mpMRI Scans Research Articles

Natural history of PIRADS-2 lesions on serial multiparametric magnetic resonance imaging: Real-life data from an Academic Center

Introduction/BackgroundThe natural history of prostate imaging reporting and data system (PIRADS) score 2 lesions on serial mpMRIs is largely unknown. Herein, we aimed to evaluate the patients with PIRADS-2 index lesions by using serial mpMRI scans to reveal the rates of mpMRI upgrade in PIRADS score and prostate cancer (PCa) detection. Methods/MaterialsAll mpMRI scans with a PIRADS-2 index lesion from our mpMRI database were evaluated retrospectively. Data from 214 biopsy-naïve patients with a PIRADS-2 index lesion on the initial mpMRI who then underwent at least 1 follow-up mpMRI were reevaluated by an experienced uroradiologist and only those (n = 172) who had a PIRADS-2 index lesion on the initial mpMRI according to PIRADS v2.1 were included in the study. mpMRI progression was defined as the detection of any PIRADS ≥3 lesion at follow-up mpMRI. Histopathological results were evaluated in patients undergoing biopsy upon mpMRI progression. ResultsA total of 172 patients with a mean age of 60.1 ± 8.6 years were evaluated. The median PSA at baseline mpMRI was 4.7 (IQR; 3.3–6.7) ng/dl. Overall mpMRI progression was detected in 54 patients (31.4%), 37 were upgraded to PIRADS-3, 16 to PIRADS-4, and one to PIRADS-5. Multivariate logistic regression analysis revealed that a PSA increase of ≥25% during follow-up was the only predictor of mpMRI upgrade (P = 0.019, OR: 2.384). 30 out of 54 patients underwent a prostate biopsy and PCa was detected in 15 patients; 5 with ISUP grade 1, 10 with ISUP grade 2. ConclusionsAlmost half of the patients with a PIRADS-2 index lesion were upgraded to PIRADS ≥3 when evaluated with serial mpMRI when a PSA increase of ≥25% was observed during follow-up. PCa was detected in half of the patients who underwent a biopsy. Serial mpMRI can be recommended when monitoring patients with elevating PSA ≥25%, a prostate biopsy can be considered upon a mpMRI progression.

Predictive factors of stress urinary incontinence after Holmium Laser Enucleation of the Prostate: a magnetic resonance imaging-based retrospective study.

Stress urinary incontinence (SUI) remains a prevalent complication after Holmium Laser Enucleation of the Prostate (HoLEP). This retrospective analysis aims to delineate perioperative and anatomical determinants of SUI as observed on preoperative multiparametric magnetic resonance imaging (mpMRI) in patients subjected to HoLEP. We reviewed 216 benign prostatic hyperplasia (BPH) cases managed via HoLEP by a singular urologist at Sun Yat-sen Memorial Hospital from January 2021 to September 2022. Comprehensive medical documentation, including age, body mass index (BMI), prostate volume (PV), total prostate-specific antigen (tPSA), and perioperative variables: operative time (OT), enucleated prostate volume (EPV), were assessed. Detailed analyses of preoperative prostate mpMRI scans were conducted to measure factors such as thickness of the posterior wall of the membranous urethral sphincter (TPWMUS), membranous urethral length (MUL), membranous urethral volume (MUV), and prostatic apex morphology. The cohort encompassed 216 participants, among whom 45 (20.83%) experienced SUI subsequent to one month of HoLEP therapy. At three months, 23 individuals exhibited recovery, reducing the prevalence of SUI to 10.19%. By the six-month milestone, the incidence further declined to 1.38%, with 19 patients reporting normalization of continence. Binary logistic regression analysis identified OT, TPWMUS, and prostatic apex and membranous urethral overlap (PAOMU) emerged as independent risk factors for SUI, while MUV was identified as a protective factor. The risk of SUI post-HoLEP is significantly associated with OT, TPWMUS, and PAOMU, while MUV imparting a protective effect.

Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.

Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated. We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence. Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48). The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.

Comparing Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen-Positron Emission Tomography for Prediction of Extraprostatic Extension of Prostate Cancer and Surgical Guidance: A Prospective Nonrandomized Clinical Trial.

Survivors of surgically managed prostate cancer may experience urinary incontinence and erectile dysfunction. Our aim was to determine if 68Ga-prostate-specific membrane antigen-11 positron emission tomography CT (PSMA-PET) in addition to multiparametric (mp) MRI scans improved surgical decision-making for nonnerve-sparing or nerve-sparing approach. We prospectively enrolled 50 patients at risk for extraprostatic extension (EPE) who were scheduled for prostatectomy. After mpMRI and PSMA-PET images were read for EPE prediction, surgeons prospectively answered questionnaires based on mpMRI and PSMA-PET scans on the decision for nerve-sparing or nonnerve-sparing approach. Final whole-mount pathology was the reference standard. Sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic curves were calculated and McNemar's test was used to compare imaging modalities. The median age and PSA were 61.5 years and 7.0 ng/dL. The sensitivity for EPE along the posterior neurovascular bundle was higher for PSMA-PET than mpMRI (86% vs 57%, P = .03). For MRI, the specificity, positive predictive value, negative predictive value, and area under the curve for the receiver operating characteristic curves were 77%, 40%, 87%, and 0.67, and for PSMA-PET were 73%, 46%, 95%, and 0.80. PSMA-PET and mpMRI reads differed on 27 nerve bundles, with PSMA-PET being correct in 20 cases and MRI being correct in 7 cases. Surgeons predicted correct nerve-sparing approach 74% of the time with PSMA-PET scan in addition to mpMRI compared to 65% with mpMRI alone (P = .01). PSMA-PET scan was more sensitive than mpMRI for EPE along the neurovascular bundles and improved surgical decisions for nerve-sparing approach. Further study of PSMA-PET for surgical guidance is warranted in the unfavorable intermediate-risk or worse populations. NCT04936334.

Radio-anatomical evaluation of clinical and radiomic profile of multi-parametric magnetic resonance imaging of de novo glioblastoma multiforme

BackgroundGlioblastoma (GBM) is a fatal, fast-growing, and aggressive brain tumor arising from glial cells or their progenitors. It is a primary malignancy with a poor prognosis. The current study aims at evaluating the neuroradiological parameters of de novo GBM by analyzing the brain multi-parametric magnetic resonance imaging (mpMRI) scans acquired from a publicly available database analysis of the scans.MethodsThe dataset used was the mpMRI scans for de novo glioblastoma (GBM) patients from the University of Pennsylvania Health System, called the UPENN-GBM dataset. This was a collection from The Cancer Imaging Archive (TCIA), a part of the National Cancer Institute. The MRIs were reviewed by a single diagnostic radiologist, and the tumor parameters were recorded, wherein all recorded data was corroborated with the clinical findings.ResultsThe study included a total of 58 subjects who were predominantly male (male:female ratio of 1.07:1). The mean age with SD was 58.49 (11.39) years. Mean survival days with SD were 347 (416.21) days. The left parietal lobe was the most commonly found tumor location with 11 (18.96%) patients. The mean intensity for T1, T2, and FLAIR with SD was 1.45E + 02 (20.42), 1.11E + 02 (17.61), and 141.64 (30.67), respectively (p = < 0.001). The tumor dimensions of anteroposterior, transverse, and craniocaudal gave a z-score (significance level = 0.05) of − 2.53 (p = 0.01), − 3.89 (p < 0.001), and 1.53 (p = 0.12), respectively.ConclusionThe current study takes a third-party database and reduces physician bias from interfering with study findings. Further prospective and retrospective studies are needed to provide conclusive data.

Fully automatic mpMRI analysis using deep learning predicts peritumoral glioblastoma infiltration and subsequent recurrence.

Glioblastoma (GBM) is most aggressive and common adult brain tumor. The standard treatments typically include maximal surgical resection, followed adjuvant radiotherapy and chemotherapy. However, the efficacy of these treatment is often limited, as tumor often infiltrate into the surrounding brain tissue, often extending beyond the radiologically defined margins. This infiltration contributes to the high recurrence rate and poor prognosis associated with GBM patients, necessitating advanced methods for early and accurate detection of tumor infiltration. Despite the great promise traditional supervised machine learning shows in predicting tumor infiltration beyond resectable margins, these methods are heavily reliant on expert-drawn Regions of Interest (ROIs), which are used to construct multi-variate models of different Magnetic Resonance (MR) signal characteristics associated with tumor infiltration. This process is both time consuming and resource intensive. Addressing this limitation, our study proposes a novel integration of fully automatic methods for generating ROIs with deep learning algorithms to create predictive maps of tumor infiltration. This approach uses pre-operative multi-parametric MRI (mpMRI) scans, encompassing T1, T1Gd, T2, T2-FLAIR, and ADC sequences, to fully leverage the knowledge from previously drawn ROIs. Subsequently, a patch based Convolutional Neural Network (CNN) model is trained on these automatically generated ROIs to predict areas of potential tumor infiltration. The performance of this model was evaluated using a leave-one-out cross-validation approach. Generated predictive maps binarized for comparison against post-recurrence mpMRI scans. The model demonstrates robust predictive capability, evidenced by the average cross-validated accuracy of 0.87, specificity of 0.88, and sensitivity of 0.90. Notably, the odds ratio of 8.62 indicates that regions identified as high-risk on the predictive map were significantly more likely to exhibit tumor recurrence than low-risk regions. The proposed method demonstrates that a fully automatic mpMRI analysis using deep learning can successfully predict tumor infiltration in peritumoral region for GBM patients while bypassing the intensive requirement for expert-drawn ROIs.

Abstract 6223: Personalized MRI-informed forecasting of prostate cancer progression during active surveillance

Abstract Low and intermediate-risk prostate cancer (PCa) patients are eligible for active surveillance (AS), whereby treatment is delayed until progression to higher-risk disease is detected via longitudinal multiparametric magnetic resonance imaging (mpMRI) scans, biopsies, and prostate specific antigen (PSA) tests. However, AS relies on a population-based observational paradigm in which standard testing frequencies may not match the patient’s tumor dynamics, resulting in a delayed diagnosis of progressive disease. To address these issues, we propose to advance AS towards a patient-specific predictive paradigm by leveraging computational tumor forecasts obtained with a biomechanistic model informed by mpMRI and clinical data collected during standard AS. Here, we present a retrospective study with n = 16 PCa cases. All patients had three mpMRI scans obtained over 2.6 to 5.6 years during AS. Our biomechanistic model describes PCa growth in terms of the dynamics of tumor cell density as a combination of tumor cell mobility and net proliferation. The model is defined on the patient’s prostate geometry, which is segmented on T2-weighted MRI data. Tumor cell density estimates are derived from apparent diffusion coefficient (ADC) maps obtained from diffusion-weighted MRI data. To facilitate modeling, the longitudinal imaging datasets are non-rigidly co-registered for each patient. We initialize the model with the tumor cell density map obtained from the ADC map of the first scan. The model is parameterized by minimizing the model-data mismatch in tumor cell density at the second scan date, and then we perform a tumor forecast up to the third scan date. Finally, we build a logistic classifier from a panel of model-based biomarkers calculated from the personalized model forecasts (e.g., tumor volume, total proliferation activity) at the times of histopathological assessment (i.e., biopsy, surgery) to classify tumors as low risk (Gleason score 3+3, n = 16) or intermediate-high risk (Gleason score ≥ 3+4, n = 15). We obtained a concordance correlation coefficient (CCC) for tumor volume of 0.89 at both model calibration and forecasting horizon. The spatial fit of tumor cell density yielded a median Dice score and CCC of 0.80 and 0.59 at the second mpMRI date, and of 0.76 and 0.60 at the third mpMRI date, respectively. The logistic classifier of PCa risk yielded an area under the ROC curve of 0.90 and operates at optimal sensitivity, specificity, and accuracy of 86.7%, 93.8%, and 90.3%, respectively. The time trajectories of PCa risk obtained from the temporal predictions of the model-based biomarkers enabled identification of PCa progression by more than 1 year. Thus, while further development and validation over larger cohorts are required, these results suggest that our forecasting technology is a promising tool to predict PCa progression in AS and, hence, optimally guide monitoring and treatment decisions during AS. Citation Format: Guillermo Lorenzo Gomez, Chengyue Wu, Joshua P. Yung, John F. Ward, Hector Gomez, Alessandro Reali, Thomas E. Yankeelov, Aradhana M. Venkatesan, Thomas J. Hughes. Personalized MRI-informed forecasting of prostate cancer progression during active surveillance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6223.

Multi-Parametric Evaluation of Prostate Cancer on MRI

Background: Prostate cancer is the most common malignancy among men globally and the second leading cause of cancer-related deaths. The increasing prevalence, particularly in aging populations, underscores the urgent need for accurate diagnostic tools to facilitate early detection, precise staging, and effective management. Traditional diagnostic methods, including Digital Rectal Examination (DRE) and Prostate-Specific Antigen (PSA) testing, have limitations in sensitivity and specificity, leading to unnecessary procedures and missed diagnoses. Objective: This study aims to evaluate the efficacy of multiparametric Magnetic Resonance Imaging (mpMRI) in the diagnosis and staging of prostate cancer, comparing its accuracy with conventional diagnostic methods and examining its role in improving patient outcomes. Methods: A retrospective descriptive cross-sectional study was conducted at the Diagnostic Center of Combined Military Hospital, Lahore, from January 2021 to June 2023. Sixty male patients suspected of having prostate cancer, based on elevated PSA levels (&gt;10 ng/mL) or clinical symptoms, were included. mpMRI scans were performed using a Siemens Avanto MRI Machine 1.5T, incorporating T2-weighted imaging, Diffusion-Weighted Imaging (DWI), and Dynamic Contrast-Enhanced (DCE) MRI. The Prostate Imaging Reporting and Data System (PI-RADS) was employed for interpretation. Statistical analysis was conducted using SPSS 26.0, with chi-square tests applied to assess the association between mpMRI findings and prostate cancer diagnosis. Results: mpMRI identified prostate cancer with a sensitivity of 85% and specificity of 88.3%, as evidenced by abnormal MRI findings in 53 out of 60 patients. Elevated PSA levels were confirmed in 90% of the cases. T2-weighted imaging demonstrated hypointensity in the peripheral zone in 70% of patients, while DWI and ADC mapping showed restricted diffusion in 85% of the cases. DCE-MRI revealed post-contrast enhancement in 40% of the participants. The PI-RADS scoring system effectively graded the severity of prostate lesions, with 38.3% of cases categorized as PI-RADS IV. Conclusion: Multiparametric MRI significantly enhances the accuracy of prostate cancer diagnosis and staging, surpassing traditional diagnostic methods in sensitivity and specificity. By providing detailed anatomical and functional information, mpMRI facilitates early detection, accurate localization, and appropriate management of prostate cancer, potentially improving patient outcomes.

Evaluating the clinical applicability of neural networks for meningioma tumor segmentation on 3D MRI

Magnetic resonance imaging (MRI), in both 2D and 3D configurations, serves as the leading non-invasive and non-ionizing technique for the detection and clinical assessment of brain tumors. 3D multiparametric MRIs (mpMRIs) offer enhanced spatial and biological context over other MRIs, which can help clinicians plan tailored treatment and therapy. Presently, surgical intervention remains the primary treatment method for brain tumors, requiring accurate segmentation of tumor tissue on MRI scans. However, manual tumor segmentation and traditional machine learning techniques are labor intensive and subject to observer bias. Deep learning models can provide enhanced accuracy and precision in tumor segmentation due to their ability to automatically extract intricate features and patterns from MRI scans. In this study, MRI scans from 358 meningioma patients were used to train a series of deep learning models to segment the tumor subregions. We hypothesized that a deep learning framework designed for meningioma tumor segmentation and trained utilizing 3D mpMRIs would demonstrate superior segmentation accuracy compared to a framework trained on 2D MRIs or single-sequence 3D MRIs. We compared the model predictions with the ground truth labels using similarity metrics to assess accuracy and clinical applicability. The model trained on 3D mpMRI scans showed reliable performance for meningioma subregion segmentation with a Dice Similarity Coefficient score of 0.91 and a median Sensitivity of 91.38%. It outperformed the other models due to the additional spatial context provided by 3D mpMRIs, suggesting it could be readily used in clinical practice to help guide treatment strategy.

Recent Developments in Speeding up Prostate MRI.

The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI-RADS (Prostate Imaging Reporting and Data System) guidelines. Speeding up techniques in the reviewed studies rely on more efficient sampling of data, ranging from the acquisition of fewer averages or b-values to adjustment of the pulse sequence. Novel acquisition methods based on undersampling techniques are often followed by suitable reconstruction methods typically incorporating synthetic priori information. These reconstruction methods often use artificial intelligence for various tasks such as denoising, artifact correction, improvement of image quality, and in the case of DWI, for the generation of synthetic high b-value images or apparent diffusion coefficient maps. Reduction of mpMRI scan time is possible, but it is crucial to maintain diagnostic quality, confirmed through radiological evaluation, to integrate the proposed methods into the standard mpMRI protocol. Additionally, before clinical integration, prospective studies are recommended to validate undersampling techniques to avoid potentially inaccurate results demonstrated by retrospective analysis. This review provides an overview of recently proposed techniques, discussing their implementation, advantages, disadvantages, and diagnostic performance according to PI-RADS guidelines compared to conventional methods. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

NIMG-48. ASSOCIATIONS BETWEEN HETEROGENEITY OBSERVED IN O-6-METHYLGUANINE-DNA METHYLTRANSFERASE (MGMT) ASSAY MEASUREMENTS AND MGMT-BASED IMAGING SIGNATURE BASED ON MRI AND DEEP LEARNING

Abstract BACKGROUND O-6-methylguanine-DNA methyltransferase(MGMT), a gene promoter, corresponds with the efficacy of standard therapy for Glioblastoma (GBM) patients. If a single sample is sequenced, an appropriate diagnosis of MGMT methylation may be hampered by intratumoral heterogeneity. This work proposes a non-invasive in-vivo estimation of MGMT methylation using deep learning and multi-parametric MRI (mpMRI). Further, it quantifies spatial heterogeneity and establishes the associations between MGMT assay measurement and MGMT imaging signature heterogeneity. METHODS For this study, mpMRI structural scans (T1, T2, FLAIR and T1-Gd) of 498 newly diagnosed GBM patients were considered. These patients underwent surgical tumor resection, and MGMT methylation testing was retrospectively collected. By overlaying local and regional 2D patches of size 32 X 32 over the whole tumor on mpMRI scans, a 10-fold cross-validated deep convolutional neural network model was trained considering five layers on the discovery cohort (n=458). The trained model was applied to an independent multi-sample cohort (n=40) to compute the MGMT methylation at the pixel level for imaging-derived maps generation and heterogeneity quantification. The independent cohort included the patients for whom multiple tissue specimens were sequenced for MGMT methylation. RESULTS The patch-based deep model produced an area under the ROC curve of 0.76 (95% CI: 0.71–0.82) for MGMT status prediction. This model showed a strong association between homogeneously MGMT methylated and heterogeneous cases with MGMT Assay measurement of multiple samples with Pearson’s correlation coefficients of 0.64 (p &amp;lt; 0.05) and 0.78 (p &amp;lt; 0.01), respectively. Further, heterogeneity with a significantly higher variance was also observed in heterogeneous cases in developed MGMT imaging signature. CONCLUSION The proposed model efficiently determines MGMT promoter methylation and quantifies GBM spatial heterogeneity non-invasively. Also, it showed a significant association between the heterogeneity observed in MGMT-based imaging signature and MGMT assay measurements.

NIMG-83. ELUCIDATING INTRA-TUMORAL HETEROGENEITY AND DISTINCT RADIOMIC FEATURES IN GLIOBLASTOMA METHYLATION SUBCLASSES

Abstract Glioblastoma, IDH-wildtype (GBM), is characterized by the inter- and intra-tumoral molecular heterogeneity. Genome-wide DNA methylation profiling has recently emerged as a promising tool offering complementary value to the classification of central nervous system tumors. In adult GBM patients, the most common methylation subclasses comprise RTK I, RTK II, and MES. We hypothesize that quantification of intricate and spatially complex radiomic features extracted from multi-parametric MRI (mpMRI) is informative to non-invasively determining characteristics of GBM methylation subclasses and their heterogeneity. In this study, mpMRI scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI) of newly diagnosed 23 GBM patients were retrospectively collected. We derived 6339 radiomic features, including histograms, morphologic and textural descriptors. For each case, 3-4 multiple samplings from different parts of the tumor were conducted. DNA was extracted from FFPE for each of sample and analyzed for genome-wide DNA methylation patterns using the Illumina EPIC array. Classification of methylation subclasses was performed using the MNP brain tumor classifier v12b6 of the DKFZ. Analysis of variance (ANOVA) with Bonferroni correction was employed to extract subclass-specific radiomic features. Of the 23 patients, 7 indicated intra-tumoral heterogeneity in methylation subclasses. We identified 68 radiomic features (p&amp;lt; 0.05) and 12 radiomic features (p&amp;lt; 0.01) that showed statistically significant differences among the methylation subclasses as determined by ANOVA with Bonferroni correction. A heatmap illustrating the relationships between subclasses and extracted radiomic features displayed distinct trends among subclasses and between heterogeneous and homogeneous cases within the same subclass. Our findings suggest the presence of heterogeneity within methylation subclasses and distinctive imaging patterns for each subclass. These findings potentially pave the way for a more personalized approach to glioblastoma management, highlighting the importance of further validation through the analysis of additional cases.

Multiparametric Magnetic Resonance Imaging in Prostate Cancer Screening at the Age of 45 Years: Results from the First Screening Round of the PROBASE Trial

BackgroundMagnetic resonance imaging (MRI) has been suggested as a tool for guiding biopsy recommendations in prostate cancer (PC) screening. ObjectiveTo determine the performance of multiparametric MRI (mpMRI) in young men at age 45 yr who participated in a PC screening trial (PROBASE) on the basis of baseline prostate-specific antigen (PSA). Design, setting, and participantsParticipants with confirmed PSA ≥3 ng/ml were offered mpMRI followed by MRI/transrectal ultrasound fusion biopsy (FBx) with targeted and systematic cores. mpMRI scans from the first screening round for men randomised to an immediate PSA test in PROBASE were evaluated by local readers and then by two reference radiologists (experience >10 000 prostate MRI examinations) blinded to the histopathology. The PROBASE trial is registered as ISRCTN37591328 Outcome measurements and statistical analysisThe local and reference Prostate Imaging-Data and Reporting System (PI-RADS) scores were compared, and the sensitivity, negative predictive value (NPV), and accuracy were calculated for both readings for different cutoffs (PI-RADS 3 vs 4). Results and limitationsOf 186 participants, 114 underwent mpMRI and FBx. PC was detected in 47 (41%), of whom 33 (29%) had clinically significant PC (csPC; International Society of Urological Pathology grade group ≥2). Interobserver reliability between local and reference PI-RADS scores was moderate (k = 0.41). At a cutoff of PI-RADS 4, reference reading showed better performance for csPC detection (sensitivity 79%, NPV 91%, accuracy of 85%) than local reading (sensitivity 55%, NPV 80%, accuracy 68%). Reference reading did not miss any PC cases for a cutoff of PI-RADS <3. If PI-RADS ≥4 were to be used as a biopsy cutoff, mpMRI would reduce negative biopsies by 68% and avoid detection of nonsignificant PC in 71% of cases. ConclusionsProstate MRI in a young screening population is difficult to read. The MRI accuracy of for csPC detection is highly dependent on reader experience, and double reading might be advisable. More data are needed before MRI is included in PC screening for men at age 45 yr. Patient summaryMeasurement of prostate specific antigen (PSA) is an effective screening test for early detection of prostate cancer (PC) and can reduce PC-specific deaths, but it can also lead to unnecessary biopsies and treatment. Magnetic resonance imaging (MRI) after a positive PSA test has been proposed as a way to reduce the number of biopsies, with biopsy only recommended for men with suspicious MRI findings. Our results indicate that MRI accuracy is moderate for men aged 45 years but can be increased by a second reading of the images by expert radiologists. For broad application of MRI in routine screening, double reading may be advisable.

The Feasibility of Dose-Escalated Radiation Therapy for Glioblastoma Using Biological Image Guided Adaptive Radiotherapy (BIGART)

Radiotherapy dose escalation (DE) for glioblastoma (GBM) has been an area of active research. We aimed to study the imaging changes within residual gross tumor volumes (GTV) through the course of concurrent chemo-radiotherapy (CCRT) using multiparametric magnetic resonance imaging (mpMRI). Diffusion, perfusion and chemical exchange saturation transfer (CEST) characteristics of the tumor and its microenvironment were investigated to identify a GTV subvolume potentially associated with radio resistance. We used biological image-guided adaptive radiotherapy (BIGART) to study the feasibility of DE to this GTV subvolume using either photon or proton beam therapy (PBT). We prospectively identified GBM patients with >5cc residual tumor post-resection who were candidates for radical CCRT (60 Gy in 30 daily fractions over 6-weeks with concurrent temozolomide 75mg/m2 daily). We observed the imaging changes with serial mpMRI scans done at baseline, after 2, 4 and 6-weeks of CCRT. Regions of interest (ROIs) within the GTV associated with the following abnormal values at week 2 were identified: apparent diffusion coefficient (ADC): 750-1000 ×10-6 mm2/s; relative cerebral blood volume (rCBV): 1.75-6; and APT-w CEST signal intensity >1.79%. The overlap regions of these ROIs were defined as a novel biological target volume (BTV), identifying the potential area of maximal radioresistance. An in silico study was performed using a technology company's treatment planning system to evaluate the feasibility of planning adaptive treatment to the BTV to total dose of 75 Gy in 30 fractions. This is given in two phases: 30 Gy/15# to the whole PTV as per standard practice, followed by a simultaneous integrated boost (SIB) of 45 Gy/15 fractions while maintaining the dose to the rest of the of the PTV to 60 Gy. Either photons or PBT were used to keep doses to organs at risk (OARs) within standard clinical tolerances. Nine patients were recruited for this analysis and a total of 27 mpMRI scans were studied. Median BTVs to GTVs ratio was 35% (range 22-47%). Volumetric-modulated arc (VMAT) photon and PBT adaptive plans for dose escalation to BTVs were created in all cases whilst maintaining OAR tolerances. Both VMAT and PBT provided acceptable target coverage with average BTV-PTV D98% of 73 Gy (range 71.5-73.8 Gy) and average D2% of 76 Gy (range 75.4-77 Gy) while effectively sparing OARs. Sharper dose gradient between DE-BTV and PTV was achieved with VMAT. PBT was particularly advantageous in minimizing the low-dose spillage outside the BTV. We hereby propose a platform for adaptive radiotherapy to GBM tumors with biological-image guidance through the utilization of mpMRI to evaluate the tumor and its microenvironment during CCRT. We identified thresholds for tumor sub volumes showing the most resistant imaging features and created precise BTVs that allowed for dose escalation. PBT represents an additional useful tool for BIGART planning that will be investigated further in our ongoing work.

Occult Lymph Node Metastasis in cN0 Tongue Squamous Cell Carcinoma: A Prospective Observational Study

Tongue squamous cell carcinoma (TSCC) is prone to occult lymph node metastasis, and preoperative evaluation of cervical nodes is critical for determining treatment strategies. There is scarce report of detecting occult lymph node metastasis in TSCC by using multi-parameter magnetic resonance imaging (mpMRI), spectral computed tomography (spectral CT) or other diagnostic functional imaging. In this study, we aimed to analyze the incidence and risk factors of occult lymph node metastasis in cN0 TSCC by comparing preoperative imaging with postoperative pathology results. This study prospectively enrolled newly-diagnosed cN0 TSCC patients admitted to the Hunan Cancer Hospital between May, 2022 and December, 2022. All patients underwent primary resection and selective dissection of cervical lymph nodes. MpMRI and spectral CT scan of oral cavity and neck were performed prior to surgery. Preoperative evaluation of lymph node metastasis was conducted by two senior radiologists independently. The location of cervical lymph nodes was assigned based on the 2013 consensus guidelines. A total of 26 cN0 TSCC patients (6 cT1 stage, 13 cT2 stage, and 7 cT3 stage) were enrolled. The median age was 53 (range, 36-64), and there were 25 males. Among all patients, 13 patients underwent unilateral cervical lymph node dissection, while 13 patients underwent bilateral cervical lymph node dissection. A total of 208 lymphatic drainage areas were resected, and 1003 lymph nodes were removed. There were 7 of pT1, 12 of pT2, 7 of pT3 based on postoperative pathological stages. Besides, there are 21 cases staged pN0, 2 cases staged pN1, 2 cases staged pN2, and 1 case staged pN3. Among the 26 patients, 5 (19.23%) cases had occult lymph node metastasis. A total of 8 metastatic lymph nodes (4 in ipsilateral Ib level, 1 in contralateral Ib level, 3 in IIa level ipsilateral side) were detected in the whole group. No lymph node metastasis was detected in level IIb, III and IV. The median maximum diameter of metastatic lymph nodes was 12 mm (range 5 to 15 mm), and 1 extra-nodal extension was observed. Moreover, all occult lymph node metastases occurred in patients with a primary invasion depth of ≥ 5 mm (29.41%, 5/17). The incidence of occult lymph node metastasis in cN0 TSCC remains high under functional imaging diagnostic technology. Preventive neck dissection is necessary for patients with cN0 disease, especially those with primary tumor invasion depth exceeding 5 mm.

The Predictive Value of Prostate-Specific Antigen Density: A Retrospective Analysis of Likert 3 Multiparametric MRI of the Prostate.

Background Many international studies have covered the predictors of prostate cancer, but there is limited information pertaining to Likert 3 MRI scores and the diagnosis of clinically significant prostate cancer (cs-PCa). Therefore, this study aimed to assess the detection rate of significant prostate cancer in men with a Likert 3 score multiparametric MRI (mp-MRI) and the predictive value of prostate-specific antigen (PSA) density in detecting significant prostate cancer. Methods This is a retrospective analysis of patients referred for suspected confined prostate cancer. Inclusion criteria were patients with prostate mp-MRIscore of Likert 3 and a prostate biopsy performed. Exclusion criteria included grossly abnormal feeling prostate, no biopsy performed, and an mp-MRI score (Prostate Imaging-Reporting and Data System/Likert) of 1, 2, 4, and 5. cs-PCawas defined as ≥ Gleason 3+4 prostate cancer. PSA density (PSAD) was calculated from MRI estimation of prostate volume. PSAD and histology results were subjected to receiver operating characteristic (ROC) curve analysis with the intention to assess the detection rate of significant prostate cancer in men with Likert 3 mp-MRI and the predictive value of PSAD in detecting significant prostate cancer. Results A total of 819 eligible men had a pre-biopsy mp-MRI scan taken between October 2019 and March 2022. A total of 177 men (21.6%, n = 819) were Likert 3 positive, and 31 did not proceed to take prostate biopsies. A total of 146 patients were included in the study. The median PSAD was 0.19 in men with cs-PCa. Prostate cancer was detected in 42 men (28.8% of the total included set), of which 27 (18.5%) had a Gleason 3+3 prostate cancer and 15 (10.3%) had Gleason ≥ 3+4 prostate cancer. Therefore, 35.7% (n = 42) of biopsy-positive men with Likert 3 mp-MRI had cs-PCa. The ROC curve analysis confirms that PSAD is a predictor of cs-PCa. The optimal PSAD threshold was 0.16 (95% CI: 0.14-0.19), which gives an accuracy of 0.7371, a sensitivity of 0.7333, and a specificity of 0.7375. Conclusion The specificity of PSAD is arguably insufficient for it to stand alone as a decision-making tool when counseling men with equivocal mp-MRI on whether or not to undergo prostate biopsy. A predictive model will need to incorporate other independent risk factors.These may include lesion size, multiplicity, location of lesion(s), and age.

Biology and performance of pre- and post-pembrolizumab (pembro) vesical imaging–reporting and data system (VI-RADS) to predict the pathological response in muscle-invasive urothelial bladder cancer (MIBC): Full data analysis from a clinical trials pipeline.

4591 Background: The VI-RADS is a standardized reporting system that uses multiparametric magnetic resonance imaging (mpMRI) parameters to predict the probability of MIBC. It has been also used in studies using standard neoadjuvant chemotherapy. No studies have analyzed the performance of VI-RADS to predict the pT0 or pT≤1 response post-immunotherapy (IO) in MIBC. Methods: In PURE-01 study patients (pts) were staged with mpMRI before and after treatment (3 cycles of pembro) prior to radical cystectomy (RC). All mpMRI scans were centrally reviewed. Logistic regression models analyzed pre- and post-pembro VI-RADS against pT≤1 (primary endpoint) and pT0 (secondary endpoint). VI-RADS scores were dichotomized between 0-3 (0=no evidence of disease) and 4-5. Covariates included cT-stage, age, gender, PD-L1 combined positive score (CPS) and tumor mutational burden (TMB). Model performance was also tested in the ongoing NureCombo (nivolumab+nab-paclitaxel; NCT04876313) and SURE-01 (sacituzumab govitecan; NCT05226117) studies. Transcriptome-wide expression profiling was matched with VI-RADS scores. Results: In PURE-01, 115 pts had centrally-reviewed MRI scans (N=230 mpMRI), treated between 02/17 and 08/18. In total, 32 (27.8%) had pure/predominant non-urothelial carcinoma (UC) histology, 54 (47%) had a cT3-4N0 MIBC. Pre-pembro: 21 pts (18.3%) had no measurable disease (VI-RADS=0), 37 (32.2%) a VI-RADS 1-3 score, and 57 (49.5%) had a VI-RADS 4-5 score. 20 pts (17.4%) had a downstage from VI-RADS 4-5 to VIRADS 0-3 post-pembro. Pre-pembro VIRADS 4-5 were associated with higher angiogenesis and epithelial-mesenchymal transition (EMT) activity vs VI-RADS 0-3 (p=0.011 and 0.033). Both pre-pembro and post-pembro VI-RADS 0-3 scores were the only significant covariates against pT≤1 endpoint on multivariable analyses (MVA): the strongest effect was seen with post-pembro VI-RADS 0-3 against pT≤1 response (OR: 23.4, 95%CI: 7-95.3, p&lt;0.0001). The AUC of this model was 0.90. Model-based decisions were supported in the range of threshold probabilities of 10-80%. Post-pembro VI-RADS 4-5 were characterized by higher stromal signature scores vs VI-RADS 0-3. When post-therapy VI-RADS were tested in evaluable pts from the combined NureCombo+SURE-01 cohort (N=17pts, 34 mpMRI): 90% of post-VI-RADS 0-3 revealed a pT≤1 stage at radical cystectomy. Conclusions: VI-RADS scores mirrored distinct biological features in MIBC, recapitulating tissue biomarkers of IO response. VI-RADS scores post-IO revealed a strong association with pathological downstaging and represented a robust feature based on which selecting pts for bladder-sparing strategies. Results seemed to be also applicable to other novel neoadjuvant therapies beyond IO monotherapy. Clinical trial information: NCT02736266 .

Occult lymph node metastasis in cN0 tongue squamous cell carcinoma: A prospective observational study.

e18066 Background: Tongue squamous cell carcinoma (TSCC) is prone to occult lymph node metastasis, and preoperative evaluation of cervical nodes is critical for determining treatment strategies. There is scarce report of detecting occult lymph node metastasis in TSCC by using multi-parameter magnetic resonance imaging (mpMRI), spectral computed tomography (spectral CT) or other diagnostic functional imaging. In this study, we aimed to analyze the incidence and risk factors of occult lymph node metastasis in cN0 TSCC by comparing preoperative imaging with postoperative pathology results. Methods: This study prospectively enrolled newly-diagnosed cN0 TSCC patients admitted to the Hunan Cancer Hospital between May, 2022 and December, 2022. All patients underwent primary resection and selective dissection of cervical lymph nodes. MpMRI and spectral CT scan of oral cavity and neck were performed prior to surgery. Preoperative evaluation of lymph node metastasis was conducted by two senior radiologist independently. The location of cervical lymph nodes was assigned based on the 2013 consensus guidelines. Results: A total of 26 cN0 TSCC patients (6 cT1 stage, 13 cT2 stage, and 7 cT3 stage) were enrolled. The median age was 53 (range, 36-64), and there were 25 males. Among all patients, 13 patients underwent unilateral cervical lymph node dissection, while 13 patients underwent bilateral cervical lymph node dissection. A total of 208 lymphatic drainage areas were resected, and 1003 lymph nodes were removed. There were 7 of pT1, 12 of pT2, 7 of pT3 based on postoperative pathological stages. Besides, there are 21 cases staged pN0, 2 cases staged pN1, 2 cases staged pN2, and 1 case staged pN3. Among the 26 patients, 5 (19.23%) cases had occult lymph node metastasis. A total of 8 metastatic lymph nodes (4 in ipsilateral Ib level, 1 in contralateral Ib level, 3 in IIa level ipsilateral side) were detected in the whole group. No lymph node metastasis was detected in level IIb, III and IV. The median maximum diameter of metastatic lymph nodes was 12 mm (range 5 to 15 mm), and 1 extra-nodal extension was observed. Moreover, all occult lymph node metastases occurred in patients with a primary invasion depth of ≥ 5 mm (29.41%, 5/17). Conclusions: The incidence of occult lymph node metastasis in cN0 TSCC remains high under functional imaging diagnostic technology. Preventive neck dissection is necessary for patients with cN0 disease, especially those with primary tumor invasion depth exceeding 5 mm.

Head-to-head comparison of perfluorobutane contrast-enhanced US and multiparametric MRI for breast cancer: a prospective, multicenter study

BackgroundMultiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency.Patients and methodsThis was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions.ResultsIn total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online.ConclusionsPFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability.Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020.IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.

INTEGRATING PROSTATE SPECIFIC ANTIGEN DENSITY BIOMARKER INTO DEEP LEARNING PROSTATE MRI LESION SEGMENTATION MODELS.

Prostate cancer lesion segmentation in multi-parametric magnetic resonance imaging (mpMRI) is crucial for pre-biopsy diagnosis and targeted biopsy guidance. Deep convolution neural networks have been widely utilized for lesion segmentation. However, these methods fail to achieve a high Dice coefficient because of the large variations in lesion size and location within the gland. To address this problem, we integrate the clinically-meaningful prostate specific antigen density (PSAD) biomarker into the deep learning model using feature-wise transformations to condition the features in latent space, and thus control the size of lesion prediction. We tested our models on a public dataset with 214 annotated mpMRI scans and compared the segmentation performance to a baseline 3D U-Net model. Results demonstrate that integrating the PSAD biomarker significantly improves segmentation performance in both Dice coefficient and centroid distance metric.