Preoperative Multiparametric Magnetic Resonance Imaging Research Articles

VI-RADS followed by Photodynamic Transurethral Resection of Non-Muscle-Invasive Bladder Cancer vs White-Light Conventional and Second-resection: the 'CUT-less' Randomised Trial Protocol.

A second transurethral resection of bladder tumour (Re-TURBT) is recommended by European Association of Urology (EAU) Guidelines on non-muscle-invasive bladder cancers (NMIBCs) due to the risk of understaging and/or persistent disease following the primary resection. However, in many cases this may be unnecessary, potentially harmful, and significantly expensive constituting overtreatment. The CUT-less trial aims to combine the preoperative staging accuracy of Vesical Imaging-Reporting and Data System (VI-RADS) and the intraoperative enhanced ability of photodynamic diagnosis (PDD) to overcome the primary TURBT pitfalls thus potentially re-defining criteria for Re-TURBT indications. Single-centre, non-inferiority, phase IV, open-label, randomised controlled trial with 1:1 ratio. The primary endpoint is short-term BC recurrence between the study arms to assess whether patients preoperatively categorised as VI-RADS Score 1 and/or Score 2 (i.e., very-low and low likelihood of MIBC) could safely avoid Re-TURBT by undergoing primary PDD-TURBT. Secondary endpoints include mid- and long-term BC recurrences and progression (i-ii). Also, health-related quality of life (HRQoL) outcomes (iii) and health-economic cost-benefit analysis (iv) will be performed. All patients will undergo preoperative Multiparametric Magnetic Resonance Imaging of the bladder with VI-RADS score determination. A total of 327 patients with intermediate-/high-risk NMIBCs, candidate for Re-TURBT according to EAU Guidelines, will be enrolled over a 3-year period. Participants will be randomised (1:1 ratio) to either standard of care (SoC), comprising primary white-light (WL) TURBT followed by second WL Re-TURBT; or the Experimental arm, comprising primary PDD-TURBT and omitting Re-TURBT. Both groups will receive adjuvant intravesical therapy and surveillance according to risk-adjusted schedules. Measure of the primary outcome will be the relative proportion of BC recurrences between the SoC and Experimental arms within 4.5 months (i.e., any 'early' recurrence detected at first follow-up cystoscopy). Secondary outcomes measures will be the relative proportion of late BC recurrences and/or BC progression detected after 4.5 months follow-up. Additionally, we will compute the HRQoL variation from NMIBC questionnaires modelled over a patient lifetime horizon and the health-economic analyses including a short-term cost-benefit assessment of incremental costs per Re-TURBT avoided and a longer-term cost-utility per quality-adjusted life year gained using 2-year clinical outcomes to drive a lifetime model across the two arms of treatment. ClinicalTrial.gov identifier (ID): NCT05962541; European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) ID: 2023-507307-64-00.

Preoperative prostate magnetic resonance imaging does not impact surgical outcomes of radical prostatectomy

ABSTRACT Objective: We reviewed our institutional experience of radical prostatectomy with and without preoperative multiparametric magnetic resonance imaging (mpMRI) to assess the impact of preoperative prostate mpMRI on surgical outcomes of radical prostatectomy. Methods: We identified patients at our institution who underwent radical prostatectomy for prostate cancer (PCa) between January 2012 and December 2017 (n = 1044). Using propensity scoring analysis, patients who underwent preoperative mpMRI (n = 285) were matched 1:1 to patients who did not receive preoperative mpMRI (n = 285). Multivariable regression analysis was performed to identify factors predictive of operative time, estimated blood loss (EBL), lymph node yield, rates of complications within 30 days, and positive surgical margin (PSM). Results: There were no significant differences in operative time, EBL, PSM, lymph node yield, or complication rates between the two cohorts. Multivariable analysis demonstrated that preoperative mpMRI was not predictive of the measured perioperative outcomes. Significant comorbidity (Charlson Comorbidity Index ≥3) was the sole predictor of perioperative complications (P = 0.015). Increasing biopsy Gleason score predicted increased lymph node yield (P < 0.001). The probability of PSM was associated with increasing preoperative prostate-specific antigen (odds ratio 1.036, P = 0.009). Body mass index was a predictor of operative time (P = 0.016) and EBL (P = 0.001). Conclusions: Although preoperative mpMRI has an important role in the diagnosis and staging of PCa, it does not impact perioperative radical prostatectomy outcomes. Our findings do not support the routine use of preoperative mpMRI for surgical planning in patients already diagnosed with clinically localized PCa.

Preoperative multiparametric magnetic resonance imaging based risk stratification system for predicting biochemical recurrence after radical prostatectomy

BackgroundMultiparametric magnetic resonance imaging (mpMRI) is used as a current marker in preoperative staging and surgical decision-making, but current evidence on predicting post-surgical oncological outcomes based on preoperative mpMRI findings is limited. In this study We aimed to develop a risk classification based on mpMRI and mpMRI-derived biopsy findings to predict early biochemical recurrence (BCR) after radical prostatectomy. MethodsBetween January 2017 and January 2023, the data of 289 patients who underwent mpMRI, transrectal ultrasound-guided cognitive and fusion targeted biopsies, and subsequent radical prostatectomy (RP) with or without pelvic lymph node dissection in a single center were retrospectively re-evaluated. BCR was defined as a prostate specific-antigen (PSA) ≥ 0.2 ng/mL at least twice after RP. Multivariate logistic regression models tested the predictors of BCR. The regression tree analysis stratified patients into risk groups based on preoperative mpMRI characteristics. Receiver operating characteristic (ROC)-derived area under the curve (AUC) estimates were used to test the accuracy of the regression tree–derived risk stratification tool. ResultsBCR was detected in 47 patients (16.2 %) at a median follow-up of 24 months. In mpMRI based multivariate analyses, the maximum diameter of the index lesion (HR 1.081, 95%Cl 1.015–1.151, p = 0.015) the presence of PI-RADS 5 lesions (HR 2.604, 95%Cl 1.043–6.493, p = 0.04), ≥iT3a stage (HR 2.403, 95%Cl 1.013–5.714, p = 0.046) and ISUP grade ≥4 on biopsy (HR 2.440, 95%Cl 1.123–5.301, p = 0.024) were independent predictors of BCR. In regression tree analysis, patients were stratified into three risk groups: maximum diameter of index lesion, biopsy ISUP grade, and clinical stage on mpMRI. The regression tree–derived risk stratification model had moderate-good accuracy in predicting early BCR (AUC 77 %) ConclusionStraightforward mpMRI and mpMRI-derived biopsy-based risk stratification for BCR prediction provide an additional clinical predictive model to the currently available pathological risk tools.

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.

Predicting histological grade in pediatric glioma using multiparametric radiomics and conventional MRI features

Prediction of glioma is crucial to provide a precise treatment plan to optimize the prognosis of children with glioma. However, studies on the grading of pediatric gliomas using radiomics are limited. Meanwhile, existing methods are mainly based on only radiomics features, ignoring intuitive information about tumor morphology on traditional imaging features. This study aims to utilize multiparametric magnetic resonance imaging (MRI) to identify high-grade and low-grade gliomas in children and establish a classification model based on radiomics features and clinical features. A total of 85 children with gliomas underwent tumor resection, and part of the tumor tissue was examined pathologically. Patients were categorized into high-grade and low-grade groups according to World Health Organization guidelines. Preoperative multiparametric MRI data, including contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted images, and apparent diffusion coefficient sequences, were obtained and labeled by two radiologists. The images were preprocessed, and radiomics features were extracted for each MRI sequence. Feature selection methods were used to select radiomics features, and statistically significant clinical features were identified using t-tests. The selected radiomics features and conventional MRI features were used to train the AutoGluon models. The improved model, based on radiomics features and conventional MRI features, achieved a balanced classification accuracy of 66.59%. The cross-validated areas under the receiver operating characteristic curve for the classifier of AutoGluon frame were 0.8071 on the test dataset. The results indicate that the performance of AutoGluon models can be improved by incorporating conventional MRI features, highlighting the importance of the experience of radiologists in accurately grading pediatric gliomas. This method can help predict the grade of pediatric glioma before pathological examination and assist in determining the appropriate treatment plan, including radiotherapy, chemotherapy, drugs, and gene surgery.

CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.

Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model's performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).

Abstract 299: FAP/αSMA association with tumor visibility and progression in prostate cancer

Abstract Introduction Prostate cancer (PCa) ranks as the second most frequently diagnosed cancer in the male population worldwide. The challenge in effectively managing PCa is the absence of well-established biological markers, which often leads to both over and under treatment. To address this issue, a deeper understanding of the molecular alterations within PCa is essential for more accurate patient stratification. In this research, we embark on a comprehensive examination of how the presence of fibroblast activation protein (FAP) and alpha smooth muscle actin (αSMA) in PCa tissue associates with the visibility of the disease in Magnetic Resonance Imaging (MRI) and their impact on cancer progression. The objective of this study is to analyze cell compartmental expression of FAP and aSMA using an AI based image analysis method. We also study the role of FAP and αSMA expression in predicting disease progression and mortality. Materials and Methods We applied FAP (ab207178, Abcam)/αSMA (M0851, DAKO) double chromogen immunohistochemical (IHC) staining to three independent cohorts of formalin-fixed paraffin-embedded (FFPE) prostate cancer tissue microarrays. All together, we analyzed 835 patients’ samples. First cohort consists of 387 patients with preoperative multiparametric MRI and robot-assisted laparoscopic prostatectomy (RALP) as primary treatment. Second cohort was case vs control cohort of Grade group 2-4 localized PCa at radical prostatectomy (RP) with 168 patients. Third cohort consists PCa patents from continuous, population-based collection of radical prostatectomies of 319 patients. Image analysis was performed with Aiforia Create, which is cloud-based deep-learning artificial intelligence software that provides platform for training and validating automated image analysis pipelines using convolutional neural networks. Analysis algorithm in this project was based on semantic, pixel-level area segmentation. Results FAP expression was higher and αSMA lower in MRI-visible tumors compared with MRI-invisible tumors. Stromal and epithelial FAP expression associate with tumor progression. We could also see statistically significant differences in FAP positivity between clinically significant (GS ≥7) and insignificant (GS ≤6) tumors. Hazard ratios imply that FAP and αSMA have significant impact on risk of biochemical recurrence in patients with MRI-visible tumors. Conclusions FAP is closely linked to tumor MRI visibility, cancer progression, and Gleason scores, marking it as a valuable biomarker in PCa assessment. High stromal αSMA is primarily associated with MRI visibility. Our automated AI-based image analysis method has demonstrated reliability, aligning with our previous findings. This advances the potential for more accurate and efficient prostate cancer diagnosis and prognosis, offering a potential benefit to future patient care. Citation Format: Jenni Säilä, Timo-Pekka Lehto, Annabrita Schoonenberg, Katja Välimäki, Andrew Erickson, Antti Rannikko, Olli Kallioniemi, Tuomas Mirtti, Teijo Pellinen. FAP/αSMA association with tumor visibility and progression in prostate cancer [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 299.

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

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

Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery.

The aim of this study was to assess the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics signature model to predict disease-free survival (DFS) in patients with rectal cancer treated by surgery. We evaluated data of 194 patients with rectal cancer who had undergone radical surgery between April 2016 and September 2021. The mean age of all patients was 62.6 ± 9.7 years (range: 37-86 years). The study endpoint was DFS and 1132 radiomic features were extracted from preoperative MRIs, including contrast-enhanced T1- and T2-weighted imaging and apparent diffusion coefficient values. The study patients were randomly allocated to training (n=97) and validation cohorts (n=97) in a ratio of 5:5. A multivariable Cox regression model was used to generate a radiomics signature (rad score). The associations of rad score with DFS were evaluated using Kaplan-Meier analysis. Three models, namely a radiomics nomogram, radiomics signature, and clinical model, were compared using the Akaike information criterion. The rad score, which was composed of four MRI features, stratified rectal cancer patients into low- and high-risk groups and was associated with DFS in both the training (p = 0.0026) and validation sets (p = 0.036). Moreover, a radiomics nomogram model that combined rad score and independent clinical risk factors performed better (Harrell concordance index [C-index] =0.77) than a purely radiomics signature (C-index=0.73) or clinical model (C-index=0.70). An MRI radiomics model that incorporates a radiomics signature and clinicopathological factors more accurately predicts DFS than does a clinical model in patients with rectal cancer.

Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer.

To investigate the performance of multiparametric magnetic resonance imaging (MRI)-based radiomics models in differentiating early stage of cervical cancer (Stage I-IIa vs. IIb-IV). One hundred patients with cervical cancer who underwent preoperative MRI between June 2020 and March 2022 were retrospectively enrolled. Training (n = 70) and testing cohorts (n = 30) were assigned by stratified random sampling. The clinical and pathological features, including age, histological subtypes, tumor grades, and node status, were compared between the two cohorts by t-test or chi-square test. Radiomics features were extracted from each volume of interest (VOI) on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps. The data balance of the training cohort was resampled by synthesizing minority oversampling techniques. Subsequently, the adiomics signatures were constructed by the least absolute shrinkage and selection operator algorithm and minimum-redundancy maximum-relevance with 10-fold cross-validation. Logistic regression was applied to predict the cervical cancer stages (low [I-IIa]) and (high [IIb-IV] FIGO stages). The receiver operating characteristic curve (area under the curve [AUC]) and decision curve analysis were used to assess the performance of the radiomics model. The characteristics of age, histological subtypes, tumor grades, and node status were not significantly different between the low [I-IIa] and high [IIb-IV] FIGO stages (p > 0.05 for both the training and test cohorts). Three models based on T2WI, ADC maps, and the combined were developed based on six radiomics features from T2WI and three radiomics features from ADC maps, with AUCs of 0.855 (95% confidence interval [CI], 0.777-0.934) and 0.823 (95% CI, 0.727-0.919), 0.861 (95% CI, 0.785-0.936) and 0.81 (95% CI, 0.701-0.918), 0.934 (95% CI, 0.884-0.984) and 0.902 (95% CI, 0.832-0.972) in the training and test cohorts. The radiomics models combined T2W and ADC maps had good predictive performance in differentiating the early stage from locally advanced cervical cancer.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma?

Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis. To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC). Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome. Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size (P <0.001), clustered ring enhancement (P <0.001), segmental distribution (P <0.001), diffusion restriction (P = 0.005), and ADC values <1.3 × 10-3 mm2/s (P = 0.004). Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning.

Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer.

To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC). Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction. This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05). A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.

The importance of periprostatic fat tissue thickness measured by preoperative multiparametric magnetic resonance imaging in upstage prediction after robot-assisted radical prostatectomy.

We analyzed the surgical results of patients who were treated and followed up for prostate cancer in our clinic to predict the relationship between periprostatic adipose tissue and patients with and without pathologically upstaged disease. The study included patients who had undergone robot-assisted radical prostatectomy and preoperative multiparametric prostate magnetic resonance imaging between 18 February 2019 and 1 April 2022. The patients were divided into two groups, and the surgical and transrectal ultrasound-guided biopsy pathology results were compared according to tumor grade and distribution in 124 patients who met the selection criteria. We analyzed the relationships between upgrading/upstaging and periprostatic adipose tissue thickness (PPATT) and subcutaneous adipose tissue thickness (SATT) as measured in magnetic resonance imaging. The median PPATT was 4.03 mm, whereas the median SATT was 36.4 mm. Upgrading was detected in 45 patients (36.3%), and upstaging was detected in 42 patients (33.9%). A receiver operating characteristic regression analysis revealed that a PPATT >3 mm was a predictive factor for upstaging after radical prostatectomy (area under curve=0.623, 95% confidence interval [CI] 0.519-0.727, p=0.025). Multivariate logistic regression analyses revealed that prostate specific antigen density ≥0.15 ng/mL/cm3 (odds ratio [OR] 5.054, 95% CI 2.008-12.724, p=0.001), International Society of Urological Pathology grade ≥4 (OR 9.369, 95% CI 2.109-21.626, p=0.003) and higher PPATT (OR 1.358, 95% CI 1.081-1.707, p=0.009) were independent risk factors for upstaging after radical prostatectomy. We believe that the PPATT may be a predictive indicator for upstaging after robot-assisted laparoscopic radical prostatectomy.

Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma.

The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators. As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility. Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75th percentile ADC value < 1.48 ×103 mm2/s and R2* value ≥ 38.6 sec-1 were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness. The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients.

Amide Proton Transfer-Weighted MRI and Diffusion-Weighted Imaging in Bladder Cancer: A Complementary Tool to the VI-RADS

To investigate the feasibility of amide proton transfer-weighted (APTw) and diffusion-weighted Magnetic Resonance Imaging (MRI) as a means by which to add value to the Vesical Imaging Reporting and Data System (VI-RADS) for discriminating muscle invasive bladder cancer (MIBC) from nonmuscle invasive bladder cancer (NMIBC). This prospective study enrolled participants with pathologically confirmed bladder cancer (BCa) who underwent preoperative multiparametric MRI, including APTw and diffusion-weighted MRI, from July 2020 to January 2023. The exclusion criteria were lesions smaller than 10mm, missing smooth muscle layer in the operation specimen, neoadjuvant therapy before MRI, inadequate image quality, and malignancy other than urothelial neoplasm. Two radiologists independently assigned the VI-RADS score for each participant. Quantitative parameters derived from APTw and diffusion-weighted MRI were obtained by another two radiologists. Receiver operating characteristic (ROC) curve analysis with the area under the ROC curve (AUC) was performed to evaluate the diagnostic performances of quantitative parameters for discriminating BCa detrusor muscle invasion status. A total of 106 participants were enrolled (mean age, 64 ± 12 years [SD]; 90 men): 32 with MIBC and 74 with NMIBC. Lower apparent diffusion coefficient (ADC)values (0.88×10-3 mm2/s±0.12 vs. 1.08×10-3 mm2/s±0.25; P<0.001) and higher APTw values (6.89% [interquartile range {IQR}, 5.05%-12.17%] vs. 3.61% [IQR, 2.23%-6.83%]; P<0.001) were observed in the MIBC group. Compared to VI-RADS alone, both APTw (P=0.003) and ADC (P=0.020) values could improve the diagnostic performance of VI-RADS in differentiating MIBC from NMIBC. The combination of the three yielded the highest diagnostic performance (AUC, 0.93; 95% CI:0.87,0.97) for evaluating muscle invasion status. The addition of the APTw values to the combination of VI-RADS and ADC values notably improved the diagnostic performance for differentiating NMIBC from MIBC (VI-RADS+ADC vs. VI-RADS+APTw+ADC, P=0.046). MRI parameters derived from APTw and diffusion-weighted MRI can be used to accurately assess muscle invasion status in BCa and provide additional value to VI-RADS.

Open Extended Pelvic Lymph Node Dissection as Validation of the Results of Preoperative Multiparametric Contrast-enhanced Magnetic Resonance Imaging in Detection of Lymph Node Metastases in Intermediate- and High-risk Prostate Cancer

Open Extended Pelvic Lymph Node Dissection as Validation of the Results of Preoperative Multiparametric Contrast-enhanced Magnetic Resonance Imaging in Detection of Lymph Node Metastases in Intermediate- and High-risk Prostate Cancer

Four-quadrant vector mapping of hybrid multidimensional MRI data for the diagnosis of prostate cancer.

The interpretation of prostate multiparametric magnetic resonance imaging (MRI) is subjective in nature, and there is large inter-observer variability among radiologists and up to 30% of clinically significant cancers are missed. This has motivated the development of new MRI techniques and sequences, especially quantitative approaches to improve prostate cancer diagnosis. Using hybrid multidimensional MRI, apparent diffusion coefficient (ADC) and T2 have been shown to change as a function of echo time (TE) and b-values, and that this dependence is different for cancer and benign tissue, which can be exploited for prostate cancer diagnosis. The purpose of this study is to investigate whether four-quadrant vector mapping of hybrid multidimensional MRI (HM-MRI) data can be used to diagnose prostate cancer (PCa) and determine cancer aggressiveness. Twenty-one patients with confirmed PCa underwent preoperative MRI prior to radical prostatectomy. Axial HM-MRI were acquired with all combinations of TE=47, 75, 100ms and b-values of 0, 750, 1500s/mm2 , resulting in a 3×3 data matrix associated with each voxel. Prostate Quadrant (PQ) mapping analysis represents HM-MRI data for each voxel as a color-coded vector in the four-quadrant space of HM-MRI parameters (a 2D matrix of signal values for each combination of b-value and TE) with associated amplitude and angle information representing the change in T2 and ADC as a function of b-value and TE, respectively. Cancers have a higher PQ4 (22.50%±21.27%) and lower PQ2 (69.86%±28.24%) compared to benign tissue: peripheral, transition, and central zone (PQ4=0.13%±0.56%, 5.73%±15.07%, 2.66%±4.05%, and PQ2=98.51%±3.05%, 86.18%±21.75%, 93.38%±9.88%, respectively). Cancers have a higher vector angle (206.5±41.8°) and amplitude (0.017±0.013) compared to benign tissue. PQ metrics showed moderate correlation with Gleason score (|ρ|=0.388-0.609), with more aggressive cancers being associated with increased PQ4 and angle and reduced PQ2 and amplitude. A combination of four-quadrant analysis metrics provided an area under the curve of 0.904 (p<0.001) for the differentiation of prostate cancer from benign prostatic tissue. Four-quadrant vector mapping of HM-MRI data provides effective cancer markers, with cancers associated with high PQ4 and high vector angle and lower PQ2 and vector amplitude.

Three-dimensional automatic artificial intelligence driven augmented-reality selective biopsy during nerve-sparing robot-assisted radical prostatectomy: A feasibility and accuracy study

ObjectiveTo evaluate the accuracy of our new three-dimensional (3D) automatic augmented reality (AAR) system guided by artificial intelligence in the identification of tumour's location at the level of the preserved neurovascular bundle (NVB) at the end of the extirpative phase of nerve-sparing robot-assisted radical prostatectomy. MethodsIn this prospective study, we enrolled patients with prostate cancer (clinical stages cT1c–3, cN0, and cM0) with a positive index lesion at target biopsy, suspicious for capsular contact or extracapsular extension at preoperative multiparametric magnetic resonance imaging. Patients underwent robot-assisted radical prostatectomy at San Luigi Gonzaga Hospital (Orbassano, Turin, Italy), from December 2020 to December 2021. At the end of extirpative phase, thanks to our new AAR artificial intelligence driven system, the virtual prostate 3D model allowed to identify the tumour's location at the level of the preserved NVB and to perform a selective excisional biopsy, sparing the remaining portion of the bundle. Perioperative and postoperative data were evaluated, especially focusing on the positive surgical margin (PSM) rates, potency, continence recovery, and biochemical recurrence. ResultsThirty-four patients were enrolled. In 15 (44.1%) cases, the target lesion was in contact with the prostatic capsule at multiparametric magnetic resonance imaging (Wheeler grade L2) while in 19 (55.9%) cases extracapsular extension was detected (Wheeler grade L3). 3D AAR guided biopsies were negative in all pathological tumour stage 2 (pT2) patients while they revealed the presence of cancer in 14 cases in the pT3 cohort (14/16; 87.5%). PSM rates were 0% and 7.1% in the pathological stages pT2 and pT3 (<3 mm, Gleason score 3), respectively. ConclusionWith the proposed 3D AAR system, it is possible to correctly identify the lesion's location on the NVB in 87.5% of pT3 patients and perform a 3D-guided tailored nerve-sparing even in locally advanced diseases, without compromising the oncological safety in terms of PSM rates.