BACKGROUND: Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.
METHODS: Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.
RESULTS: The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.
CONCLUSIONS: The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.

OBJECTIVE: Despite advancements in prostate multiparametric magnetic resonance imaging (mpMRI) and fusion biopsy (FB), the management of incidental prostate cancer (IPCa) after surgery for benign prostatic obstruction (BPO) remains unclear. The aim of this retrospective study is to determine the prevalence of IPCa in our cohort and identify potential predictors for its occurrence.
METHODS: We enrolled patients underwent TURP or simple prostatectomy for BPO at our high-volume center between January 2020-December 2022. Data on age, pre-operative total PSA (tPSA) and PSA density (PSAd) levels, prostate volume, previous MRI, biopsies, specimen weight, rates of positive tissue slices, ISUP score and three-month tPSA were collected.
RESULTS: Of 454 patients with negative digital rectal examination who underwent BPO surgery, 74 patients (16.3%) were found to have IPCa. Of these, 33 patients (44.6%) had undergone previous mpMRI. Among the patients who had mpMRI, 23 had negative mpMRI results for suspected prostate cancer, while 10 had positive mpMRI findings (PIRADS ≥ 3) but no evidence of tumor upon FB. KW analysis indicates that PSAd was statistically associated with higher ISUP score, while at univariable regression analysis negative mpMRI (p = 0.03) was the only potential predictor for IPCa.
CONCLUSIONS: Among the ISUP groups, PSAd showed a correlation with the tumor, while negative mpMRI was protective against clinically significant PCa. In the era of mpMRI and FB, the IPCa rates found at our center is higher than reported in existing literature and if it were confirmed with further studies, maybe there is a need for expansion in urology guidelines.

BACKGROUND: The landscape of prostate cancer (PCa) segmentation within multiparametric magnetic resonance imaging (MP-MRI) was fragmented, with a noticeable lack of consensus on incorporating background details, culminating in inconsistent segmentation outputs. Given the complex and heterogeneous nature of PCa, conventional imaging segmentation algorithms frequently fell short, prompting the need for specialized research and refinement.
OBJECTIVE: This study sought to dissect and compare various segmentation methods, emphasizing the role of background information and gland masks in achieving superior PCa segmentation. The goal was to systematically refine segmentation networks to ascertain the most efficacious approach.
METHODS: A cohort of 232 patients (ages 61-73 years old, prostate-specific antigen: 3.4-45.6 ng/mL), who had undergone MP-MRI followed by prostate biopsies, was analyzed. An advanced segmentation model, namely Attention-Unet, which combines U-Net with attention gates, was employed for training and validation. The model was further enhanced through a multiscale module and a composite loss function, culminating in the development of Matt-Unet. Performance metrics included Dice Similarity Coefficient (DSC) and accuracy (ACC).
RESULTS: The Matt-Unet model, which integrated background information and gland masks, outperformed the baseline U-Net model using raw images, yielding significant gains (DSC: 0.7215 vs. 0.6592; ACC: 0.8899 vs. 0.8601, p < 0.001).
CONCLUSIONS: A targeted and practical PCa segmentation method was designed, which could significantly improve PCa segmentation on MP-MRI by combining background information and gland masks. The Matt-Unet model showcased promising capabilities for effectively delineating PCa, enhancing the precision of MP-MRI analysis.

OBJECTIVE: To compare transperineal (TP) vs transrectal (TR) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion-guided prostate biopsy (PBx) in a large, ethnically diverse and multiracial cohort.
METHODS: Consecutive patients who underwent multiparametric (mp) MRI followed by TP or TR TRUS-fusion guided PBx, were identified from a prospective database (IRB #HS-13-00663). All patients underwent mpMRI followed by 12-14 core systematic PBx. A minimum of two additional target-biopsy cores were taken per PIRADS≥3 lesion. The endpoint was the detection of clinically significant prostate cancer (CSPCa; Grade Group, GG≥2). Statistical significance was defined as p<0.05.
RESULTS: A total of 1491 patients met inclusion criteria, with 480 undergoing TP and 1011 TR PBx. Overall, 11% of patients were Asians, 5% African Americans, 14% Hispanic, 14% Others, and 56% White, similar between TP and TR (p=0.4). For PIRADS 3-5, the TP PBx CSPCa detection was significantly higher (61% vs 54%, p=0.03) than TR PBx, but not for PIRADS 1-2 (13% vs 13%, p=1.0). After adjusting for confounders on multivariable analysis, Black race, but not the PBx approach (TP vs TR), was an independent predictor of CSPCa detection. The median maximum cancer core length (11 vs 8mm; p<0.001) and percent (80% vs 60%; p<0.001) were greater for TP PBx even after adjusting for confounders.
CONCLUSIONS: In a large and diverse cohort, Black race, but not the biopsy approach, was an independent predictor for CSPCa detection. TP and TR PBx yielded similar CSPCa detection rates; however the TP PBx was histologically more informative.

OBJECTIVE: To validate the Barcelona magnetic resonance imaging predictive model (BCN-MRI PM) in men with pre-biopsy multiparametric MRI (mpMRI) reported with the Prostate Imaging Reporting and Data System (PI-RADS) v2.1, followed by transrectal and transperineal prostate biopsies.
METHODS: Prospective analysis of 3,264 men with PSA >3.0 ng/mL and/or abnormal digital rectal examination who were referred to ten participant centers in the csPCa early detection program of Catalonia (Spain), between 2021 and 2023. MpMRI was reported with the PI-RADS v2.1, and 2- to 4-core MRI-transrectal ultrasound (TRUS) fusion-targeted biopsy of suspected lesions and/or 12-core systematic biopsy were conducted. 2,295 (70.3%) individuals were referred to six centers for transrectal prostate biopsies, while 969 (39.7%) were referred to four centers for transperineal prostate biopsies. CsPCa was classified whenever the International Society of Urologic Pathology grade group was 2 or higher.
RESULTS: CsPCa was detected in 41% of transrectal prostate biopsies and in 45.9% of transperineal prostate biopsies (p < 0.016). Both BCN-MRI PM calibration curves were within the ideal correlation between predicted and observed csPCa. Areas under the curve and 95% confidence intervals were 0.847 (0.830-0.857) and 0.830 (0.823-0.855), respectively (p = 0.346). Specificities corresponding to 95% sensitivity were 37.6 and 36.8%, respectively (p = 0.387). The Net benefit of the BCN-MRI PM was similar with both biopsy methods.
CONCLUSIONS: The BCN-MRI PM has been successfully validated when mpMRI was reported with the PI-RADS v2.1 and prostate biopsies were conducted via the transrectal and transperineal route.

OBJECTIVE: There is an unmet clinical need for non-invasive imaging biomarkers that could replace liver biopsy in the management of patients with autoimmune hepatitis (AIH). In this study, we sought to evaluate the diagnostic accuracy of a simple uncorrected, non-contrast T1 mapping for detecting fibrosis and inflammation in AIH patients using histopathology as a reference standard.
METHODS: Over 3 years, 33 patients with AIH were prospectively studied using a multiparametric liver MRI protocol which included T1 mapping. Biopsies were performed up to 3 months before imaging, and a standardized histopathological score for fibrosis (F0-F4) and inflammatory activity (PPA0-4) was used as a reference. Statistical analysis included independent t test, Mann-Whitney U-test, and ROC (receiver operating characteristic) analysis.
RESULTS: T1 mapping values were significantly higher in patients with advanced fibrosis (F0-2 vs. F3-4; p < 0.015), significant fibrosis (F0-1 vs. F2-4; p < 0.005), and significant inflammatory activity (PPA 0-1 vs. PPA 2-4 p = 0.048). Moreover, the technique demonstrated a good diagnostic performance in detecting significant (AUC 0.856) and advanced fibrosis (AUC 0.835), as well as significant inflammatory activity (AUC 0.763).
CONCLUSIONS: A rapid, simple, uncorrected, non-contrast T1 mapping sequence showed satisfactory diagnostic performance in comparison with histopathology for detecting significant tissue inflammation and fibrosis in AIH patients, being a potential non-invasive imaging biomarker for monitoring disease activity in such individuals.

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.

暂无摘要。

OBJECTIVE: To investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features.
METHODS: A total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time-signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models.
RESULTS: Two models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953-0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005-1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276-12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets.
CONCLUSIONS: The changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning.

OBJECTIVE: The aim of this study is to describe the anatomical and functional changes observed in multiparametric magnetic resonance imaging (mpMRI) during follow-up after focal therapy (FT) for localized prostate cancer (PCa).
METHODS: In this prospective study, we analyzed pre- and postoperatively acquired mpMRI of 10 patients after FT (7 days; 3, 6, 9, 12 months). 7/10 (70%) patients underwent vascular-targeted photodynamic therapy (VTP). 3/10 (30%) patients underwent high-intensity focused ultrasound (HIFU). MpMR image analysis was performed using a semi-automatic software for segmentation of the prostate gland (PG) and tumor zones. Signal intensities (SI) of T2-weighted (T2w), T1-weighted (T1w),diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) images as well as volumes of the prostate gland (PGV) and tumor volumes (TV) were evaluated at each time point.
RESULTS: The results showed a significant increase of PGV 7 days after FT (p = 0.042) and a significant reduction of PGV between 7 days and 6, 9 and 12 months after FT (p < 0.001). The TV increased significantly 7 days after FT (p < 0.001) and decreased significantly between 7 days and 12 months after FT (p < 0.001). There was a significant increase in SI of the ADC in the ablation zone after 6, 9 and 12 months after FT (p < 0.001). 1/9 patients (11%) had recurrent tumor on rebiopsy characterized as a a small focal lesion on mpMRI with strong diffusion restriction (low SI on ADC map and high SI on b-value DWI).
CONCLUSIONS: MpMRI is able to represent morphologic changes of the ablated zone after FT and might be helpful to detect recurrent tumor.