European Radiology

We present the case of a 44-year-old woman with chondromyxoid fibroma of temporal bone origin. Since this is the least common bone tumor of cartilaginous origin, it is highly unusual to find this tumor in the skull. In fact, the literature describes 18 cases of this form of neoplasia arising in the skull, only 4 of these having originated in the temporal bone. To date, the radiological features of these tumors, and especially features detected using the latest imaging modalities, have not been described in detail. This report is unique in that it is the first to present a case of chondromyxoid fibroma of the temporal bone accompanied by detailed CT and MRI findings.

To determine whether 3D-CT multi-level anatomical features can provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. This is a retrospective study based on multi-center cohorts. A total of 473 participants with pathologically proved renal cell carcinoma were split into the internal training and the external testing set. The training set contains 412 cases from five open-source cohorts and two local hospitals. The external testing set includes 61 participants from another local hospital. The proposed automatic analytic framework contains the following modules: a 3D kidney and tumor segmentation model constructed by 3D-UNet, a multi-level feature extractor based on the region of interest, and a partial or radical nephrectomy prediction classifier by XGBoost. The fivefold cross-validation strategy was used to get a robust model. A quantitative model interpretation method called the Shapley Additive Explanations was conducted to explore the contribution of each feature. In the prediction of partial versus radical nephrectomy, the combination of multi-level features achieved better performance than any single-level feature. For the internal validation, the AUROC was 0.93 ± 0.1, 0.94 ± 0.1, 0.93 ± 0.1, 0.93 ± 0.1, and 0.93 ± 0.1, respectively, as determined by the fivefold cross-validation. The AUROC from the optimal model was 0.82 ± 0.1 in the external testing set. The tumor shape Maximum 3D Diameter plays the most vital role in the model decision. The automated surgical decision framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features exhibits robust performance in renal cell carcinoma. The framework points the way towards guiding surgery through medical images and machine learning. We proposed an automated analytic framework that can assist surgeons in partial or radical nephrectomy decision-making. The framework points the way towards guiding surgery through medical images and machine learning. • The 3D-CT multi-level anatomical features provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. • The data from multicenter study and a strict fivefold cross-validation strategy, both internal validation set and external testing set, can be easily transferred to different tasks of new datasets. • The quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature.

Trong nhiều năm qua, các lựa chọn thay thế cho phương tiện cản quang X-quang iod đã được tìm kiếm. Trong số các phương tiện cản quang đã được nghiên cứu đến nay, chỉ có CO2 và các chelate gadolinium mới được chứng minh là những lựa chọn khả thi cho một số xét nghiệm X-quang nhất định. Do đó, chúng tôi đã xem xét tài liệu chung và tài liệu chuyên về gadopentetate (Magnevist) đặc biệt, vì tác nhân này đã được nghiên cứu nhiều nhất. Đánh giá này chỉ ra rằng các xét nghiệm CT chẩn đoán có thể được thực hiện sau khi tiêm tĩnh mạch các phương tiện cản quang chứa gadolinium để đánh giá các bất thường ở động mạch chủ. Các phương tiện cản quang chứa gadolinium với liều lượng được phê duyệt cho hình ảnh MR không hữu ích cho việc đánh giá các cơ quan parenchymal bụng bằng CT. Tiêm tĩnh mạch/tiêm qua động mạch cũng đã được sử dụng trong nhiều thủ thuật chụp mạch và can thiệp. Chất lượng hình ảnh, tuy nhiên, thường bị kém hơn so với phương tiện cản quang iod. Các phương tiện cản quang chứa gadolinium không yêu cầu xử lý đặc biệt và có thể được tiêm bằng tay hoặc thông qua các máy tiêm tự động angiographic thông thường với các lưu lượng và áp suất giống như được sử dụng với phương tiện cản quang iod. Đối với CT, một tiêm bolus ngoại vi của một tác nhân gadolinium được pha loãng (1:1 với dung dịch muối) với 60–90 ml ở tốc độ 3–5 ml/s thường được thực hiện. Giống như tất cả các chelate gadolinium khác, việc sử dụng gadopentetate (Magnevist) không phải MRI không được các cơ quan quản lý phê duyệt. Tuy nhiên, tài liệu cho thấy rằng một liều 0.3–0.4 mmol/kg trọng lượng cơ thể đã được tiêm an toàn cho CT cũng như cho chụp mạch và các thủ thuật can thiệp qua đường tĩnh mạch và động mạch. Ngay cả ở liều này, tuy nhiên, thể tích tổng thể cần tiêm là tương đối nhỏ, điều này phần nào hạn chế tính hữu dụng của nó.

To determine the value of intravenous contrast medium in (68)Ga-DOTA-Phe(1)-Tyr(3)-octreotide – (68)Ga-DOTATOC – PET/CT for the detection of abdominal neuroendocrine tumours (NET). In fifty-five patients with known or suspected NETs of the abdomen PET/CT was performed on a 64-row multi-detector hybrid system. For PET, 150 MBq of (68)Ga-DOTATOC were injected intravenously. Full-dose unenhanced, and arterial- and venous-phase contrast-enhanced CT images were obtained. Unenhanced and contrast-enhanced PET/CT images were evaluated separately for the presence of NETs on a per-region basis, by two separate teams with different experience levels. On unenhanced PET/CT, sensitivity and specificity ranged from 89.3% (junior team) to 92% (senior team), and 99.1% (junior team) to 99.2% (senior team), respectively. On contrast-enhanced PET/CT, sensitivity and specificity ranged from 92.3% (junior team) to 98.5% (senior team), and 99.4% (junior team) to 99.5% (senior team), respectively. These increases in sensitivity and specificity, due to the use of contrast-enhanced images, were statistically significant (P < 0.05). Intravenous contrast medium only moderately, aleit significantly, improves the sensitivity of (68)Ga-DOTATOC PET/CT for the detection of abdominal NETs, and hardly affects specificity. Thus, while contrast enhancement is justified to achieve maximum sensitivity, unenhanced images may be sufficient for routine PET/CT in NET patients. • Contrast media moderately improve the sensitivity of (68)Ga-DOTATOC PET/CT for neuroendocrine tumours. • Contrast media hardly affect the specificity of (68)Ga-DOTATOC PET/CT for neuroendocrine tumours. • Unenhanced PET/CT is sufficient for routine imaging of patients with neuroendocrine tumours.

The aim of this article is to report the MRI features of bilateral parotid haemangiomas of infancy and highlight the diagnostic and prognostic value of MRI especially with regard to those lesions that may cause respiratory compromise. Retrospective review of MRI scans in six infants with bilateral parotid haemangiomas. The bilateral haemangiomas were symmetrical in all but one patient. On T1-weighted images the masses were isointense to muscle and on T2 they were hyperintense with numerous small vessels giving a septated appearance. There was vivid enhancement with gadolinium in all cases. In three patients, deep bilateral extensions were demonstrated involving the parapharyngeal spaces resulting in respiratory compromise which required tracheostomy. Bilateral parotid haemangiomas are rare, but when present may have deep extensions, which can result in respiratory compromise requiring tracheostomy.

(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies. In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed. Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93–0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27–9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from RCC. Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures. • Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons. • A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score. • Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity.

To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning–based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers. • With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm–reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm–induced distortion artifacts were more frequently observed on deep learning algorithm–reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.