How artificial intelligence improves radiological interpretation in suspected pulmonary embolism.

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.

What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing.

Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with natural language processing for epidemiological purposes, activity monitoring or data collection. These reports are obtained under various on-call conditions by radiologists with various backgrounds. Our aim was to investigate what influences the radiologists' written expressions. To do so, this retrospective multicentric study included 30,227 emergency radiological reports of computed tomography scans and magnetic resonance imaging involving exactly one body region, only with pathological findings, interpreted from 2019-09-01 to 2020-02-28 by 165 radiologists. After text pre-processing, one-word tokenization and use of dictionaries for stop words, polarity, sentiment and uncertainty, 11 variables depicting the structure and content of words and sentences in the reports were extracted and summarized to 3 principal components capturing 93.7% of the dataset variance. In multivariate analysis, the 1st principal component summarized the length and lexical diversity of the reports and was significantly influenced by the weekday, time slot, workload, number of examinations previously interpreted by the radiologist during the on-call period, type of examination, emergency level and radiologists' gender (P value range: < 0.0001-0.0029). The 2nd principal component summarized negative formulations, polarity and sentence length and was correlated with the number of examination previously interpreted by the radiologist, type of examination, emergency level, imaging modality and radiologists' experience (P value range: < 0.0001-0.0032). The last principal component summarized questioning, uncertainty and polarity and was correlated with the type of examination and emergency level (all P values < 0.0001). Thus, the length, structure and content of emergency radiological reports were significantly influenced by organizational, radiologist- and examination-related characteristics, highlighting the subjectivity and variability in the way radiologists express themselves during their clinical activity. These findings advocate for more homogeneous practices in radiological reporting and stress the need to consider these influential features when developing models based on natural language processing.

The accuracy of teleradiologists in diagnosing COVID-19 based on a French multicentric emergency cohort.

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, 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. 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).

Whole-Body CT in Patients with Multiple Traumas: Factors Leading to Missed Injury.

Purpose To determine radiologic and clinical markers predictive of missed injuries at early whole-body CT image interpretation. Materials and Methods For this retrospective study, 2354 consecutive whole-body CT examinations were performed in patients with multiple traumas from 26 hospitals interpreted at a teleradiology center study during on-call period from February 2011 to September 2016. All whole-body CT images were interpreted by the on-call radiologist and reviewed within 12-48 hours by another radiologist to detect missed injury as the standard of reference. The first and review reports of all examinations were retrospectively reviewed. Univariable and multivariable logistic regression with a stepwise selection method were performed to identify clinical and radiologic predictors of missed injury. Results This study included 639 women (27.1%) and 1715 men (72.8%). The median age of men, women, and the entire population was 34 years (age range, 1-96 years). On a per-scan basis, there were 304 (12.9%) missed injuries and 59 (2.5%) were clinically significant. On a per-injury basis, the missed injury rate was 530 of 5979 (8.8%). More than two injured body parts (odds ratio, 1.4 [95% confidence interval: 1.1, 1.8]; P = .01), patient age older than 30 years (odds ratio, 2.8 [95% confidence interval: 2.1, 3.8]; P < .001), and an initial clinical severity class of 1 (odds ratio, 1.9 [95% confidence interval: 1.3, 2.8]; P < .001) were independent predictive factors of missed injury. Conclusion Multiple traumas with more than two injured body parts, age older than 30 years, or an initial clinical severity class of 1 were associated with missed injury at whole-body CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Novelline in this issue.

Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France.

BACKGROUND: COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors. RESULTS: A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90-0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29). CONCLUSION: Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators.

Teleradiology as a relevant indicator of the impact of COVID-19 pandemic management on emergency room activities: a nationwide worrisome survey.

OBJECTIVES: To evaluate the impact of COVID-19's lockdown on radiological examinations in emergency services. METHODS: Retrospective, multicentre analysis of radiological examinations requested, via our teleradiology network, from 2017 to 2020 during two timeframes (calendar weeks 5-8 and then 12-15). We included CT scans or MRIs performed for strokes, multiple traumas (Body-CT), cranial traumas (CTr) and acute non-traumatic abdominal pain (ANTAP). We evaluated the number and percentages of examinations performed, of those with a pathological conclusion, and of examinations involving the chest. RESULTS: Our study included 25 centres in 2017, 29 in 2018, 43 in 2019 and 59 in 2020. From 2017 to 2019, the percentages of examinations were constant, which was also true for chest CTs. Each centre's number of examinations, gender distribution and patient ages were unchanged. In 2020, examinations significantly decreased: suspected strokes decreased by 36% (1052 vs 675, p < 0.001), Body-CT by 62% (349 vs 134, p < 0.001), CTr by 52% (1853 vs 895, p < 0.001) and for ANTAP, appendicitis decreased by 38% (45 vs 90, not statistically significant (NS)) sigmoiditis by 44% (98 vs 55, NS), and renal colic by 23% (376 vs 288, NS). The number of examinations per centre decreased by 13% (185.5 vs 162.5, p < 0.001), whereas the number of examinations of the "chest" region increased by 170% (1205 vs 3766, p < 0.001). CONCLUSION: Teleradiology enabled us to monitor the impact of the COVID-19 pandemic management on emergency activities, showing a global decrease in the population's use of care.

Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort.

Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions' semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.

[Lung metastases of epithelioid sarcoma revealed by bilateral spontaneous pneumothorax: a pathological diagnosis]. / Métastases pulmonaires d'un sarcome épithélioïde révélées par un pneumothorax spontané bilatéral : un diagnostic anatomopathologique exclusif.

Spontaneous pneumothorax may be one of the presenting manifestation of metastatic epithelioid sarcoma even if imaging does not show evident metastatic nodules. We report the case of a 24-year-old man presenting a bilateral spontaneous pneumothorax in association with an epithelioid sarcoma developed in the right foot. CT-scan revealed bilateral microcystic lesions with no evidence of metastatic disease. A left thoracoscopy and a pleurodesis were performed. Persistent air leakage led to a thoracotomy during which lung biopsies were carried out. Histopathological examination of the pulmonary biopsies revealed rare millimetric nodules, composed of very atypical epithelioid cells, growing along alveolar walls reminiscent of a bronchiolo-alveolar carcinoma. However, these cells and primary neoplastic cells of the foot tumour were morphologically and immunohistochemically similar and the atypical pulmonary cells were TTF1 negative. All these constatations allowed a diagnosis of pulmonary metastases of the epithelioid sarcoma. This very unusual case underlines that sarcomatous metastases may be a clinical and pathological pitfall.

Usefulness of MRI to demonstrate the mechanisms of myocardial ischemia in hypertrophic cardiomyopathy with myocardial bridge.

We present a case of symptomatic primary hypertrophic cardiomyopathy (HCM) associated with myocardial bridging of the left anterior descending (LAD) artery and suspected ischemia that could be related either to LAD artery compression or to microvascular perfusion abnormalities. MRI demonstrated the morphological appearance of myocardial hypertrophy, and coronary MR angiography evidenced the myocardial bridge and its functional consequences with stress MR perfusion. In conclusion, as a non-invasive comprehensive imaging technique, MRI should be considered in identifying the mechanisms of myocardial ischemia in HCM with myocardial bridge.

Biological, electrical and echocardiographic indices versus cardiac magnetic resonance imaging in diagnosing left ventricular hypertrophy.

The aim of this study was to compare the diagnostic performance of N-terminal pro-brain natriuretic peptide (NT-proBNP), electrocardiographic (ECG) criteria and transthoracic echocardiography (TTE) versus cardiac magnetic resonance imaging in detecting left ventricular hypertrophy (LVH). The study included 42 hypertensive subjects with mean±s.d. age 48.1±12.3 years, 57.1% men, 24-h ambulatory blood pressure 144/89 mm Hg, left ventricular ejection fraction >50%, without symptoms of heart failure, and not taking any drugs that interfere with hormonal regulation. The accuracies of the methods in detecting LVH were compared at two diagnostic LVH cutoffs: low, 83 g m(-2) in men and 67 g m(-2) in women; and high, 96 g m(-2) in men and 81 g m(-2) in women. With the low and high LVH cutoffs, the areas under the receiver-operating characteristic curves and the optimal values for NT-proBNP were 0.761, 0.849, 200 and 421 pg ml(-1), respectively. An NT-proBNP level under 30 pg ml(-1) ruled out LVH with 100% sensitivity. The optimal values and literature-based values of NT-proBNP allowed a correct classification of 73-81% of the subjects. In 80-90% of the cases, the diagnostic accuracy of NT-proBNP was close to that of ECG criteria but lower than that of TTE criteria. Interestingly, combining ECG criteria and NT-proBNP level improved the diagnostic performance to be at least comparable to that of TTE: the percentages of correctly classified subjects were 73-95% vs. 67-86%, respectively. Of note, the range considers both diagnostic LVH cutoffs. The simultaneous use of ECG criteria and NT-proBNP plasma levels seemed to be powerful enough to detect LVH in most hypertensive subjects.

[Whole-body diffusion-weighted magnetic resonance imaging in oncology]. / Intérêts de l'imagerie par résonance magnétique corps entier avec séquences de diffusion en cancérologie.

Because of its excellent diagnostic performance, whole-body MRI will probably become an alternative to or replacement for positron emission tomography and computed tomography (PET-CT). The sensitivity of diffusion sequences is high. Whole body MRI is used increasingly often in oncology, for initial or follow-up staging, finding primary tumors after identifying metastases, staging for pregnant woman and children, and therapeutic follow-up. Advantages of whole-body MRI over PET-CT include the absence of irradiation as well as the greater availability, and lower cost of MRI.

Whole-body MRI for metastases screening: a preliminary study using 3D VIBE sequences with automatic subtraction between noncontrast and contrast enhanced images.

OBJECTIVES: To evaluate 3D Volumetric Interpolated Breath-hold Examination (VIBE) whole-body MRI (WB-MRI) acquisition for the metastases staging. METHODS: Thirty-two consecutive patients with solid tumor were examined from head to feet before and after contrast injection. An automatic subtraction occurred between the 2 series of images. WB-MRI was compared with conventional staging techniques (CT, scintigraphy, brain MRI, and whole-body PET in 4 cases). RESULTS: WB-MRI and the reference techniques depicted metastases in 25 patients. WB-MRI depicted more bone lesions in the spine, pelvis, skull, femur, and tibia, whereas scintigraphy detected more rib lesions. WB-MRI depicted 27 cerebral metastases, whereas brain MRI depicted 40 cerebral metastases. WB-MRI depicted a total of 8 hepatic metastases, 8 adrenal lesions, and conventional staging 7 hepatic metastases and 10 adrenal lesions. WB-MRI examination depicted lung metastases in 10 patients, and CT examination in 13 patients. CONCLUSION: The results of this study indicate that WB-MRI is a feasible and promising technique for tumor staging.

Compensation for herbivory by Cucumis sativus through increased photosynthetic capacity and efficiency.

Herbivory is an important selective pressure in the life history of most plant species, as it usually results in reduced plant fitness. In some situations, however, plants are able to compensate for the resources lost to herbivory and do not suffer any reduction in growth or reproduction after attack. We examined the ability of Lebanese cucumber (Cucumis sativus) to compensate for both pre-flowering and during-flowering foliar herbivory through increased photosynthetic efficiency and capacity. Plants that were damaged before flowering were able to compensate, in terms of vegetative biomass and fruit production for up to 80% leaf area loss. Plants that were damaged during the flowering period were less able to compensate and fruit production declined with increasing herbivory. Damaged plants had higher photosynthetic efficiency and capacity, and dissipated less light energy as heat. Herbivore-damaged plants may be induced to use a greater proportion of the absorbed light energy for photosynthesis as a result of altered carbohydrate source-sink relationships.