Utilization trend of prebiopsy multiparametric magnetic resonance imaging and its impact on prostate cancer detection: Real-world insights from a high-volume center in southwest China.

BACKGROUND: Data on the utilization and effects of prebiopsy prostate multiparametric magnetic resonance imaging (mpMRI) to support its routine use in real-world setting are still scarce. OBJECTIVE: To evaluate the change of clinical practice of prebiopsy mpMRI over time, and assess its diagnostic accuracy. DESIGN, SETTING, AND PARTICIPANTS: We retrospectively analyzed data from 6168 patients who underwent primary prostate biopsy (PBx) between January 2011 and December 2021 and had prostate-specific antigen (PSA) values ranging from 3 to 100 ng/mL. INTERVENTION: Prebiopsy MRI at the time of PBx. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We performed general linear regression and to elucidate trends in the annual use of prebiopsy mpMRI and conducted multivariable logistic regression to evaluate the potential benefits of incorporating prebiopsy mpMRI for prostate cancer (PCa) detection. RESULTS AND LIMITATIONS: The utilization of prebiopsy mpMRI significantly increased from 9.2% in 2011 to 75.0% in 2021 (p < 0.001). In addition, prebiopsy mpMRI significantly reduced negative PBx by 8.6% while improving the detection of clinically significant PCa (csPCa) by 7.0%. Regression analysis showed that the utilization of prebiopsy mpMRI was significantly associated with a 48% (95% confidence interval [CI]: 1.19-1.84) and 36% (95% CI: 1.12-1.66) increased PCa detection rate in the PSA 3-10 ng/mL and 10-20 ng/mL groups, respectively; and a 34% increased csPCa detection rate in the PSA 10-20 ng/mL group (95% CI: 1.09-1.64). The retrospective design and the single center cohort constituted the limitations of this study. CONCLUSIONS: Our study demonstrated a notable rise in the utilization of prebiopsy mpMRI in the past decade. The adoption of this imaging technique was significantly associated with an increased probability of detecting prostate cancer. PATIENT SUMMARY: From 2011 to 2021, we demonstrated a steady increase in the utilization of prebiopsy mpMRI among biopsy-naïve men. We also confirmed the positive impact of prebiopsy mpMRI utilization on the detection of prostate cancer.

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.

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.

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.

Multiparametric Magnetic Resonance Investigations on Acute and Long-Term Kidney Injury.

Acute kidney injury (AKI) is a frequent complication of critical illness and carries a significant risk of short- and long-term mortality. The prediction of the progression of AKI to long-term injury has been difficult for renal disease treatment. Radiologists are keen for the early detection of transition from AKI to long-term kidney injury, which would help in the preventive measures. The lack of established methods for early detection of long-term kidney injury underscores the pressing needs of advanced imaging technology that reveals microscopic tissue alterations during the progression of AKI. Fueled by recent advances in data acquisition and post-processing methods of magnetic resonance imaging (MRI), multiparametric MRI is showing great potential as a diagnostic tool for many kidney diseases. Multiparametric MRI studies offer a precious opportunity for real-time noninvasive monitoring of pathological development and progression of AKI to long-term injury. It provides insight into renal vasculature and function (arterial spin labeling, intravoxel incoherent motion), tissue oxygenation (blood oxygen level-dependent), tissue injury and fibrosis (diffusion tensor imaging, diffusion kurtosis imaging, T1 and T2 mapping, quantitative susceptibility mapping). The multiparametric MRI approach is highly promising but the longitudinal investigation on the transition of AKI to irreversible long-term impairment is largely ignored. Further optimization and implementation of renal MR methods in clinical practice will enhance our comprehension of not only AKI but chronic kidney diseases. Novel imaging biomarkers for microscopic renal tissue alterations could be discovered and benefit the preventative interventions. This review explores recent MRI applications on acute and long-term kidney injury while addressing lingering challenges, with emphasis on the potential value of the development of multiparametric MRI for renal imaging on clinical systems. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Defining the role of multiparametric MRI in predicting prostate cancer extracapsular extension.

OBJECTIVES: To identify the predictive factors of prostate cancer extracapsular extension (ECE) in an institutional cohort of patients who underwent multiparametric MRI of the prostate prior to radical prostatectomy (RP). PATIENTS AND METHODS: Overall, 126 patients met the selection criteria, and their medical records were retrospectively collected and analysed; 2 experienced radiologists reviewed the imaging studies. Logistic regression analysis was conducted to identify the variables associated to ECE at whole-mount histology of RP specimens; according to the statistically significant variables associated, a predictive model was developed and calibrated with the Hosmer-Lomeshow test. RESULTS: The predictive ability to detect ECE with the generated model was 81.4% by including the length of capsular involvement (LCI) and intraprostatic perineural invasion (IPNI). The predictive accuracy of the model at the ROC curve analysis showed an area under the curve (AUC) of 0.83 [95% CI (0.76-0.90)], p < 0.001. Concordance between radiologists was substantial in all parameters examined (p < 0.001). Limitations include the retrospective design, limited number of cases, and MRI images reassessment according to PI-RADS v2.0. CONCLUSION: The LCI is the most robust MRI factor associated to ECE; in our series, we found a strong predictive accuracy when combined in a model with the IPNI presence. This outcome may prompt a change in the definition of PI-RADS score 5.

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.

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.

The impact of the relationship between lesion diameter and total core length on the detection rate of clinically significant prostate cancer for PI-RADS 3 lesions.

BACKGROUND: The aim of our study was to determine the effect of total core length (TCL) for prostate imaging reporting and data system (PI-RADS) 3 lesions to facilitate clinically significant prostate cancer (csPCa) detection based on the lesion diameter. MATERIALS AND METHODS: A total of 149 patients with at least 1 lesion with a PI-RADS 3 were evaluated retrospectively. The lesions with diameters of < 1 cm were categorized as small lesions and lesions of ≥ 1 cm were categorized as large lesions. The lengths of biopsy cores from PI-RADS 3 lesions were summed for each lesion separately, and TCL was calculated. The relationship between TCL and csPCa was analyzed separately for the small and large groups with multiple logistic regression analyses. RESULTS: A total of 208 lesions were detected by multiparametric magnetic resonance imaging (MpMRI) in 149 males included in the study. The mean TCL was 44.68 mm (26-92) and the mean lesion diameter was 10.73 mm (4-27) in PIRADS 3 lesions. For small diameter lesions (< 1 cm), the odds of finding clinically insignificant prostate cancer (ciPCa) increase by 1.67 times if TCL increases by one unit. Hence, increasing TCL for small lesions only increases the odds of ciPCa detection. For large diameter lesions (≥ 1 cm), if TCL increases by one unit, the odds of finding ciPCa increase 1.13 times and the odds of finding csPCa increases1.16 times. Accordingly, large lesions are more likely to have both csPCa and ciPCa as TCL increases. CONCLUSIONS: Our study showed that for PI-RADS 3 lesions, both more csPCa and more ciPCa were detected as TCL increased. However, in lesions with a size of < 1 cm, only ciPCa was detected more frequently as TCL increased. In conclusion, taking more and longer biopsy cores in PI-RADS 3 lesions below 1 cm does not contribute to the detection of csPCa.

Contrast-enhanced cardiac MRI is superior to non-contrast mapping to predict left ventricular remodeling at 6 months after acute myocardial infarction.

OBJECTIVES: Parametric mapping constitutes a novel cardiac magnetic resonance (CMR) technique enabling quantitative assessment of pathologic alterations of left ventricular (LV) myocardium. This study aimed to investigate the clinical utility of mapping techniques with and without contrast agent compared to standard CMR to predict adverse LV remodeling following acute myocardial infarction (AMI). MATERIALS AND METHODS: A post hoc analysis was performed on sixty-four consecutively enrolled patients (57 ± 12 years, 54 men) with first-time reperfused AMI. Baseline CMR was obtained at 8 ± 5 days post-AMI, and follow-up CMR at 6 ± 1.4 months. T1/T2 mapping, T2-weighted, and late gadolinium enhancement (LGE) acquisitions were performed at baseline and cine imaging was used to determine adverse LV remodeling, defined as end-diastolic volume increase by 20% at 6 months. RESULTS: A total of 11 (17%) patients developed adverse LV remodeling. At baseline, patients with LV remodeling showed larger edema (30 ± 11 vs. 22 ± 10%LV; p < 0.05), infarct size (24 ± 11 vs. 14 ± 8%LV; p < 0.001), extracellular volume (ECVinfarct; 63 ± 12 vs. 47 ± 11%; p < 0.001), and native T2infarct (95 ± 16 vs. 78 ± 17 ms; p < 0.01). ECVinfarct and infarct size by LGE were the best predictors of LV remodeling with areas under the curve (AUCs) of 0.843 and 0.789, respectively (all p < 0.01). Native T1infarct had the lowest AUC of 0.549 (p = 0.668) and was inferior to edema size by T2-weighted imaging (AUC = 0.720; p < 0.05) and native T2infarct (AUC = 0.766; p < 0.01). CONCLUSION: In this study, ECVinfarct and infarct size by LGE were the best predictors for the development of LV remodeling within 6 months after AMI, with a better discriminative performance than non-contrast mapping CMR. CLINICAL RELEVANCE STATEMENT: This study demonstrates the predictive value of contrast-enhanced and non-contrast as well as conventional and novel CMR techniques for the development of LV remodeling following AMI, which might help define precise CMR endpoints in experimental and clinical myocardial infarction trials. KEY POINTS: • Multiparametric CMR provides insights into left ventricular remodeling at 6 months following an acute myocardial infarction. • Extracellular volume fraction and infarct size are the best predictors for adverse left ventricular remodeling. • Contrast-enhanced T1 mapping has a better predictive performance than non-contrast standard CMR and T1/T2 mapping.

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. METHODS: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. RESULTS: The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. CONCLUSIONS: Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. CLINICAL RELEVANCE STATEMENT: The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. KEY POINTS: • Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics.

OBJECTIVES: To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. METHODS: A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n1 = 407) and an independent validation dataset (n1 = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively. CONCLUSIONS: Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients. CLINICAL RELEVANCE STATEMENT: The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies. KEY POINTS: • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.

Key learning on the promise and limitations of MRI in prostate cancer screening.

MRI retains its ability to reduce the harm of prostate biopsies by decreasing biopsy rates and the detection of indolent cancers in population-based screening studies aiming to find clinically significant prostate cancers. Limitations of low positive predictive values and high reader variability in diagnostic performance require optimisations in patient selection, imaging protocols, interpretation standards, diagnostic thresholds, and biopsy methods. Improvements in diagnostic accuracy could come about through emerging technologies like risk calculators and polygenic risk scores to select men for MRI. Furthermore, artificial intelligence and workflow optimisations focused on streamlining the diagnostic pathway, quality control, and assurance measures will improve MRI variability. CLINICAL RELEVANCE STATEMENT: MRI significantly reduces harm in prostate cancer screening, lowering unnecessary biopsies and minimizing the overdiagnosis of indolent cancers. MRI maintains the effective detection of high-grade cancers, thus improving the overall benefit-to-harm ratio in population-based screenings with or without using serum prostate-specific antigen (PSA) for patient selection. KEY POINTS: • The use of MRI enables the harm reduction benefits seen in individual early cancer detection to be extended to both risk-stratified and non-stratified prostate cancer screening populations. • MRI limitations include a low positive predictive value and imperfect reader variability, which require standardising interpretations, biopsy methods, and integration into a quality diagnostic pathway. • Current evidence is based on one-time point use of MRI in screening; MRI effectiveness in multiple rounds of screening is not well-documented.

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.

Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.

PURPOSE: To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. METHODS: This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. RESULTS: All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93). CONCLUSION: The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. CLINICAL RELEVANCE STATEMENT: Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. KEY POINTS: • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.

Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study.

PURPOSE: This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)-based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients. METHODS: We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient's T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles. RESULTS: TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 - 0.923; p = 0.025). CONCLUSION: MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles.

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

BACKGROUND: 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. PURPOSE: To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC). MATERIAL AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer.

BACKGROUND: It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. PURPOSE: To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer. MATERIAL AND METHODS: This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model. RESULTS: Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. CONCLUSION: The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

Estimated Prostate Volume by Semiautomatic Segmentation of MRI Is More Accurately Correlated with Radical Prostatectomy Specimen Weight than the Volume Calculated by Ellipsoid Formula.

INTRODUCTION: Accurate in vivo prostate volume (PV) estimation is important for obtaining prostate-specific antigen density (PSAD) and further predicting clinically significant prostate cancer (csPCa). We aimed to evaluate the accuracy of multiparametric magnetic resonance imaging (mpMRI)-estimated PV compared to both volume and weight of radical prostatectomy (RP). METHODS: We identified 310 PCa patients who underwent RP following combined targeted and systematic biopsy in our institution from September 2019 to February 2021. The MRI PV was determined using a semiautomated segmentation algorithm. RP PV was calculated using the prolate ellipsoid formula (length × width × height × π/6). Formula (prostate weight = [actual weight-3.8 g]/1.05 g/mL) was applied, and the resulting volume was used in further analysis. RESULTS: The median PV from MRI, RP, and RP weight were 39 mL, 38 mL, and 44 mL, respectively. Spearman's rank correlation coefficients (ρ) were 0.841 (MRI PV vs. RP weight), 0.758 (RP PV vs. RP weight), and 0.707 (MRI PV vs. RP PV) (all p < 0.001). Decreased correlation between the MRI PV and RP PV was observed in the larger (more than 55 mL) prostate. The PSAD derived from MRI PV showed most efficient to detect csPCa in RP specimen (57.9% vs. 57.6% vs. 45.4%). CONCLUSION: MRI PV is correlated better with RP weight than calculated RP PV, especially in larger prostate. The high csPCa detection rate in final pathology suggested that PSAD derived from MRI PV can be confidently used in clinical practice.

MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis.

PURPOSE: We to systematically evaluate the diagnostic performance of MRI radiomics in detecting extracapsular extension (EPE) of prostate cancer (PCa). METHODS: A literature search of online databases of PubMed, EMBASE, Cochrane Library, Web of Science, and Google Scholar online scientific publication databases was performed to identify studies published up to July 2023. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic (HSROC) model. This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, the quality of included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2) and the radiomics quality score (RQS). Meta-regression and subgroup analyses were performed to explore the impact of varying clinical settings. RESULTS: A total of ten studies met the inclusion criteria. The pooled sensitivity and specificity were 0.77 (95% CI 0.68-0.84, I2 = 83.5%) and 0.75 (95% CI 0.67-0.82, I2 = 83.5%), respectively, with an area under the HSROC curve of 0.88 (95% CI 0.85-0.91). Study quality was not high while assessing with the RQS. Substantial heterogeneity was observed between studies; however, meta-regression analysis did not reveal any significant contributing factors. CONCLUSIONS: MRI radiomics demonstrated moderate sensitivity and specificity, offering similar diagnostic performance with previous risk stratifications and models that primarily based on radiologists' subjective experience. However, all studies included were retrospective, thus the performance of radiomics needs to validate in prospective, multicenter studies.