Radiology. Imaging cancer最新文献

Purpose To perform a systematic review evaluating current digital twin (DT) implementations, highlighting clinical relevance and technical strategies, and identifying opportunities to advance personalized, predictive care in neuro-oncology. Materials and Methods PubMed, Scopus, and Web of Science databases were systematically screened for English-language original research articles published from inception through June 2025 focused on DT development, validation, or patient-specific computational models in neuro-oncology. Extracted variables included computational frameworks, data sources, clinical or predictive tasks, and reported outcomes. Risk of bias and applicability were assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), which revealed well-defined predictors and outcomes but frequent concerns regarding participants and analysis. Results Of the 73 articles reviewed, 21 met eligibility criteria. DTs simulated tumor growth, radiation response, immune interactions, and drug transport.Most models (n = 20) relied on mechanistic or biophysical frameworks, with increasing adoption of artificial intelligence-driven and hybrid approaches. A total of 12 studies focused on glioblastomas or high-grade gliomas, and 17 relied primarily on MRI data. Tumor-growth and treatment-response simulations were the most common DT applications. Only six studies provided publicly available code, and closed-loop calibration was reported in eight studies. Predictive accuracy and correlation with clinical data were generally high, but real-time integration, multimodal data fusion, and external validation were limited. Conclusion DTs showed promise for advancing personalized neuro-oncology, with demonstrated potential in modeling tumor behavior and optimizing therapies. Applications relied mainly on mechanistic artificial intelligence methods. Despite strong predictive performance, reproducibility, multimodal integration, and external validation remained limited, reflecting method heterogeneity. Keywords: Digital Twins, Neuro-oncology, Computational Modeling, Mechanistic Models, Brain Tumor, Precision Medicine Supplemental material is available for this article. © RSNA, 2026.

Purpose To evaluate the clinical utility of deep learning-based ultrafast multiple arterial phase (AP) MRI for diagnosing hepatocellular carcinoma (HCC), compared with conventional single AP imaging, using both extracellular agents (ECAs) and hepatobiliary agents (HBAs). Materials and Methods This prospective study included participants with suspected HCC who underwent either ECA- or HBA-enhanced MRI between September 2024 and March 2025. Outcomes included late AP capture rate, image quality (overall quality, motion artifacts, noise, liver and lesion edge sharpness, and lesion conspicuity), diagnostic performance (lesion, arterial phase hyperenhancement [APHE], and HCC detection rates), and hepatic arterial visualization scoring. Wilcoxon rank sum, Pearson χ², and Fisher exact tests compared characteristics from multiple and single AP MRI. Results The final analysis included 128 participants who underwent ECA-enhanced MRI (64 multiphase, 64 single-phase; median age, 61 years [IQR, 54-69 years]; 103 male) and 108 participants who underwent HBA-enhanced MRI (54 multiphase, 54 single-phase; median age, 62 years [IQR, 56-69 years]; 83 male). In the ECA group, multiple AP MRI resulted in greater late AP capture (98% vs 81%; P = .001); higher scores for overall image quality (P = .03), motion artifacts (P < .001), lesion edge sharpness (P < .001), and lesion conspicuity (P = .007); and higher lesion (98% vs 90%; P = .01), APHE (96% vs 88%; P = .03), and HCC (96% vs 81%; P < .001) detection rates. In the HBA group, multiple AP MRI examinations also resulted in greater late AP capture (98% vs 85%; P = .04); higher scores for overall image quality (P = .01), motion artifacts (P = .04), and lesion edge sharpness (P = .005); and higher lesion (97% vs 88%; P = .04) and APHE (97% vs 86%; P = .02) detection rates. Multiphase imaging consistently achieved satisfactory hepatic arterial visualization in both contrast agent groups (mean scores > 3 on a four-point Likert scale for all categories). Conclusion Deep learning-based ultrafast multiphase arterial MRI improved late AP capture, image quality, and HCC diagnosis and enabled reliable hepatic arterial visualization within a single scan compatible with ECA and HBA. Keywords: MR-Imaging, Abdomen/GI, Liver Supplemental material is available for this article. © RSNA, 2026.

Editor's Note. This issue of Radiology: Imaging Cancer brings a new feature that we term "A Look Behind the Paper." We invite authors of selected manuscripts to provide more details about their research and the thought process that led to the final manuscript. In this inaugural Look Behind the Paper, Liang and Li and colleagues describe the motivation for the published research, any unexpected challenges they encountered, and future directions for their study developing biosynthetic gas vesicles to detect glypican-3 in hepatocellular carcinoma. Their description of the critical decision to switch the targeting molecule for glypican-3 from an RNA aptamer to a peptide helps readers understand the barriers researchers must overcome to move a project forward. We hope you enjoy the authors' description of their research goals. We look forward to bringing you more insights from A Look Behind the Paper in future issues of the journal.

Purpose To compare published CT-based systems for small solid renal mass (SoRM) assessment, propose modifications that may increase specificity and interreader agreement, and validate the revised system. Materials and Methods Our retrospective study included patients with histologically confirmed SoRMs measuring ≤4 cm who underwent CT imaging (single-institution internal dataset, n = 194; external dataset from The Cancer Imaging Archive, n = 55). Two blinded radiologists (readers 1 [R1] and 2 [R2]) compared four CT systems (CT score, modified CT score, abbreviated CT score, and UCLA CT score) for diagnostic accuracy in clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC) and for interreader agreement (Gwet agreement coefficient [AC1]). We also evaluated the addition of two decision rules to the best-performing algorithm (noncontrast CT [NCCT] attenuation ≤ 20 HU and corticomedullary phase-NCCT attenuation at two thresholds, ≤20 HU and ≤30 HU) to create a modified algorithm (CT-Score version 2.0). Results The abbreviated CT score had the best combination of accuracy for ccRCC (R1: 85% [95% CI: 79, 89], R2: 72% [95% CI: 65, 78]) and pRCC (R1: 86% [95% CI: 80, 91], R2: 86% [95% CI: 80, 91]) and interreader agreement (Gwet AC1 = 0.53). CT-Score version 2.0 (derived by adding decision rules to the abbreviated CT score) demonstrated substantial agreement (Gwet AC1 = 0.63). Specificity of CT-Score version 2.0 was higher for ccRCC (R1: 99% [95% CI: 94, 100], R2: 99% [95% CI: 94, 100] vs R1: 92% [95% CI: 84, 96], R2: 81% [95% CI: 72, 89]; P = .02, P < .001) and pRCC (R1: 100% [95% CI: 98, 100], R2: 100% [95% CI: 98, 100] vs R1: 93% [95% CI: 87, 96], R2: 93% [95% CI: 87, 96]; P = .003, P = .003) when compared with the abbreviated CT score. Validation in the external dataset showed similar results: Gwet AC1 = 0.53; specificity for ccRCC (R1: 100% [95% CI: 83, 100], R2: 100% [95% CI: 83, 100]); and specificity for pRCC (R1: 100% [95% CI: 82, 100], R2: 100% [95% CI: 92, 100]). Conclusion Application of CT-Score version 2.0 resulted in modest improvements in interreader agreement and high specificity for ccRCC and pRCC diagnosis. Keywords: CT, Kidney, Urinary, Oncology, Renal Mass, Algorithm, Clear Cell RCC, Papillary RCC Supplemental material is available for this article. © RSNA, 2026.

Purpose To examine whether time-dependent diffusion MRI (T_d-dMRI) and macromolecular proton fraction (MPF) mapping-derived quantitative metrics can effectively distinguish between cervical cancer with and without lymph node metastasis (LNM) before treatment. Materials and Methods In this prospective study of adults with clinically suspected cervical cancer who underwent T_d-dMRI, MPF mapping, and pulsed gradient spin-echo diffusion-weighted imaging (DWI_PGSE) examinations between October 2023 and June 2025, authors calculated T_d-dMRI-derived parameters (cellularity, diameter, intracellular volume fraction [V_in], and extracellular diffusivity [D_ex]), MPF, and DWI_PGSE-derived parameter (pulsed gradient spin-echo apparent diffusion coefficient [ADC_PGSE]). Through Ridge regression analysis, the authors identified independent predictors of LNM and developed a composite diagnostic tool using logistic regression analysis. To evaluate tool performance, the area under the receiver operating characteristic curve was determined. Results Among 98 female individuals with cervical cancer (mean age, 56.69 years ± 11.63 [SD]), participants who were LNM positive exhibited higher cellularity, V_in, and MPF but lower diameter, D_ex, and ADC_PGSE than their counterparts who were LNM negative (P < .001 to P = .007). Cellularity, maximum tumor diameter, and MPF were independent predictors of LNM status, with their combination yielding the best diagnostic performance (area under the receiver operating characteristic curve, 0.95; 95% CI: 0.89, 0.98). The performance of this combination surpassed that of individual imaging modality, including DWI_PGSE (ADC_PGSE), and MPF, as well as any individual parameter, including cellularity, V_in, diameter, and D_ex. Conclusion T_d-dMRI and MPF mapping were effective for predicting LNM in cervical cancer, with the combination of cellularity, maximum tumor diameter, and MPF showing the best diagnostic performance. Keywords: Time-Dependent Diffusion MRI, Macromolecular Proton Fraction, Cervical Cancer, Lymph Node Metastases © RSNA, 2026.

Purpose To compare gallium 68 (⁶⁸Ga) fibroblast activation protein inhibitor (FAPI) PET/CT with plasma Epstein-Barr virus (EBV)-DNA load measurement, contrast-enhanced MRI/CT, and fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/CT for nasopharyngeal carcinoma (NPC) initial assessment and recurrence surveillance. Materials and Methods Participants with NPC who underwent both ¹⁸F-FDG and ⁶⁸Ga-FAPI PET/CT examinations within 1 week for initial assessment (treatment-naive cohort) and recurrence surveillance (posttreatment cohort) were enrolled in this prospective study conducted between July 2020 and November 2023 (Chinese Clinical Trial Registry identifier no. ChiCTR2100054163). The data of plasma EBV DNA load and contrast-enhanced MRI/CT were analyzed. Diagnostic performance was compared using the McNemar test. The relationships between plasma EBV DNA and the two tracers were assessed by using simple linear regression analysis. Results Sixty-five participants (median age, 50 years [IQR, 41.50-61 years]; 50 male) were included, with 31 in the treatment-naive cohort and 34 in the posttreatment cohort. In participant-level analysis, ⁶⁸Ga-FAPI PET/CT demonstrated the highest accuracy rate in identifying the participants with de novo (100% [31 of 31] vs 84% [26 of 31], 77% [24 of 31], and 84% [26 of 31], P = .06, .02, and .06, respectively) and recurrent (94% [32 of 34] vs 53% [18 of 34], 68% [23 of 34], and 85% [29 of 34], P = .001, .02, and .25, respectively) NPC, compared with plasma EBV DNA, contrast-enhanced MRI/CT, and ¹⁸F-FDG PET/CT. Furthermore, compared with contrast-enhanced MRI/CT and ¹⁸F-FDG PET/CT, ⁶⁸Ga-FAPI PET/CT led to upstaging in five of 31 (16%) and downstaging in four of 31 (13%) treatment-naive participants. Lesion uptake at ⁶⁸Ga-FAPI PET/CT did not correlate with plasma EBV DNA load (P > .05). Conclusion ⁶⁸Ga-FAPI PET/CT outperformed plasma EBV DNA level, contrast-enhanced MRI/CT, and ¹⁸F-FDG PET/CT in initial assessment and recurrence surveillance of NPC. Keywords: Head/Neck, PET/CT, Nasopharyngeal Carcinoma, Epstein-Barr Virus, Fibroblast Activation Protein Inhibitor, FDG Chinese Clinical Trial Registry identifier no. ChiCTR2100054163 Supplemental material is available for this article. © RSNA, 2026.