Radiology. Imaging cancer最新文献

Purpose To identify clinical and radiologic factors associated with outcomes and develop a prognostic scoring system for patients with proficient mismatch repair or microsatellite stable intrahepatic cholangiocarcinoma (iCCA) undergoing preoperative immunotherapy. Materials and Methods This retrospective study included patients who underwent MRI and immunotherapy before surgery at a tertiary referral academic center between October 2019 and May 2023. Least absolute shrinkage and selection operator regression was performed to identify relevant clinical and radiologic features associated with recurrence-free survival (RFS), followed by multivariable Cox proportional hazards analysis to construct a nomogram incorporating all independent prognostic factors. Performance of the nomogram model was assessed using the concordance index, and RFS was analyzed using Kaplan-Meier curves and log-rank testing. Results This study included 77 patients (45 male patients) with a median age of 60 years (IQR: 51-66 years). Least absolute shrinkage and selection operator regression identified 14 potential factors, and multivariable Cox analysis determined seven independent prognostic factors: male sex (hazard ratio [HR]: 2.1, P = .04), history of hepatitis B (HR: 2.3, P = .04), microvascular invasion (HR: 3.7, P = .02), preoperative carbohydrate antigen 19-9 levels (HR: 1.1, P = .04), number of tumors (HR: 1.1, P = .03), tumor size (HR: 1.2, P = .006), and lobulated or irregular morphology (HR: 4.4, P = .004). The nomogram incorporating these factors predicted RFS with a concordance index of 0.72 (95% CI: 0.65, 0.80). Nomogram-based risk stratification revealed significant differences in RFS between groups (P < .001), with a median RFS of 42 months in the low-risk group and 10 months in the high-risk group. Conclusion The proposed prognostic scoring system provides a valuable tool for evaluating prognosis in patients with proficient mismatch repair or microsatellite stable iCCA undergoing preoperative immunotherapy, which can aid clinical decision-making. Keywords: MR Imaging, Abdomen/GI, Outcomes Analysis Supplemental material is available for this article. © RSNA, 2025.

Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communications in Medicine-based deep learning-based denoising (DLD), in evaluating small renal masses (SRMs) during active surveillance. Materials and Methods In this retrospective study, CT scans from patients undergoing active surveillance for an SRM were included. Using a validated simulation technique, LD CT images were generated from the RD images to simulate 75% (LD75) and 90% (LD90) radiation dose reductions. Two additional LD image sets, in which the DLD was applied (LD75-DLD and LD90-DLD), were generated. Between January 2023 and June 2024, nine radiologists from three institutions independently evaluated 350 CT scans across five datasets for tumor size, tumor nearness to the collecting system (TN), and tumor shape irregularity (TSI), and interobserver reproducibility and agreement were assessed using the 95% limits of agreement with the mean (LOAM) and Gwet AC2 coefficient, respectively. Subjective and quantitative image quality assessments were also performed. Results The study sample included 70 patients (mean age, 73.2 years ± 9.2 [SD]; 48 male, 22 female). LD75 CT was found to be in agreement with RD scans for assessing SRM diameter, with a LOAM of ±2.4 mm (95% CI: 2.3, 2.6) for LD75 compared with ±2.2 mm (95% CI: 2.1, 2.4) for RD. However, a 90% dose reduction compromised reproducibility (LOAM ±3.0 mm; 95% CI: 2.8, 3.2). LD90-DLD preserved measurement reproducibility (LOAM ±2.4 mm; 95% CI: 2.3, 2.6). Observer agreement was comparable between TN and TSI assessments across all image sets, with no statistically significant differences identified (all comparisons P ≥ .35 for TN and P ≥ .02 for TSI; Holm-corrected significance threshold, P = .013). Subjective and quantitative image quality assessments confirmed that DLD effectively restored image quality at reduced dose levels: LD75-DLD had the highest overall image quality, significantly lower noise, and improved contrast-to-noise ratio compared with RD (P < .001). Conclusion A 75% reduction in radiation dose is feasible for SRM assessment in active surveillance using CT with a conventional iterative reconstruction technique, whereas applying DLD allows submillisievert dose reduction. Keywords: CT, Urinary, Kidney, Radiation Safety, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2025 See also commentary by Muglia in this issue.

Purpose To develop a combined contrast-enhanced US (CEUS) clinical model for the prediction of macrotrabecular-massive hepatocellular carcinoma (MTM HCC) and evaluate its diagnostic and prognostic values. Materials and Methods This secondary analysis of a prospective multicenter study (ClinicalTrials.gov: NCT04682886) included participants from three independent cohorts who underwent CEUS and surgical resection for HCC between January 2017 and December 2022. Two radiologists independently reviewed CEUS data, and the interreader agreement was evaluated. Logistic regression was performed using the training cohort to determine the predictors associated with MTM HCC, while the validation cohort was used to evaluate the diagnostic and prognostic values of the predictors. Results A total of 387 participants (mean age, 55.09 years ± 10.33 [SD]; 342 male) were included. Four clinical and CEUS features were associated with MTM HCC: early washout (before 60 seconds) (odds ratio [OR]: 8.82 [95% CI: 4.22, 18.64], P < .001), hypoenhancing component (OR: 4.03 [95% CI: 1.78, 9.49], P < .001), tumor size (OR: 1.28 [95% CI: 1.04, 1.59], P = .02), and serum α-fetoprotein level greater than 100 ng/mL (OR: 3.01 [95% CI: 1.41, 6.63], P = .004). The combined predictive model yielded an area under the receiver operating characteristic curve of 0.89 (95% CI: 0.85, 0.93) in the training cohort and 0.81 (95% CI: 0.73, 0.89) in the validation cohort. The model also achieved a negative predictive value of 94.2% (147 of 156) in the training cohort and 88.0% (66 of 75) in the validation cohort, with high prognostic accuracy for overall survival (hazard ratio: 2.26 [95% CI: 1.07, 4.79], P = .03). Conclusion The combined CEUS-clinical predictive model could be used to characterize the MTM HCC subtype and determine prognosis. Keywords: Molecular Imaging-Angiogenesis, Ultrasound-Contrast, Liver, Macrotrabecular-Massive Hepatocellular Carcinoma, Contrast-enhanced US Clinical trial registration no. NCT04682886 Supplemental material is available for this article. © RSNA, 2025.

Cancer-associated cachexia (CAC) is a prevalent condition that accelerates cancer progression and heightens treatment-related adverse effects in patients by affecting multiple organ systems. Despite the profound impact of CAC on clinical management and treatment outcomes of patients with cancer, the current understanding of mechanisms associated with the condition, as well as the tools necessary for early diagnosis, are limited. Currently, the clinical diagnosis of CAC relies on weight change-based assessments, which have limited sensitivity and cannot identify patients at risk for CAC. In this context, noninvasive imaging-based biomarkers, such as the composition and properties of adipose and muscle tissues, may allow for diagnosis of CAC before substantial weight loss occurs. Such early detection can potentially enable more timely and effective interventions. Furthermore, imaging allows for quantitative assessment of CAC, enabling monitoring of prognosis and treatment response. This article reviews current applications and future developments of imaging techniques, particularly those employed in current clinical radiology, that can reveal diagnostic information and facilitate early detection of CAC and quantitative evaluation of associated metabolic alterations. Keywords: Molecular Imaging, Cancer, MRI, PET/CT, Ultrasound, Muscular, Oncology © RSNA, 2025.