The objective was to evaluate T2-weighted (T2w) and dynamic contrast-enhanced (DCE) MRI in detecting local cancer recurrences after prostate high-intensity focused ultrasound (HIFU) ablation. Fifty-nine patients with biochemical recurrence after prostate HIFU ablation underwent T2-weighted and DCE MRI before transrectal biopsy. For each patient, biopsies were performed by two operators: operator 1 (blinded to MR results) performed random and colour Doppler-guided biopsies ("routine biopsies"); operator 2 obtained up to three cores per suspicious lesion on MRI ("targeted biopsies"). Seventy-seven suspicious lesions were detected on DCE images (n = 52), T2w images (n = 2) or both (n = 23). Forty patients and 41 MR lesions were positive at biopsy. Of the 36 remaining MR lesions, 20 contained viable benign glands. Targeted biopsy detected more cancers than routine biopsy (36 versus 27 patients, p = 0.0523). The mean percentages of positive cores per patient and of tumour invasion of the cores were significantly higher for targeted biopsies (p < 0.0001). The odds ratios of the probability of finding viable cancer and viable prostate tissue (benign or malignant) at targeted versus routine biopsy were respectively 3.35 (95% CI 3.05-3.64) and 1.38 (95% CI 1.13-1.63). MRI combining T2-weighted and DCE images is a promising method for guiding post-HIFU biopsy towards areas containing recurrent cancer and viable prostate tissue.

We assessed the accuracy of T2-weighted (T2w) and dynamic contrast-enhanced (DCE) 1.5-T magnetic resonance imaging (MRI) in localizing prostate cancer before transrectal ultrasound-guided repeat biopsy. Ninety-three patients with abnormal PSA level and negative prostate biopsy underwent T2w and DCE prostate MRI using pelvic coil before repeat biopsy. T2w and DCE images were interpreted using visual criteria only. MR results were correlated with repeat biopsy findings in ten prostate sectors. Repeat biopsy found prostate cancer in 23 patients (24.7%) and 44 sectors (6.6%). At per patient analysis, the sensitivity, specificity, positive and negative predictive values were 47.8%, 44.3%, 20.4% and 79.5% for T2w imaging and 82.6%, 20%, 24.4% and 93.3% for DCE imaging. When all suspicious areas (on T2w or DCE imaging) were taken into account, a sensitivity of 82.6% and a negative predictive value of 100% could be achieved. At per sector analysis, DCE imaging was significantly less specific (83.5% vs. 89.7%, p < 0.002) than T2w imaging; it was more sensitive (52.4% vs. 32.1%), but the difference was hardly significant (p = 0.09). T2w and DCE MRI using pelvic coil and visual diagnostic criteria can guide prostate repeat biopsy, with a good sensitivity and NPV.

• When a 6-mm apical safety margin is used, residual cancer after HIFU ablation is found significantly more frequently in the apex.

Objectives To evaluate the accuracy of diagnoses of COVID-19 based on chest CT as well as inter-observer agreement between teleradiologists during on-call duty and senior radiologists in suspected COVID-19 patients. Materials and methods From March 13, 2020, to April 14, 2020, consecutive suspected COVID-19 adult patients who underwent both an RT-PCR test and chest CT from 15 hospitals were included in this prospective study. Chest CTs were immediately interpreted by the on-call teleradiologist and were systematically blind reviewed by a senior radiologist. Readings were categorised using a five-point scale: (1) normal; (2) non-infectious findings; (3) infectious findings but not consistent with COVID-19 infection; (4) consistent with COVID-19 infection; and (5) typical appearance of COVID-19 infection. The diagnostic accuracy of chest CT and inter-observer agreement using the kappa coefficient were evaluated over the study period. Results In total, 513 patients were enrolled, of whom 244/513 (47.6%) tested positive for RT-PCR. First readings were scored 4 or 5 in 225/244 (92%) RT-PCR+ patients, and between 1 and 3 in 201/269 (74.7%) RT-PCR− patients. The data were highly consistent (weighted kappa = 0.87) and correlated with RT-PCR ( p < 0.001, AUC 1st-reading = 0.89, AUC 2nd-reading = 0.93). The negative predictive value for scores of 4 or 5 was 0.91–0.92, and the PPV for a score of 5 was 0.89–0.96 at the first and second readings, respectively. Diagnostic accuracy was consistent over the study period, irrespective of a variable prevalence rate. Conclusion Chest CT demonstrated high diagnostic accuracy with strong inter-observer agreement between on-call teleradiologists with varying degrees of experience and senior radiologists over the study period. Key Points • The accuracy of readings by on-call teleradiologists, relative to second readings by senior radiologists, demonstrated a sensitivity of 0.75–0.79, specificity of 0.92–0.97, NPV of 0.80–0.83, and PPV of 0.89–0.96, based on “typical appearance,” as predictive of RT-PCR+. • Inter-observer agreement between the first reading in the emergency setting and the second reading by the senior emergency teleradiologist was excellent (weighted kappa = 0.87). Electronic supplementary material The online version of this article (10.1007/s00330-020-07345-z) contains supplementary material, which is available to authorized users.

Objectives To evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice. Methods This was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality. Results Overall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]). Conclusion Instead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists. Key Points • Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%). • The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality. • Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08645-2.

Objectives: To evaluate the accuracy of diagnoses of COVID-19 based on chest CT as well as inter-observer agreement between teleradiologists during on-call duty and senior radiologists in suspected COVID-19 patients. Materials and Methods: From 03/13/2020 to 04/14/2020, consecutive suspected COVID-19 adult patients who underwent both an RT-PCR test and chest CT from 15 hospitals were included in this prospective study. Chest CTs were immediately interpreted by the on-call teleradiologist and were systematically blind reviewed by a senior radiologist. Readings were categorised using a five-point scale: (1) normal; (2) non-infectious findings; (3) infectious findings but not consistent with COVID-19 infection; (4) consistent with COVID-19 infection; and (5) typical appearance of COVID-19 infection. The diagnostic accuracy of chest CT and inter-observer agreement using the Kappa coefficient were evaluated over the study period.Results: In total, 513 patients were enrolled, of whom 244/513 (47.6%) tested positive for RT-PCR. First readings were scored 4 or 5 in 225/244 (92%) RT-PCR+ patients, and between 1 and 3 in 201/269 (74.7%) RT-PCR- patients. The data were highly consistent (weighted Kappa=0.87) and correlated with RT-PCR (p<0.001, AUC1st-reading=0.89, AUC2nd-reading=0.93). The negative predictive value for scores of 4 or 5 was 0.91–0.92, and the PPV for a score of 5 was 0.89–0.96 at the first and second readings, respectively. Diagnostic accuracy was consistent over the study period, irrespective of a variable prevalence rate.Conclusion: Chest CT demonstrated high diagnostic accuracy with strong inter-observer agreement between on-call teleradiologists with varying degrees of experience and senior radiologists over the study period.

No abstract

This case demonstrates the limitations of the EEG for this indication and suggests that angiography should be preferred. French legislation is probably maladjusted and would benefit by incorporating guidelines of other countries like Canada. International harmonization of criteria for brain death diagnosis would also be welcome.