European Radiology最新文献

Objective: To investigate the prognostic value of preoperative body composition and serum tumor markers (STM) in patients undergoing surgical treatment for colorectal cancer (CRC) and to establish the prognostic score for patients with CRC.

Methods: This study enrolled 365 patients (training set 245, validation set 120) with CRC who underwent surgical resection. The predictive value of various body composition features and STM for determining CRC prognosis were compared. A novel index score based on the independent risk factors from Cox regression for CRC patients was established and evaluated for its usefulness.

Results: Multivariate Cox regression showed that low skeletal muscle radiodensity (SMD) (p = 0.020), low subcutaneous fat area (SFA) (p = 0.029), high carcinoembryonic antigen (CEA) (p = 0.008), and high alpha-fetoprotein (AFP) (p = 0.039) were all independent prognostic factors for poor overall survival (OS). The multifactorial analysis indicated that high intermuscular fat area (IMFA) (p = 0.033) and high CEA (p = 0.009) were independent prognostic factors for poor disease-free survival (DFS). Based on these findings, two scoring systems for OS and DFS were established in the training datasets. CRC patients who scored higher on the new scoring systems had lower OS and DFS (both p < 0.001) as shown in the Kaplan-Meier survival curves in the training and validation datasets.

Conclusion: In predicting the prognosis of CRC patients, SFA and SMD are superior to other body composition measurements. A scoring system based on body composition and STM can have prognostic value and clinical applicability.

Clinical relevance statement: This scoring system, combining body composition and serum tumor markers, may help predict postoperative survival of CRC patients and help clinicians make well-informed decisions regarding the treatment of patients.

Key points: Colorectal cancer prognosis can be related to body composition. High intermuscular fat area and CEA were independent prognostic factors for poor disease-free survival. This scoring system, based on body composition and tumor markers, can prognosticate for colorectal cancer patients.

Objective: To review the components of past and present active surveillance (AS) protocols, provide an overview of the current studies employing artificial intelligence (AI) in AS of prostate cancer, discuss the current challenges of AI in AS, and offer recommendations for future research.

Methods: Research studies on the topic of MRI-based AI were reviewed to summarize current possibilities and diagnostic accuracies for AI methods in the context of AS. Established guidelines were used to identify possibilities for future refinement using AI.

Results: Preliminary results show the role of AI in a range of diagnostic tasks in AS populations, including the localization, follow-up, and prognostication of prostate cancer. Current evidence is insufficient to support a shift to AI-based AS, with studies being limited by small dataset sizes, heterogeneous inclusion and outcome definitions, or lacking appropriate benchmarks.

Conclusion: The AI-based integration of prostate MRI is a direction that promises substantial benefits for AS in the future, but evidence is currently insufficient to support implementation. Studies with standardized inclusion criteria and standardized progression definitions are needed to support this. The increasing inclusion of patients in AS protocols and the incorporation of MRI as a scheduled examination in AS protocols may help to alleviate these challenges in future studies.

Clinical relevance statement: This manuscript provides an overview of available evidence for the integration of prostate MRI and AI in active surveillance, addressing its potential for clinical optimizations in the context of established guidelines, while highlighting the main challenges for implementation.

Key points: Active surveillance is currently based on diagnostic tests such as PSA, biopsy, and imaging. Prostate MRI and AI demonstrate promising diagnostic accuracy across a variety of tasks, including the localization, follow-up and risk estimation in active surveillance cohorts. A transition to AI-based active surveillance is not currently realistic; larger studies using standardized inclusion criteria and outcomes are necessary to improve and validate existing evidence.

Objective: To assess the performance of computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) among patients with non-cirrhotic steatotic liver disease (SLD).

Materials and methods: This IRB-approved, retrospective study included 119 observations from 77 adult patients (36 women, 41 men; median 64 years) who underwent liver CT or MRI from 2010 to 2023. All patients had histopathologic evidence of SLD without cirrhosis. Three board-certified abdominal radiologists blinded to tissue diagnosis and imaging follow-up assessed observations with LI-RADS. The positive predictive value (PPV), sensitivity, specificity, accuracy, and inter-reader agreement were calculated.

Results: Seventy-five observations (63%) were benign and 44 (37%) were malignant. PPV for hepatocellular carcinoma (HCC) was 0-0% for LR-1, 0-0% for LR-2, 0-7% for LR-3, 11-20% for LR-4, 75-88% for LR-5, 0-8% for LR-M, and 50-75% for LR-TIV. For LR-5 in identifying HCC, sensitivity was 79-83%, specificity was 91-97%, and accuracy was 89-92%. For composite categories of LR-5, LR-M, or LR-TIV in identifying malignancy, sensitivity was 86-89%, specificity was 85-96%, and accuracy was 86-93%. The most common false positives for LR-5 were hepatocellular adenomas. Only 59-65% of HCCs showed non-peripheral washout at CT versus 67-83% at MRI, though nearly all had an enhancing capsule. PPV and accuracy of LR-5 for HCC did not differ by modality. Inter-reader agreement for major features ranged from 0.667 to 0.830 and was 0.766 for the final category.

Conclusion: Despite challenges such as the lower prevalence of non-peripheral washout at CT and overlapping imaging features between HCC and hepatocellular adenomas, LI-RADS may serve as an effective tool in assessing focal liver lesions in SLD.

Clinical relevance statement: LI-RADS in non-cirrhotic steatotic liver disease can effectively diagnose hepatocellular carcinoma and malignancy at computed tomography and magnetic resonance imaging, thereby guiding clinical management decisions and expediting patient care pathways.

Key points: Performance of LI-RADS is unknown in non-cirrhotic patients with steatotic liver disease. LI-RADS 5 category showed a high pooled specificity of 91-97% for hepatocellular carcinoma. LI-RADS can non-invasively risk stratify focal liver observations in non-cirrhotic patients with steatotic liver disease.

Objectives: To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD).

Materials and methods: This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups.

Results: A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002).

Conclusion: Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD.

Clinical relevance statement: The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD.

Key points: Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.

Active surveillance (AS) is a conservative management option recommended for patients diagnosed with low-risk prostate cancer (PCa) and selected cases with intermediate-risk PCa. The adoption of prostate MRI in the primary diagnostic setting has sparked interest in its application during AS. This review aims to examine the role and performance of multiparametric MRI (mpMRI) across the entire AS pathway, from initial stratification to follow-up, also relative to the utilization of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) criteria. Given the high negative predictive value of mpMRI in detecting clinically significant PCa (csPCa), robust evidence supports its use in patient selection and risk stratification at the time of diagnosis or confirmatory biopsy. However, conflicting results have been observed when using MRI in evaluating disease progression during follow-up. Key areas requiring clarification include addressing the clinical significance of MRI-negative csPCa, optimizing MRI quality, determining the role of biparametric MRI (bpMRI) or mpMRI protocols, and integrating artificial intelligence (AI) for improved performance. CLINICAL RELEVANCE STATEMENT: MRI plays an essential role in the selection, stratification, and follow up of patients in active surveillance (AS) for prostate cancer. However, owing to existing limitations, it cannot fully replace biopsies in the context of AS. KEY POINTS: Multiparametric MRI (mpMRI) has become a crucial tool in active surveillance (AS) for prostate cancer (PCa). Conflicting results have been observed regarding multiparametric MRI efficacy in assessing disease progression. Standardizing MRI-guided protocols will be critical in addressing current limitations in active surveillance for prostate cancer.

Objectives: To assess 3-Tesla (3-T) ultra-small superparamagnetic iron oxide (USPIO)-enhanced MRI in detecting lymph node (LN) metastases for resectable adenocarcinomas of the pancreas, duodenum, or periampullary region in a node-to-node validation against histopathology.

Methods: Twenty-seven consecutive patients with a resectable pancreatic, duodenal, or periampullary adenocarcinoma were enrolled in this prospective single expert centre study. Ferumoxtran-10-enhanced 3-T MRI was performed pre-surgery. LNs found on MRI were scored for suspicion of metastasis by two expert radiologists using a dedicated scoring system. Node-to-node matching from in vivo MRI to histopathology was performed using a post-operative ex vivo 7-T MRI of the resection specimen. Sensitivity and specificity were calculated using crosstabs.

Results: Eighteen out of 27 patients (median age 65 years, 11 men) were included in the final analysis (pre-surgery withdrawal n = 4, not resected because of unexpected metastases peroperatively n = 2, and excluded because of inadequate contrast-agent uptake n = 3). On MRI 453 LNs with a median size of 4.0 mm were detected, of which 58 (13%) were classified as suspicious. At histopathology 385 LNs with a median size of 5.0 mm were found, of which 45 (12%) were metastatic. For 55 LNs node-to-node matching was possible. Analysis of these 55 matched LNs, resulted in a sensitivity and specificity of 83% (95% CI: 36-100%) and 92% (95% CI: 80-98%), respectively.

Conclusion: USPIO-enhanced MRI is a promising technique to preoperatively detect and localise LN metastases in patients with pancreatic, duodenal, or periampullary adenocarcinoma.

Clinical relevance statement: Detection of (distant) LN metastases with USPIO-enhanced MRI could be used to determine a personalised treatment strategy that could involve neoadjuvant or palliative chemotherapy, guided resection of distant LNs, or targeted radiotherapy.

Registration: The study was registered on clinicaltrials.gov NCT04311047. https://clinicaltrials.gov/ct2/show/NCT04311047?term=lymph+node&cond=Pancreatic+Cancer&cntry=NL&draw=2&rank=1 .

Key points: LN metastases of pancreatic, duodenal, or periampullary adenocarcinoma cannot be reliably detected with current imaging. This technique detected LN metastases with a sensitivity and specificity of 83% and 92%, respectively. MRI with ferumoxtran-10 is a promising technique to improve preoperative staging in these cancers.