Prostatic Lesions Research Articles

Comparison of early and standard 18F-PSMA-11 PET/CT imaging in treatment-naïve patients with prostate cancer.

To evaluate the diagnostic performance of dual-time-point 18F-PSMA-11 PET/CT imaging at 30 and 60min post-injection (p.i.) in treatment-naïve patients with prostate cancer (PCa). Twenty treatment-naïve patients with histology-proven PCa who underwent 18F-PSMA-11 PET/CT scans at both 30 and 60-min p.i. were retrospectively analyzed. Lesion detection, semi-quantitative analysis of lesion and background, and unspecific bone uptake (UBU) between two time points were evaluated and compared. Besides, interrater reliability was also evaluated. Lesion detection was consistent at both 30 and 60-min p.i. imaging of 20 patients (mean age 72 ± 9), identifying 27 primary prostate lesions, 84 lymph node metastases, bone metastases in 8 patients, and other metastases in 2 patients. Primary prostate lesions showed no significant difference in SUVmax and target-to-blood pool (T/B) ratios between the two imaging times, while these parameters significantly increased over time in bone metastases. Lymph node metastases showed no significant difference in SUVmax but higher T/B ratios at 60min compared to 30min. A higher frequency of UBU was observed at 60min (37.3%) compared to 30min (32.3%), with significantly higher SUVmax and T/B ratios at 60min. 85.6% UBU was categorized as PSMA-RADS 2 at 60min, and the others were PSMA-RADS 3 or 4. The most frequent localization was vertebrae, followed by ribs. Interrater reliability was almost perfect for lesion detection at both time points. Early 30-min 18F-PSMA-11 PET/CT imaging provided comparable PCa lesion detection and semi-quantitative analysis with reduced UBU to the standard 60-min imaging.

Factors of interobserver variability in prostate tumor MRI delineation: impact of PI-QUAL score.

Prostate cancer ranks as the second most common cancer in men worldwide. Dose escalation to the tumor and/or the prostate improves biochemical recurrence-free survival. However, interobserver variability in lesion contouring poses a significant limitation to such therapeutic approaches. Therefore, a study of factors influencing this variability is necessary. Three independent readers delineated the index prostate lesion (DIL) using T2w and ADC sequences for each patient. Clinical data were retrospectively collected for all participants. Logistic regression analysis was employed to examine the correlation between clinical features and a mean DICE coefficient > 0.7. Features exhibiting a p value < 0.05 on univariate analysis were subjected to multivariate analysis. The study comprised 68 patients, with a median DICE coefficient of 0.69 (95% CI 0.65-0.71), wherein 42.6% (29/68) attained a mean DICE > 0.7. Univariate analysis identified the PI-QUAL score, maximum diameter of DIL, and mean DIL volume as significant (p < 0.05) predictors. In multivariate analysis, only the PI-QUAL score (p = 0.008) remained statistically associated with a DICE coefficient > 0.7. The PI-QUAL score emerges as the primary predictive factor for minimizing inter-reader variability in intraprostatic dominant lesion segmentation. These findings underscore the importance of considering PI-QUAL scores when devising focal treatment plans. Adoption of a multi-reader approach involving diverse medical specialists (radiologists, radiotherapists, urologists) is advocated, particularly for MRIs with low PI-QUAL scores. Radiotherapy is a major treatment for patients with localized prostate cancer. Dose escalation to the tumor leads to improved cancer control. Precise delineation of the dominant intraprostatic lesion (DIL) remains a limitation to focal treatments. Features influencing inter-reader variability were never evaluated. In this study, we identified that the PI-QUAL score was the sole predictor of the inter-reader delineation variability of the DIL.

Magnetic Resonance Elastography Combined With PI-RADS v2.1 for the Identification of Clinically Significant Prostate Cancer.

Multiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). To investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI-RADS v2.1 for diagnosing csPCa. Prospective. One hundred forty-seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa. T1-weighted fast spin-echo, T2-weighted fast spin-echo, single-shot echo-planar diffusion-weighted imaging, fast 3D gradient-echo T1-weighted dynamic contrast-enhanced imaging, and 3D single-shot spin-echo based echo-planar MR elastography at 3.0 T. The PI-RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI-RADS integrating stiffness with PI-RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI-RADS v2.1 and the modified PI-RADS. Spearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one-way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05. In the peripheral zone, csPCa (N = 35) had significantly higher SS than non-csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI-RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI-RADS, combing SS at 60 Hz with PI-RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI-RADS v2.1, with a sensitivity of 97% and specificity of 75%. Stiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI-RADS v2.1 improved the diagnostic accuracy and specificity for csPCa. 1 TECHNICAL EFFICACY: Stage 2.

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.

Nanostructured biloalbuminosomes loaded with berberine and berberrubine for Alleviating heavy Metal-Induced male infertility in rats

Despite the remarkable biological effects of berberine (BBR), particularly on fertility, its bioavailability is low. This study aims to test the effectiveness of novel nanostructured biloalbuminosomes (BILS) of BBR and its metabolite berberrubine (M1) in treatment of testicular and prostatic lesions. M1 was semi-synthesized from BBR using microwave-assisted reaction. The solvent evaporation method was used to prepare BBR-BILS and M1-BILS by three different concentrations of sodium cholate (SC) or glycocholate (SG), along with the incorporation of bovine serum albumin (BSA). The prepared BILS were fully characterized. Male infertility was induced by cadmium (Cd) at 5 mg/kg and lead (Pb) at 20 mg/kg contaminated water for 90 days, followed by treatment with BBR, M1, and their BILS (BBR-BILS and M1-BILS) for 45 days. Blood male infertility markers, testicular and prostatic oxidative stress status, autophagy, inflammation, along with testicular and prostatic concentrations of Cd and Pb, and histopathology of both tested tissues were determined using standardized protocols. The optimal BBR-BILS and M1-BILS nano-preparations, containing 30 mg SC, were chosen based on the best characterization properties of the preparations. Both nano-preparations improved heavy metals-induced testicular and prostatic deformities, as they reduced Bax and elevated Bcl-2 expressions in both tissues. Moreover, they activated the mTOR/PI3K pathway with a marked reduction in AMPK and activated LC-3II protein levels. Consequently, testicular and prostatic architecture and functions were improved. This study is the first to report the preparation of BBR and M1 BILS nano-preparations and proved their superior efficacy compared to free drugs against testicular and prostatic deformities by attenuating oxidative stress-induced excessive autophagy, offering a new hope to manage male infertility.

Comparison in prostate cancer diagnosis with PSA 4–10 ng/mL: radiomics-based model VS. PI-RADS v2.1

BackgroundTo evaluate accuracy of MRI-based radiomics in diagnosing prostate cancer (PCa) in patients with PSA levels between 4 and 10 ng/mL and compare it with the latest Prostate Imaging Reporting and Data System (PI-RADS v2.1) score.Methods221 patients with prostate lesions and PSA levels in 4–10 ng/mL, including 154 and 67 cases in the training and validation groups. Pathological confirmation of all patients was accomplished by the use of MRI-TRUS fusion targeted biopsy or systematic transrectal ultrasound (TRUS) guided biopsy. 851 radiomic features were extracted from each lesion of ADC and T2WI images. The least absolute shrinkage and selection operator (LASSO) regression algorithm and logistic regression were employed to select features and build the ADC and T2WI model. The combined model was obtained based on the ADC and T2WI features. The clinical benefit and diagnostic accuracy of the three radiomics models and PI-RADS v2.1 score were evaluated.Results10 radiomic features were ultimately selected from the ADC images, 13 from the T2WI images and 7 from the combined models. The ADC, T2WI and combined models achieved satisfactory diagnostic accuracy in the training [AUC:0.945 (ADC), 0.939 (T2WI), 0.979 (combined)] and validation groups [AUC: 0.942 (ADC), 0.943 (T2WI), 0.959 (combined)], which was significantly higher than those in PI-RADS v2.1 model (0.825 for training cohort and 0.853 for validation cohort). Compared with the PI-RADS v2.1 score, the three radiomics models generated superior PCa diagnostic performance in both the training (p = 0.002, p = 0.005, p < 0.001) and validation groups (p = 0.045, p = 0.035, p = 0.015).ConclusionRadiomics based on ADC and T2WI images can better identify PCa in patients with PSA 4–10 ng/mL, and MRI-based radiomics significantly outperforms the PI-RADS v2.1 score.Clinical trial numberNot applicable.

Reliability of mpMRI in diagnosing cancer prostate following intravesical BCG for bladder cancer.

Detecting carcinoma prostate (CaP) after intravesical Bacillus Calmette Guerin (BCG) immunotherapy for non-muscle invasive bladder cancer (NMIBC) poses diagnostic challenges. Granulomatous prostatitis (GP) has an incidence of 0.8%-3.3% in post-intravesical BCG patients and 6% incidence in a PIRADS 5 lesion on multiparametric MRI (mpMRI). Patients with GP after intravesical BCG may have clinical, biochemical, and radiological features similar to CaP. In our study, we evaluate the reliability of mpMRI in diagnosing CaP after intravesical BCG therapy. We reviewed the NMIBC patients treated with intravesical BCG therapy between 2017 and 2023 and investigated those who underwent mpMRI and MR fusion biopsy in suspicion of CaP. A total of 120 patients had intravesical BCG immunotherapy, and 10 patients met our selection criteria. We performed a descriptive analysis of these patients and assessed the sensitivity and specificity of mpMRI in diagnosing CaP. The sensitivity of mpMRI in detecting CaP was 100%, and the specificity was 28.6%. Similarly, the negative predictive value for detecting CaP was 100%, and the positive predictive value was 37.5%. Among patients evaluated with mpMRI, a PIRADS 4 or 5 lesion was seen in 8 (80%) patients, and there was no lesion in 2 (20%) patients. The mpMRI detected 1 lesion in 6 patients (60%) and 2 (20%) in 2 patients. The lesions had a PIRADS score of 4 and 5 in 6 (60%) and 2 (20%) patients, respectively. Among these lesions, 8 (80%) were in the peripheral zone and 2 (20%) in the transition zone. In the MR fusion biopsy of these 10 patients, 7 (70%) had granulomatous prostatitis, and 3 (30%) had CaP. In our study on evaluating the reliability of mpMRI in diagnosing CaP among post-intravesical BCG patients, we noted that although PIRADS in mpMRI had high sensitivity in identifying prostate lesions, its specificity for detecting CaP is limited.

6463 A Case of Tumour-Induced Osteomalacia Which Remitted After Treatment of Metastatic Prostate Cancer

Abstract Disclosure: J. Chua: None. M. Chuah: None. A case Tumour-induced osteomalacia (TIO) which remitted after treatment of metastatic prostate cancer. Dr Chua Jia Min, Dr Matthew Chuah Bingfeng Abstract Case report: A 69-year-old gentleman with a known history of bladder cancer presented to the emergency department with fatigue, lower back pain and bilateral lower limb weakness worsening over the past one month. MRI and bone scan revealed extensive osteoblastic bone metastases across the skeletal system. This gentleman was referred to the Endocrinology service for the evaluation of high PTH. His corrected calcium was normal at 2.17mmol/L (2.09-2.46mmol/L) and iPTH was raised at 8.74pmol/L (1.60-6.90pmol/L). He was noted to have severe hypophosphatemia 0.34mmol/L (0.94-1.50mmol/L). Bicarbonate levels were normal, there was no glycosuria or proteinuria. There was no history of IV ferrous carboxymaltose use. Further investigations were consistent with urinary phosphate loss - TRP was 0.398 and TMP-GFR was 0.199. ALP was elevated at 282ug/L (39-99ug/L). 25-hydroxyvitamin D was low at 12 ug/L, which accounted for the high PTH levels (secondary hyperparathyroidism). FGF-23 levels returned high at 1293 RU/ml (&amp;lt;180 RU/ml). BMD showed osteoporosis with T-score of -2.6 at the left femoral neck. He was subsequently diagnosed with metastatic prostate adenocarcinoma, based on histology from a bone biopsy of an affected vertebrae. PSA was markedly raised at 606ug/L. A diagnosis of TIO secondary to metastatic prostate cancer was made. The patient was started on PO sodium-phosphate solution 4ml TDS (approximately 3mmol/kg/day of elemental phosphorus), calcitriol 0.25mcg BD and vitamin D was replaced. After phosphate was replete, the patient's weakness and fatigue improved. He was subsequently started on androgen deprivation therapy with leuprorelin acetate injection once every 3 months and apalutamide daily. SC denosumab 60mg Q6monthly was also commenced in view of extensive bone metastases. Repeat imaging showed response to treatment with decrease in the size of the prostate and bone lesions. His PSA levels are now undetectable and TMP-GFR has normalised. His oral phosphate and calcitriol doses were gradually reduced and subsequently stopped. His calcium and phosphate levels remain within normal limits on no supplementation, suggesting remission of his TIO. PTH levels normalised after vitamin D repletion. Learning points TIO is a rare paraneoplastic syndrome usually associated with mesenchymal tumours, and treatment involves resection of the causative tumour. Cases of TIO secondary to prostate cancer (an epithelial cancer) are exceedingly rare. Despite metastatic disease, this patient responded exceedingly well to his cancer therapy. Suppression of the tumour cells with androgen deprivation therapy resulted in the lack of phosphotonin production and remission of his TIO. Presentation: 6/1/2024

Combined developmental exposure to estrogenic endocrine disruptor and nutritional imbalance induces long term adult prostate inflammation through inflammasome activation

Increasing number of studies suggested that environmental deleterious impacts (such as estrogen-like endocrine disruptors, EDCs, unhealthy diet) during early human development affect the risk of developing non-communicable diseases including prostate cancer (PCa) later in life. To test if the combination of EDCs and unhealthy induces adult prostate lesions, we developed an experimental model of adult male Sprague Dawley rats exposed during gestation (from day 7) to weaning to high fat diet (HFD 60 % fat), or to a xenoestrogen (estradiol benzoate, EB, 2.5 µg/d) from post-natal days 1–5, or to a combination of both. EB and EB+HFD exposures induced decreased prostate weight in adult rats along with inflammatory status. A white blood cell infiltrate was observed after EB exposure and more dramatic lesions were observed with the combined exposure, along with a gland destruction. The lesions, following EB or EB+HFD exposure, are associated with elevated mRNA levels for TNFa, IL6 and CCL2/MCP1 pro-inflammatory cytokines while the levels of the anti-inflammatory IL10 cytokine remained unchanged. This activation of NLRP3 and elevated levels of CASP1 were observed following EB or EB+HFD exposures associated with elevated mRNA levels for IL1b, substrates for the NLRP3 complex. HFD exposure alone has mild if not pro-inflammatory effects in adult prostate. In conclusion, we showed that developmental combined exposure to EB and HFD programmed prostate inflammatory lesions in adult prostate. Since proliferative inflammatory atrophy and chronic inflammation of prostate may drive cell to become cancer cells, our model might be useful for study onset of PCa.

Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model

Abstract Nowadays, Dynamic Contrast Enhanced MRI (DCE-MRI) is becoming the most widely explored technique in clinical practice for tumor assessment. In acquiring DCE-MRI, a contrast agent (CA), also called tracer, is injected into the blood flow before or during the acquisition of a time series of T 1 {T_{1}} -weighted images with fast imaging techniques. When the CA goes through the tissue, MR signal intensity measurements in voxels of the region of interest (ROI) are registered and used to calculate the CA concentration in each voxel. The Tofts models have become standard for the analysis of DCE-MRI and which express tissue CA concentration C ⁢ ( t ) {C(t)} as function of time t. The analysis of quantitative parameters in DCE-MRI provides the quantitative criterion as a reference rather than relying only on the shape of the DCE-curve, as it is used for diagnosis of prostate cancer (PCa). This study aim to provide a new thinking in quantitative analysis which may therefore improve diagnostic accuracy for detection of prostate cancer and could be used in patient baseline prediction and guide management. A hierarchical Bayesian model was built to estimate the values of the four pharmacokinetic parameters ( K trans {K_{\mathrm{trans}}} , k ep {k_{\mathrm{ep}}} , υ p {\upsilon_{\mathrm{p}}} , υ e {\upsilon_{\mathrm{e}}} ) for both prostate healthy and lesion tissues in the peripheral zone. This estimation is important because it help to understand the behavior of the CA in the body and how this latter reacts to the CA in order to emphasize the expectation or the absence of prostate lesion during the diagnosis step.

Multi-label semantic segmentation of magnetic resonance images of the prostate gland

Prostate segmentation is a substantial factor in the diagnostic pathway of suspicious prostate lesions. Medical doctors are assisted by computer-aided detection and diagnosis systems systems and methods derived from artificial intelligence deep learning-based systems have to be trained on existing data. Especially freely available labeled prostate magnetic resonance imaging data is rare. With regard to this problem, we show a method to combine two existing small datasets to form a bigger one. We present a data processing pipeline consisting of a cascaded network architecture that is able to perform multi-label semantic segmentation, hence, capable of classifying each pixel in a T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{T}_{2}$$\\end{document}-weighted image not only to one class but to a subset of the prostate zones and classes of interest. This delivers richer information such as overlapping zones that are key to medical radiological examination. Additionally, we describe how to integrate expert knowledge in our deep learning system. To increase data variety for training and evaluation we use image augmentation on our two datasets—a freely available dataset and our new open-source dataset. To combine the datasets we denoise the contourings in our dataset by using an effective yet simple algorithm based on standard computer vision methods only. The performance of the presented methodology is compared and evaluated using the dice score metric and 5-fold cross-validation on all datasets. Although we trained on tiny datasets our method achieves excellent segmentation quality and is even able to detect prostate cancer. Our method to combine the two datasets reduces segmentation errors and increases data variety. The proposed architecture significantly improves performance by including expert knowledge via feature-map concatenation. On the initiative for collaborative computer vision benchmarking dataset we achieve on average dice scores of approximately 91% for the whole prostate gland, 67% for the peripheral zone and 75% for the prostate central gland. We find that image augmentation except contrast limited adaptive histogram equalisation did not have much influence on the segmentation quality. Derived and enhanced from existing methods we present an approach that is able to deliver multi-label semantic segmentation results for prostate magnetic resonance imaging. This approach is simple and could be applied to other applications of deep learning as well. It improves the segmentation results by a large margin. Once tweaked to the data, our denoising and combination algorithm delivers robust and accurate results even on data with segmentation errors.

Extra-Skeletal Osteosarcoma of the Prostate After Treated Prostatic Acinar Adenocarcinoma: A Case Report and Review of the Literature.

Extra-skeletal osteosarcoma is a rare form of malignant soft tissue sarcoma. Its occurrence in the prostate gland is particularly uncommon. In this case report, we present a patient diagnosed with osteosarcoma arising within the prostatic gland. A 58-year-old man was initially diagnosed with Gleason 8 prostate acinar adenocarcinoma following a transurethral resection (TUR) of the prostate. This diagnosis was accompanied by locoregional involvement and multiple bone metastases. The patient underwent a treatment regimen including complete androgen blockade, chemotherapy, greenlight laser prostate vaporization, and palliative radiotherapy. After treatment, he achieved a complete biochemical response, and his bone metastases remained stable. However, at 16 months post-diagnosis, clinical follow-up by means of radiological examinations revealed an increase in the size of the prostatic lesion, along with additional infiltration of the tumor into the rectum and bladder walls. Remarkably, a mesenchymal tumor proliferation with intratumor calcifications was observed. A subsequent TUR biopsy of the prostate showed a malignant tumor spindle and ovoid cell proliferation with high-grade nuclear atypia, necrosis, and islets of osteoid formation, leading to a final diagnosis of high-grade prostatic extra-skeletal osteosarcoma. Despite undergoing chemotherapy, the patient's condition progressed with the development of pulmonary and liver metastases, culminating in his demise.

The QIBA Profile for Diffusion-Weighted MRI: Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker.

The apparent diffusion coefficient (ADC) provides a quantitative measure of water mobility that can be used to probe alterations in tissue microstructure due to disease or treatment. Establishment of the accepted level of variance in ADC measurements for each clinical application is critical for its successful implementation. The Diffusion-Weighted Imaging Biomarker Committee of the Quantitative Imaging Biomarkers Alliance (QIBA) has recently advanced the ADC Profile from the consensus to clinically feasible stage for the brain, liver, prostate, and breast. This profile distills multiple studies on ADC repeatability and describes detailed procedures to achieve stated performance claims on an observed ADC change within acceptable confidence limits. In addition to reviewing the current ADC Profile claims, this report has used recent literature to develop proposed updates for establishing metrology benchmarks for mean lesion ADC change that account for measurement variance. Specifically, changes in mean ADC exceeding 8% for brain lesions, 27% for liver lesions, 27% for prostate lesions, and 15% for breast lesions are claimed to represent true changes with 95% confidence. This report also discusses the development of the ADC Profile, highlighting its various stages, and describes the workflow essential to achieving a standardized implementation of advanced quantitative diffusion-weighted MRI in the clinic. The presented QIBA ADC Profile guidelines should enable successful clinical application of ADC as a quantitative imaging biomarker and ensure reproducible ADC measurements that can be used to confidently evaluate longitudinal changes and treatment response for individual patients.

Evaluating Prostate Cancer: The Diagnostic Impact of MRI and Its Relationship With Transrectal Ultrasound (TRUS)-Guided Biopsy.

Background Prostate disorders, including benign enlargement and malignancy, are commonly evaluated through imaging techniques. Historically, transrectal ultrasound (TRUS) has been used for prostate imaging and biopsy. However, multiparametric MRI (mpMRI), which integrates structural and functional imaging methods, offers enhanced diagnostic capabilities. This study evaluates the effectiveness of mpMRI, including its grading via Prostate Imaging - Reporting and Data System (PI-RADS) or Likert scoring, in distinguishing between benign and malignant prostatic conditionsand compares these findings with TRUS outcomes. Methodology This prospective study enrolled 30 male patients aged 45 to 75 years (mean age 60 years), selected based on prostatic abnormalities, elevated prostate-specific antigen (PSA) levels (>4 ng/dL), or palpable nodules detected via digital rectal examination. MRI, including PI-RADS or Likert scoring, was utilized to assess prostatic lesions, and results were compared with histopathological data obtained from TRUS-guided biopsies. Results Among the 30 patients, common symptoms included urinary retention (60%) and painful urination (53.3%). Malignant tumors were diagnosed in 12 patients (40%). MRI identified eight cases with enlarged transitional zones and irregular signals in peripheral zones (benign prostatic hyperplasia with tumor)and four cases with irregular signals in both zones (sarcoma). Concordance between MRI T2-weighted (T2W) observations and biopsy results showed 60% malignancy detection. Sensitivity assessments revealed MRI detected 15 true-positives (50%), TRUS detected six true positives (20%), and multivoxel spectroscopic analysis (MVS) identified 14 true-positives (46.7%). PI-RADS or Likert scoring of mpMRI was correlated with TRUS outcomes, highlighting its enhanced diagnostic accuracy compared to TRUS alone. Conclusion While TRUS remains a standard diagnostic tool, it is limited by significant sampling errors and complications. The integration of mpMRI, with its grading system, significantly improves diagnostic accuracy and treatment planning. Although mpMRI alone has limitations, its combination with contrast-enhanced MRI, diffusion-weighted imaging, and MR spectroscopy offers a comprehensive approach to enhanced prostate cancer detection.

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 (P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 (P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

Boundary-aware semantic clustering network for segmentation of prostate zones from T2-weighted MRI

Objective. Automatic segmentation of prostatic zones from MRI can improve clinical diagnosis of prostate cancer as lesions in the peripheral zone (PZ) and central gland (CG) exhibit different characteristics. Existing approaches are limited in their accuracy in localizing the edges of PZ and CG. The proposed boundary-aware semantic clustering network (BASC-Net) improves segmentation performance by learning features in the vicinity of the prostate zonal boundaries, instead of only focusing on manually segmented boundaries. Approach. BASC-Net consists of two major components: the semantic clustering attention (SCA) module and the boundary-aware contrastive (BAC) loss. The SCA module implements a self-attention mechanism that extracts feature bases representing essential features of the inner body and boundary subregions and constructs attention maps highlighting each subregion. SCA is the first self-attention algorithm that utilizes ground truth masks to supervise the feature basis construction process. The features extracted from the inner body and boundary subregions of the same zone were integrated by BAC loss, which promotes the similarity of features extracted in the two subregions of the same zone. The BAC loss further promotes the difference between features extracted from different zones. Main results. BASC-Net was evaluated on the NCI-ISBI 2013 Challenge and Prostate158 datasets. An inter-dataset evaluation was conducted to evaluate the generalizability of the proposed method. BASC-Net outperformed nine state-of-the-art methods in all three experimental settings, attaining Dice similarity coefficients of 79.9% and 88.6% for PZ and CG, respectively, in the NCI-ISBI dataset, 80.5% and 89.2% for PZ and CG, respectively, in Prostate158 dataset, and 73.2% and 87.4% for PZ and CG, respectively, in the inter-dataset evaluation. Significance. As prostate lesions in PZ and CG have different characteristics, the zonal boundaries segmented by BASC-Net will facilitate prostate lesion detection.

PSMA-PET/CT response after metastasis-directed radiotherapy of bone oligometastases in prostate cancer

ObjectiveBone metastases are very common in advanced prostate cancer and can sensitively be detected utilizing PSMA-PET/CT. Therefore, our goal was to evaluate the suitability of PSMA-PET/CT-guided metastasis-directed external beam radiotherapy (MDT) as treatment option for patients with biochemical recurrence and oligometastatic bone lesions.Materials & methodsWe retrospectively examined 32 prostate cancer patients with biochemical recurrence and PSMA-positive oligometastatic disease limited to the bone (n = 1–3). A total of 49 bone lesions were treated with MDT. All patients received a post-radiotherapy PSMA-PET/CT-Scan. Changes in SUVmax, PSMA-positive tumor volume per lesion and PSA, as well as the correlation between the PET/CT-interval and SUVmax response were calculated.ResultsMDT lead to a SUVmax decrease in 46/49 (94%) of the lesions. The median relative decline of SUVmax was 60.4%, respectively. Based on PSMA-positive lesion volume with a SUV cut-off of 4, 46/49 (94%) of lesions showed complete response, two (4%) partial response and one lesion (2%) was stable on PSMA-PET/CT after MDT. Most of the treated patients (56.3%) showed an initial PSA decline at three months and a PSA nadir of median 0.14 ng/ml after a median time of 3.6 months after MDT. The median relative PSA change at three months after MDT was 3.9%.ConclusionMDT is a very effective treatment modality for prostate cancer bone oligometastases and lesion response to MDT can be assessed using the (semi-)quantitative parameters SUVmax and PSMA-positive lesion volume with established SUV cut-offs.

Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

BackgroundProstate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.MethodsWe conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People’s Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40–100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.ResultsVirtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).ConclusionsVirtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

Impact of the diagnostic label for a low-risk prostate lesion: protocol for two online factorial randomised experiments

IntroductionMany types of prostate cancer present minimal risk to a man’s lifespan or well-being, but existing terminology makes it difficult for men to distinguish these from high-risk prostate cancers. This...

Interactive Cascaded Network for Prostate Cancer Segmentation from Multimodality MRI with Automated Quality Assessment.

The accurate segmentation of prostate cancer (PCa) from multiparametric MRI is crucial in clinical practice for guiding biopsy and treatment planning. Existing automated methods often lack the necessary accuracy and robustness in localizing PCa, whereas interactive segmentation methods, although more accurate, require user intervention on each input image, thereby limiting the cost-effectiveness of the segmentation workflow. Our innovative framework addresses the limitations of current methods by combining a coarse segmentation network, a rejection network, and an interactive deep network known as Segment Anything Model (SAM). The coarse segmentation network automatically generates initial segmentation results, which are evaluated by the rejection network to estimate their quality. Low-quality results are flagged for user interaction, with the user providing a region of interest (ROI) enclosing the lesions, whereas for high-quality results, ROIs were cropped from the automatic segmentation. Both manually and automatically defined ROIs are fed into SAM to produce the final fine segmentation. This approach significantly reduces the annotation burden and achieves substantial improvements by flagging approximately 20% of the images with the lowest quality scores for manual annotation. With only half of the images manually annotated, the final segmentation accuracy is statistically indistinguishable from that achieved using full manual annotation. Although this paper focuses on prostate lesion segmentation from multimodality MRI, the framework can be adapted to other medical image segmentation applications to improve segmentation efficiency while maintaining high accuracy standards.