Insights into Imaging最新文献

Objective: To assess the utility of clinical and MRI features in distinguishing ovarian clear cell carcinoma (CCC) from adnexal masses with ovarian-adnexal reporting and data system (O-RADS) MRI scores of 4-5.

Methods: This retrospective study included 850 patients with indeterminate adnexal masses on ultrasound. Two radiologists evaluated all preoperative MRIs using the O-RADS MRI risk stratification system. Patients with O-RADS MRI scores of 4-5 were divided into a training set (n = 135, hospital A) and a test set (n = 86, hospital B). Clinical and MRI features were compared between CCC and non-CCC patients. Analysis of variance and support vector machine were used to develop four CCC prediction models. Tenfold cross-validation was applied to determine the hyperparameters. Model performance was evaluated by the area under the curve (AUC) and decision curve.

Results: 221 patients were included (30 CCCs, 191 non-CCCs). CA125, HE4, CEA, ROMA, endometriosis, shape, parity, unilocular, component, the growth pattern of mural nodules, high signal on T1WI, number of nodules, the ratio of signal intensity, and the ADC value were significantly different between CCCs and non-CCCs. The kappa and interobserver correlation coefficient of each MRI feature exceeded 0.85. The comprehensive model combining clinical and MRI features had a greater AUC than the clinical model and tumour maker model (0.92 vs 0.66 and 0.78 in the test set; both p < 0.05), displaying improved net benefit.

Conclusions: The comprehensive model combining clinical and MRI features can effectively differentiate CCC from adnexal masses with O-RADS MRI scores of 4-5.

Critical relevance statement: CCC has a high incidence rate in Asians and has limited sensitivity to platinum chemotherapy. This comprehensive model improves CCC prediction ability and clinical applicability for facilitating individualised clinical decision-making.

Key points: Identifying ovarian CCC preoperatively is beneficial for treatment planning. Ovarian CCC tends to be high-signal on T1WI, unilocular, big size, with endometriosis and low CEA. This model, integrating clinical and MRI features, can differentiate ovarian CCC from adnexal masses with O-RADS MRI scores 4-5.

Purposes: The presence of clinically significant prostate cancer (csPCa) is equivocal for patients with prostate imaging reporting and data system (PI-RADS) category 3. We aim to develop deep learning models for re-stratify risks in PI-RADS category 3 patients.

Methods: This retrospective study included a bi-parametric MRI of 1567 consecutive male patients from six centers (Centers 1-6) between Jan 2015 and Dec 2020. Deep learning models with double channel attention modules based on MRI (AttenNet) for predicting PCa and csPCa were constructed separately. Each model was first pretrained using 1144 PI-RADS 1-2 and 4-5 images and then retrained using 238 PI-RADS 3 images from three training centers (centers 1-3), and tested using 185 PI-RADS 3 images from the other three testing centers (centers 4-6).

Results: Our AttenNet models achieved excellent prediction performances in testing cohort of center 4-6 with the area under the receiver operating characteristic curves (AUC) of 0.795 (95% CI: [0.700, 0.891]), 0.963 (95% CI: [0.915, 1]) and 0.922 (95% CI: [0.810, 1]) in predicting PCa, and the corresponding AUCs were 0.827 (95% CI: [0.703, 0.952]) and 0.926 (95% CI: [0.846, 1]) in predicting csPCa in testing cohort of center 4 and center 5. In particular, 71.1% to 92.2% of non-csPCa patients were identified by our model in three testing cohorts, who can spare from invasive biopsy or RP procedure.

Conclusions: Our model offers a noninvasive screening clinical tool to re-stratify risks in PI-RADS 3 patients, thereby reducing unnecessary invasive biopsies and improving the effectiveness of biopsies.

Critical relevance statement: The deep learning model with MRI can help to screen out csPCa in PI-RADS category 3.

Key points: AttenNet models included channel attention and soft attention modules. 71.1-92.2% of non-csPCa patients were identified by the AttenNet model. The AttenNet models can be a screen clinical tool to re-stratify risks in PI-RADS 3 patients.

Objectives: To develop and validate radiomics and deep learning models based on contrast-enhanced MRI (CE-MRI) for differentiating dual-phenotype hepatocellular carcinoma (DPHCC) from HCC and intrahepatic cholangiocarcinoma (ICC).

Methods: Our study consisted of 381 patients from four centers with 138 HCCs, 122 DPHCCs, and 121 ICCs (244 for training and 62 for internal tests, centers 1 and 2; 75 for external tests, centers 3 and 4). Radiomics, deep transfer learning (DTL), and fusion models based on CE-MRI were established for differential diagnosis, respectively, and their diagnostic performances were compared using the confusion matrix and area under the receiver operating characteristic (ROC) curve (AUC).

Results: The radiomics model demonstrated competent diagnostic performance, with a macro-AUC exceeding 0.9, and both accuracy and F1-score above 0.75 in the internal and external validation sets. Notably, the vgg19-combined model outperformed the radiomics and other DTL models. The fusion model based on vgg19 further improved diagnostic performance, achieving a macro-AUC of 0.990 (95% CI: 0.965-1.000), an accuracy of 0.935, and an F1-score of 0.937 in the internal test set. In the external test set, it similarly performed well, with a macro-AUC of 0.988 (95% CI: 0.964-1.000), accuracy of 0.875, and an F1-score of 0.885.

Conclusions: Both the radiomics and the DTL models were able to differentiate DPHCC from HCC and ICC before surgery. The fusion models showed better diagnostic accuracy, which has important value in clinical application.

Critical relevance statement: MRI-based deep learning radiomics were able to differentiate DPHCC from HCC and ICC preoperatively, aiding clinicians in the identification and targeted treatment of these malignant hepatic tumors.

Key points: Fusion models may yield an incremental value over radiomics models in differential diagnosis. Radiomics and deep learning effectively differentiate the three types of malignant hepatic tumors. The fusion models may enhance clinical decision-making for malignant hepatic tumors.

Objectives: This article aims to evaluate the use and effects of an artificial intelligence system supporting a critical diagnostic task during radiology resident training, addressing a research gap in this field.

Materials and methods: We involved eight residents evaluating 150 CXRs in three scenarios: no AI, on-demand AI, and integrated-AI. The considered task was the assessment of a multi-regional severity score of lung compromise in patients affected by COVID-19. The chosen artificial intelligence tool, fully integrated in the RIS/PACS, demonstrated superior performance in scoring compared to the average radiologist. Using quantitative metrics and questionnaires, we measured the 'upskilling' effects of using AI support and residents' resilience to 'deskilling,' i.e., their ability to overcome AI errors.

Results: Residents required AI in 70% of cases when left free to choose. AI support significantly reduced severity score errors and increased inter-rater agreement by 22%. Residents were resilient to AI errors above an acceptability threshold. Questionnaires indicated high tool usefulness, reliability, and explainability, with a preference for collaborative AI scenarios.

Conclusion: With this work, we gathered quantitative and qualitative evidence of the beneficial use of a high-performance AI tool that is well integrated into the diagnostic workflow as a training aid for radiology residents.

Critical relevance statement: Balancing educational benefits and deskilling risks is essential to exploit AI systems as effective learning tools in radiology residency programs. Our work highlights metrics for evaluating these aspects.

Key points: Insights into AI tools' effects in radiology resident training are lacking. Metrics were defined to observe residents using an AI tool in different settings. This approach is advisable for evaluating AI tools in radiology training.

Objectives: The aim of this study was to determine the status of tertiary lymphoid structures (TLSs) using radiomic features in patients with invasive pulmonary adenocarcinoma (IA).

Methods: In this retrospective study, patients with IA from November 2015 to March 2024 were recruited from two independent centers (center 1, training and internal test data set; center 2, external test data set). TLS was divided into two groups according to hematoxylin-eosin staining. Radiomic features were extracted, and support vector machine (SVM) were implemented to predict the status of TLSs. Receiver operating characteristic (ROC) curves were used to analyze diagnostic performance. Furthermore, visual assessments of the test set were also conducted by two thoracic radiologists and compared with the radiomics results.

Results: A total of 456 patients were included (training data set, n = 278; internal test data set, n = 115; external test data set, n = 63). The area under the curve (AUC) of the radiomics model on the validation set, the internal test set, and the external test set were 0.781 (95% confidence interval (CI): 0.659-0.905;), 0.804 (95% CI: 0.723-0.884;) and 0.747 (95% CI: 0.621-0.874;), respectively. In the visual assessments, the mean CT value and air bronchogram were important indicators of TLS, the AUC was 0.683. In the external test set, the AUC of the clinical model was 0.632.

Conclusions: The radiomics model has a higher AUC than the clinical model and effectively discriminates TLSs in patients with IA.

Critical relevance statement: This study demonstrates that the radiomics-based model can differentiate TLSs in patients with IA. As a non-invasive biomarker, it enhances our understanding of tumor prognosis and management.

Key points: TLSs are closely related to favorable clinical outcomes in non-small cell lung cancer. Radiomics from Chest CT predicted TLSs in patients with IA. This study supports individualized clinical decision-making for patients with IA.

Objectives: To compare and correlate bone edema volume detected by 3D-short-tau-inversion-recovery (STIR) sequence to osseous decay detected by a T1-based sequence and conventional panoramic radiography (OPT).

Materials and methods: Patients with clinical evidence of apical periodontitis were included retrospectively and received OPT as well as MRI of the viscerocranium including a 3D-STIR and a 3D-T1 gradient echo sequence. Bone edema was visualized using the 3D-STIR sequence and periapical hard tissue changes were evaluated using the 3D-T1 sequence. Lesions were segmented and volumes were calculated for bone edema and structural decay. OPTs were assessed for corresponding periapical radiolucencies using the periapical index (PAI).

Results: Of the 42 patients of the initial cohort 21 patients with 38 periapical lesions were included in the analysis (mean age 57.2 ± 13.8 years, 9 women). Reactive bone edema was detected on MRI in 23 periapical lesions with corresponding radiolucency on OPT. Fifteen periapical lesions were detected only in the STIR sequence. The volume of edema measured in the STIR was significantly larger in OPT-positive lesions (mean: STIR (OPT+) 207.3 ± 191.1 mm³) compared to OPT-negative lesions (mean: STIR (OPT-) 29.5 ± 34.2 mm³, p < 0.001). The ROC curve analysis demonstrated that Volume T1 (0.905, p < 0.01) and Volume STIR (0.857, p < 0.01) measurements have strong diagnostic performance for distinguishing OPT-positive from OPT-negative lesions.

Conclusion: Clinically symptom-free patients without pathologic changes in OPT can show signs of inflammation within the periapical bone. Bone edema volume visualized by STIR sequence exceeds bone architecture changes indicated in T1-based imaging and might precede osteolysis in dental radiography.

Critical relevance statement: These results show that subtle intraosseous inflammation within the periapical tissue might remain undetected by conventional dental radiography and T1-based sequences. This emphasizes the potential of MRI in secondary prevention in dentistry.

Key points: Conventional panoramic radiography (OPT) may show only delayed findings of pathological periapical changes. MRI detected bone edema in 23 radiolucent lesions on OPT. MRI revealed 15 lesions only visible with STIR sequences. STIR sequences showed bone inflammation undetectable by conventional radiography or T1 imaging. MRI offers diagnostic advantages for early dental pathology detection.

Objectives: To determine whether deep learning-based reconstructions of zero-echo-time (ZTE-DL) sequences enhance image quality and bone visualization in cervical spine MRI compared to traditional zero-echo-time (ZTE) techniques, and to assess the added value of ZTE-DL sequences alongside standard cervical spine MRI for comprehensive pathology evaluation.

Methods: In this retrospective study, 52 patients underwent cervical spine MRI using ZTE, ZTE-DL, and T2-weighted 3D sequences on a 1.5-Tesla scanner. ZTE-DL sequences were reconstructed from raw data using the AirReconDL algorithm. Three blinded readers independently evaluated image quality, artifacts, and bone delineation on a 5-point Likert scale. Cervical structures and pathologies, including soft tissue and bone components in spinal canal and neural foraminal stenosis, were analyzed. Image quality was quantitatively assessed by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

Results: Mean image quality scores were 2.0 ± 0.7 for ZTE and 3.2 ± 0.6 for ZTE-DL, with ZTE-DL exhibiting fewer artifacts and superior bone delineation. Significant differences were observed between T2-weighted and ZTE-DL sequences for evaluating intervertebral space, anterior osteophytes, spinal canal, and neural foraminal stenosis (p < 0.05), with ZTE-DL providing more accurate assessments. ZTE-DL also showed improved evaluation of the osseous components of neural foraminal stenosis compared to ZTE (p < 0.05).

Conclusions: ZTE-DL sequences offer superior image quality and bone visualization compared to ZTE sequences and enhance standard cervical spine MRI in assessing bone involvement in spinal canal and neural foraminal stenosis.

Critical relevance statement: Deep learning-based reconstructions improve zero-echo-time sequences in cervical spine MRI by enhancing image quality and bone visualization. This advancement offers additional insights for assessing bone involvement in spinal canal and neural foraminal stenosis, advancing clinical radiology practice.

Key points: Conventional MRI encounters challenges with osseous structures due to low signal-to-noise ratio. Zero-echo-time (ZET) sequences offer CT-like images of the C-spine but with lower quality. Deep learning reconstructions improve image quality of zero-echo-time sequences. ZTE sequences with deep learning reconstructions refine cervical spine osseous pathology assessment. These sequences aid assessment of bone involvement in spinal and foraminal stenosis.

Entrapment neuropathies at the elbow are common in clinical practice and require an accurate diagnosis for effective management. Understanding the imaging characteristics of these conditions is essential for confirming diagnoses and identifying underlying causes. Ultrasound serves as the primary imaging modality for evaluating nerve structure and movement, while MRI is superior for detecting muscle denervation. Plain radiography and CT play a minor role and can be used for the evaluation of bony structures and calcifications/ossifications. Comprehensive knowledge of anatomical landmarks, nerve pathways, and compression sites is crucial for clinicians to accurately interpret imaging and guide appropriate treatment strategies for entrapments of ulnar, median, and radial nerves, and their branches. CRITICAL RELEVANCE STATEMENT: Accurate imaging and anatomical knowledge are essential for diagnosing elbow entrapment neuropathies. Ultrasound is the preferred modality for assessing nerve structure and motion, while MRI excels in detecting muscle denervation and guiding effective management of ulnar, median, and radial nerve entrapments. KEY POINTS: Ultrasound is the primary modality for assessing nerve structure and stability. Findings include nerve structural loss, isoechogenicity, thickening, and hyper-vascularization. MRI provides a comprehensive evaluation of the elbow and accurate muscle assessment. Imaging allows the identification of compressive causes, including anatomical variants, masses, or osseous anomalies. Awareness of anatomical landmarks, nerve pathways, and compression sites is essential.

Objectives: Renal cell carcinoma (RCC) with extrarenal fat (perinephric or renal sinus fat) invasion is the main evidence for the T3a stage. Currently, computed tomography (CT) is still the primary modality for staging RCC. This study aims to determine the diagnostic performance of CT in RCC patients with extrarenal fat invasion.

Methods: The PubMed, Web of Science, Cochrane Library, and EMBASE databases were systematically searched up to October 11, 2023. Study quality was assessed by the QUADAS-2 tool. Standard methods recommended for meta-analyses of diagnostic evaluation were used. Heterogeneity was analyzed through meta-regression analysis.

Results: Fifteen studies were included in this meta-analysis. Among them, six studies focused on perinephric fat invasion (PFI) only, four on renal sinus fat invasion (RSFI) only, and five on both. Pooled weighted estimates of sensitivity, specificity, area of SROC curve, PLR, and negative likelihood ratio (NLR) of CT for PFI were 0.69 (95% CI: 0.55-0.79), 0.82 (95% CI: 0.69-0.90), 0.81 (95% CI: 0.77-0.84), 3.85 (95% CI: 2.22-6.67), and 0.38 (95% CI: 0.27-0.55). Pooled weighted estimates of sensitivity, specificity, area of SROC curve, PLR, and NLR of CT for RSFI were 0.81 (95% CI: 0.76-0.85), 0.79 (95% CI: 0.66-0.88), 0.82 (95% CI: 0.78-0.85), 3.91 (95% CI: 2.26-6.77), and 0.24 (95% CI: 0.18-0.31).

Conclusion: CT has the ability to detect the PFI and RSFI in patients with RCC. However, the diagnostic performance of CT has suffered from the limitation of slightly lower accuracy, resulting from the low positive sample in the current studies. Additionally, the current PLR is low.

Critical relevance statement: This study provides radiologists and urologists with a systematic and comprehensive summary of CT and CT-related morphological features in assessing extrarenal fat invasion in patients with RCC.

Key points: CT can detect extrarenal fat invasion in patients with RCC, but the diagnostic performance is inconsistent. The diagnostic performance of CT is acceptable, but primarily affected by the low positive rate of included patients. Further large-scale trials are necessary to determine the true diagnostic capabilities of CT for extrarenal fat invasion.