Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

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.

Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data.

OBJECTIVES: MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining "real-world" and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extensive curation. METHODS: Curation involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods. RESULTS: A total of 796 whole-body MR imaging sessions from 462 subjects were curated. A major change in scan protocol part way through the retrospective window meant that approximately 30% of available imaging sessions had properties that differed significantly from the remainder of the data. Issues were found with a vendor-supplied clinical algorithm for "composing" whole-body images from multiple imaging stations. Historic weaknesses in a digital video disk (DVD) research archive (already addressed by the mid-2010s) were highlighted by incomplete datasets, some of which could not be completely recovered. The final dataset contained 736 imaging sessions for 432 subjects. Software was written to clean and harmonise data. Implications for the subsequent machine learning activity are considered. CONCLUSIONS: MALIMAR exemplifies the vital role that curation plays in machine learning studies that use real-world data. A research repository such as XNAT facilitates day-to-day management, ensures robustness and consistency and enhances the value of the final dataset. The types of process described here will be vital for future large-scale multi-institutional and multi-national imaging projects. CRITICAL RELEVANCE STATEMENT: This article showcases innovative data curation methods using a state-of-the-art image repository platform; such tools will be vital for managing the large multi-institutional datasets required to train and validate generalisable ML algorithms and future foundation models in medical imaging. KEY POINTS: • Heterogeneous data in the MALIMAR study required the development of novel curation strategies. • Correction of multiple problems affecting the real-world data was successful, but implications for machine learning are still being evaluated. • Modern image repositories have rich application programming interfaces enabling data enrichment and programmatic QA, making them much more than simple "image marts".

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features.

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.

An Integrated Clinical-MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer.

BACKGROUND: Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning. PURPOSE: To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects. STUDY TYPE: Retrospective. POPULATION: Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years). FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence. ASSESSMENT: Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets. STATISTICAL TESTS: A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model. RESULTS: Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively. DATA CONCLUSION: The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

COVID-protected pathways for image-guided lung cancer intervention during the COVID-19 pandemic: A cohort study.

OBJECTIVES: To compare the experience of COVID-protected and mixed cohort pathways in COVID-19 transmission at a tertiary referral hospital for elective CT-guided lung biopsy and ablation during the COVID-19 pandemic. METHODS: From September 2020 to August 2021, patients admitted for elective thoracic intervention were treated at a tertiary hospital (Site 1). Site 1 received patients for extracorporeal membrane oxygenation (ECMO) and invasive ventilation in the treatment of COVID-19. Shared imaging, theater, and hallway facilities were used.From April 2020 to August 2020, patients admitted for elective thoracic intervention were treated at a COVID-protected hospital (Site 2). No patients with suspected or confirmed COVID-19 were treated at Site 2.Patients were surveyed for clinical and laboratory signs of COVID-19 infection up to 30 days post-procedure. RESULTS: At Sites 1 and 2, patients (2.4%) were tested positive for COVID-19 at 10 and 14 days post-procedure.At Site 2, there were no COVID-19 positive cases within 30 days of undergoing elective thoracic intervention. CONCLUSION: A mixed-site method for infection control could represent a pragmatic approach to the management of elective procedures during the COVID-19 pandemic or for similar illnesses. ADVANCES IN KNOWLEDGE: Mixed-cohort infection control is possible in the prevention of nosocomial COVID-19 infection.

Comparison of automated ASPECTS, large vessel occlusion detection and CTP analysis provided by Brainomix and RapidAI in patients with suspected ischaemic stroke.

OBJECTIVES: The ischaemic core and penumbra volumes derived from CTP aid the selection of patients with an arterial occlusion for mechanical thrombectomy. Different post-processing software packages may give different CTP outputs, potentially causing variable patient selection for mechanical thrombectomy. The study aims were, firstly, to assess the correlation in CTP outputs from software packages provided by Brainomix and RapidAI. Secondly, the correlation between automated ASPECTS and neuroradiologist-derived ASPECTS and accuracy in detecting large vessel occlusion was assessed. MATERIALS AND METHODS: This retrospective study included patients undergoing CTP for suspected anterior circulation large vessel occlusion. Pearson's correlation coefficient was used for testing the correlation in CTP outputs, ASPECTS/automated ASPECTS, and-in those with complete or near complete occlusion-final infarct volume. Diagnostic statistics were calculated for large vessel occlusion detection. RESULTS: Correlation was high for ischaemic core and penumbra volumes (0.862 and 0.832, respectively) but lower for the mismatch ratio (0.477). Agreement in mechanical thrombectomy eligibility was achieved in 85% of cases (46/54). Correlation between ischaemic core and final infarct volume was higher for Brainomix (0.757) than for RapidAI (0.595). The correlation between ASPECTS and automated ASPECTS (0.738 and 0.659) and the accuracy of detecting large vessel occlusion (77% and 71%) was higher for Brainomix than for RapidAI. CONCLUSION: There was high correlation between the CTP output from Brainomix and RapidAI. However, there was a difference in MT eligibility in 15% of cases, which highlights that the decision regarding MT should not be based on imaging parameters alone.

Ultrasound Diagnosis of a Retained Migrated Foreign Body Following Penetrating Injury to The Upper Thigh in a Child: A Case Report Demonstrating an Underused Indication for Diagnostic Ultrasound.

We present a case of delayed diagnosis of retained glass foreign body in the inguinal region of a child using ultrasonography following penetrating trauma to the upper thigh. The foreign body had traversed significantly by the time of diagnosis, from the medial upper thigh to the inguinal region at the level of the inguinal ligament. Ultrasound can be an effective initial imaging modality for the diagnosis of foreign bodies in children, allowing the potential to reduce ionizing radiation exposure.

Symptomatic, biochemical and radiographic recovery in patients with COVID-19.

BACKGROUND: The symptoms, radiography, biochemistry and healthcare utilisation of patients with COVID-19 following discharge from hospital have not been well described. METHODS: Retrospective analysis of 401 adult patients attending a clinic following an index hospital admission or emergency department attendance with COVID-19. Regression models were used to assess the association between characteristics and persistent abnormal chest radiographs or breathlessness. RESULTS: 75.1% of patients were symptomatic at a median of 53 days post discharge and 72 days after symptom onset and chest radiographs were abnormal in 47.4%. Symptoms and radiographic abnormalities were similar in PCR-positive and PCR-negative patients. Severity of COVID-19 was significantly associated with persistent radiographic abnormalities and breathlessness. 18.5% of patients had unscheduled healthcare visits in the 30 days post discharge. CONCLUSIONS: Patients with COVID-19 experience persistent symptoms and abnormal blood biomarkers with a gradual resolution of radiological abnormalities over time. These findings can inform patients and clinicians about expected recovery times and plan services for follow-up of patients with COVID-19.