Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting.

OBJECTIVE: To develop a multiparametric machine-learning (ML) framework using high-resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD). MATERIALS: We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age- and gender-matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole-brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z-score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual-level classification. Fivefold cross-validation was performed and performance metrics were reported using receiver-operating-characteristic-curve analyses. RESULTS: FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF-ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross-validation trials. Type II versus non-type-II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases. INTERPRETATION: The MRF-ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024;96:944-957.

Temporal dynamics of white and gray matter plasticity during motor skill acquisition: a comparative diffusion tensor imaging and multiparametric mapping analysis.

Learning new motor skills relies on neural plasticity within motor and limbic systems. This study uniquely combined diffusion tensor imaging and multiparametric mapping MRI to detail these neuroplasticity processes. We recruited 18 healthy male participants who underwent 960 min of training on a computer-based motion game, while 14 were scanned without training. Diffusion tensor imaging, which quantifies tissue microstructure by measuring the capacity for, and directionality of, water diffusion, revealed mostly linear changes in white matter across the corticospinal-cerebellar-thalamo-hippocampal circuit. These changes related to performance and reflected different responses to upper- and lower-limb training in brain areas with known somatotopic representations. Conversely, quantitative MRI metrics, sensitive to myelination and iron content, demonstrated mostly quadratic changes in gray matter related to performance and reflecting somatotopic representations within the same brain areas. Furthermore, while myelin and iron-sensitive multiparametric mapping MRI was able to describe time lags between different cortical brain systems, diffusion tensor imaging detected time lags within the white matter of the motor systems. These findings suggest that motor skill learning involves distinct phases of white and gray matter plasticity across the sensorimotor network, with the unique combination of diffusion tensor imaging and multiparametric mapping MRI providing complementary insights into the underlying neuroplastic responses.

This Is What Metabolic Dysfunction-Associated Steatotic Liver Disease Looks Like: Potential of a Multiparametric MRI Protocol.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent condition with a broad spectrum defined by liver biopsy. This gold standard method evaluates three features: steatosis, activity (ballooning and lobular inflammation), and fibrosis, attributing them to certain grades or stages using a semiquantitative scoring system. However, liver biopsy is subject to numerous restrictions, creating an unmet need for a reliable and reproducible method for MASLD assessment, grading, and staging. Noninvasive imaging modalities, such as magnetic resonance imaging (MRI), offer the potential to assess quantitative liver parameters. This review aims to provide an overview of the available MRI techniques for the three criteria evaluated individually by liver histology. Here, we discuss the possibility of combining multiple MRI parameters to replace liver biopsy with a holistic, multiparametric MRI protocol. In conclusion, the development and implementation of such an approach could significantly improve the diagnosis and management of MASLD, reducing the need for invasive procedures and paving the way for more personalized treatment strategies.

Whole brain multiparametric mapping in two minutes using a dual-flip-angle stack-of-stars blipped multi-gradient-echo acquisition.

A new MRI technique is presented for three-dimensional fast simultaneous whole brain mapping of myelin water fraction (MWF), T1, proton density (PD), R2*, magnetic susceptibility (QSM), and B1 transmit field (B1+). Phantom and human (N = 9) datasets were acquired using a dual-flip-angle blipped multi-gradient-echo (DFA-mGRE) sequence with a stack-of-stars (SOS) trajectory. Images were reconstructed using a subspace-based algorithm with a locally low-rank constraint. A novel joint-sparsity-constrained multicomponent T2*-T1 spectrum estimation (JMSE) algorithm is proposed to correct for the T1 saturation effect and B1+/B1- inhomogeneities in the quantification of MWF. A tissue-prior-based B1+ estimation algorithm was adapted for B1 correction in the mapping of T1 and PD. In the phantom study, measurements obtained at an acceleration factor (R) of 12 using prospectively under-sampled SOS showed good consistency (R2 > 0.997) with Cartesian reference for R2*/T1app/M0app. In the in vivo study, results of retrospectively under-sampled SOS with R = 6, 12, 18, showed good quality (structure similarity index measure > 0.95) compared with those of fully-sampled SOS. Besides, results of prospectively under-sampled SOS with R = 12 showed good consistency (intraclass correlation coefficient > 0.91) with Cartesian reference for T1/PD/B1+/MWF/QSM/R2*, and good reproducibility (coefficient of variation < 7.0 %) in the test-retest analysis for T1/PD/B1+/MWF/R2*. This study has demonstrated the feasibility of simultaneous whole brain multiparametric mapping with a two-minute scan using the DFA-mGRE SOS sequence, which may overcome a major obstacle for neurological applications of multiparametric MRI.

The detection rate for prostate cancer in systematic and targeted prostate biopsy in biopsy-naive patients, according to the localization of the lesion at the mpMRI: A single-center retrospective observational study.

OBJECTIVE: Evaluate the detection rates of systematic, targeted and combined cores at biopsy according to tumor positions in biopsy-naïve patients. MATERIAL AND METHODS: A retrospective analysis of a single-center patient cohort (n = 501) that underwent transrectal prostate biopsy between January 2017 and December 2019 was performed. Multi-parametric MRI was executed as a prebiopsy investigation. Biopsy protocol included, for each patient, 12 systematic cores plus 3 to 5 targeted cores per lesion identified at the mpMRI. Pearson and McNemar chi-squared tests were used for statistical analysis to compare tumor location-related detection rates of systematic, targeted and combined (systematic + targeted) cores at biopsy. RESULTS: Median age of patients was 70 years (IQR 62-72), with a median PSA of 8.5 ng/ml (IQR 5.7-15.6). Positive biopsies were obtained in 67.7% of cases. Overall, targeted cores obtained higher detection rates compared to systematic cores (54.3% vs. 43.1%, p < 0.0001). Differences in detection rates were, however, higher for tumors located at the apex (61.1% vs. 26.3%, p < 0.05) and anteriorly (44.4% vs. 19.3%, p < 0.05). Targeted cores similarly obtained higher detection rates in the posterior zone of the prostate gland for clinically significant prostate cancer. A poor agreement was reported between targeted and systematic cores for the apex and anterior zone of the prostate with, respectively κ = 0.028 and κ = -0.018. CONCLUSION: A combined approach of targeted and systematic biopsy delivers the highest detection rate in prostate cancer (PCa). The location of the tumor could however greatly influence overall detection rates, indicating the possibility to omit (as for the base or posterior zone of the gland) or add (as for the apex or anterior zone of the gland) further targeted cores.

Image-guided multiparametric magnetic resonance imaging-transrectal ultrasound fusion biopsy augmented with a sextant versus an extended template random biopsy: Comparison of cancer detection rates, complication and functional outcomes.

PURPOSE: To compare the efficacy of a novel fusion template "reduced six-core systemic template and multiparametric magnetic resonance imaging/transrectal ultrasound (mpMRI/TRUS) fusion targeted biopsy" (TBx+6c), with mpMRI/TRUS fusion-targeted biopsy and 12-core systematic biopsy template (TBx+12c) in the diagnosis of prostate cancer (PCa). MATERIALS AND METHODS: This is an institutional review board approved single-center observational study involving adult men undergoing fusion-targeted biopsies for the diagnosis of PCa. Patients were sorted into cohorts of TBx+6c or TBx+12c based on the systematic biopsy template used. The study's main objective was to determine the cancer detection rate (CDR) for overall PCa and clinically significant PCa (csPCa) and the secondary objectives were to compare complication rates and functional outcome differences between the cohort. RESULTS: A total of 204 patients met study's inclusion criteria. TBx+6c group had 120 patients, while TBx+12c cohort had 84 patients. The groups had similar baseline characteristics and overall CDR in the TBx+6c cohort was 71.7% versus 79.8%, compared to the TBx+12c (p = 0.18) whereas, the csPCa detection rate in the TBx+6c group was 50.8% versus 54.8% in the TBx+12c group (p = 0.5). TBx+6c cohort had lower overall complication rate of 3% versus 13%, (p = 0.01) and ≥ grade 2 complication rates (1 (1%) vs. 3(4%), p = 0.03) compared to the TBx+12c cohort. There were no differences in IIEF-5 (p = 0.5) or IPSS (p = 0.1) scores at baseline and 2-weeks and 6-weeks post-biopsy. CONCLUSION: TBx+6c cohort, when compared to the TBx+12c cohort, demonstrated comparable diagnostic performance along with similar functional outcomes and lower complication rates. These results suggest the importance of further exploring the clinical implications of adopting a TBx+6c schema for PCa diagnosis in comparison to the widely used TBx+12c schema through a multicenter randomized controlled trial.

Natural history of histologically benign PIRADS 4-5 lesions in multiparametric MRI: Real-life experience in an academic center.

INTRODUCTION: The follow-up findings of patients who underwent prostate biopsy for prostate image reporting and data system (PIRADS) 4 or 5 multiparametric magnetic resonance imaging (mpMRI) findings and had benign histology were retrospectively reviewed. METHODS: There were 190 biopsy-naive patients. Patients with at least 12 months of follow-up between 2012 and 2023 were evaluated. All MRIs were interpreted by two very experienced uroradiologists. Of the patients, 125 had either cognitive or software fusion MR-targeted biopsies with 4 + 8/10 cores. The remaining 65 patients had in-bore biopsies with 4-5 cores. Prostate-specific antigen (PSA) levels below 4 ng/mL were defined as PSA regression following biopsy. PIRADS 1-3 lesions on new MRI images were classified as MRI regression. RESULTS: Median patient age and PSA were 62 (39-82) years and six (0.4-33) ng/mL, respectively, at the initial work-up. During a median follow-up period of 44 months, 37 (19.4%) patients were lost to follow-up. Of the remaining 153 patients, 82 (53.6%) had persistently high PSA. Among them, 72 (87.8%) had repeat mpMRI within 6-24 months which showed regressive findings (PIRADS 1-3) in 53 patients (73.6%) and PIRADS 4-5 index lesion persistence in 19 cases (26.4%). The latter group was recommended to have rebiopsy. Of these 19 patients, 16 underwent MRI-targeted rebiopsy. Prostate cancer was diagnosed in six (37.5%) patients and of these four (25%) were clinically significant (>Grade Group 1). Totally, clinically significant prostate cancer was detected in 4/153 (2.6%) patients followed up. CONCLUSION: Patients should be warned against the relative relaxing effect of a negative biopsy after identification of PIRADS 4-5 index lesion. While PSA decrease was observed in many patients during follow-up, persistent MRI findings were present in nearly a quarter of patients with persistently high PSA. A rebiopsy is warranted in these patients, with significant prostate cancer diagnosed in a quarter of patients with rebiopsy.

The predictive impact of hematological inflammatory markers in detecting prostate cancer in patients with PI-RADS 3 lesions on multiparametric magnetic resonance imaging.

BACKGROUND: The diagnostic accuracy of suspicious lesions that are classified as PI-RADS 3 in multiparametric prostate magnetic-resonance imaging (mpMRI) is controversial. This study aims to assess the predictive capacity of hematological inflammatory markers such as neutrophil-lymphocyte ratio (NLR), pan-immune-inflammation value (PIV), and systemic immune-response index (SIRI) in detecting prostate cancer in PI-RADS 3 lesions. METHODS: 276 patients who underwent mpMRI and subsequent prostate biopsy after PI-RADS 3 lesion detection were included in the study. According to the biopsy results, the patients were distributed to two groups as prostate cancer (PCa) and no cancer (non-PCa). Data concerning age, PSA, prostate volume, PSA density, PI-RADS 3 lesion size, prostate biopsy results, monocyte counts (109/L), lymphocyte counts (109/L), platelet counts (109/L), neutrophils count (109/L) were recorded from the complete blood count. From these data; PIV value is obtained by monocyte × neutrophil × platelet/lymphocyte, NLR by neutrophil/lymphocyte, and SIRI by monocyte number × NLR. RESULTS: Significant variations in neutrophil, lymphocyte, and monocyte levels between PCa and non-PCa patient groups were detected (p = 0.009, p = 0.001, p = 0.005 respectively, p < 0.05). NLR, PIV, and SIRI exhibited significant differences, with higher values in PCa patients (p = 0.004, p = 0.001, p < 0.001 respectively, p < 0.05). The area under curve of SIRI was 0.729, with a cut-off value of 1.20 and with a sensitivity 57.70%, and a specificity of 68.70%. CONCLUSION: SIRI outperformed NLR and PIV in detecting PCa in PI-RADS 3 lesions, showcasing its potential as a valuable biomarker. Implementation of this parameter to possible future nomograms has the potential to individualize and risk-stratify the patients in prostate biopsy decision.

Multiparametric MRI and 18F-PSMA-1007 PET/CT for the Detection of Clinically Significant Prostate Cancer.

Background Multiparametric MRI (mpMRI) is effective for detecting prostate cancer (PCa); however, there is a high rate of equivocal Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions and false-positive findings. Purpose To investigate whether fluorine 18 (18F) prostate-specific membrane antigen (PSMA) 1007 PET/CT after mpMRI can help detect localized clinically significant PCa (csPCa), particularly for equivocal PI-RADS 3 lesions. Materials and Methods This prospective study included participants with elevated prostate-specific antigen (PSA) levels referred for prostate mpMRI between September 2020 and February 2022. 18F-PSMA-1007 PET/CT was performed within 30 days of mpMRI and before biopsy. PI-RADS category and level of suspicion (LOS) were assessed. PI-RADS 3 or higher lesions at mpMRI and/or LOS 3 or higher lesions at 18F-PSMA-1007 PET/CT underwent targeted biopsies. PI-RADS 2 or lower and LOS 2 or lower lesions were considered nonsuspicious and were monitored during a 1-year follow-up by means of PSA testing. Diagnostic accuracy was assessed, with histologic examination serving as the reference standard. International Society of Urological Pathology (ISUP) grade 2 or higher was considered csPCa. Results Seventy-five participants (median age, 67 years [range, 52-77 years]) were assessed, with PI-RADS 1 or 2, PI-RADS 3, and PI-RADS 4 or 5 groups each including 25 participants. A total of 102 lesions were identified, of which 80 were PI-RADS 3 or higher and/or LOS 3 or higher and therefore underwent targeted biopsy. The per-participant sensitivity for the detection of csPCa was 95% and 91% for mpMRI and 18F-PSMA-1007 PET/CT, respectively, with respective specificities of 45% and 62%. 18F-PSMA-1007 PET/CT was used to correctly differentiate 17 of 26 PI-RADS 3 lesions (65%), with a negative and positive predictive value of 93% and 27%, respectively, for ruling out or detecting csPCa. One additional significant and one insignificant PCa lesion (PI-RADS 1 or 2) were found at 18F-PSMA-1007 PET/CT that otherwise would have remained undetected. Two participants had ISUP 2 tumors without PSMA uptake that were missed at PET/CT. Conclusion 18F-PSMA-1007 PET/CT showed good sensitivity and moderate specificity for the detection of csPCa and ruled this out in 93% of participants with PI-RADS 3 lesions. Clinical trial registration no. NCT04487847 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists. Materials and Methods Data from patients without known csPCa who underwent MRI from January 2017 to December 2019 at one of multiple sites of a single academic institution were retrospectively reviewed. A convolutional neural network was trained to predict csPCa from T2-weighted images, diffusion-weighted images, apparent diffusion coefficient maps, and T1-weighted contrast-enhanced images. The reference standard was pathologic diagnosis. Radiologist performance was evaluated as follows: Radiology reports were used for the internal test set, and four radiologists' PI-RADS ratings were used for the external (ProstateX) test set. The performance was compared using areas under the receiver operating characteristic curves (AUCs) and the DeLong test. Gradient-weighted class activation maps (Grad-CAMs) were used to show tumor localization. Results Among 5735 examinations in 5215 patients (mean age, 66 years ± 8 [SD]; all male), 1514 examinations (1454 patients) showed csPCa. In the internal test set (400 examinations), the AUC was 0.89 and 0.89 for the DL classifier and radiologists, respectively (P = .88). In the external test set (204 examinations), the AUC was 0.86 and 0.84 for the DL classifier and radiologists, respectively (P = .68). DL classifier plus radiologists had an AUC of 0.89 (P < .001). Grad-CAMs demonstrated activation over the csPCa lesion in 35 of 38 and 56 of 58 true-positive examinations in internal and external test sets, respectively. Conclusion The performance of a DL model was not different from that of radiologists in the detection of csPCa at MRI, and Grad-CAMs localized the tumor. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Johnson and Chandarana in this issue.

AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer.

Background An artificial intelligence (AI)-based method for measuring intraprostatic tumor volume based on data from MRI may provide prognostic information. Purpose To evaluate whether the total volume of intraprostatic tumor from AI-generated segmentations (VAI) provides independent prognostic information in patients with localized prostate cancer treated with radiation therapy (RT) or radical prostatectomy (RP). Materials and Methods For this retrospective, single-center study (January 2021 to August 2023), patients with cT1-3N0M0 prostate cancer who underwent MRI and were treated with RT or RP were identified. Patients who underwent RT were randomly divided into cross-validation and test RT groups. An AI segmentation algorithm was trained to delineate Prostate Imaging Reporting and Data System (PI-RADS) 3-5 lesions in the cross-validation RT group before providing segmentations for the test RT and RP groups. Cox regression models were used to evaluate the association between VAI and time to metastasis and adjusted for clinical and radiologic factors for combined RT (ie, cross-validation RT and test RT) and RP groups. Areas under the receiver operating characteristic curve (AUCs) were calculated for VAI and National Comprehensive Cancer Network (NCCN) risk categorization for prediction of 5-year metastasis (RP group) and 7-year metastasis (combined RT group). Results Overall, 732 patients were included (combined RT group, 438 patients; RP group, 294 patients). Median ages were 68 years (IQR, 62-73 years) and 61 years (IQR, 56-66 years) for the combined RT group and the RP group, respectively. VAI was associated with metastasis in the combined RT group (median follow-up, 6.9 years; adjusted hazard ratio [AHR], 1.09 per milliliter increase; 95% CI: 1.04, 1.15; P = .001) and the RP group (median follow-up, 5.5 years; AHR, 1.22; 95% CI: 1.08, 1.39; P = .001). AUCs for 7-year metastasis for the combined RT group for VAI and NCCN risk category were 0.84 (95% CI: 0.74, 0.94) and 0.74 (95% CI: 0.80, 0.98), respectively (P = .02). Five-year AUCs for the RP group for VAI and NCCN risk category were 0.89 (95% CI: 0.80, 0.98) and 0.79 (95% CI: 0.64, 0.94), respectively (P = .25). Conclusion The volume of AI-segmented lesions was an independent, prognostic factor for localized prostate cancer. © RSNA, 2024 Supplemental material is available for this article.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Comparison of Multiparametric MRI-targeted and Systematic Biopsies for Detection of Cribriform and Intraductal Carcinoma Prostate Cancer.

Background Intraductal carcinoma (IDC) and invasive cribriform (Cr) subtypes of prostate cancer (PCa) are an indication of aggressiveness, but the evidence regarding whether MRI can be used to detect Cr/IDC-pattern PCa is contradictory. Purpose To compare the detection of Cr/IDC-pattern PCa at multiparametric MRI (mpMRI)-targeted biopsy versus systematic biopsy in biopsy-naive men at risk for PCa. Materials and Methods This study was a secondary analysis of a prospective randomized controlled trial that recruited participants with a clinical suspicion of PCa between April 2017 and November 2019 at five centers. Participants were randomized 1:1 to either the MRI arm or the systematic biopsy arm. Targeted biopsy was performed in participants with a Prostate Imaging Reporting and Data System score of at least 3. MRI features were recorded, and biopsy slides and prostatectomy specimens were reviewed for the presence or absence of Cr/IDC histologic patterns. Comparison of Cr/IDC patterns was performed using generalized linear mixed modeling. Results A total of 453 participants were enrolled, with 226 in the systematic biopsy arm (median age, 65 years [IQR, 59-70 years]; 196 biopsies available for assessment) and 227 in the mpMRI-targeted biopsy arm (median age, 67 years [IQR, 60-72 years]; 132 biopsies available for assessment). Identification of Cr/IDC PCa was lower in the systematic biopsy arm compared with the mpMRI arm (31 of 196 biopsies [16%] vs 33 of 132 biopsies [25%]; P = .01). No evidence of a difference in mean cancer core length (CCL) (11.3 mm ± 4.4 vs 9.7 mm ± 4.5; P = .09), apparent diffusion coefficient (685 µm2/sec ± 178 vs 746 µm2/sec ± 245; P = .52), or dynamic contrast-enhanced positivity (27 [82%] vs 37 [90%]; P = .33) for clinically significant PCa (csPCa) was observed between participants with or without Cr/IDC disease in the MRI arm. Cr/IDC-positive histologic patterns overall had a higher mean CCL compared with Cr/IDC-negative csPCa (11.1 mm ± 4.4 vs 9.2 mm ± 4.1; P = .009). Conclusion MRI-targeted biopsy showed increased detection of Cr/IDC histologic patterns compared with systematic biopsy. Clinical trial registration no. NCT02936258 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Scialpi and Martorana in this issue.

Comparison of Positive Predictive Values of Biparametric MRI and Multiparametric MRI-directed Transrectal US-guided Targeted Prostate Biopsy.

Background Biparametric MRI (bpMRI) of the prostate is an alternative to multiparametric MRI (mpMRI), with lower cost and increased accessibility. Studies investigating the positive predictive value (PPV) of bpMRI-directed compared with mpMRI-directed targeted biopsy are lacking in the literature. Purpose To compare the PPVs of bpMRI-directed and mpMRI-directed targeted prostate biopsies. Materials and Methods This retrospective cross-sectional study evaluated men who underwent bpMRI-directed or mpMRI-directed transrectal US (TRUS)-guided targeted prostate biopsy at a single institution from January 2015 to December 2022. The PPVs for any prostate cancer (PCa) and clinically significant PCa (International Society of Urological Pathology grade ≥2) were calculated for bpMRI and mpMRI using mixed-effects logistic regression modeling. Results A total of 1538 patients (mean age, 67 years ± 8 [SD]) with 1860 lesions underwent bpMRI-directed (55%, 849 of 1538) or mpMRI-directed (45%, 689 of 1538) prostate biopsy. When adjusted for the number of lesions and Prostate Imaging Reporting and Data System (PI-RADS) score, there was no difference in PPVs for any PCa or clinically significant PCa (P = .61 and .97, respectively) with bpMRI-directed (55% [95% CI: 51, 59] and 34% [95% CI: 30, 38], respectively) or mpMRI-directed (56% [95% CI: 52, 61] and 34% [95% CI: 30, 39], respectively) TRUS-guided targeted biopsy. PPVs for any PCa and clinically significant PCa stratified according to clinical indication were as follows: biopsy-naive men, 64% (95% CI: 59, 69) and 43% (95% CI: 39, 48) for bpMRI, 67% (95% CI: 59, 75) and 51% (95% CI: 43, 59) for mpMRI (P = .65 and .26, respectively); and active surveillance, 59% (95% CI: 49, 69) and 30% (95% CI: 22, 39) for bpMRI, 73% (95% CI: 65, 89) and 38% (95% CI: 31, 47) for mpMRI (P = .04 and .23, respectively). Conclusion There was no evidence of a difference in PPV for clinically significant PCa between bpMRI- and mpMRI-directed TRUS-guided targeted biopsy. © RSNA, 2024 Supplemental material is available for this article.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

PURPOSE: To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. METHODS: A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. RESULTS: In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. CONCLUSION: The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

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.

Prospective longitudinal analysis of imaging-based spatiotemporal tumor habitats in glioblastoma, IDH-wild type: implication in patient outcome using multiparametric physiologic MRI.

BACKGROUND: Physiologic MRI-based tumor habitat analysis has the potential to predict patient outcomes by identifying the spatiotemporal habitats of glioblastoma. This study aims to prospectively validate the cut-off for tumor progression obtained from tumor habitat analysis based on physiologic MRI in ascertaining time-to-progression (TTP) and the site of progression in glioblastoma patients following concurrent chemoradiotherapy (CCRT). METHODS: In this prospective study (ClinicalTrials.gov ID: NCT02613988), we will recruit patients with IDH-wild type glioblastoma who underwent CCRT and obtained immediate post-operative and three serial post-CCRT MRI scans within a three-month interval, conducted using diffusion-weighted imaging and dynamic susceptibility contrast imaging. Voxels from cerebral blood volume and apparent diffusion coefficient maps will be grouped using k-means clustering into three spatial habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue). The spatiotemporal habitats of the tumor will be evaluated by comparing changes in each habitat between the serial MRI scans (post-operative and post-CCRT #1, #2, and #3). Associations between spatiotemporal habitats and TTP will be analyzed using cox proportional hazard modeling. The site of progression will be matched with spatiotemporal habitats. DISCUSSION: The perfusion- and diffusion-derived tumor habitat in glioblastoma is expected to stratify TTP and may serve as an early predictor for tumor progression in patients with IDH wild-type glioblastoma. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02613988.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Detecting Double Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning.

BACKGROUND: Double expression lymphoma (DEL) is a subtype of primary central nervous system lymphoma (PCNSL) that often has a poor prognosis. Currently, there are limited noninvasive ways to detect protein expression. PURPOSE: To detect DEL in PCNSL using multiparametric MRI-based machine learning. STUDY TYPE: Retrospective. POPULATION: Forty PCNSL patients were enrolled in the study among whom 17 were DEL (9 males and 8 females, 61.29 ± 14.14 years) and 23 were non-DEL (14 males and 9 females, 55.57 ± 14.16 years) with 59 lesions (28 DEL and 31 non-DEL). FIELD STRENGTH/SEQUENCE: ADC map derived from DWI (b = 0/1000 s/mm2 ), fast spin echo T2WI, T2FLAIR, and contrast-enhanced T1 weighted imaging (T1CE) were collected at 3.0 T. ASSESSMENT: Two raters manually segmented lesions by ITK-SNAP on ADC, T2WI, T2FLAIR and T1CE. A total of 2234 radiomics features from the tumor segmentation area were extracted. The t-test was conducted to filter the features, and elastic net regression algorithm combined with recursive feature elimination was used to calculate the essential features. Finally, 12 groups with combinations of different sequences were fitted to 6 classifiers, and the optimal models were selected. STATISTICAL TESTS: Continuous variables were assessed by the t-test, while categorical variables were assessed by the non-parametric test. Interclass correlation coefficient tested variables' consistency. Sensitivity, specificity, accuracy F1-score, and area under the curve (AUC) were used to evaluate model performance. RESULTS: DEL status could be identified to varying degrees with 72 models based on radiomics, and model performance could be improved by combining different sequences and classifiers. Both SVMlinear and logistic regression (LR) combined with four sequence group had similar largest AUCmean (0.92 ± 0.09 vs. 0.92 ± 0.05), and SVMlinear was considered as the optimal model in this study since the F1-score of SVMlinear (0.88) was higher than that of LR (0.83). DATA CONCLUSION: Multiparametric MRI-based machine learning is promising in DEL detection. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY STAGE: 2.