Insights into Imaging最新文献

Objectives: Gut-brain axis dysfunction has emerged as a key contributor to the pathogenesis of Crohn's disease (CD). The elucidation of neural alterations may provide novel insights into its management. We aimed to develop a multiparameter brain MRI-based radiomics model (RM) for characterizing neural alterations in CD patients and to interpret these alterations using multiomics traits.

Methods: This prospective study enrolled 230 CD patients and 46 healthy controls (HCs). Participants voluntarily underwent brain MRI and psychological assessment (n = 155), blood metabolomics analysis (n = 260), and/or fecal 16S rRNA sequencing (n = 182). The RM was developed using 13 features selected from 13,870 first-order features extracted from multiparameter brain MRI in training cohort (CD, n = 75; HCs, n = 32) and validated in test cohort (CD, n = 34; HCs, n = 14). Multiomics data (including gut microbiomics, blood metabolomics, and brain radiomics) were compared between CD patients and HCs.

Results: In the training cohort, area under the receiver operating characteristic curve (AUC) of RM for distinguishing CD patients from HCs was 0.991 (95% confidence interval (CI), 0.975-1.000). In test cohort, RM showed an AUC of 0.956 (95% CI, 0.881-1.000). CD-enriched blood metabolites such as triacylglycerol (TAG) exhibited significant correlations with both brain features detected by RM and CD-enriched microbiota (e.g., Veillonella). One notable correlation was found between Veillonella and Ctx-Lh-Middle-Temporal-CBF-p90 (r = 0.41). Mediation analysis further revealed that dysbiosis, such as of Veillonella, may regulate the blood flow in the middle temporal cortex through TAG.

Conclusion: We developed a multiparameter MRI-based RM that characterized the neural alterations of CD patients, and multiomics data offer potential evidence to support the validity of our model. Our study may offer clues to help provide potential therapeutic targets.

Critical relevance statement: Our brain-gut axis study developed a novel model using multiparameter MRI and radiomics to characterize brain changes in patients with Crohn's disease. We validated this model's effectiveness using multiomics data, making it a potential biomarker for better patient management.

Key points: Utilizing multiparametric MRI and radiomics techniques could unveil Crohn's disease's neurophenotype. The neurophenotype radiomics model is interpreted using multiomics data. This model may serve as a novel biomarker for Crohn's disease management.

Objectives: To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses.

Methods: A total of 170 patients with pathologically and endoscopically confirmed proximal esophageal cancer from two centers were enrolled. Radiomics models were established by five machine learning approaches. The optimal radiomics model was selected using receiver operating curve analysis. Bioinformatics methods were applied to explore the potential biological mechanisms. Nomograms based on radiomics and clinical-radiomics features were constructed and assessed by receiver operating characteristics, calibration, and decision curve analyses net reclassification improvement, and integrated discrimination improvement evaluations.

Results: The peritumoral models performed well with the majority of classifiers in the training and validation sets, with the dual-region radiomics model showing the highest integrated area under the curve values of 0.9763 and 0.9471, respectively, and outperforming the single-region models. The clinical-radiomics nomogram showed better predictive performance than the clinical nomogram, with a net reclassification improvement of 34.4% (p = 0.02) and integrated discrimination improvement of 10% (p = 0.007). Gene ontology enrichment analysis revealed that lipid metabolism-related functions are potentially crucial in the process by which the radiomics score could stratify patients.

Conclusions: A combination of peritumoral radiomics features could improve the predictive performance of intratumoral radiomics to estimate overall survival after definitive chemoradiotherapy in patients with proximal esophageal cancer. Radiomics features could provide insights into the lipid metabolism associated with radioresistance and hold great potential to guide personalized care.

Critical relevance statement: This study demonstrates that incorporating peritumoral radiomics features enhances the predictive accuracy of overall survival in proximal esophageal cancer patients after chemoradiotherapy, and suggests a link between radiomics and lipid metabolism in radioresistance, highlighting its potential for personalized treatment strategies.

Key points: Peritumoral region radiomics features could predict the prognosis of proximal esophageal cancer. Dual-region radiomics features showed significantly better predictive performance. Radiomics features can provide insights into the lipid metabolism associated with radioresistance.

Objectives: To implement and evaluate a super-fast and high-quality biparametric MRI (bpMRI) protocol for prostate imaging acquired at a new ultra-high gradient 3.0-T MRI system.

Methods: Participants with clinically suspected prostate cancer prospectively underwent a multiparametric MRI (mpMRI) on a new 3.0-T MRI scanner (maximum gradient strength: 200 mT/m, maximum slew rate: 200 T/m/s). The bpMRI protocol was extracted from the full mpMRI protocol, including axial T2-weighted and diffusion-weighted (DWI) sequences (b0/800, b1500). Overall image quality was rated by two readers on a five-point Likert scale from (1) non-diagnostic to (5) excellent. PI-RADS 2.1 scores were assessed by three readers separately for the bpMRI and mpMRI protocols. Cohen's and Fleiss' κ were calculated for PI-RADS agreement between protocols and interrater reliability between readers, respectively.

Results: Seventy-seven male participants (mean age, 66 ± 8 years) were included. Acquisition time of the bpMRI protocol was reduced by 62% (bpMRI: 5 min, 33 ± 21 s; mpMRI: 14 min, 50 ± 42 s). The bpMRI protocol showed excellent overall image quality for both the T2-weighted (median score both readers: 5 [IQR: 4-5]) and DWI (b1500) sequence (median score reader 1: 4 [IQR: 4-5]; reader 2: 4 [IQR: 4-4]). PI-RADS score agreement between protocols was excellent (Cohen's κ range: 0.91-0.95 [95% CI: 0.89, 0.99]) with an overall good interrater reliability (Fleiss' κ, 0.86 [95% CI: 0.80, 0.92]).

Conclusion: Ultra-high gradient MRI allows the establishment of a high-quality and rapidly acquired bpMRI with high PI-RADS agreement to a full mpMRI protocol.

Trials registration: Clinicaltrials.gov, NCT06244680, Registered 06 February 2024, retrospectively registered, https://classic.

Clinicaltrials: gov/ct2/show/NCT06244680 .

Critical relevance statement: A novel 3.0-Tesla MRI system with an ultra-high gradient performance enabled high-quality biparametric prostate MRI in 5.5 min while achieving excellent PI-RADS agreement with a standard multiparametric protocol.

Key points: Multi- and biparametric prostate MRIs were prospectively acquired utilizing a maximum gradient of 200 mT/m. Super-fast biparametric MRIs showed excellent image quality and had high PI-RADS agreement with multiparametric MRIs. Implementation of high gradient MRI in clinical routine allows accelerated and high-quality biparametric prostate examinations.

Objectives: To investigate the potential of intratumoral and peritumoral radiomics derived from T2-weighted MRI to preoperatively predict extrapelvic peritoneal metastasis (EPM) in patients with epithelial ovarian cancer (EOC).

Methods: In this retrospective study, 488 patients from four centers were enrolled and divided into training (n = 245), internal test (n = 105), and external test (n = 138) sets. Intratumoral and peritumoral models were constructed based on radiomics features extracted from the corresponding regions. A combined intratumoral and peritumoral model was developed via a feature-level fusion. An ensemble model was created by integrating this combined model with specific independent clinical predictors. The robustness and generalizability of these models were assessed using tenfold cross-validation and both internal and external testing. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanation method was employed for model interpretation.

Results: The ensemble model showed superior performance across the tenfold cross-validation, with the highest mean AUC of 0.844 ± 0.063. On the internal test set, the peritumoral and ensemble models significantly outperformed the intratumoral model (AUC = 0.786 and 0.832 vs. 0.652, p = 0.007 and p < 0.001, respectively). On the external test set, the AUC of the ensemble model significantly exceeded those of the intratumoral and peritumoral models (0.843 vs. 0.750 and 0.789, p = 0.008 and 0.047, respectively).

Conclusion: Peritumoral radiomics provide more informative insights about EPM than intratumoral radiomics. The ensemble model based on MRI has the potential to preoperatively predict EPM in EOC patients.

Critical relevance statement: Integrating both intratumoral and peritumoral radiomics information based on MRI with clinical characteristics is a promising noninvasive method to predict EPM to guide preoperative clinical decision-making for EOC patients.

Key points: Peritumoral radiomics can provide valuable information about extrapelvic peritoneal metastasis in epithelial ovarian cancer. The ensemble model demonstrated satisfactory performance in predicting extrapelvic peritoneal metastasis. Combining intratumoral and peritumoral MRI radiomics contributes to clinical decision-making in epithelial ovarian cancer.

Objectives: Total tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV.

Methods: We developed an auto-segmentation model to segment CRLM using 783 contrast-enhanced portal venous phase CTs (CT-PVP) of 373 patients. We used a self-learning setup whereby we first trained a teacher model on 99 manually segmented CT-PVPs from three radiologists. The teacher model was then used to segment CRLM in the remaining 663 CT-PVPs for training the student model. We used the DICE score and the intraclass correlation coefficient (ICC) to compare the student model's segmentations and the TTV obtained from these segmentations to those obtained from the merged segmentations. We evaluated the student model in an external test set of 50 CT-PVPs from 35 patients from the Oslo University Hospital and an internal test set of 21 CT-PVPs from 10 patients from the Amsterdam University Medical Centers.

Results: The model reached a mean DICE score of 0.85 (IQR: 0.05) and 0.83 (IQR: 0.10) on the internal and external test sets, respectively. The ICC between the segmented volumes from the student model and from the merged segmentations was 0.97 on both test sets.

Conclusion: The developed colorectal cancer liver metastases auto-segmentation model achieved a high DICE score and near-perfect agreement for assessing TTV.

Critical relevance statement: AI model segments colorectal liver metastases on CT with high performance on two test sets. Accurate segmentation of colorectal liver metastases could facilitate the clinical adoption of total tumor volume as an imaging biomarker for prognosis and treatment response monitoring.

Key points: Developed colorectal liver metastases segmentation model to facilitate total tumor volume assessment. Model achieved high performance on internal and external test sets. Model can improve prognostic stratification and treatment planning for colorectal liver metastases.

Purpose: To investigate over 10 years the safety and efficacy of percutaneous cryoablation for the treatment of abdominal wall endometriosis (AWE).

Methods: A systematic review was conducted of literature published between March 2014 and March 2024. Inclusion criteria focused on treatment efficacy studies, while exclusion criteria targeted case reports and studies lacking pertinent outcome data. Methodological quality was assessed using the Newcastle-Ottawa Scale for cohort studies.

Results: A total of eight studies were included. Local pain scores decreased from a median of 8/10 (interquartile range (IQR) 7-9) on the visual analog scale to 1/10 (IQR 0-2) at the last follow-up (p < 0.0001). Median complete local pain response rates ranged from 80% to 100%, with median local pain-free survival rates reaching 76.8% (IQR 55.3-83.8) at the longest follow-up. Notably, no patient reported a post-procedure pain score higher than that they reported pre-cryoablation. The studies indicated minor complications in 3.5 to 11% of cases, with major complications occurring in less than 2% of cases, graded following the guidelines of the Society of Interventional Radiology.

Conclusion: In the last decade, percutaneous image-guided cryoablation has offered consistent results and appears to be a promising, minimally invasive option for AWE treatment. Prospective trials are now essential to establish cryoablation as a new standard in patient care for AWE.

Critical relevance statement: Over a decade-long study, percutaneous cryoablation has proven to be a safe and effective minimally invasive treatment for abdominal wall endometriosis, significantly reducing pain with minimal complications.

Key points: Percutaneous cryoablation significantly reduced local pain scores for abdominal wall endometriosis. The procedure demonstrated a favorable safety profile with minor complications. Cryoablation has emerged as a minimally invasive alternative to traditional treatments.

Objective: To develop a domain-specific large language model (LLM) for LI-RADS v2018 categorization of hepatic observations based on free-text descriptions extracted from MRI reports.

Material and methods: This retrospective study included 291 small liver observations, divided into training (n = 141), validation (n = 30), and test (n = 120) datasets. Of these, 120 were fictitious, and 171 were extracted from 175 MRI reports from a single institution. The algorithm's performance was compared to two independent radiologists and one hepatologist in a human replacement scenario, and considering two combined strategies (double reading with arbitration and triage). Agreement on LI-RADS category and dichotomic malignancy (LR-4, LR-5, and LR-M) were estimated using linear-weighted κ statistics and Cohen's κ, respectively. Sensitivity and specificity for LR-5 were calculated. The consensus agreement of three other radiologists served as the ground truth.

Results: The model showed moderate agreement against the ground truth for both LI-RADS categorization (κ = 0.54 [95% CI: 0.42-0.65]) and the dichotomized approach (κ = 0.58 [95% CI: 0.42-0.73]). Sensitivity and specificity for LR-5 were 0.76 (95% CI: 0.69-0.86) and 0.96 (95% CI: 0.91-1.00), respectively. When the chatbot was used as a triage tool, performance improved for LI-RADS categorization (κ = 0.86/0.87 for the two independent radiologists and κ = 0.76 for the hepatologist), dichotomized malignancy (κ = 0.94/0.91 and κ = 0.87) and LR-5 identification (1.00/0.98 and 0.85 sensitivity, 0.96/0.92 and 0.92 specificity), with no statistical significance compared to the human readers' individual performance. Through this strategy, the workload decreased by 45%.

Conclusion: LI-RADS v2018 categorization from unlabelled MRI reports is feasible using our LLM, and it enhances the efficiency of data curation.

Critical relevance statement: Our proof-of-concept study provides novel insights into the potential applications of LLMs, offering a real-world example of how these tools could be integrated into a local workflow to optimize data curation for research purposes.

Key points: Automatic LI-RADS categorization from free-text reports would be beneficial to workflow and data mining. LiverAI, a GPT-4-based model, supported various strategies improving data curation efficiency by up to 60%. LLMs can integrate into workflows, significantly reducing radiologists' workload.

Objectives: To explore the impact of hepatic steatosis measured by MRI-proton density fat fraction (MRI-PDFF) on liver stiffness measurement (LSM) value and its diagnostic performance for staging liver fibrosis in patients with chronic hepatitis B (CHB).

Methods: A total of 914 patients with CHB who underwent liver biopsy and MRI-PDFF were retrospectively reviewed. The influence of MRI-PDFF on LSM value was assessed using univariate and multivariate linear analyses. To assess the influence of liver steatosis on the diagnostic performance of LSM, a series of ROC analyses were performed and compared by stratifying patients into non-steatosis (PDFF < 5%) and steatosis (PDFF ≥ 5%) groups according to MRI-PDFF values. The effects of different LSM cut-off values on the false-positive rate in the steatosis cohort were compared using McNemar's test.

Results: LSM values were significantly affected by MRI-PDFF in the entire cohort (B-coefficient: 0.003, p < 0.001), F1 cohort (B-coefficient: 0.005, p < 0.001), and F2 cohort (B-coefficient: 0.003, p = 0.002). Hepatic steatosis was not observed to have a significant influence on the ROC curve of LSM for staging liver fibrosis. Compared with using the cut-off values for the CHB cohort, using relatively higher cut-off values for hepatic steatosis significantly improved the false-positive rate of LSM in the steatosis cohort.

Conclusion: Steatosis significantly influenced LSM, with a higher value in the early stage of liver fibrosis but did not affect the diagnostic efficiency of LSM for staging liver fibrosis. Moreover, using relatively high cut-off values significantly improved the false-positive rate of LSM in CHB patients with steatosis.

Clinical relevance statement: The identified correlation between MRI-PDFF and VCTE-measured LSM is not clinically relevant since the diagnostic performance of LSM in staging liver fibrosis is not affected by steatosis. A higher cut-off should be applied in CHB patients with steatosis to improve the false-positive rate.

Key points: Steatosis can affect liver stiff measurement (LSM) values in the early stage of liver fibrosis. The diagnostic performance of LSM in staging liver fibrosis is not affected by steatosis. LSM's cutoffs should be increased in patients with steatosis to improve the false-positive rate.

Background: Percutaneous cryoablation (PCA), having shown effectiveness in treating liver, lung, prostate, breast, and kidney tumors, is now gaining attention for the treatment of soft tissue tumors. PCA functions by freezing tissue, which induces ice crystal formation and cell death without damaging collagen structures. Technical considerations include the selection and handling of cryoprobes and cryogenic agents, procedural duration, and choice of image guidance for precision. This review aims to synthesize the mechanisms, applications, and technical aspects of PCA in the treatment of soft tissue tumors.

Methods: Adhering to PRISMA 2020 guidelines, a review was conducted of studies published prior to March 2024 that investigated PCA of soft tissue tumors. The review focused on technical and procedural aspects of cryoablation, cryobiological principles, cellular and tissue responses to extreme cold, intra- and post-procedure physiological mechanisms during and post-procedure, and main clinical applications.

Results: PCA is efficient in treating soft tissue tumors, including desmoid tumors, vascular malformations, and abdominal wall endometriosis. Several cryobiological mechanisms are involved, notably ice crystal formation, cellular dehydration, osmotic effects, and the inflammatory response, all of which contribute to its efficacy. Key technical aspects include the choice of cryoprobes, cryogenic agents (argon gas or liquid nitrogen), and the duration and control of freezing/thawing cycles. PCA also frequently outperformed traditional treatments like surgery and radiotherapy in terms of pain reduction, tumor size reduction, and patient outcomes. Moreover, its nerve sideration properties make it effective under local anesthesia.

Conclusion: Demonstrating substantial pain reduction, tumor size decrease, and high technical success rates, PCA offers a promising and minimally invasive alternative for soft tissue tumor treatment.

Critical relevance statement: Percutaneous cryoablation provides a minimally invasive, precise alternative for soft tissue tumor management, advancing clinical radiology by offering effective treatment with reduced patient risk and enhanced outcomes through image-guided procedures.

Key points: Percutaneous cryoablation (PCA) offers a promising, minimally invasive alternative for managing soft tissue tumors. PCA employs image-guided techniques to accurately target and treat tumors, ensuring high precision and control. PCA preserves structures like collagen, reduces pain, decreases tumor size, and generally enhances patient outcomes.

Objectives: To investigate the value of vascular morphology on high frame rate contrast-enhanced ultrasound (H-CEUS) and CEUS Li-RADS in predicting microvascular invasion (MVI), Ki-67 expression and recurrence of hepatocellular carcinoma (HCC).

Methods: This retrospective study enrolled 78 patients with single HCC diagnosed by postoperative pathology between January 1, 2021, and June 30, 2022. All patients underwent ultrasound and H-CEUS examination before operation. H-CEUS image features and CEUS Li-RADS were compared in different MVI status and Ki-67 level. Multiple logistic regression analysis was performed to select independent variables for MVI. Differences in recurrence among different H-CEUS image features, MVI status and Ki-67 level were further analyzed.

Results: Tumor shape, vascular morphology, LR-M category, necrosis and AFP level were different between the MVI-positive group and MVI-negative group (p < 0.05). Vascular morphology and LR-M category were independent risk factors related to MVI (p < 0.05). Vascular morphology was also different between the high Ki-67 expression group and low Ki-67 expression group (p < 0.05). Vascular morphology, MVI status and Ki-67 expression were different between the recurrence group and no recurrence group (p < 0.05).

Conclusion: The vascular morphology of HCC on H-CEUS can indicate the risk of MVI status, Ki-67 expression and recurrence, which provides a feasible imaging technique for predicting the prognosis before operation.

Critical relevance statement: H-CEUS shows the different vascular morphology of HCC in arterial phase and indicates the risk of MVI, Ki-67 expression and recurrence, which provides a feasible imaging technique for clinician to judge the risk of MVI pre-operation and adopt appropriate treatment.

Key points: H-CEUS can clearly show different vascular morphology of HCC in arterial phase. Vascular morphology on H-CEUS is associated with MVI status, Ki-67 expression and HCC recurrence. Preoperative MVI and Ki-67 expression prediction could help surgeons choose a more appropriate treatment plan.