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Background Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis. Methods Training ( n  = 411) and testing ( n  = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. Results Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. Conclusions The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. Trial registration ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Purpose: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. Methods: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models. Results: Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively. Conclusion: It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.

In 2021, the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) MRI Committee developed a risk stratification system and lexicon for assessing adnexal lesions using MRI. Like the BI-RADS classification, O-RADS MRI provides a standardized language for communication between radiologists and clinicians. It is essential for radiologists to be familiar with the O-RADS algorithmic approach to avoid misclassifications. Training, like that offered by International Ovarian Tumor Analysis (IOTA), is essential to ensure accurate and consistent application of the O-RADS MRI system. Tools such as the O-RADS MRI calculator aim to ensure an algorithmic approach. This review highlights the key teaching points, pearls, and pitfalls when using the O-RADS MRI risk stratification system.Critical relevance statement This article highlights the pearls and pitfalls of using the O-RADS MRI scoring system in clinical practice.Key points• Solid tissue is described as displaying post- contrast enhancement.• Endosalpingeal folds, fimbriated end of the tube, smooth wall, or septa are not solid tissue.• Low-risk TIC has no shoulder or plateau. An intermediate-risk TIC has a shoulder and plateau, though the shoulder is less steep compared to outer myometrium.

The main prognostic factor in ovarian cancer is the stage of disease at diagnosis. The staging system in use (FIGO classification, updated in 2014) is based on the surgical-pathological findings. Although surgical staging is the gold standard in ovarian cancer, the initial patient management depends on the imaging-based pre-surgical staging assessment, in order to identify unresectable or difficult to resect disease. Radiologists need to be aware of the strengths of the available imaging modalities, as well as the imaging pitfalls. Clear understanding of pattern of disease spread and review areas are critical for accurate staging and treatment planning. The current standard of care for pre-surgical staging is CT of the thorax, abdomen, and pelvis. This allows a rapid evaluation of disease extent and is fairly accurate in identifying bulky disease but has definite limitations in assessing the extent of small volume disease and in the confirmation of certain sites of disease beyond the abdomen. Functional MRI has been reported to be superior in detecting small peritoneal deposits. PET/CT may be used as a problem-solving tool in some patients where determination remains unclear, particularly in confirmation of advanced stage beyond the abdomen.

The urgency for climate action is recognised by international government and healthcare organisations, including the United Nations (UN) and World Health Organisation (WHO). Climate change, biodiversity loss, and pollution negatively impact all life on earth. All populations are impacted but not equally; the most vulnerable are at highest risk, an inequity further exacerbated by differences in access to healthcare globally. The delivery of healthcare exacerbates the planetary health crisis through greenhouse gas emissions, largely due to combustion of fossil fuels for medical equipment production and operation, creation of medical and non-medical waste, and contamination of water supplies. As representatives of radiology societies from across the globe who work closely with industry, and both governmental and non-governmental leaders in multiple capacities, we advocate together for urgent, impactful, and measurable changes to the way we deliver care by further engaging our members, policymakers, industry partners, and our patients. Simultaneous challenges including global health disparities, resource allocation, and access to care must inform these efforts. Climate literacy should be increasingly added to radiology training programmes. More research is required to understand and measure the environmental impact of radiological services and inform mitigation, adaptation and monitoring efforts. Deeper collaboration with industry partners is necessary to support innovations in the supply chain, energy utilization, and circular economy. Many solutions have been proposed and are already available, but we must understand and address barriers to implementation of current and future sustainable innovations. Finally, there is a compelling need to partner with patients, to ensure that trust in the excellence of clinical care is maintained during the transition to sustainable radiology. By fostering a culture of global cooperation and rapid sharing of solutions amongst the broader imaging community, we can transform radiological practice to mitigate its environmental impact, adapt and develop resilience to current and future climate and environmental threats, and simultaneously improve access to care.

Objectives: The aim of this study was to construct a diagnostic model from MRI features to distinguish complex leiomyomas/degenerating fibroids (DF) from leiomyosarcoma (LMS). Methods: A retrospective case-controlled study was performed comparing MRI features of patients with pathologically proven DF or LMS. MRI in 42 patients with DF (control group) and 46 with LMS (study group) was used to generate a diagnostic model. Imaging features reported in the literature to distinguish these two entities were scored for each uterine mass by two radiologists unaware of the histological diagnosis. Inter observer variation and univariate analysis was undertaken. Imaging characteristics identified on univariate analysis were used to build a multi-variable diagnostic model and sensitivity and specificity of this model calculated. Results: Taking the features identified on the univariate analysis, the final diagnostic model was based on AP length (p = .053), intermediate T2 signal (IT2), volume (p = .002), and nodular border (p = .001). When the model was implemented back into the training dataset it demonstrated a sensitivity of 70.7%, and a specificity of 76.2%. The sensitivity and specificity of radiologist suspicion score was 74.7% and 70.4%. In addition, morphological features showed only poor or moderate inter observer agreement at best. Conclusions: Morphological MRI imaging features alone are not sufficient to obviate the need for pathological confirmation prior to non-surgical management of complex uterine mass lesions. Trial registration: IRAS project ID 251778 Protocol number: CCR 4992 REC reference 19/YH/0134 Date of HRA approval: 29.4.19. Advances in knowledge Morphological features of uterine masses on MRI are not sufficient to distinguish leiomyosarcoma from degenerating fibroids.

Background and aims Rapid and accurate cancer staging following diagnosis underpins patient management, in particular the identification of distant metastatic disease. Current staging guidelines recommend sequential deployment of various imaging platforms such as computerised tomography (CT) and positron emission tomography (PET) which can be time and resource intensive and onerous for patients. Recent studies demonstrate that whole body magnetic resonance Imaging (WB-MRI) may stage cancer efficiently in a single visit, with potentially greater accuracy than current staging investigations. The Streamline trials aim to evaluate whether WB-MRI increases per patient detection of metastases in non-small cell lung and colorectal cancer compared to standard staging pathways. Methods The Streamline trials are multicentre, non-randomised, single-arm, prospective diagnostic accuracy studies with a novel design to capture patient management decisions during staging pathways. The two trials recruit adult patients with proven or highly suspected new diagnosis of primary colorectal (Streamline C) or non-small cell lung cancer (Streamline L) referred for staging. Patients undergo WB-MRI in addition to standard staging investigations. Strict blinding protocols are enforced for those interpreting the imaging. A first major treatment decision is made by the multi-disciplinary team prior to WB-MRI revelation based on standard staging investigations only, then based on the WB-MRI and any additional tests precipitated by WB-MRI, and finally based on all available test results. The reference standard is derived by a multidisciplinary consensus panel who assess 12 months of follow-up data to adjudicate on the TNM stage at diagnosis. Health psychology assessment of patients’ experiences of the cancer staging pathway will be undertaken via interviews and questionnaires. A cost (effectiveness) analysis of WB-MRI compared to standard staging pathways will be performed. Discussion We describe a novel approach to radiologist and clinician blinding to ascertain the ‘true’ diagnostic accuracy of differing imaging pathways and discuss our approach to assessing the impact of WB-MRI on clinical decision making in real-time. The Streamline trials will compare WB-MRI and standard imaging pathways in the same patients, thereby informing the most accurate and efficient approach to staging. Trial registration Streamline C ISRCTN43958015 (registered 25/7/2012). Streamline L ISRCTN50436483 (registered 31/7/2012).

Adnexal lesions are a common occurrence in radiology practice and imaging plays a crucial role in triaging women appropriately. Current trends toward early detection and characterization have increased the need for accurate imaging assessment of adnexal lesions prior to treatment. Ultrasound is the first-line imaging modality for assessing adnexal lesions; however, approximately 20% of lesions are incompletely characterized after ultrasound evaluation. Secondary assessment with MR imaging using the ADNEx MR Scoring System has been demonstrated as highly accurate in the characterization of adnexal lesions and in excluding ovarian cancer. This review will address the role of MR imaging in further assessment of adnexal lesions discovered on US, and the utility of the ADNEx MR Scoring System.

The syndrome of ectopic adrenocorticotrophin secretion (EAS) is rare and is due to excess adrenocorticotrophin (ACTH) production from a nonpituitary tumour. These tumours can be covert, where the tumours are not readily apparent, and very small making them challenging to image. It is clinically and biochemically difficult to distinguish between covert EAS and Cushing’s disease. The first-line investigation in locating the source of ACTH production is computed tomography (CT). The aim of this pictorial review is to illustrate the likely covert sites and related imaging findings. We review the CT appearances of tumours resulting in covert EAS and the associated literature. The most common tumours were bronchial carcinoid tumours, which appear as small, well-defined, round or ovoid pulmonary lesions. Rarer causes included thymic carcinoids, gastrointestinal carcinoids and pancreatic neuroendocrine tumours. Awareness of the imaging characteristics will aid identification of the source of ACTH production and allow potentially curative surgical resection.

The purpose of this study was to describe the range of appearances of adrenal phaeochromocytomas on T2-weighted MRI, correlate appearances with histopathology, and quantify the incidence of the previously described hyperintense appearance. The appearance and MR characteristics of 44 phaeochromocytomas were reviewed retrospectively. T2-weighted appearances were grouped: (1) ‘classical’, homogeneous, high signal intensity, isointense to CSF; (2) homogeneous, isointense or minimally hyperintense to spleen, hypointense to CSF; (3) heterogeneous, marbled appearance; (4) heterogeneous, multiple high signal intensity pockets. All 44 adrenal phaeochromocytomas were well circumscribed, 1.2–15 cm in maximum diameter, with no visual or quantitative signal loss on chemical shift imaging. On T2-weighted MRI 5/44 (11%) had group 1 appearance; 15/44 (34%) group 2, 7/44 (16%) group 3; and 17/44 (39%) group 4. Homogeneous group 1 and 2 lesions were smaller (mean 4.5 cm) than heterogeneous group 3 and 4 lesions (mean 6.3 cm). Increasing MRI heterogeneity correlated pathologically with increasing amounts of haemorrhage, necrosis and fibrosis. No MRI features were predictive of malignancy. Non-functioning phaeochromocytomas were larger than functioning lesions. No size difference was seen between syndrome and sporadic lesions. In this large series we report a wide range of appearances of adrenal phaeochromocytomas on T2-weighted MRI. The previously described classical hyperintense phaeochromocytoma is relatively uncommon.