Improving workflow in prostate MRI: AI-based decision-making on biparametric or multiparametric MRI.
Ontology highlight
ABSTRACT: Objectives
To develop and validate an artificial intelligence algorithm to decide on the necessity of dynamic contrast-enhanced sequences (DCE) in prostate MRI.Methods
This study was approved by the institutional review board and requirement for study-specific informed consent was waived. A convolutional neural network (CNN) was developed on 300 prostate MRI examinations. Consensus of two expert readers on the necessity of DCE acted as reference standard. The CNN was validated in a separate cohort of 100 prostate MRI examinations from the same vendor and 31 examinations from a different vendor. Sensitivity/specificity were calculated using ROC curve analysis and results were compared to decisions made by a radiology technician.Results
The CNN reached a sensitivity of 94.4% and specificity of 68.8% (AUC: 0.88) for the necessity of DCE, correctly assigning 44%/34% of patients to a biparametric/multiparametric protocol. In 2% of all patients, the CNN incorrectly decided on omitting DCE. With a technician reaching a sensitivity of 63.9% and specificity of 89.1%, the use of the CNN would allow for an increase in sensitivity of 30.5%. The CNN achieved an AUC of 0.73 in a set of examinations from a different vendor.Conclusions
The CNN would have correctly assigned 78% of patients to a biparametric or multiparametric protocol, with only 2% of all patients requiring re-examination to add DCE sequences. Integrating this CNN in clinical routine could render the requirement for on-table monitoring obsolete by performing contrast-enhanced MRI only when needed.
SUBMITTER: Hotker AM
PROVIDER: S-EPMC8353049 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:PurposeThis study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI).MethodsThis retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1.ResultsIn all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases (P < 0.05) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade.ConclusionsThe results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.
| S-EPMC8718299 | biostudies-literature
Project description:Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective is to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.
2016-06-07 | GSE80683 | GEO
Project description:To compare the diagnostic outcomes of the current approach of transrectal ultrasound (TRUS)-guided biopsy in men with suspected prostate cancer to an alternative approach using multiparametric MRI (mpMRI), followed by MRI-targeted biopsy if positive.Clinical decision analysis was used to synthesise data from recently emerging evidence in a format that is relevant for clinical decision making.A hypothetical cohort of 1000 men with suspected prostate cancer.mpMRI and, if positive, MRI-targeted biopsy compared with TRUS-guided biopsy in all men.We report the number of men expected to undergo a biopsy as well as the numbers of correctly identified patients with or without prostate cancer. A probabilistic sensitivity analysis was carried out using Monte Carlo simulation to explore the impact of statistical uncertainty in the diagnostic parameters.In 1000 men, mpMRI followed by MRI-targeted biopsy 'clinically dominates' TRUS-guided biopsy as it results in fewer expected biopsies (600 vs 1000), more men being correctly identified as having clinically significant cancer (320 vs 250), and fewer men being falsely identified (20 vs 50). The mpMRI-based strategy dominated TRUS-guided biopsy in 86% of the simulations in the probabilistic sensitivity analysis.Our analysis suggests that mpMRI followed by MRI-targeted biopsy is likely to result in fewer and better biopsies than TRUS-guided biopsy. Future research in prostate cancer should focus on providing precise estimates of key diagnostic parameters.
| S-EPMC4067835 | biostudies-other
Project description:PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.
| S-EPMC8473816 | biostudies-literature
Project description:Background. Multiple studies have shown that digitally mediated decision aids help prepare patients for medical decision making with their providers. However, few studies have investigated whether decision-support preferences differ between non-English-speaking and English-speaking Latino men with limited literacy. Objective. To identify and compare health information seeking patterns, preferences for information presentation, and interest in digital decision aids in a sample of Southern Californian underserved Latino men with newly diagnosed prostate cancer at a county hospital. Methods. We conducted semistructured, in-depth telephone interviews with 12 Spanish-speaking and 8 English-speaking Latino men using a purposive sampling technique. Following transcription of taped interviews, Spanish interviews were translated. Using a coding protocol developed by the team, two bilingual members jointly analyzed the transcripts for emerging themes. Coder agreement exceeded 80%. Differences were resolved through discussion. Results. Thematic differences between groups with different preferred languages emerged. Most respondents engaged in online health information seeking using cellphones, perceived a paternalistic patient-provider relationship, and expressed willingness to use hypothetical digital decision aids if recommended by their provider. English speakers reported higher digital technology proficiency for health-related searches. They also more frequently indicated family involvement in digital search related to their condition and preferred self-guided, web-based decision aids. In comparison, Spanish speakers reported lower digital technology proficiency and preferred family-involved, coach-guided, paper and visual decision aids. English speakers reported substantially higher levels of formal education. Conclusion. Preferences regarding the use of digital technology to inform prostate cancer treatment decision making among underserved Latino men varied depending on preferred primary language. Effective preparation of underserved Latino men for shared decision making requires consideration of alternative approaches depending on level of education attainment and preferred primary language.
| S-EPMC8165846 | biostudies-literature
Project description:There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng∕ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accurately localizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.
| S-EPMC3390048 | biostudies-other
Project description:In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.
| S-EPMC6602944 | biostudies-literature
Project description:Objective:To compare prostate cancer detection rates (CDRs) and pathology results with targeted prostate biopsy (TB) and systematic prostate biopsy (SB) in biopsy-naive men. Methods:An in-patient control study of 82 men undergoing SB and subsequent TB in case of positive prostate MRI between 2015 and 2017 in the Jeroen Bosch Hospital, the Netherlands. Results:Prostate cancer (PCa) was detected in 54.9% with 70.7% agreement between TB and SB. Significant PCa (Gleason score ?7) was detected in 24.4%. The CDR with TB and SB was 35.4% and 48.8%, respectively (p=0.052). The CDR of significant prostate cancer with TB and SB was both 20.7%. Clinically significant pathology upgrading occurred in 7.3% by adding TB to SB and 22.0% by adding SB to TB. Conclusions:There is no statistically significant difference between CDRs of SB and TB. Both SB and TB miss significant PCas. Moreover, pathology upgrading occurred more often by adding SB to TB than vice versa. This indicates that the omission of SB in this study population might not be justified.
| S-EPMC7157788 | biostudies-literature
Project description:ObjectivesThe optimal technique to administer image-guided radiation therapy for prostate cancer remains poorly defined. This study assessed outcomes after multiparametric prostate MRI-based planning was delivered with image-guided radiation therapy using prostatic calculi observed on cone beam CT (CBCT).MethodsBetween January 2015 and December 2017, 94 consecutive patients were treated with CBCT-based image-guided radiation therapy (IGRT) without fiducial markers. MRI was routinely incorporated for target delineation and intraprostatic tumor nodules were boosted to allow reduced doses to normal appearing prostate. The primary endpoint was the prevalence of prostatic calcifications while toxicity and biochemical control were secondary endpoints.ResultsMedian follow-up was 39.7 months with 82% NCCN intermediate to very high risk. Intraprostatic calculi were noted in 68% of patients. The 3-year biochemical control, late grade ≥2 rectal toxicity and late grade ≥2 urinary toxicity rates were 96%, 3 and 7%, respectively. Biochemical control and toxicity were not significantly impacted by the presence of prostatic calculi.ConclusionProstatic calcifications can serve as natural fiducial markers to allow for non-invasive IGRT for prostate cancer with promising early disease control and toxicity outcomes.Advances in knowledgeProstate calcification-guided IGRT is technically feasible.
| S-EPMC7715998 | biostudies-literature
Project description:BACKGROUND:Radiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. PURPOSE:To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. STUDY TYPE:Retrospective. SUBJECTS:In all, 120 PCa patients from two institutions, I1 and I2 , partitioned into training set D1 (N?=?70) from I1 and independent validation set D2 (N?=?50) from I2 . All patients were followed for ?3 years. SEQUENCE:3T, T2 -weighted (T2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences. ASSESSMENT:PCa regions of interest (ROIs) on T2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T2 WI and ADC maps) were extracted from the ROIs. A machine-learning classifier (CBCR ) was trained with the best discriminating set of radiomic features to predict BCR (pBCR ). STATISTICAL TESTS:Wilcoxon rank-sum tests with P?<?0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using pBCR was assessed using the Kaplan-Meier method and compared with Gleason Score (GS), PSA, and PIRADS-v2. RESULTS:Distribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T2 WI and ADC were associated with BCR (P?<?0.05) on D1 . CBCR predictions resulted in a mean AUC?=?0.84 on D1 and AUC?=?0.73 on D2 . A significant difference in BCR-free survival between the predicted classes (BCR?+?and BCR-) was observed (P?=?0.02) on D2 compared to those obtained from GS (P?=?0.8), PSA (P?=?0.93) and PIRADS-v2 (P?=?0.23). DATA CONCLUSION:Radiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy. LEVEL OF EVIDENCE:4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.
| S-EPMC6222024 | biostudies-literature