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PubMed2026

Optimizing prostate biopsy decision making - (MUSIC-screen): A 1:1 randomized controlled trial comparing micro-ultrasound versus multiparametric magnetic resonance imaging for prostate cancer diagnosis.

BACKGROUND: Micro-ultrasound (microUS) represents a potential alternative to multiparametric magnetic resonance (mpMRI) in guiding prostate biopsy, with level 1 evidence demonstrating non-inferiority to detect Grade Group ≥2 (GG ≥ 2) prostate cancer in biopsy-naïve men. However, a critical gap remains in the screening pathway, in which imaging is needed to identify men at risk and determine whether biopsy is warranted. METHODS: MUSIC-Screen is a phase 3, open-label, noninferiority 1:1 randomized controlled trial evaluating microUS as an alternative imaging compared to mpMRI for determining the need for prostate biopsy in biopsy- and imaging- naïve men at risk for GG ≥ 2. A total of 1284 men will be randomized to undergo either microUS or mpMRI. Men with PRI-MUS 3-5 or PI-RADS 3-5 lesion will undergo targeted and systematic biopsy. Men with negative imaging and PSA density ≥ 0.15 will undergo systematic biopsy, while those with PSA density < 0.15 will defer biopsy. RESULTS: The primary outcome is GG ≥ 2 detection in each study arm. The primary hypothesis is that microUS is non-inferior to mpMRI for screening and detection of GG ≥ 2. Secondary objectives include comparison of GG ≥ 2 detection rates in targeted cores among patients with PRI-MUS or PI-RADS scores of 3-5, proportion of men who defer biopsy but are diagnosed with GG ≥ 2 within 8 years, assessment of the negative predictive value of each imaging modality, and health economic analyses. CONCLUSION: MUSIC-Screen will determine whether microUS can be used as an imaging modality to inform biopsy decision making that is non-inferior to mpMRI for GG ≥ 2 prostate cancer detection. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06626022.

Humans↗

PubMed2024

Histopathologic Features and Transcriptomic Signatures Do Not Solve the Issue of Magnetic Resonance Imaging-Invisible Prostate Cancers: A Matched-Pair Analysis.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is pivotal in prostate cancer (PCa) diagnosis, but some clinically significant (cs) PCa remain undetected. This study aims to understand the pathological and molecular basis for csPCa visibility at mpMRI. METHODS: We performed a retrospective matched-pair cohort study, including patients undergoing radical prostatectomy (RP) for csPCa (i.e., ISUP grade group ≥ 2) from 2015 to 2020, in our tertiary-referral center. We screened for inclusion in the "mpMRI-invisible" cohort all consecutive men (N = 45) having a negative preoperative mpMRI. The "mpMRI-visible" cohort was matched based on age, PSA, prostate volume, ISUP grade group. Included patients underwent radiological and pathological open-label revisions and characterization of the tumor mRNA expression profile (analyzing 780 gene transcripts, signaling pathways, and cell-type profiling). We compared the clinical-pathological variables and the gene expression profile between matched pairs. The analysis was stratified according to histological characteristics and lesion diameter. RESULTS: We included 34 patients (17 per cohort); mean age at RP and PSA were 70.5 years (standard deviation [SD] = 7.7), 7.1 ng/mL (SD = 3.3), respectively; 65% of men were ISUP 2. Overall, no significant differences in histopathological features, tumor diameter and location, mRNA profile, pathways, and cell-type scores emerged between cohorts. In the stratified analysis, an upregulation of cell adhesion and motility, of extracellular matrix remodeling and of metastatic process pathways was present in specific subgroups of mpMRI-invisible cancers. CONCLUSIONS: No PCa pathological or gene-expression hallmarks explaining mp-MRI invisibility were identified. Aggressive features can be present both in mpMRI-invisible and -visible tumors.

Humans↗

PubMed2026

The 'Prostate Cancer Screening for People at Genetic Risk of Aggressive Disease' (PATROL) study.

BACKGROUND: Inherited (germline) pathogenic and likely pathogenic variants (gPVs) in key genes associated with increased risk of prostate cancer (PCa) now warrant more attentive PCa screening per National Comprehensive Cancer Network (NCCN) guidelines-e.g., BRCA2, HOXB13, ATM, BRCA1, MSH2, MSH6, CHEK2 and TP53. However, the optimal early detection strategy for gPV carriers, including use of age-adjusted PSA thresholds and prostate imaging may be refined. and as a means to investigate novel biomarkers. STUDY DESIGN: 'Prostate Cancer Screening for People at Genetic Risk of Aggressive Disease' (PATROL) is a multicentre, prospective early detection study for individuals at increased risk for PCa due to carrying a gPV in a PCa risk gene. ENDPOINTS: The primary endpoint is to determine the positive predictive value of pre-defined age-directed prostate-specific antigen (PSA) level thresholds and prostate-specific imaging, e.g., multiparametric magnetic resonance imaging (MRI) for clinically significant PCa on biopsy for individuals at risk of PCa due to a gPV. Exploratory endpoints include characterising clinicopathological characteristics of PCa and patient-reported outcomes. Biospecimens will be collected to evaluate emerging clinical and research biomarkers. PATIENTS AND METHODS: Key eligibility includes: individuals aged ≥40 years who carry a gPV in an eligible gene, who have no prior diagnosis of PCa, do not have another active malignancy, and provide informed consent. Study procedures include annual physical examination and PSA. Imaging with MRI is optional at baseline and recommended if the PSA level is above the protocol-recommended PSA level threshold. Participants will be offered prostate biopsy for any clinical concern, PSA level >1.0 ng/mL if aged <50 years; PSA level >1.5 ng/mL if aged 50-59 years; PSA level >2.0 ng/mL if aged ≥60 years. If PCa is diagnosed, clinical care is determined by the participant and treating physician. If opting for active surveillance, study procedures will be collected annually for 10 years or until definitive treatment. If definitive treatment, study procedures will be collected for an additional 1 year. Long-term clinical outcomes will be collected annually until the study closes.

Humans↗

PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗

PubMed2026

PCa Detection in PI-RADS 4 and 5 Lesions: Comparison of [68Ga]Ga-PSMA-11 PET/CT-Guided Robot-Assisted Biopsy Versus mpMRI Cognitive-Fusion TRUS-Guided Prostate Biopsy.

Lesions with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 4 or greater on multiparametric MRI (mpMRI) indicate a high likelihood of prostate cancer (PCa), and guidelines recommend a targeted biopsy. We aimed to compare the diagnostic performance of robotic arm-assisted [68Ga]Ga-PSMA-11 PET/CT-guided prostate biopsy (PGPB) with mpMRI-directed cognitive-fusion transrectal ultrasound-guided biopsy (MCFB) in biopsy-naïve men with clinical findings suggestive of PCa. Methods: This prospective, single-center, randomized clinical trial (NCT05137561) enrolled biopsy-naïve men age 50-90 y with elevated levels of prostate-specific antigen (≥4 ng/mL) and abnormal digital rectal examination findings. All participants underwent mpMRI, and those with a PI-RADS score of 4 or greater were randomized into 2 arms. In arm 1, participants underwent PGPB for a [68Ga]Ga-PSMA-avid lesion, and participants in arm 2 underwent MCFB. Participants in arm 1 with PET-negative findings subsequently underwent MCFB, and participants with negative biopsy results underwent PET and PGPB. The primary outcome was the detection of PCa. Secondary outcomes included complication rates and participant-reported pain. Result: Of the 267 participants enrolled, 81.3% (217) had lesions with a PI-RADS score of 4 or greater and were randomized to either PGPB (n = 112) or MCFB (n = 105). PCa was detected in 97.1% of participants (101/104) in arm 1 and 81.0% (85/105) in arm 2 (P < 0.05). PGPB showed higher diagnostic accuracy for PI-RADS 5 lesions (100% vs. 95.1%, P = 0.09). Major complications were observed in arm 2 only (n = 5). Arm 1 had significantly fewer complications (10.8% vs. 51.4%, P < 0.01), a lower median visual analog scale score for pain (3 vs. 5), and shorter procedure times. The core positivity rate was higher in arm 1 (60% ± 20%), despite obtaining fewer cores. Conclusion: [68Ga]Ga-PSMA-11 PGPB demonstrated higher diagnostic performance, fewer complications, and better tolerability compared with MCFB. This approach enables integrated diagnosis and staging, offering a promising alternative for efficient, safe, and accurate evaluation of prostate cancer.

Humans↗

PubMed2026

Integrative Genomic Profiling of Newly Diagnosed Prostate Cancers Progressing on Surveillance.

OBJECTIVE: To identify molecular features associated with earlier progression to definitive therapy amongst patients with localized prostate cancer (PCa) managed on active surveillance (AS). METHODS: We performed a retrospective pilot study of 7 patients with low- to intermediate-risk PCa undergoing serial multiparametric MRI (mpMRI)-targeted biopsies of the same lesion while on AS, who all proceeded to definitive therapy. Time-to-treatment (TTT) was defined as years from first biopsy on AS to definitive therapy. Laser-capture microdissection was used to separate tumor epithelium, benign glands, high-grade prostatic intraepithelial neoplasia, and stroma in each biopsy specimen. DNA from the tumor and matched benign tissue underwent whole-exome sequencing, and RNA from all compartments underwent whole-transcriptome sequencing. Somatic mutations and copy-number alterations were compared across serial biopsies and used to reconstruct phylogenies and quantify clonal complexity. RESULTS: Tumors exhibited substantial intratumoral heterogeneity, and in 3 of 6 paired cases, serial mpMRI-targeted biopsies showed discordant somatic profiles consistent with sampling distinct major clones over time. By contrast, no single gene-level alteration, and few large-scale chromosomal events, were associated with TTT. High clonal complexity, defined as ≥3 subclones, was associated with significantly shorter TTT than low complexity (median 1.9 vs 7.2 years; P = .0082). Exploratory pathway analyses of individual tissue components suggested TTT-associated differences in inflammatory signaling and stromal-epithelial cross-talk. CONCLUSION: In this small, hypothesis-generating cohort, clonal complexity was more closely associated with earlier definitive therapy than individual genomic alterations. Larger prospective studies are needed to validate whether multiomic measures of clonal architecture can improve AS risk stratification.

Humans↗

PubMed2026

Cine-derived mitral annular relaxation velocity for detection of preclinical left ventricular diastolic dysfunction.

OBJECTIVES: Imaging diastolic dysfunction in pre-clinical heart failure (HF) is challenging. We evaluated a novel cardiac MRI (CMR) biomarker, CMR e-prime (CMR-MARV), in patients at risk of HF. METHODS: In this substudy of the PARABLE trial (NCT04687111), 236 patients (71.6 ± 7.7 years, 61.6% male) fulfilling trial-defined ALVDD citeria underwent CMR with measurement of mitral annular relaxation velocity (CMR-MARV) at four mitral annular anchor points. Diastolic strain rates from FT were also assessed. Twenty-five age- and sex-matched controls were included (73.8 ± 3.1 years, 52% male). Group differences were tested with t-tests, diagnostic accuracy with ROC analysis, and predictors of diastolic dysfunction with adjusted logistic regression. RESULTS: Compared with controls, patients had significantly higher indexed maximal left atrial volume (LAVimax), LV end-diastolic and end-systolic volumes, and LV mass (all p < 0.001). Of FT variables, only peak diastolic longitudinal velocity differed between groups (p < 0.001). In multivariate models, CMR-MARV correlated with radial, circumferential, and longitudinal diastolic strain rates, radial and longitudinal diastolic velocities (all p < 0.001), echocardiographic e' (r = 0.20, p = 0.007), LV mass (r = -0.18, p = 0.008), LAVimax (r = -0.18, p = 0.008), and NT-proBNP (r = -0.30, p < 0.0001). LAVimax and CMR-MARV were strongly independently associated with ALVDD (AUC 0.89 and 0.76, respectively; p < 0.0001). A combined model (LAVimax + CMR-MARV) achieved excellent discrimination (AUC 0.91, 95% CI 0.86-0.97, p < 0.0001). Independent predictors included LAVimax, CMR-MARV, and peak diastolic longitudinal velocity (all p < 0.001). CONCLUSION: CMR-MARV provides a simple cine-derived measure of longitudinal relaxation that correlates with established structural and biochemical markers of diastolic burden. Within an at-risk population, it offers incremental functional information beyond conventional parameters and may support multiparametric CMR phenotyping of preclinical diastolic dysfunction.

Aged↗

PubMed2026

Integrating Radiogenomics and CSF-Based Liquid Biopsy Sequencing for Precision Neuro-Oncology.

Glioblastoma and diffuse gliomas pose major therapeutic challenges due to marked intratumoral heterogeneity, limited tissue accessibility, and the blood-brain barrier. Tissue-based next-generation sequencing (NGS) remains essential for WHO CNS5 molecular classification, yet it is invasive and poorly suited to serial monitoring. Two complementary non- or minimally invasive approaches have advanced rapidly: radiogenomics, which correlates multiparametric MRI features with genomic alterations, and cerebrospinal fluid (CSF) liquid biopsy sequencing, which detects circulating tumor DNA with high tissue concordance. This review examines the independent progress and synergistic integration of radiogenomics and CSF-NGS. Imaging signatures can non-invasively predict key drivers (IDH1/2, EGFR, TERT, PTEN, TP53) and molecular subtypes, while CSF-ctDNA sequencing enables real-time assessment of clonal evolution, therapy resistance (including post-temozolomide hypermutation), and residual disease. We discuss technical considerations, performance metrics, multimodal artificial-intelligence fusion, and emerging clinical applications for diagnosis, prognosis, treatment selection, and longitudinal surveillance. Critical challenges, standardization, prospective validation, and workflow integration are highlighted. By combining the spatial phenotypic information of radiogenomics with the temporal genomic resolution of CSF sequencing, this multimodal strategy offers a promising path toward precision neuro-oncology and reduced reliance on repeated invasive sampling.

Humans↗

PubMed2026

Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.

BACKGROUND: Glioblastoma (GBM) exhibits profound molecular and spatial heterogeneity, complicating prognostic evaluations. While multiparametric MRI provides crucial multidimensional biological information, conventional end-to-end deep learning integration strategies, such as early or late fusion, often fail to capture complex nonlinear cross-modal interactions. We aimed to systematically evaluate a cross-attention fusion (CAF) architecture for GBM survival prediction and quantify its incremental prognostic value relative to existing clinical tools. METHODS: In this multicenter retrospective study, 386 adults with IDH-wildtype, WHO grade 4 GBM were assembled from an institutional cohort (n = 226), the Chinese Glioma Genome Atlas (CGGA, n = 62), and The Cancer Genome Atlas (TCGA, n = 98). Using a unified 3D ResNet-18 backbone, we compared single-modality models, early fusion, late fusion, and CAF on preoperative T1-weighted, contrast-enhanced T1-weighted (T1CE), and T2-weighted MRI, and integrated the resulting deep learning risk score with routine clinical variables through multivariable Cox regression. Performance was assessed using Harrell's C-index, time-dependent AUC, and decision curve analysis. RESULTS: CAF showed numerically higher, more consistent C-index trends than early fusion, late fusion, and single-modality models (pooled C-index 0.629, 95% CI 0.594-0.664), although pairwise differences in time-dependent AUC were not statistically significant. Integrating clinical variables raised the pooled C-index to 0.691 (95% CI 0.660-0.721) in the treatment-era model, with comparable performance across the three cohorts (Local 0.688; CGGA 0.716; TCGA 0.689); a pre-treatment configuration excluding adjuvant therapy yielded a pooled C-index of 0.642. Under leave-one-cohort-out external validation, the combined model retained significant risk stratification in all held-out cohorts (C-index 0.63-0.71; all log-rank P < 0.01), albeit with attenuated discrimination. The deep learning risk score remained independent after multivariable adjustment (HR 1.41 per SD, 95% CI 1.26-1.57; P < 0.001). Kaplan-Meier analysis confirmed significant high- versus low-risk separation in all cohorts, and decision curve analysis showed greater net benefit than clinical-only and deep-learning-only models. CONCLUSION: The CAF-derived risk score offers prognostic information complementary to routine clinical variables, representing a promising noninvasive tool for individualized risk stratification when molecular profiling is incomplete or unavailable; these findings warrant prospective external validation before clinical use.

cross-attention fusion↗