Insights into Imaging最新文献

Objectives: To test the feasibility of 60 kVp double-low-dose coronary CT angiography (CCTA) with a deep learning reconstruction (DLR) algorithm.

Materials and methods: Eighty-nine patients (44 females, 59.9 ± 13.2 years, 23.1 ± 3.3 kg/m²) with known or suspected coronary artery disease were prospectively enrolled. Each patient underwent the double-low-dose CCTA (60-kVp, 28 mL contrast at 2.5 mL/s) and was immediately followed by routine-dose CCTA (100-kVp, 44 mL contrast at 4.0 mL/s). Routine-dose data were reconstructed using hybrid iterative reconstruction (RD-HIR), and double-low-dose data were reconstructed using both HIR (LD-HIR) and DLR (LD-DLR). The consistency of both coronary stenosis assessments and CT-derived fractional flow reserve (CT-FFR) values between low-dose and routine-dose images was quantified using receiver operating characteristic analysis at various levels. Segment-level image quality scores (IQS), signal-noise-ratio (SNR), and contrast-noise-ratio (CNR) were compared among three groups.

Results: Double-low-dose CCTA achieved a significant reduction in both radiation dose (0.60 ± 0.12 mSv vs 4.43 ± 1.42 mSv) and contrast volume compared to routine-dose CCTA. For the per-segment level, LD-DLR showed significantly higher specificity (0.99 vs 0.94), positive predictive value (0.91 vs 0.68), and accuracy (0.98 vs 0.94) for ≥ 50% coronary stenosis compared to LD-HIR. The area under the curve of LD-DLR was significantly higher than LD-HIR for ≥ 50% stenosis at per-segment (0.94 vs 0.92), per-vessel (0.92 vs 0.89), and per-patient (0.92 vs 0.85) levels; and for CT-FFR ≤ 0.80 at per-vessel (0.94 vs 0.74), LAD-vessel (0.94 vs 0.71), and LCX-vessel (0.99 vs 0.67) levels. The IQS, SNR, and CNR of LD-DLR were not inferior to those of RD-HIR in all segments.

Conclusions: The 60 kVp double-low-dose CCTA with DLR can significantly reduce radiation dose and simultaneously maintain the high consistency of coronary stenosis and CT-FFR assessments with routine-dose CCTA.

Critical relevance statement: The 60 kVp double-low-dose CCTA protocol is feasible with a novel DLR algorithm without compromising the performance of coronary stenosis and CT-FFR assessments.

Key points: Is a 60 kVp double-low-dose CCTA protocol with a DLR algorithm feasible for routine clinical application? The 60 kVp CCTA protocol with the DLR algorithm reduced radiation dose by 86.5% and contrast dose by 36.4%. The 60 kVp CCTA with DLR achieved high consistency of coronary stenosis and CT-FFR values with the routine-dose 100 kVp CCTA.

Objectives: The study aimed to assess the predictive performance of transition zone PSA density (TZ-PSAD) compared to conventional PSA density (PSAD) in detecting clinically significant prostate cancer (csPCa) among patients with negative pre-biopsy MRI findings.

Materials and methods: The study included 606 patients with negative MRI findings who subsequently underwent transrectal ultrasound-guided systematic biopsy. AI software automatically measured prostate and zonal volumes, from which PSAD and TZ-PSAD (total PSA/transition zone volume) were calculated. Diagnostic performances were evaluated using ROC curve analysis, risk stratification was applied to select patients needing biopsy, and independent predictors of imaging-invisible csPCa were determined through univariate and multivariate analyses.

Results: 51 patients (8.4%) were diagnosed with csPCa. TZ-PSAD demonstrated significant superior discriminative ability (AUC = 0.718) compared to PSAD (AUC = 0.686; p = 0.019). Patients with TZ-PSAD ≥ 0.35 ng/mL/cc had a csPCa detection rate of 20.1%, while those below this threshold had a rate of 4.1%. The optimal TZ-PSAD threshold of 0.35 ng/mL/cc showed superior performance than the guideline-recommended PSAD threshold of 0.2 ng/mL/cc. Multivariate analysis identified TZ-PSAD as a strong independent predictor of imaging-invisible csPCa.

Conclusions: TZ-PSAD outperforms conventional PSAD in predicting csPCa among men with negative MRI, offering a valuable tool for risk stratification. This facilitates individualized risk assessment, potentially reducing unnecessary biopsies and optimizing patient management.

Critical relevance statement: Our AI system delivers accurate and reproducible prostate zone segmentation, while TZ-PSAD derived from AI outperforms conventional PSAD in detecting csPCa in MRI-negative patients and serves as an effective triage tool to optimize biopsy decision-making and reduce unnecessary systematic biopsies.

Key points: Our AI system enables accurate and reproducible segmentation and measurement of prostate zones. TZ-PSAD demonstrates significantly superior diagnostic performance over conventional PSAD for identifying men with a negative MRI who will have csPCa on a systematic biopsy. TZ-PSAD represents an effective triage metric to reduce unwarranted systematic biopsies in MRI-negative patients.

Objectives: To evaluate the impact of a law amendment that reduced the eye lens dose limit on the use of personal dosimeters among radiation workers in medical settings.

Materials and methods: A repeated cross-sectional survey was conducted at medical institutions across three periods: before the law amendment (control) and during the promulgation and implementation periods. Surveyors (radiological technologists) at each participating medical institution recorded dosimeter-wearing status among radiation workers. Data were collected via mail or email and analysed. The observed workers included physicians, nurses, and radiological technologists.

Results: The surveys were collected from 1194 workers in the control period, 1374 in the promulgation period, and 1194 in the implementation period, totalling 3762 workers. Post-law amendment, the overall wearing rate of primary personal dosimeters significantly increased from 64.6% to 77.9% (p < 0.001). Significant increases in wearing rates were observed among physicians and radiological technologists (p < 0.001). Among occupations, physicians showed the lowest wearing rates across all periods (control: 35.8%, promulgation: 56.7%, implementation: 62.6%), whereas radiological technologists showed the highest (control: 92.7%, promulgation: 98.5%, implementation: 99.5%). Regarding physician specialities, orthopaedic surgery exhibited the lowest compliance (control: 11.3%, promulgation: 35.4%, implementation: 24.7%). The proportion of workers without provision of a personal dosimeter declined from 5.9% to 1.9% (p < 0.001).

Conclusions: Despite overall improvement following the law amendment, low compliance among physicians, particularly in orthopaedics, indicates the need for targeted interventions.

Critical relevance statement: Although dosimeter-wearing rates improved after Japan's eye dose limit revision, persistent low physician compliance-especially in orthopaedics-highlights the need for targeted strategies to strengthen radiation protection in clinical practice.

Key points: The effect of reduced eye dose limits on dosimeter use remains unclear. Personal dosimeter usage increased significantly after the law amendment. Compliance remained low among orthopaedic physicians despite regulatory tightening. Targeted interventions are needed for low-compliance groups to ensure radiation protection.

Objectives: To develop and validate a primary tumor-derived, multiparametric MRI-based deep learning-radiomics-clinical (DLRC) model for predicting pelvic lymph node metastasis (LNM) in early-stage cervical cancer.

Materials and methods: This retrospective five-center study selected 1095 patients (Jan 2020-Dec 2022), divided into training (n = 481), internal validation (n = 204), and external validation (n = 410) cohorts. Radiomics and deep learning (DL) features were extracted from the volumetric segmentations of the primary cervical tumors on three MRI sequences (CE-T1WI, DWI, FS-T2WI). After constructing individual radiomics and DL models, the DLRC model was developed by integrating the radiomics_score, optimal DL model, and significant clinical features. Model performance was evaluated using ROC analysis, calibration curves, and decision curve analysis.

Results: The DLRC model demonstrated superior predictive performance, achieving AUCs of 0.807 (95% CI: 0.766-0.849) in the training cohort, 0.789 (95% CI: 0.721-0.857) in the internal validation cohort, and 0.807 (95% CI: 0.761-0.853) in the external validation cohort. It significantly outperformed both the radiomics model and the optimal DL model (all p < 0.001) in the external validation cohort. The calibration curves indicated good agreement between predictions and observations. The decision curve analysis showed that the DLRC model provided the highest net clinical benefit across most decision thresholds.

Conclusions: The DLRC model, which integrates tumor-derived multiparametric MRI features with clinical features, represents a robust and generalizable tool for the preoperative prediction of LNM. Its comparable accuracy to standardized radiological assessment and clinical utility shows potential to aid in personalized treatment planning for patients with early-stage cervical cancer.

Critical relevance statement: The combined model (DLRC) integrating deep learning and radiomics features from the primary tumor with clinical characteristics enables preoperative LNM risk stratification, supporting personalized surgical planning and reducing unnecessary lymphadenectomy.

Key points: Accurate preoperative prediction of lymph node metastasis in early-stage cervical cancer remains a significant clinical challenge. The model integrating deep learning and radiomics features derived from the primary tumor with clinical features achieved robust and generalizable predictive performance. The accuracy of a deep learning-radiomics-clinical nomogram for lymph node metastasis risk stratification in early-stage cervical cancer is comparable to standardized radiological assessment.

Colorectal cancer (CRC) is the third most common malignancy worldwide, and early detection is vital to prevent metastasis and postoperative recurrence. This review summarizes current applications of spectral computed tomography (CT) in CRC, including its principles, spectral parameters used for evaluating primary and metastatic lesions, and key findings from recent literature. A systematic search of PubMed, Web of Science, and Google Scholar identified English-language studies published between April 2018 and April 2025 using the keywords: "spectral CT," "spectral imaging," "dual-layer spectral CT," "dual-energy spectral CT," "colorectal cancer," and "colon cancer." Spectral CT has shown promise in improving CRC detection and T staging accuracy, increasing sensitivity for lesion characterization, and aiding prognostic assessment after chemotherapy using baseline spectral parameters. Early evidence suggests it may also help predict lymph node metastasis and identify patients at risk of early postoperative metastases or surgical complications. Spectral parameters have been correlated with KRAS mutation, Ki-67 index, microsatellite instability, lymphovascular, perineural, and extramural vascular invasion, as well as microvessel density. However, most studies remain small and observational, highlighting the need for validation in larger, multicenter cohorts. Standardization and the time-intensive nature of image segmentation currently limit widespread adoption. Nevertheless, spectral CT is expected to play an increasing role in CRC evaluation by providing quantitative, predictive imaging biomarkers. Integration with artificial intelligence, particularly deep learning and automated segmentation, will likely expand both research and clinical applications. CRITICAL RELEVANCE STATEMENT: This article explores the current applications of spectral CT in colorectal cancer by outlining the fundamentals of spectral CT, the spectral parameters used to assess, stage, and predict the prognosis of primary and metastatic disease, as well as the main findings from the current literature. KEY POINTS: Spectral CT may be helpful in the detection of colorectal primary tumors, lymph node metastases, and liver metastases, as well as in predicting treatment response. Spectral CT offers a non-invasive method to assess genetic mutations and prognostic factors associated with colorectal primaries. The lack of standardization in technology and measurement methods limits its applicability in clinical practice.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors originating from neural crest-derived chromaffin tissue, marked by clinical heterogeneity and substantial genetic underpinnings. With up to 70% of cases linked to germline or somatic mutations, including Succinate DeHydrogenase genetic alterations (SDHx), and Von Hippel-Lindau (VHL), genetic profiling is central to diagnosis, risk stratification, and therapeutic planning. Clinical presentation varies by tumor location and secretory status-from catecholamine-driven crises to mass effect in head and neck paragangliomas (H&N PGLs). The diagnostic workflow begins with biochemical testing, followed by high-resolution anatomical and functional imaging. Computed tomography (CT) and magnetic resonance imaging (MRI) remain essential for localization and staging, while radiopharmaceuticals such as ⁶⁸Ga-DOTA⁰-Tyr³-octreotate (⁶⁸Ga-DOTATATE), ¹⁸F-fluoro-L-dihydroxyphenylalanine (¹⁸F-FDOPA), and ¹³¹I-metaiodobenzylguanidine (¹³¹I-MIBG) refine tumor characterization and guide peptide receptor radiopharmaceutical therapy (RPT) with radiolabeled octreotide derivatives or therapeutic MIBG Imaging features such as size, necrosis, and diffusion restriction correlate with malignancy risk, but novel molecular imaging offer promise for more precise prognostication. Therapeutic options span from curative surgery to systemic therapies, including temozolomide, tyrosine kinase inhibitors, and nuclide therapy. Minimally invasive, image-guided interventions provide palliation for metastatic or inoperable disease. Importantly, artificial intelligence and molecular assays such as the NETest and ¹H-MRS are emerging as pivotal tools in real-time tumor monitoring, early relapse detection, and biomarker discovery. This review underscores the necessity of a multidisciplinary, genomics-informed, and imaging-guided approach to PPGL management. With the integration of advanced imaging and AI-driven analytics, precision oncology for PPGLs is transitioning from potential to practice. CRITICAL RELEVANCE STATEMENT: This article offers an overview of the diverse manifestations of paragangliomas, illustrated with examples from various anatomical locations. It also highlights different patterns of tumor evolution and provides an up-to-date review of current management and therapeutic strategies, with a special focus on emerging AI-guided approaches. KEY POINTS: Review the genetic associations, including Von Hippel-Lindau, Multiple Endocrine Neoplasia, Neurofibromatosis, and Carney Triad. Overview of anatomical imaging features (CT and MRI) of paragangliomas. Improve knowledge about the different Nuclear Medicine and functional imaging techniques in detecting lesions, depending on their location, secretory function and underlying genetic mutation. Discuss the multiple radiopharmaceuticals available for Scintigraphy and PET-CT, according to the paraganglioma site and mutational pattern.

Objectives: Renal biopsy has certain limitations for diagnosing membranous nephropathy (MN). The aim is to explore the value of MRI for diagnosing MN.

Materials and methods: MN patients were divided into two subgroups based on estimated glomerular filtration rate, including the mild group and moderate to severe group. Quantitative T1 mapping and renal blood flow (RBF) of bilateral kidneys were measured, including renal cortical T1 mapping (cT1) value, medullary T1 mapping (mT1) value, cortical RBF value (cRBF), and medullary RBF (mRBF) value. The Student's t-test, Mann-Whitney U test, chi-square test, and one-way analysis of variance were used.

Results: Forty-seven MN patients and 54 matched healthy controls (HC) were prospectively enrolled. The cT1 and mT1 average values of HC were significantly lower than those of both MN subgroups (all p < 0.001) after adjusting for age and sex. Compared with the mild group and HC group, the moderate to severe group had lower cRBF (all p < 0.050) and mRBF average values (p = 0.012 and p < 0.001, respectively). The combination model of the T1 mapping and RBF values for differentiating MN from HC had a higher area under the curve of 0.87 (95% confidence intervals, 0.80-0.95) than single-parameter models (all p < 0.050), except the mT1 value model.

Conclusions: Multiparametric MRI shows potential as a noninvasive adjunct tool for assessing MN, offering a possibility to guide clinical decision-making.

Critical relevance statement: Multiparametric MRI provides a noninvasive approach to renal structural and perfusion changes in membranous nephropathy and offers an alternative to guide clinical treatment strategies.

Key points: Renal biopsy has certain limitations for diagnosing membranous nephropathy, and there is an urgent need to develop a noninvasive method. Membranous nephropathy patients had higher cortex, medullary T1 mapping values and lower cortex, medullary renal blood flow values than healthy controls. Quantitative MRI parameters show potential as a noninvasive biomarker for assessing membranous nephropathy.

Objectives: To evaluate the prognostic significance of tumour mutation burden (TMB) in pancreatic ductal adenocarcinoma (PDAC) and explore the performance of dual-layer spectral CT (DLCT) for noninvasive TMB evaluation.

Materials and methods: This retrospective analysis enroled patients with histopathologically confirmed PDAC who underwent DLCT between June 2019 and December 2023. Clinical, qualitative radiological, and quantitative conventional CT and DLCT parameters were evaluated. Survival analysis evaluated TMB's association with progression-free survival (PFS) and identified an optimal TMB cutoff. Independent TMB predictors were identified through univariable and LASSO regression. Predictive performance was quantified via receiver operating characteristic and precision-recall curve assessments.

Results: Among 75 patients (mean age 60.4 ± 11.2 years; 41 males, 34 females), median TMB was 2.13 mut/Mb (interquartile range: 1.00-4.26). A 5 mut/Mb cutoff revealed distinct prognostic groups, with high-TMB cases exhibiting better PFS (median PFS: 7 vs 5 months, p = 0.02). Normalised iodine concentration in the pancreatic phase (nICa) was the sole independent TMB predictor (area under the curve [AUC] = 0.901; cutoff = 0.089; accuracy = 89.3% [89.1-89.6%], sensitivity = 81.8% [59.0-100%], specificity = 90.6% [83.5-97.8%]), surpassing conventional CT attenuation metrics (nCTa, AUC = 0.834), peripancreatic tumour infiltration (AUC = 0.679), and their combined model (AUC = 0.864) with significant net reclassification improvement (all p < 0.05). Precision-recall curve validation reinforced nICa's superior predictive capacity. Patients classified by nICa-predicted high TMB status demonstrated better PFS (median PFS: 7 vs 5 months, p = 0.04).

Conclusion: Elevated TMB is a positive biomarker for PFS in PDAC. DLCT-derived nICa facilitates precise, noninvasive TMB prediction, outperforming conventional imaging parameters and supporting its potential role in therapeutic stratification.

Critical relevance statement: Elevated tumour mutational burden (TMB) in PDAC correlated with prolonged PFS. DLCT provided noninvasive, accurate TMB quantification, enabling meaningful survival stratification.

Key points: High TMB in patients with PDAC portends better PFS, particularly those receiving combination immunotherapy. A clinically applicable TMB cutoff of 5 mut/Mb was identified, stratifying patients into biologically distinct low- and high-TMB prognostic groups. DLCT-derived pancreatic phase normalized iodine concentration emerged as a superior noninvasive TMB biomarker compared to conventional imaging parameters.