and treating patients.

Conclusion

Standardized protocols and checklists for mpMRI interpretation are essential for achieving greater consensus among radiologists, ultimately leading to improved diagnostic outcomes in prostate cancer.

Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework

Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multiparametric magnetic resonance imaging (mpMRI) is actively being investigated as a means to provide clinical decision support to radiologists. Typically, these systems are trained using lesion annotations. However, lesion annotations are expensive to obtain and inadequate for characterizing certain tumor types e.g. diffuse tumors and MRI invisible tumors. In this work, we introduce a novel patient-level classification framework, denoted PCF, that is trained using patient-level labels only. In PCF, features are extracted from three-dimensional mpMRI and derived parameter maps using convolutional neural networks and subsequently, combined with clinical features by a multi-classifier support vector machine scheme. The output of PCF is a probability value that indicates whether a patient is harboring clinically significant PCa (Gleason score ≥3+4) or not. PCF achieved mean area under the receiver operating characteristic curves of 0.79 and 0.86 on the PICTURE and PROSTATEx datasets respectively, using five-fold cross-validation. Clinical evaluation over a temporally separated PICTURE dataset cohort demonstrated comparable sensitivity and specificity to an experienced radiologist. We envision PCF finding most utility as a second reader during routine diagnosis or as a triage tool to identify low-risk patients who do not require a clinical read.

Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study

We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.

Dynamic contrast-enhanced (DCE) MR imaging: the role of qualitative and quantitative parameters for evaluating prostate tumors stratified by Gleason score and PI-RADS v2

PURPOSE

To investigate the role of qualitative and quantitative DCE-MRI parameters in prostate cancer (PCa) stratified by whole-mount histopathology (WMHP) Gleason score (GS) and PI-RADSv2.

METHODS

This retrospective study included 323 PCa tumors in 254 men, who underwent 3T MRI prior to prostatectomy, 7/2009-12/2016. Qualitative DCE curve types included type 1 (progressive), type 2 (plateau) and type 3 (washout). Quantitative DCE-MRI pharmacokinetic (PK) parameters included K^trans (influx volume transfer coefficient), K_ep (efflux reflux rate constant) and iAUC (initial area under the curve). DCE-MRI features of true positive lesions were evaluated for overall, index, transition zone (TZ) and peripheral zone (PZ), based on GS grade (low = 6, high > 6) and PI-RADSv2 score using SPSSv24.

RESULTS

There were 57 (17.6%) low-grade and 266 (82.4%) high-grade PCa lesions. PI-RADSv2 3, 4 and 5 included 106, 120 and 97 lesions, respectively. 251 (77.7%) and 72 (22.3%) lesions were located in PZ and TZ, respectively. High-grade lesions had significantly higher proportion of Type 3 curves compared to low-grade lesions in overall (70.3% vs. 54.4%) and TZ (73.5% vs. 43.5%). As PI-RADSv2 increased, the proportion of type 3 curve significantly increased for overall (80.4-51.9%), index (80.4-54.7%) and PZ (78.7-52.1%) lesions. Among PK parameters, K^trans (0.43 vs 0.32) and iAUC (8.99 vs 6.9) for overall PCa, K^trans (0.43 vs 0.31) and iAUC (9 vs 6.67) for PZ PCa, and iAUC (8.94 vs 7.42) for index PCa were significantly higher for high-grade versus low-grade lesions. Also, K^trans (0.51-0.34), K_ep (1.75-1.29) and iAUC (9.79-7.6) for overall PCa, K^trans (0.53-0.32), K_ep (1.81-1.26) and iAUC (9.83-7.34) for PZ PCa; and K_ep (1.79-1.17) and iAUC (11.3-8.45) for index PCa increased significantly with a higher PI-RADSv2 score.

CONCLUSIONS

The results of study show the possible utility of qualitative and quantitative DCE-MRI parameters for assessment of PCa GS and PI-RADSv2 categorization.

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Background:

Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer.

Aim:

The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening.

Methods:

This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results.

Results:

CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist.

Conclusion:

When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study

Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detection of PCa will use an engineered scalar image called K^trans to summarize the information in the DCE time-series images. This work proposes a new model that amalgamates the U-net and the convGRU neural network architectures for the purpose of interpreting DCE time-series in a temporal and spatial basis for segmenting PCa in MR images. Ultimately, experiments show that the proposed model using the DCE time-series images can outperform a baseline U-net segmentation model using K^trans. However, when other types of scalar MR images are considered by the models, no significant advantage is observed for the proposed model.

Identification of Potential MR-Derived Biomarkers for Tumor Tissue Response to 177Lu-Octreotate Therapy in an Animal Model of Small Intestine Neuroendocrine Tumor

Magnetic resonance (MR) methods enable noninvasive, regional tumor therapy response assessment, but associations between MR parameters, underlying biology, and therapeutic effects must be investigated. The aim of this study was to investigate response assessment efficacy and biological associations of MR parameters in a neuroendocrine tumor (NET) model subjected to radionuclide treatment. Twenty-one mice with NETs received 177Lu-octreotate at day 0. MR experiments (day -1, 1, 3, 8, and 13) included T2-weighted, dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) and relaxation measurements (T1/T2*). Tumor tissue was analyzed using proteomics. MR-derived parameters were evaluated for each examination day and for different radial distances from the tumor center. Response assessment efficacy and biological associations were evaluated using feature selection and protein expression correlations, respectively. Reduced tumor growth rate or shrinkage was observed until day 8, followed by reestablished growth in most tumors. The most important MR parameter for response prediction was DCE-MRI-derived pretreatment signal enhancement ratio (SER) at 40% to 60% radial distance, where it correlated significantly also with centrally sampled protein CCD89 (association: DNA damage and repair, proliferation, cell cycle arrest). The second most important was changed diffusion (D) between day -1 and day 3, at 60% to 80% radial distance, where it correlated significantly also with peripherally sampled protein CATA (association: oxidative stress, proliferation, cell cycle arrest, apoptotic cell death). Important information regarding tumor biology in response to radionuclide therapy is reflected in several MR parameters, SER and D in particular. The spatial and temporal information provided by MR methods increases the sensitivity for tumor therapy response.

Head and Neck Cancer Tumor Segmentation Using Support Vector Machine in Dynamic Contrast-Enhanced MRI

OBJECTIVE

We aimed to propose an automatic method based on Support Vector Machine (SVM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to segment the tumor lesions of head and neck cancer (HNC).

MATERIALS AND METHODS

120 DCE-MRI samples were collected. Five curve features and two principal components of the normalized time-intensity curve (TIC) in 80 samples were calculated as the dataset in training three SVM classifiers. The other 40 samples were used as the testing dataset. The area overlap measure (AOM) and the corresponding ratio (CR) and percent match (PM) were calculated to evaluate the segmentation performance. The training and testing procedure was repeated for 10 times, and the average performance was calculated and compared with similar studies.

RESULTS

Our method has achieved higher accuracy compared to the previous results in literature in HNC segmentation. The average AOM with the testing dataset was 0.76 ± 0.08, and the mean CR and PM were 79 ± 9% and 86 ± 8%, respectively.

CONCLUSION

With improved segmentation performance, our proposed method is of potential in clinical practice for HNC.