Preoperative Prediction of Muscle Invasiveness in Bladder Cancer: The Role of 3D Volumetric Radiomics Using Diffusion-Weighted MRI, the VI-RADS Score, or a Combination of Both.

BACKGROUND: Bladder cancer treatment decisions hinge on detecting muscle invasion. The 2018 "Vesical Imaging Reporting and Data System" (VI-RADS) standardizes multiparametric MRI (mp-MRI) use. Radiomics, an analysis framework, provides more insightful information than conventional methods. PURPOSE: To determine how well MIBC (Muscle Invasive Bladder Cancer) and NMIBC (Non-Muscle Invasive Bladder Cancer) can be distinguished using mp-MRI radiomics features. METHODS: We conducted a study with 73 bladder cancer patients diagnosed pathologically, who underwent preoperative mp-MRI from January 2020 to July 2022. Utilizing 3D Slicer (version 4.8.1) and Pyradiomics, we manually extracted radiomic features from apparent diffusion coefficient (ADC) maps created from diffusion-weighted imaging. The LASSO approach identified optimal features, and we addressed sample imbalance using SMOTE. We developed a classification model using textural features alone or combined with VI-RADS, employing a random forest classifier with 10-fold cross-validation. Diagnostic performance was assessed using the area under the ROC curve analysis. RESULTS: Among 73 patients (63 men, 10 women; median age: 63 years), 41 had muscle-invasive and 32 had superficial bladder cancer. Muscle invasion was observed in 25 of 41 patients with VI-RADS 4 and 5 scores and 12 of 32 patients with VI-RADS 1, 2, and 3 scores (accuracy: 77.5%, sensitivity: 67.7%, specificity: 88.8%). The combined VI-RADS score and radiomics model (AUC = 0.92 ± 0.12) outperformed the single radiomics model using ADC MRI (AUC = 0.83 ± 0.22 with 10-fold cross-validation) in this dataset. CONCLUSION: Before undergoing surgery, bladder cancer invasion in muscle might potentially be predicted using a radiomics signature based on mp-MRI.

A three-dimensional Magnetic Resonance Spin Tomography in Time-domain protocol for high-resolution multiparametric quantitative magnetic resonance imaging.

Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2, and proton density from one single short scan. A typical two-dimensional (2D) MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a three-dimensional (3D) MR-STAT framework based on previous 2D work, in order to achieve better image signal-to-noise ratio, higher though-plane resolution, and better tissue characterization. Specifically, we design a 7-min, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data-splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments, and in vivo experiments. High-quality knee quantitative maps with 0.8 × 0.8 × 1.5 mm3 resolution and bilateral lower leg maps with 1.6 mm isotropic resolution can be acquired using the proposed 7-min acquisition sequence and the 3-min-per-slice decoupled reconstruction algorithm. The proposed 3D MR-STAT framework could have wide clinical applications in the future.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique.

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = -0.21, p = 0.001), Decipher (rS = -0.193, p = 0.003), and three-tier risk (rS = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

High-resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior.

Noninvasive extracellular pH (pHe) mapping with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) using MR spectroscopic imaging (MRSI) has been demonstrated on 3T clinical MR scanners at 8 × 8 × 10 mm3 spatial resolution and applied to study various liver cancer treatments. Although pHe imaging at higher resolution can be achieved by extending the acquisition time, a postprocessing method to increase the resolution is preferable, to minimize the duration spent by the subject in the MR scanner. In this work, we propose to improve the spatial resolution of pHe mapping with BIRDS by incorporating anatomical information in the form of multiparametric MRI and using an unsupervised deep-learning technique, Deep Image Prior (DIP). Specifically, we used high-resolution T 1 , T 2 , and diffusion-weighted imaging (DWI) MR images of rabbits with VX2 liver tumors as inputs to a U-Net architecture to provide anatomical information. U-Net parameters were optimized to minimize the difference between the output super-resolution image and the experimentally acquired low-resolution pHe image using the mean-absolute error. In this way, the super-resolution pHe image would be consistent with both anatomical MR images and the low-resolution pHe measurement from the scanner. The method was developed based on data from 49 rabbits implanted with VX2 liver tumors. For evaluation, we also acquired high-resolution pHe images from two rabbits, which were used as ground truth. The results indicate a good match between the spatial characteristics of the super-resolution images and the high-resolution ground truth, supported by the low pixelwise absolute error.

An Empirical Approach to Derive Water T1 from Multiparametric MR Images Using an Automated Pipeline and Comparison With Liver Stiffness.

BACKGROUND: Water T1 of the liver has been shown to be promising in discriminating the progressive forms of fatty liver diseases, inflammation, and fibrosis, yet proper correction for iron and lipid is required. PURPOSE: To examine the feasibility of an empirical approach for iron and lipid correction when measuring imaging-based T1 and to validate this approach by spectroscopy on in vivo data. STUDY TYPE: Retrospective. POPULATION: Next to mixed lipid-iron phantoms, individuals with different hepatic lipid content were investigated, including people with type 1 diabetes (N = 15, %female = 15.6, age = 43.5 ± 14.0), or type 2 diabetes mellitus (N = 21, %female = 28.9, age = 59.8 ± 9.7) and healthy volunteers (N = 9, %female = 11.1, age = 58.0 ± 8.1). FIELD STRENGTH/SEQUENCES: 3 T, balanced steady-state free precession MOdified Look-Locker Inversion recovery (MOLLI), multi- and dual-echo gradient echo Dixon, gradient echo magnetic resonance elastography (MRE). ASSESSMENT: T1 values were measured in phantoms to determine the respective correction factors. The correction was tested in vivo and validated by proton magnetic resonance spectroscopy (1 H-MRS). The quantification of liver T1 based on automatic segmentation was compared to the T1 values based on manual segmentation. The association of T1 with MRE-derived liver stiffness was evaluated. STATISTICAL TESTS: Bland-Altman plots and intraclass correlation coefficients (ICCs) were used for MOLLI vs. 1 H-MRS agreement and to compare liver T1 values from automatic vs. manual segmentation. Pearson's r correlation coefficients for T1 with hepatic lipids and liver stiffness were determined. A P-value of 0.05 was considered statistically significant. RESULTS: MOLLI T1 values after correction were found in better agreement with the 1 H-MRS-derived water T1 (ICC = 0.60 [0.37; 0.76]) in comparison with the uncorrected T1 values (ICC = 0.18 [-0.09; 0.44]). Automatic quantification yielded similar liver T1 values (ICC = 0.9995 [0.9991; 0.9997]) as with manual segmentation. A significant correlation of T1 with liver stiffness (r = 0.43 [0.11; 0.67]) was found. A marked and significant reduction in the correlation strength of T1 with liver stiffness (r = 0.05 [-0.28; 0.38], P = 0.77) was found after correction for hepatic lipid content. DATA CONCLUSION: Imaging-based correction factors enable accurate estimation of water T1 in vivo. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

Association of Pathological Features and Multiparametric MRI-Based Radiomics With TP53-Mutated Prostate Cancer.

BACKGROUND: TP53 mutations are associated with prostate cancer (PCa) prognosis and therapy. PURPOSE: To develop TP53 mutation classification models for PCa using MRI radiomics and clinicopathological features. STUDY TYPE: Retrospective. POPULATION: 388 patients with PCa from two centers (Center 1: 281 patients; Center 2: 107 patients). Cases from Center 1 were randomly divided into training and internal validation sets (7:3). Cases from Center 2 were used for external validation. FIELD STRENGTH/SEQUENCE: 3.0T/T2-weighted imaging, dynamic contrast-enhanced imaging, diffusion-weighted imaging. ASSESSMENT: Each patient's index tumor lesion was manually delineated on the above MRI images. Five clinicopathological and 428 radiomics features were obtained from each lesion. Radiomics features were selected by least absolute shrinkage and selection operator and binary logistic regression (LR) analysis, while clinicopathological features were selected using Mann-Whitney U test. Radiomics models were constructed using LR, support vector machine (SVM), and random forest (RF) classifiers. Clinicopathological-radiomics combined models were constructed using the selected radiomics and clinicopathological features with the aforementioned classifiers. STATISTICAL TESTS: Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC). P value <0.05 indicates statistically significant. RESULTS: In the internal validation set, the radiomics model had an AUC of 0.74 with the RF classifier, which was significantly higher than LR (AUC = 0.61), but similar to SVM (AUC = 0.69; P = 0.422). For the combined model, the AUC of RF model was 0.84, which was significantly higher than LR (0.64), but similar to SVM (0.80; P = 0.548). Both the combined RF and combined SVM models showed significantly higher AUCs than the radiomics models. In the external validation set, the combined RF and combined SVM models showed AUCs of 0.83 and 0.82. DATA CONCLUSION: Pathological-radiomics combined models with RF, SVM show the association of TP53 mutations and pathological-radiomics features of PCa. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.

BACKGROUND: Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated. PURPOSE: To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences. STUDY TYPE: Retrospective. POPULATION: A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female). FIELD STRENGTH/SEQUENCE: T1-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) with gradient echo sequences, T2-weighted imaging (T2WI) with spin-echo sequences, diffusion-weighted imaging with single-shot echo-planar sequence and at 1.5-T. ASSESSMENT: A convolutional neural network and long short-term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI-RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE-MRI and non-DCE sequences, respectively. STATISTICAL TESTS: Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P-value <0.05 was considered statistically significant. RESULTS: With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE-MRI, the DL-based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE-MRI/T2WI alone, respectively. DATA CONCLUSION: The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent-free combination is comparable to DCE-MRI alone and the radiologists' reading in AUC and sensitivity. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Role of Additional MRI-Based Morphologic Measurements on the Performance of VI-RADS for Muscle-Invasive Bladder Cancer.

BACKGROUND: Vesical Imaging-Reporting and Data System (VI-RADS) is a pathway for the standardized imaging and reporting of bladder cancer staging using multiparametric (mp) MRI. PURPOSE: To investigate additional role of morphological (MOR) measurements to VI-RADS for the detection of muscle-invasive bladder cancer (MIBC) with mpMRI. STUDY TYPE: Retrospective. POPULATION: A total of 198 patients (72 MIBC and 126 NMIBC) underwent bladder mpMRI was included. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted imaging with fast-spin-echo sequence, spin-echo-planar diffusion-weighted imaging and dynamic contrast-enhanced imaging with fast 3D gradient-echo sequence. ASSESSMENT: VI-RADS score and MOR measurement including tumor location, number, stalk, cauliflower-like surface, type of tumor growth, tumor-muscle contact margin (TCM), tumor-longitudinal length (TLL), and tumor cellularity index (TCI) were analyzed by three uroradiologists (3-year, 8-year, and 15-year experience of bladder MRI, respectively) who were blinded to histopathology. STATISTICAL TESTS: Significant MOR measurements associated with MIBC were tested by univariable and multivariable logistic regression (LR) analysis with odds ratio (OR). Area under receiver operating characteristic curve (AUC) with DeLong's test and decision curve analysis (DCA) were used to compared the performance of unadjusted vs. adjusted VI-RADS. A P-value <0.05 was considered statistically significant. RESULTS: TCM (OR 9.98; 95% confidence interval [CI] 4.77-20.8), TCI (OR 5.72; 95% CI 2.37-13.8), and TLL (OR 3.35; 95% CI 1.40-8.03) were independently associated with MIBC at multivariable LR analysis. VI-RADS adjusted by three MORs achieved significantly higher AUC (reader 1 0.908 vs. 0.798; reader 2 0.906 vs. 0.855; reader 3 0.907 vs. 0.831) and better clinical benefits than unadjusted VI-RADS at DCA. Specially in VI-RADS-defined equivocal lesions, MOR-based adjustment resulted in 55.5% (25/45), 70.4% (38/54), and 46.4% (26/56) improvement in accuracy for discriminating MIBC in three readers, respectively. DATA CONCLUSION: MOR measurements improved the performance of VI-RADS in detecting MIBC with mpMRI, especially for equivocal lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Understanding Spatial Correlation Between Multiparametric MRI Performance and Prostate Cancer.

BACKGROUND: Multiparametric MRI (mpMRI) has shown a substantial impact on prostate cancer (PCa) diagnosis. However, the understanding of the spatial correlation between mpMRI performance and PCa location is still limited. PURPOSE: To investigate the association between mpMRI performance and tumor spatial location within the prostate using a prostate sector map, described by Prostate Imaging Reporting and Data System (PI-RADS) v2.1. STUDY TYPE: Retrospective. SUBJECTS: One thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022. FIELD STRENGTH/SEQUENCE: 3.0 T. T2-weighted turbo spin-echo, a single-shot spin-echo EPI sequence for diffusion-weighted imaging, and a gradient echo sequence for dynamic contrast-enhanced MRI sequences. ASSESSMENT: Integrated relative cancer prevalence (rCP), detection rate (DR), and positive predictive value (PPV) maps corresponding to the prostate sector map for PCa lesions were created. The relationship between tumor location and its detection/missing by radiologists on mpMRI compared to WMHP as a reference standard was investigated. STATISTICAL TESTS: A weighted chi-square test was performed to examine the statistical differences for rCP, DR, and PPV of the aggregated sectors within the zone, anterior/posterior, left/right prostate, and different levels of the prostate with a statistically significant level of 0.05. RESULTS: A total of 1665 PCa lesions were identified in 1143 patients, and from those 1060 lesions were clinically significant (cs)PCa tumors (any Gleason score [GS] ≥7). Our sector-based analysis utilizing weighted chi-square tests suggested that the left posterior part of PZ had a high likelihood of missing csPCa lesions at a DR of 67.0%. Aggregated sector analysis indicated that the anterior or apex locations in PZ had the significantly lowest csPCa detection at 67.3% and 71.5%, respectively. DATA CONCLUSION: Spatial characteristics of the per-lesion-based mpMRI performance for diagnosis of PCa were studied. Our results demonstrated that there is a spatial correlation between mpMRI performance and locations of PCa on the prostate. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

BACKGROUND: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. PURPOSE: To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. STUDY TYPE: Retrospective. POPULATION: One hundred ninety-one patients (median age, 65 years; age range, 54-73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. FIELD STRENGTH/SEQUENCE: 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. ASSESSMENT: Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. STATISTICAL TESTS: Student's t-tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan-Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. RESULTS: The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792-0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749-0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. DATA CONCLUSION: Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Is multiparametric MRI always needed in biopsy-naïve patients with abnormal digital rectal examination? A single-institutional experience combining clinical and micro-ultrasonography-based factors to optimize prostate cancer detection.

PURPOSE: To assess the diagnostic performance of microultrasound-targeted biopsy (microUSTBx) and systematic biopsy (SBx) in detecting clinically significant prostate cancer (csPCa) among men with abnormal digital rectal examination (DRE) and suspicious lesions at multiparametric magnetic resonance imaging (mpMRI), and to compare the diagnostic performance of this approach with a mpMRI-guided targeted biopsy (MTBx) plus SBx-based strategy. METHODS: Biopsy-naïve men with suspicious lesions at mpMRI and abnormal DRE were prospectively evaluated between October 2017 and January 2023. csPCa detection rate by microUSTBx plus SBx and MTBx plus SBx was assessed and then compared by McNemar's test. The added value of prostate-specific antigen density (PSAd) was also evaluated. RESULTS: Overall, 182 biopsy naïve men were included. MicroUSTBx plus SBx achieved comparable detection rate to MTBx plus SBx in diagnosis of ciPCa and csPCa (ciPCa: 9.3% [17/182] vs 10% [19/182]; csPCa: 63% [114/182] vs 62% [113/182]). MicroUSTBx outperformed MTBx (ciPCa: 5.5% [10/182] vs 6.0% [11/182]; csPCa: 57% [103/182] vs 54% [99/182]). Using microUSTBx plus SBx would have avoided 68/182 (37%) unnecessary mpMRI, while missing only 2/116 (1.7%) csPCa. The decision curve analysis of suspicious microUS plus PSAd ≥ 0.15 ng/ml showed higher net benefit in the ability to identify true positives and reduce the number of unnecessary prostate biopsy in this subcategory of patients. CONCLUSIONS: The combination of microUSTBx and SBx showed equal diagnostic performance to an mpMRI-based approach in biopsy-naïve patients with an abnormal DRE. The combination of this approach with PSAd maximize the diagnostic accuracy while lowering the need for unnecessary biopsies.

Factors associated with pathological up-staging in MRI cT3a prostate cancer - a retrospective study from a high-volume centre.

INTRODUCTION: Multiparametric MRI (mpMRI) parameters of pT3a prostate cancer have not been examined in large cohort studies. Therefore, we aimed to identify factors associated with up-staging of mpMRI cT3a in post-operative histopathological confirmation. METHODS: Retrospective analysis of a prospectively maintained database of a single UK cancer centre. Only cT3a cases who underwent robotic-assisted radical prostatectomy (RARP) were included (N = 383). MRI and specimen histopathology was reviewed independently by expert uro-radiologists and uro-histopathologists, respectively. Factors included age, BMI, prostate-specific antigen (PSA) level, biopsy international society of urological pathology (ISUP) grade, Prostate Imaging Reporting & Data System (PI-RADS®) score, tumour size, tumour coverage of gland (%), gland weight and surgical margins were analysed as predictors of pT3a prostate cancer. RESULTS: N = 383. Mean age 66 years (58-71), mean BMI 27.1 kg/m2 (25.0-30.0). 314 (82.0%) cases down- unchanged or down-staged, and 69 (18.0%) cases upstaged. PSA level (P = 0.002), PI-RADS score (P < 0.001) and ISUP grade (P < 0.001) are positively associated with upstage categories. ISUP grade ≥3 (OR 5.45, CI 1.88, 9.29, P < 0.002), PI-RADS score ≥4 (OR 3.92, CI 1.88-9.29, P < 0.001) and tumour coverage (OR 1.06, CI 1.05-1.08, P < 0.001) significantly positively associated with upstaging disease, with concurrent decreased probability of downstaging (OR 0.55, 0.14, 0.44, respectively, P < 0.05). Tumour coverage was positively correlated with increasing positive surgical margins (P < 0.05). Capsular contact > 15 mm was very unlikely to be upstaged (OR 0.36, CI 0.21-0.62, P < 0.001), aligning with published results past the widely accepted significant level for extracapsular disease on MRI. CONCLUSION: The study has identified PSA level, ISUP, PI-RADS score, tumour volume and percentage coverage are key predictive factors in cT3a upstaging. This study uniquely shows tumour coverage percentage as a predictor of cT3a upstaging on mpMRI. ISUP is the strongest predictor, followed by PI-RADS score and tumour coverage of gland. Multi-institutional studies are needed to confirm our findings.

Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation.

OBJECTIVES: The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test-retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis. METHODS: To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs). RESULTS: Radiomics features from T2WI demonstrated higher repeatability (65-94%) than those from DWI (38-48%) or T1WI (2-92%). Across test-retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869). CONCLUSION: Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL. CLINICAL RELEVANCE STATEMENT: By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology. KEY POINTS: • Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility.

Performance of an ultra-fast deep-learning accelerated MRI screening protocol for prostate cancer compared to a standard multiparametric protocol.

OBJECTIVES: To establish and evaluate an ultra-fast MRI screening protocol for prostate cancer (PCa) in comparison to the standard multiparametric (mp) protocol, reducing scan time and maintaining adequate diagnostic performance. MATERIALS AND METHODS: This prospective single-center study included consecutive biopsy-naïve patients with suspected PCa between December 2022 and March 2023. A PI-RADSv2.1 conform mpMRI protocol was acquired in a 3 T scanner (scan time: 25 min 45 sec). In addition, two deep-learning (DL) accelerated sequences (T2- and diffusion-weighted) were acquired, serving as a screening protocol (scan time: 3 min 28 sec). Two readers evaluated image quality and the probability of PCa regarding PI-RADSv2.1 scores in two sessions. The diagnostic performance of the screening protocol with mpMRI serving as the reference standard was derived. Inter- and intra-reader agreements were evaluated using weighted kappa statistics. RESULTS: We included 77 patients with 97 lesions (mean age: 66 years; SD: 7.7). Diagnostic performance of the screening protocol was excellent with a sensitivity and specificity of 100%/100% and 89%/98% (cut-off ≥ PI-RADS 4) for reader 1 (R1) and reader 2 (R2), respectively. Mean image quality was 3.96 (R1) and 4.35 (R2) for the standard protocol vs. 4.74 and 4.57 for the screening protocol (p < 0.05). Inter-reader agreement was moderate (κ: 0.55) for the screening protocol and substantial (κ: 0.61) for the multiparametric protocol. CONCLUSION: The ultra-fast screening protocol showed similar diagnostic performance and better imaging quality compared to the mpMRI in under 15% of scan time, improving efficacy and enabling the implementation of screening protocols in clinical routine. CLINICAL RELEVANCE STATEMENT: The ultra-fast protocol enables examinations without contrast administration, drastically reducing scan time to 3.5 min with similar diagnostic performance and better imaging quality. This facilitates patient-friendly, efficient examinations and addresses the conflict of increasing demand for examinations at currently exhausted capacities. KEY POINTS: Time-consuming MRI protocols are in conflict with an expected increase in examinations required for prostate cancer screening. An ultra-fast MRI protocol shows similar performance and better image quality compared to the standard protocol. Deep-learning acceleration facilitates efficient and patient-friendly examinations, thus improving prostate cancer screening capacity.

Development and Validation of MRI Radiomics Models to Differentiate HER2-Zero, -Low, and -Positive Breast Cancer.

BACKGROUND. Breast cancer HER2 expression has been redefined using a three-tiered system, with HER2-zero cancers considered ineligible for HER2-targeted therapy, HER2-low cancers considered candidates for novel HER2-targeted drugs, and HER2-positive cancers treated with traditional HER2-targeted medications. OBJECTIVE. The purpose of this study was to assess MRI radiomics models for a three-tiered classification of HER2 expression of breast cancer. METHODS. This retrospective study included 592 patients with pathologically confirmed breast cancer (mean age, 47.0 ± 18.0 [SD] years) who underwent breast MRI at either of a health system's two hospitals from April 2016 through June 2022. Three-tiered HER2 status was pathologically determined. Radiologists assessed the conventional MRI features of tumors and manually segmented the tumors on multiparametric sequences (T2-weighted images, DWI, ADC maps, and T1-weighted delayed contrast-enhanced images) to extract radiomics features. Least absolute shrinkage and selection operator analysis was used to develop two radiomics signatures, to differentiate HER2-zero cancers from HER2-low or HER2-positive cancers (task 1) as well as to differentiate HER2-low cancers from HER2-positive cancers (task 2). Patients from hospital 1 were randomly assigned to a discovery set (task 1: n = 376; task 2: n = 335) or an internal validation set (task 1: n = 161; task 2: n = 143); patients from hospital 2 formed an external validation set (task 1: n = 55; task 2: n = 50). Multivariable logistic regression analysis was used to create nomograms combining radiomics signatures with clinicopathologic and conventional MRI features. RESULTS. AUC, sensitivity, and specificity in the discovery, internal validation, and external validation sets were as follows: for task 1, 0.89, 99.4%, and 69.0%; 0.86, 98.6%, and 76.5%; and 0.78, 100.0%, and 0.0%, respectively; for task 2, 0.77, 93.8%, and 32.3%; 0.75, 92.9%, and 6.8%; and 0.77, 97.0%, and 29.4%, respectively. For task 1, no nomogram was created because no clinicopathologic or conventional MRI feature was associated with HER2 status independent of the MRI radiomics signature. For task 2, a nomogram including an MRI radiomics signature and three pathologic features (histologic grade of III, high Ki-67 index, and positive progesterone receptor status) that were independently associated with HER2-low expression had an AUC of 0.87, 0.83, and 0.80 in the three sets. CONCLUSION. MRI radiomics features were used to differentiate HER2-zero from HER2-low cancers or HER2-positives cancers as well as to differentiate HER2-low cancers from HER2-positive cancers. CLINICAL IMPACT. MRI radiomics may help select patients for novel or traditional HER2-targeted therapies, particularly those patients with ambiguous results of immunohistochemical staining results or limited access to fluorescence in situ hybridization.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology.

BACKGROUND: The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model. METHODS: We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis. RESULTS: Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model. CONCLUSIONS: The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Diagnostic Accuracy of a Combination of Preoperative 68-Ga Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography and Multiparametric Magnetic Resonance Imaging in Localized Prostate Cancer.

INTRODUCTION: Prostate cancer (PCa) is a common and leading course of cancer-related death in men. Although there are studies on multiparametric magnetic resonance imaging (MpMRI) with good diagnostic results in detecting clinically significant PCa, new methods have been investigated due to the low positive predictive values. In this context, prostate-specific membrane antigen positron emission tomography (PSMA PET) emerges as an alternative imaging method to MpMRI. This study aimed to compare 68Ga PSMA I&T-PET/CT and MpMRI in determining tumor location. METHODS: Preoperative MpMRI and 68Ga PSMA I&T-PET/CT scans and pathology specimens of patients who underwent radical prostatectomy for PCa at our clinic between 2018 and 2021 were retrospectively evaluated. PSMA I&T-PET/CT, MpMRI, combined imaging were compared for tumor localization according to histopathological data. RESULTS: In terms of tumor localization, MpMRI demonstrated overall accuracy rates of 75.9% (p kappa [κ] 0.0001* [0.525]). 68Ga PSMA I&T-PET/CT showed 71.5% (p κ 0.0001* [0.438]). For the combined imaging approach, the overall accuracy rate was calculated as 79.2% (p κ 0.0001* [0.576]). Additionally, high diagnostic accuracy was achieved for the combined imaging approach, particularly in the intermediate ISUP group. Moreover, SUVmax was calculated as 6.37. CONCLUSION: The combined use of 68Ga PSMA I&T-PET/CT and MpMRI has high diagnostic rates. However, the high cost is a significant disadvantage that limits their routine combined use.

The Role of Multiparametric MRI (mpMRI) in the Prediction of Adverse Prostate Cancer Pathology in Radical Prostatectomy Specimen.

INTRODUCTION: Prostate cancer (PCa) risk stratification is essential in guiding therapeutic decision. Multiparametric magnetic resonance tomography (mpMRI) holds promise in the prediction of adverse pathologies (AP) after prostatectomy (RP). This study aims to identify clinical and imaging markers in the prediction of adverse pathology. METHODS: Patients with PCa, diagnosed by targeted biopsy after mpMRI and undergoing RP, were included. The predictive accuracy of mpMRI for extraprostatic extension (ECE), seminal vesicle infiltration (SVI), and lymph node positivity was calculated from the final histopathology. RESULTS: 846 patients were involved. Independent risk parameters include imaging findings such as ECE (OR 3.12), SVI (OR 2.55), and PI-RADS scoring (4: OR 2.01 and 5: OR 4.34). mpMRI parameters such as ECE, SVI, and lymph node metastases showed a high prognostic accuracy (73.28% vs. 95.35% vs. 93.38%) with moderate sensitivity compared to the final histopathology. The ROC analysis of our combined scoring system (D'Amico classification, PSA density, and MRI risk factors) improves the prediction of adverse pathology (AUC: 0.73 vs. 0.69). CONCLUSION: Our study supports the use of mpMRI for comprehensive pretreatment risk assessment in PCa. Due to the high accuracy of factors like ECE, SVI, and PI-RADS scoring, utilizing mpMRI data enabled accurate prediction of unfavorable pathology after RP.

Diagnostic performance of microUltrasound at MRI-guided confirmatory biopsy in patients under active surveillance for low-risk prostate cancer.

BACKGROUND: Active surveillance (AS) represents a standard of care of low-risk prostate cancer (PCa). However, the identification and monitoring of AS candidates remains challenging. Microultrasound (microUS) is a novel high-resolution imaging modality for transrectal ultrasonography (TRUS). We explored the impact of microUS TRUS and targeted biopsies in mpMRI-guided confirmatory biopsies. METHODS: Between October 2017 and September 2021, we prospectively enrolled 100 patients scheduled for MRI-guided confirmatory biopsy at 1 year from diagnosis of ISUP 1 PCa. TRUS was performed using the ExactVu microUS system; PRI-MUS protocol was applied to identify suspicious lesions (i.e., PRIMUS score ≥ 3). All patients received targeted biopsies of any identified microUS and mpMRI lesions and complementary systematic biopsies. The proportion of patients upgraded to clinically significant PCa (defined as ISUP ≥ 2 cancer; csPCa) at confirmatory biopsies was determined, and the diagnostic performance of microUS and mpMRI were compared. RESULTS: Ninety-two patients had a suspicious MRI lesion classified PI-RADS 3, 4, and 5 in respectively 28, 16, and 18 patients. MicroUS identified 82 patients with suspicious lesions, classified as PRI-MUS 3, 4, and 5 in respectively 20, 50, and 12 patients, while 18 individuals had no lesions. Thirty-four patients were upgraded to ISUP ≥ 2 cancer and excluded from AS. MicroUS and mpMRI showed a sensitivity of 94.1% and 100%, and an NPV of 88.9% and 100%, respectively, in detecting ISUP ≥ 2 patients. A microUS-mandated protocol would have avoided confirmatory biopsies in 18 patients with no PRI-MUS ≥ 3 lesions at the cost of missing four upgraded patients. CONCLUSIONS: MicroUS and mpMRI represent valuable imaging modalities showing high sensitivity and NPV in detecting csPCa, thus allowing their use for event-triggered confirmatory biopsies in AS patients. MicroUS offers an alternative imaging modality to mpMRI for the identification and real-time targeting of suspicious lesions in AS patients.

Is prostatic adenocarcinoma with cribriform architecture more difficult to detect on prostate MRI?

BACKGROUND: Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden. METHODS: Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy. Biopsy cores and lesions were categorized as follows: C0 = benign, C1 = PCa with no CBFM pattern, C2 = PCa with CBFM pattern. Correlation between cancer core length (CCL) and measured MRI lesion dimension were assessed using a modified Pearson correlation test for clustered data. Differences between the biopsy core groups were assessed with the Wilcoxon-signed rank test with clustering. RESULTS: Between 2015 and 2022, a total of 131 consecutive patients with CBFM pattern on prostate biopsy and pre-biopsy mpMRI were included. Clinical feature analysis included 1572 systematic biopsy cores (1149 C0, 272 C1, 151 C2) and 736 MRI-targeted biopsy cores (253 C0, 272 C1, 211 C2). Of the 131 patients with confirmed CBFM pathology, targeted biopsy (TBx) alone identified CBFM in 76.3% (100/131) of patients and detected PCa in 97.7% (128/131) patients. SBx biopsy alone detected CBFM in 61.1% (80/131) of patients and PCa in 90.8% (119/131) patients. TBx and SBx had equivalent detection in patients with smaller prostates (p = 0.045). For both PCa lesion groups there was a positive and significant correlation between maximum MRI lesion dimension and CCL (C1 lesions: p < 0.01, C2 lesions: p < 0.001). There was a significant difference in CCL between C1 and C2 lesions for T2 scores of 3 and 5 (p ≤ 0.01, p ≤ 0.01, respectively) and PI-RADS 5 lesions (p ≤ 0.01), with C2 lesions having larger CCL, despite no significant difference in MRI lesion dimension. CONCLUSIONS: The extent of disease for CBFM-containing tumors is difficult to capture on mpMRI. When comparing MRI lesions of similar dimensions and PIRADS scores, CBFM-containing tumors appear to have larger cancer yield on biopsy. Proper staging and planning of therapeutic interventions is reliant on accurate mpMRI estimation. Special considerations should be taken for patients with CBFM pattern on prostate biopsy.