Pseudo-myometrial thinning in placental site trophoblastic tumors: a case series with multiparametric MRI.

PURPOSE: Placental site trophoblastic tumor (PSTT) is a rare form of gestational trophoblastic neoplasm with few previous imaging case reports. We report multiparametric MRI findings in four cases of PSTT with special emphasis on the "pseudo-myometrial thinning" underlying the tumor. METHODS: We reviewed multiparametric MRI and pathologic findings in four cases of PSTT from four institutions. Signal intensity, enhancement pattern, margins, and location of the tumors were evaluated, and myometrial thickness underlying the tumor and normal myometrial thickness contralateral to the tumor were measured on MRI. The myometrial thickness underlying the tumor was also measured in the resected specimen and compared with the myometrial thickness measured on MRI using the Friedman test. RESULTS: All tumors showed heterogeneous signal intensity on T1-weighted imaging, T2-weighted imaging (T2WI), and diffusion-weighted imaging. Three of the four tumors had a hypervascular area on dynamic contrast-enhanced (DCE) MRI. A hypointense rim on T2WI and DCE-MRI was seen in all tumors. All tumors protruded into the uterine cavity to varying degrees and extended into the myometrium close to the serosa. The myometrial thickness underlying the tumor measured on MRI (median thickness, 1.2 mm) was significantly thinner than that measured on pathology (median thickness, 9.5 mm) and normal myometrial thickness contralateral to the tumor on MRI (median thickness, 10.3 mm) (P = 0.02), and there was no significant difference between the latter two. CONCLUSIONS: The thickness of the myometrium underlying the tumor on MRI was approximately one tenth of the thickness on pathology. Thus, the tumors appeared to have almost transmural invasion even when pathologically located within the superficial myometrium. This "pseudo-thinning" of the underlying myometrium and the hypointense rim on MRI could be caused by focal compression of the myometrium by the tumor, possibly due to the fragility of the myometrium at the placental site.

Comparing the diagnostic value of 68Ga-prostate-specific membrane antigen PET/CT and multiparametric MRI in pelvic lymph node metastasis of locally advanced prostate cancer.

Background: Multiparametric magnetic resonance imaging (mpMRI) is a commonly used method to diagnose pelvic lymph node metastasis (PLNM) in prostate cancer (PCa) patients, but there are few comparative studies on mpMRI and 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) in locally advanced PCa (LAPC) patients. Therefore, we designed a retrospective study to compare the diagnostic value of 68Ga-PSMA PET/CT and mpMRI for PLNM of LAPC. Methods: A retrospective study was performed on 50 patients with LAPC who underwent radical prostatectomy (RP) in Tongji Hospital from 2021 to 2023. All patients underwent PET/CT and mpMRI examination, and were diagnosed as LAPC before surgery, followed by robot-assisted laparoscopic prostatectomy or laparoscopic RP and extended pelvic lymph node dissection (ePLND). Routine postoperative pathological examination was performed. According to the results, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT and mpMRI for the diagnosis of PLNM of LAPC were compared. Results: Among the 50 patients, the mean age was 65.5±10.3 years, the preoperative total serum prostate-specific antigen (PSA) was 30.7±12.3 ng/mL, and the Gleason score was 7 [7, 8]. The difference in diagnostic efficacy between 68Ga-PSMA PET/CT and mpMRI in the preoperative diagnosis of PLNM of PCa was determined by postoperative pathological results. Based on the number of patients who developed PLNM, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were as follows: 93.75%, 100.00%, 100.00%, 97.14%, and 68.75%, 97.06%, 91.67%, 86.84% for mpMRI, respectively. Based on the number of pelvic metastatic lymph nodes, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were 95.24%, 100.00%, 100.00%, 99.48%, and 65.08%, 99.13%, 89.13%, 96.30% for mpMRI, respectively. It turned out that PET/CT was more sensitive than mpMRI in detecting PLNM of PCa, and the difference was statistically significant. Conclusions: 68Ga-PSMA PET/CT is more sensitive than mpMRI in the detection of PLNM in patients with LAPC. It is a promising method in the diagnosis and preoperative assessment of PLNM in LAPC.

Diagnostic accuracy of an uncorrected native T1 mapping sequence for liver fibrosis and inflammation in autoimmune hepatitis: a prospective study using histopathology as reference standard.

PURPOSE: 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. MATERIAL AND 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). CONCLUSION: 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.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features.

PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml.

OBJECTIVES: To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy. PATIENTS AND METHODS: Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit. RESULTS: A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively). CONCLUSION: The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

Diffusion-Weighted MRI for Recurrent/Persistent Head and Neck Squamous-Cell Carcinoma After Radiotherapy: Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the accuracy of diffusion-weighted magnetic resonance imaging (DWI-MRI) in diagnosing persistent/recurrent head and neck squamous cell carcinomas (HNSCCs) after primary chemoradiotherapy (CRT). DATA SOURCES: Scopus, PubMed/MEDLINE, and Cochrane Library databases were searched for relevant publications until April 18, 2023. REVIEW METHODS: A systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses of Diagnostic Test Accuracy statement. The search was conducted independently by 2 investigators. Methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Studies-2 questionnaire. Extracted data were used to calculate the pooled DWI-MRI sensitivity, specificity, diagnostic odds ratio, and positive and negative likelihood ratio. RESULTS: A total of 618 patients from 10 studies were included for calculation of diagnostic accuracy parameters. At the level of the primary tumor, the pooled sensitivity and specificity were, respectively, 0.96 (95% confidence interval [CI]: 0.89-1.00) and 0.81 (95% CI: 0.54-0.98) in the case of qualitative analysis, and, respectively, 0.79 (95% CI: 0.66-0.89) and 0.88 (95% CI: 0.77-0.96) for quantitative analysis. At the level of the neck, the pooled sensitivity and specificity were, respectively, 0.87 (95% CI: 0.75-0.95) and 0.84 (95% CI: 0.74-0.93) when images were analyzed qualitatively, and 0.79 (95% CI: 0.60-0.94) and 0.90 (95% CI: 0.82-0.97) when analyzed quantitatively. CONCLUSION: DWI-MRI showed high diagnostic accuracy and should be considered if persistent/recurrent HNSCCs is suspected after primary CRT. No significant differences were found between qualitative and quantitative imaging assessment.

Enhancing prostate cancer segmentation on multiparametric magnetic resonance imaging with background information and gland masks.

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. PURPOSE: 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). CONCLUSION: 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.

[Predictive effect of the dual-parametric MRI modified maximum diameter of the lesions with PI-RADS 4 and 5 on the clinically significant prostate cancer].

OBJECTIVE: To assess the rationality of the maximum lesion diameter of 15 mm in prostate imaging reporting and data system (PI-RADS) as a criterion for upgrading a lesion from category 4 to 5 and improve it to enhance the prediction of clinically significant prostate cancer (csPCa). METHODS: In this study, the patients who underwent prostate magnetic resonance imaging (MRI) and prostate biopsy at Peking University First Hospital from 2019 to 2022 as a development cohort, and the patients in 2023 as a validation cohort were reviewed. The localization and maximum diameter of the lesion were fully evaluated. The area under the curve (AUC) and the cut-off value of the maximum diameter of the lesion to predict the detection of csPCa were calculated from the receiver operating characteristics (ROC) curve. Confounding factors were reduced by propensity score matching (PSM). Diagnostic efficacy was compared in the validation cohort. RESULTS: Of the 589 patients in the development cohort, 358 (60.8%) lesions were located in the peripheral zone and 231 (39.2%) were located in the transition zone, and 496 (84.2%) patients detected csPCa. The median diameter of the lesions in the peripheral zone was smaller than that in the transition zone (14 mm vs. 19 mm, P < 0.001). In the ROC analysis of the maximal diameter on the csPCa prediction, there was no statistically significant difference between the peri-pheral zone (AUC=0.709) and the transition zone (AUC=0.673, P=0.585), and the cut-off values were calculated to be 11.5 mm for the peripheral zone and 16.5 mm for the migrating zone. By calcula-ting the Youden index for the cut-off values in the validation cohort, we found that the categorisation by lesion location led to better predictive results. Finally, the net reclassification index (NRI) was 0.170. CONCLUSION: 15 mm as a criterion for upgrading the PI-RADS score from 4 to 5 is reasonable but too general. The cut-off value for peripheral zone lesions is smaller than that in transitional zone. In the future consideration could be given to setting separate cut-off values for lesions in different locations.

Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer.

Objectives: This study aimed to develop and validate a radiomics nomogram based on multiparameter MRI for preoperative differentiation of type II and type I endometrial carcinoma (EC). Methods: A total of 403 EC patients from two centers were retrospectively recruited (training cohort, 70 %; validation cohort, 30 %). Radiomics features were extracted from T2-weighted imaging, dynamic contrast-enhanced T1-weighted imaging at delayed phase(DCE4), and apparent diffusion coefficient (ADC) maps. Following dimensionality reduction, radiomics models were developed by logistic regression (LR), random forest (RF), bootstrap aggregating (Bagging), support vector machine (SVM), artificial neural network (ANN), and naive bayes (NB) algorithms. The diagnostic performance of each radiomics model was evaluated using the ROC curve. A nomogram was constructed by incorporating the optimal radiomics signatures with significant clinical-radiological features and immunohistochemistry (IHC) markers obtained from preoperative curettage specimens. The diagnostic performance and clinical value of the nomogram were evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results: Among the radiomics models, the NB model, developed from 12 radiomics features derived from ADC and DCE4 sequences, exhibited strong performance in both training and validation sets, with the AUC values of 0.927 and 0.869, respectively. The nomogram, incorporating the radiomics model with significant clinical-radiological features and IHC markers, demonstrated superior performance in both the training (AUC = 0.951) and the validation sets (AUC = 0.915). Additionally, it exhibited excellent calibration and clinical utility. Conclusions: The radiomics nomogram has great potential to differentiate type II from type I EC, which may be an effective tool to guide clinical decision-making for EC patients.

Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.

OBJECTIVES: Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS. MATERIAL AND METHODS: One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis. RESULTS: Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001). CONCLUSIONS: Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment. CLINICAL RELEVANCE STATEMENT: For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure. KEY POINTS: The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.

Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer.

Clinical decisions based on the test results for prostate-specific antigen often result in overdiagnosis and overtreatment. Multiparametric magnetic resonance imaging (mpMRI) can be used to identify high-grade prostate cancer (HGPCa; Gleason score ≥3 + 4); however, certain limitations remain such as inter-reader variability and false negatives. The combination of mpMRI and prostate cancer (PCa) biomarkers (prostate-specific antigen density, Proclarix, TMPRSS2:ERG gene fusion, Michigan prostate score, ExoDX prostate intelliscore, four kallikrein score, select molecular diagnosis, prostate health index, and prostate health index density) demonstrates high accuracy in the diagnosis of HGPCa, ensuring that patients avoid unnecessary prostate biopsies with a low leakage rate. This manuscript describes the characteristics and diagnostic performance of each biomarker alone and in combination with mpMRI, with the intension to provide a basis for decision-making in the diagnosis and treatment of HGPCa. Additionally, we explored the applicability of the combination protocol to the Asian population.

Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.

This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.

Comparative Analysis of the Apparent Diffusion Coefficient and Diffusion Tensor Imaging in the Diagnosis of Prostate Cancer.

OBJECTIVE: The aim of this work was to demonstrate capabilities of diffusion tensor imaging as a diagnostic tool for prostate cancer in comparison with the apparent diffusion coefficient. METHODS: 364 patients with suspected prostate cancer underwent multiparametric magnetic resonance imaging including diffusion tensor imaging. RESULTS: The anatomical structure of the prostate obtained on T2-weighted imaging was compared with the apparent diffusion coefficient and diffusion tensor imaging maps. The rest of the gland (central and peripheral regions) were used as healthy areas. The apparent diffusion coefficient at diffusion-weighted imaging, fractional anisotropy and mean diffusivity at diffusion tensor imaging were evaluated in pathological zones. Cancer-suspicious areas of the prostate had high fractional anisotropy fractional anisotropy and low mean diffusivity compared to unaltered areas. Fractional anisotropy values were significantly elevated in central gland cancer, compared to normal tissue, and slightly elevated in peripheral zone cancer. CONCLUSION: Diffusion tensor imaging has the potential to identify prostate cancer with high accuracy and specificity. The combination of standard magnetic resonance imaging and diffusion tensor imaging can significantly improve the prognosis of the disease during active surveillance. The fractional anisotropy and mean diffusivity values can be useful in assessing the grade of malignancy and the radiolopathological correlation of the lesion.

DCE-MRI to distinguish all monoclonal plasma cell disease stages and correlation with diffusion-weighted MRI/PET-based biomarkers in a hybrid simultaneous whole body-2-[18F]FDG-PET/MRI imaging approach.

BACKGROUND: Dynamic contrast-enhanced-MRI (DCE-MRI) is able to study bone marrow angiogenesis in patients with multiple myeloma (MM) and asymptomatic precursor diseases but its role in the management of MM has not yet been established. The aims of this prospective study was to compare DCE-MRI-based parameters between all monoclonal plasma cell disease stages in order to find out discriminatory parameters and to seek correlations with other diffusion-weighted MRI and positron emission tomography (PET)-based biomarkers in a hybrid simultaneous whole-body-2-[18F]fluorodeoxyglucose (FDG)-PET/MRI (WB-2-[18F]FDG-PET/MRI) imaging approach. METHODS: Patients with newly diagnosed Monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM) or symptomatic MM according to international myeloma working group and underwent WB-2-[18F]FDG-PET/MRI imaging including bone marrow DCE sequences at the Nantes University Hospital were prospectively enrolled in this study before receiving treatment. RESULTS: One hundred and sixty-seven patients (N = 167, mean age: 64 years ± 11 [Standard deviation], 66 males) were considered for the analysis. DCE-MRI-based Peak Enhancement Intensity (PEI), Time to PEI (TPEI) and their maximum intensity time ratio (MITR: PEI/TPEI) values were significantly different between the different monoclonal plasma cell disease stages, PEI values increasing and TPEI values decreasing progressively along the spectrum of plasma cell disorders, from MGUS stage to symptomatic multiple myeloma. PEI values were significantly higher in patients with diffuse bone marrow involvement (either in PET or in MRI images) than in those without diffuse bone marrow involvement, unlike TPEI values. PEI and TPEI values were not significantly different between patients with or without focal bone lesions. CONCLUSION: Different DCE-MRI-based parameters (PEI, TPEI, MITR) could significantly differentiate all monoclonal plasma cell disease stages and complemented conventional MRI and PET-based biomarkers.

The role of VI-RADS scoring criteria for predicting oncological outcomes in bladder cancer.

PURPOSE: Our purpose was to evaluate the prognostic value of Vesical Imaging Reporting and Data System (VI-RADS) in bladder cancer (BCa) staging and predicting recurrence or progression. METHODS: We retrospectively analyzed the prospectively collected data from 96 patients with bladder tumors who underwent VI-RADS-based multiparametric magnetic resonance imaging (mpMRI) before endourological treatment from April 2021 to December 2022. Diagnostic performance was evaluated by comparing mpMRI reports with final pathology, using logistic regression for muscle-invasive bladder cancer (MIBC) predictors. Follow-up until May 2023 included Kaplan-Meier and Cox regression analysis to assess VI-RADS predictive roles for recurrence-free survival (RFS) and progression-free survival (PFS). RESULTS: A total of 96 patients (19.8% women, 80.2% men; median age 68.0 years) were included, with 71% having primary tumors and 29% recurrent BCa. Multiparametric MRI exhibited high sensitivity (92%) and specificity (79%) in predicting MIBC, showing no significant differences between primary and recurrent cancers (AUC: 0.96 vs. 0.92, P = .565). VI-RADS emerged as a key predictor for MIBC in both univariate (OR: 40.3, P < .001) and multivariate (OR: 54.6, P < .001) analyses. Primary tumors with VI-RADS ≥ 3 demonstrated significantly shorter RFS (P = .02) and PFS (P = .04). CONCLUSIONS: In conclusion, mpMRI with VI-RADS has a high diagnostic value in predicting MIBC in both primary and recurrent BCa. A VI-RADS threshold ≥ 3 is a strong predictor for MIBC, and in primary tumors predicts early recurrence and progression.

Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study.

OBJECTIVES: To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives. METHODS: One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions. RESULTS: csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only. CONCLUSION: Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

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.

A nomogram to predict the absence of clinically significant prostate cancer in males with negative MRI

ABSTRACT Purpose: To create a nomogram to predict the absence of clinically significant prostate cancer (CSPCa) in males with non-suspicion multiparametric magnetic resonance imaging (mpMRI) undergoing prostate biopsy (PBx). Materials and Methods: We identified consecutive patients who underwent 3T mpMRI followed by PBx for suspicion of PCa or surveillance follow-up. All patients had Prostate Imaging Reporting and Data System score 1-2 (negative mpMRI). CSPCa was defined as Grade Group ≥2. Multivariate logistic regression analysis was performed via backward elimination. Discrimination was evaluated with area under the receiver operating characteristic (AUROC). Internal validation with 1,000x bootstrapping for estimating the optimism corrected AUROC. Results: Total 327 patients met inclusion criteria. The median (IQR) age and PSA density (PSAD) were 64 years (58-70) and 0.10 ng/mL2 (0.07-0.15), respectively. Biopsy history was as follows: 117 (36%) males were PBx-naive, 130 (40%) had previous negative PBx and 80 (24%) had previous positive PBx. The majority were White (65%); 6% of males self-reported Black. Overall, 44 (13%) patients were diagnosed with CSPCa on PBx. Black race, history of previous negative PBx and PSAD ≥0.15ng/mL2 were independent predictors for CSPCa on PBx and were included in the nomogram. The AUROC of the nomogram was 0.78 and the optimism corrected AUROC was 0.75. Conclusions: Our nomogram facilitates evaluating individual probability of CSPCa on PBx in males with PIRADS 1-2 mpMRI and may be used to identify those in whom PBx may be safely avoided. Black males have increased risk of CSPCa on PBx, even in the setting of PIRADS 1-2 mpMRI

Deep learning model based on contrast-enhanced MRI for predicting post-surgical survival in patients with hepatocellular carcinoma.

Objective: To develop a deep learning model based on contrast-enhanced magnetic resonance imaging (MRI) data to predict post-surgical overall survival (OS) in patients with hepatocellular carcinoma (HCC). Methods: This bi-center retrospective study included 564 surgically resected patients with HCC and divided them into training (326), testing (143), and external validation (95) cohorts. This study used a three-dimensional convolutional neural network (3D-CNN) ResNet to learn features from the pretreatment MR images (T1WIpre, late arterial phase, and portal venous phase) and got the deep learning score (DL score). Three cox regression models were established separately using the DL score (3D-CNN model), clinical features (clinical model), and a combination of above (combined model). The concordance index (C-index) was used to evaluate model performance. Results: We trained a 3D-CNN model to get DL score from samples. The C-index of the 3D-CNN model in predicting 5-year OS for the training, testing, and external validation cohorts were 0.746, 0.714, and 0.698, respectively, and were higher than those of the clinical model, which were 0.675, 0.674, and 0.631, respectively (P = 0.009, P = 0.204, and P = 0.092, respectively). The C-index of the combined model for testing and external validation cohorts was 0.750 and 0.723, respectively, significantly higher than the clinical model (P = 0.017, P = 0.016) and the 3D-CNN model (P = 0.029, P = 0.036). Conclusions: The combined model integrating the DL score and clinical factors showed a higher predictive value than the clinical and 3D-CNN models and may be more useful in guiding clinical treatment decisions to improve the prognosis of patients with HCC.

Radical Prostatectomy Without Prior Biopsy in Selected Patients Evaluated by 18F-Labeled Prostate-Specific Membrane Antigen-Ligand Positron Emission Tomography/Computed Tomography and Multiparameter Magnetic Resonance Imaging: A Single-Center, Prospective, Single-Arm Trial.

PURPOSE: This study aimed to verify the feasibility and short-term prognosis of prostatectomy without biopsy. MATERIALS AND METHODS: Patients with a rising PSA level ranging from 4 to 30 ng/mL were scheduled for multiparametric (mp) MRI and 18F-labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET). Forty-seven patients (cT2N0M0) with Prostate Imaging Reporting and Data System ≥ 4 and molecular imaging PSMA score ≥ 2 were enrolled. All candidates underwent robot-assisted laparoscopic radical prostatectomy without biopsy. Prostate cancer detection rate, index tumors localization correspondence rate, positive surgical margin, complications, postoperative hospital stay, and PSA level in a 6-week postoperative follow-up visit were collected. RESULTS: All the patients with positive mpMRI and PSMA PET were diagnosed with clinically significant prostate cancer. A total of 80 lesions were verified as cancer by pathology, of which 63 cancer lesions were clinically significant prostate cancer. Fifty-one lesions were simultaneously found by mpMRI and PSMA PET. A total of 23 lesions were invisible on either image, and all lesions were ≤ International Society of Urological Pathology 2 or ≤ 15 mm. Forty-five (95.7%) index tumors found by mpMRI combined with PSMA PET were consistent with pathology. Nine patients reported positive surgical margin. CONCLUSIONS: Biopsy-free prostatectomy is safe and feasible for patients with evaluation strictly by mpMRI combined with 18F-PSMA PET/CT.