Ultrasound and Magnetic Resonance Imaging of Burned-Out Testicular Tumours: The Diagnostic Keys Based on 48 Cases.

The spontaneous regression of testicular germ-cell tumours is a rare event whose mechanisms have yet to be elucidated. In the majority of published cases, tumour regression is concomitant with the metastatic development of the disease. Residual lesions, often referred to as burned-out testicular tumours (BOTTs), are difficult to diagnose due to the paucity of published data, especially in the field of imaging. The aim of this article is to describe the radiological signs of BOTTs on multimodal ultrasound and multiparametric MRI from a series of 48 patients whose diagnosis was confirmed histologically. The demographic, clinical and laboratory characteristics of the patients are studied, as well as the data of the imaging examinations, including conventional scrotal ultrasound, shear-wave elastography, contrast-enhanced ultrasound (CEUS) and multiparametric MRI. A total of 27 out of 48 patients were referred for investigation of primary testicular lesion following the discovery of retroperitoneal metastases, 18/48 patients were referred because of lesions suspected on an ultrasound that was performed for an infertility work-up, and 3/48 were referred because of scrotal clinical signs. Of these last 21 patients (infertility work-up/scrotal clinical sign), 6 were found to be metastatic on the extension work-up. Of the 48 orchiectomy specimens, tumour involution was complete in 41 cases, and a small active contingent remained in 7 cases, with 6 suspected upon advanced US and MRI. Typically, BOTTs appear on a conventional ultrasound as ill-delineated, hypoechoic and hypovascular nodular areas. Clustered microliths (60.4%) and macrocalcifications (35.4%) were frequent. Shear-wave elastography showed areas of focal induration (13.5 ± 8.4 vs. 2.7 ± 1.2 kPa for normal parenchyma, p < 0.01) in 92.5% of the patients for whom it was performed, and contrast ultrasonography demonstrated hypoperfusion of these lesions. Of the 42 MRIs performed, BOTTs corresponded to nodules on T2-weighted sequences (hyposignal) with significantly increased ADC values compared with healthy parenchyma (2 ± 0.3 versus 1.3 ± 0.3 × 10−3 mm2/s, p < 0.01) and an enhancement defect after injection. This enhancement defect overlapped the lesions visible on T2-weighted sequences in most cases. In the case of predominant partial regression, an enhanced portion after contrast injection was visible on MRI in all seven patients of our series, and in six of them a focal diffusion restriction zone was also present. Spontaneously involuted testicular germ-cell tumours have specific radiological signs, and all of the mentioned examinations contribute to this difficult diagnosis, even histologically, because there is no tumour cell left. These signs are similar whether the patient is initially symptomatic metastatic or whether the discovery is fortuitous on the occasion of an infertility work-up, and whatever the seminomatous or non-seminomatous nature of the germ-cell tumour, when this can be determined. The appearance of regressed germ-cell tumours is often trivialized, which can lead to the wrong diagnosis of an extra gonadal germ-cell tumour (in metastatic patients) or of scarring from an acute event such as trauma or infection, which is not recognized or forgotten. In our series, two patients had an unrecognized diagnosis in their history, with local and/or distant recurrence. An improvement in diagnosing burned-out tumours, combining advanced US and MRI, is necessary in order to optimize patient management, with special attention paid to asymptomatic patients, to prompt extension screening and orchiectomy with analysis of the whole testis. This may reveal a persistent viable tumour or lesions of germinal neoplasia in situ, which are precursors of testicular germ-cell tumours.

Study of Thoracic CT in COVID-19: The STOIC Project.

Background There are conflicting data regarding the diagnostic performance of chest CT for COVID-19 pneumonia. Disease extent at CT has been reported to influence prognosis. Purpose To create a large publicly available data set and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter, observational, retrospective cohort study involved 20 French university hospitals. Eligible patients presented at the emergency departments of the hospitals involved between March 1 and April 30th, 2020, and underwent both thoracic CT and reverse transcription-polymerase chain reaction (RT-PCR) testing for suspected COVID-19 pneumonia. CT images were read blinded to initial reports, RT-PCR, demographic characteristics, clinical symptoms, and outcome. Readers classified CT scans as either positive or negative for COVID-19 based on criteria published by the French Society of Radiology. Multivariable logistic regression was used to develop a model predicting severe outcome (intubation or death) at 1-month follow-up in patients positive for both RT-PCR and CT, using clinical and radiologic features. Results Among 10 930 patients screened for eligibility, 10 735 (median age, 65 years; interquartile range, 51-77 years; 6147 men) were included and 6448 (60%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity of CT were 80.2% (95% CI: 79.3, 81.2) and 79.7% (95% CI: 78.5, 80.9), respectively, with strong agreement between junior and senior radiologists (Gwet AC1 coefficient, 0.79). Of all the variables analyzed, the extent of pneumonia at CT (odds ratio, 3.25; 95% CI: 2.71, 3.89) was the best predictor of severe outcome at 1 month. A score based solely on clinical variables predicted a severe outcome with an area under the curve of 0.64 (95% CI: 0.62, 0.66), improving to 0.69 (95% CI: 0.6, 0.71) when it also included the extent of pneumonia and coronary calcium score at CT. Conclusion Using predefined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at 1 month. ClinicalTrials.gov identifier: NCT04355507 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.