Chest X-ray in suspected lung cancer is harmful.

OBJECTIVES: The aim of this study was to analyse the use of the chest radiograph (CXR) as the first-line investigation in primary care patients with suspected lung cancer. METHODS: Of 16,945 primary care referral CXRs (June 2018 to May 2019), 1,488 were referred for suspected lung cancer. CXRs were coded as follows: CX1, normal but a CT scan is recommended to exclude malignancy; CX2, alternative diagnosis; or CX3, suspicious for cancer. Kaplan-Meier survival analysis was undertaken by stratifying patients according to their CX code. RESULTS: In the study period, there were 101 lung cancer diagnoses via a primary care CXR pathway. Only 10% of patients with a normal CXR (CX1) underwent subsequent CT and there was a significant delay in lung cancer diagnosis in these patients (p < 0.001). Lung cancer was diagnosed at an advanced stage in 50% of CX1 patients, 38% of CX2 patients and 57% of CX3 patients (p = 0.26). There was no survival difference between CX codes (p = 0.42). CONCLUSION: Chest radiography in the investigation of patients with suspected lung cancer may be harmful. This strategy may falsely reassure in the case of a normal CXR and prioritises resources to advanced disease. KEY POINTS: • Half of all lung cancer diagnoses in a 1-year period are first investigated with a chest X-ray. • A normal chest X-ray report leads to a significant delay in the diagnosis of lung cancer. • The majority of patients with a normal or abnormal chest X-ray have advanced disease at diagnosis and there is no difference in survival outcomes based on the chest X-ray findings.

Automated calculation of the right ventricle to left ventricle ratio on CT for the risk stratification of patients with acute pulmonary embolism.

OBJECTIVES: To assess the feasibility and reliability of the use of artificial intelligence post-processing to calculate the RV:LV diameter ratio on computed tomography pulmonary angiography (CTPA) and to investigate its prognostic value in patients with acute PE. METHODS: Single-centre, retrospective study of 101 consecutive patients with CTPA-proven acute PE. RV and LV volumes were segmented on 1-mm contrast-enhanced axial slices and maximal ventricular diameters were derived for RV:LV ratio using automated post-processing software (IMBIO LLC, USA) and compared to manual analysis in two observers, via intraclass coefficient correlation analysis. Each CTPA report was analysed for mention of the RV:LV ratio and compared to the automated RV:LV ratio. Thirty-day all-cause mortality post-CTPA was recorded. RESULTS: Automated RV:LV analysis was feasible in 87% (n = 88). RV:LV ratios ranged from 0.67 to 2.43, with 64% (n = 65) > 1.0. There was very strong agreement between manual and automated RV:LV ratios (ICC = 0.83, 0.77-0.88). The use of automated analysis led to a change in risk stratification in 45% of patients (n = 40). The AUC of the automated measurement for the prediction of all-cause 30-day mortality was 0.77 (95% CI: 0.62-0.99). CONCLUSION: The RV:LV ratio on CTPA can be reliably measured automatically in the majority of real-world cases of acute PE, with perfect reproducibility. The routine use of this automated analysis in clinical practice would add important prognostic information in patients with acute PE. KEY POINTS: • Automated calculation of the right ventricle to left ventricle ratio was feasible in the majority of patients and demonstrated perfect intraobserver variability. • Automated analysis would have added important prognostic information and altered risk stratification in the majority of patients. • The optimal cut-off value for the automated right ventricle to left ventricle ratio was 1.18, with a sensitivity of 100% and specificity of 54% for the prediction of 30-day mortality.

Assessing the role of an artificial intelligence assessment tool for thoracic aorta diameter on routine chest CT.

OBJECTIVE: To assess the diagnostic accuracy and clinical impact of automated artificial intelligence (AI) measurement of thoracic aorta diameter on routine chest CT. METHODS: A single-centre retrospective study involving three cohorts. 210 consecutive ECG-gated CT aorta scans (mean age 75 ± 13) underwent automated analysis (AI-Rad Companion Chest CT, Siemens) and were compared to a reference standard of specialist cardiothoracic radiologists for accuracy measuring aortic diameter. A repeated measures analysis tested reporting consistency in a second cohort (29 patients, mean age 61 ± 17) of immediate sequential pre-contrast and contrast CT aorta acquisitions. Potential clinical impact was assessed in a third cohort of 197 routine CT chests (mean age 66 ± 15) to document potential clinical impact. RESULTS: AI analysis produced a full report in 387/436 (89%) and a partial report in 421/436 (97%). Manual vs AI agreement was good to excellent (ICC 0.76-0.92). Repeated measures analysis of expert and AI reports for the ascending aorta were moderate to good (ICC 0.57-0.88). AI diagnostic performance crossed the threshold for maximally accepted limits of agreement (>5 mm) at the aortic root on ECG-gated CTs. AI newly identified aortic dilatation in 27% of patients on routine thoracic imaging with a specificity of 99% and sensitivity of 77%. CONCLUSION: AI has good agreement with expert readers at the mid-ascending aorta and has high specificity, but low sensitivity, at detecting dilated aortas on non-dedicated chest CTs. ADVANCES IN KNOWLEDGE: An AI tool may improve the detection of previously unknown thoracic aorta dilatation on chest CTs vs current routine reporting.

Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography relative to cardiac magnetic resonance imaging.

OBJECTIVES: To assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard. METHODS: Consecutive patients with contrast-enhanced CT thorax and CMR within 31 days (2016-2020) were analysed (n = 84). LV dilatation was defined against age-, sex- and body surface area-specific values for CMR. CTs underwent automated artificial intelligence(AI)-derived analysis that segmented ventricular chambers, presenting maximal LV diameter and volume. Area under the receiver operator curve (AUC-ROC) analysis identified CT thresholds with ≥90% sensitivity and highest specificity and ≥90% specificity with highest sensitivity. Youden's Index was used to identify thresholds with optimised sensitivity and specificity. RESULTS: Automated diameter analysis was feasible in 92% of cases (77/84; 45 men, age 61 ± 14 years, mean CT to CMR interval 10 ± 8 days). Relative to CMR as a reference standard, 45% had LV dilatation. In males, an automated LV diameter measurement of ≥55.5 mm was ≥90% specific for CMR-defined LV dilatation (positive predictive value (PPV) 85.7%, negative predictive value (NPV) 61.2%, accuracy 68.9%). In females, an LV diameter of ≥49.7 mm was ≥90% specific for CMR-defined LV dilatation (PPV 66.7%, NPV 73.1%, accuracy 71.9%). AI CT volumetry data did not significantly improve AUC performance. CONCLUSION: Fully automated AI-derived analysis LV dilatation on routine unselected non-gated contrast-enhanced CT thorax studies is feasible. We have defined thresholds for the detection of LV dilatation on CT relative to CMR, which could be used to routinely screen for dilated cardiomyopathy at the time of CT. ADVANCES IN KNOWLEDGE: We show, for the first time, that a fully-automated AI-derived analysis of maximal LV chamber axial diameter on non-ECG-gated thoracic CT is feasible in unselected real-world cases and that the derived measures can predict LV dilatation relative to cardiac magnetic resonance imaging, the non-invasive reference standard for determining cardiac chamber size. We have derived sex-specific cut-off values to screen for LV dilatation on routine contrast-enhanced thoracic CT. Future work should validate these thresholds and determine if technology can alter clinical outcomes in a cost-effective manner.

Impact of an ultra-low dose unenhanced planning scan on CT coronary angiography scan length and effective radiation dose.

Objective: Imaged scan length (z-axis coverage) is a simple parameter that can reduce CT dose without compromising image quality. In CT coronary angiography (CTCA), z-axis coverage may be planned using non-contrast calcium score scan (CaCS) to identify the relevant coronary anatomy. However, standardised Agatston CaCS is acquired at 120 kV which adds a relatively high contribution to total study dose and CaCS is no longer routinely recommended in UK guidelines. We evaluate an ultra-low dose unenhanced planning scan on CTCA scan length and effective radiation dose. Methods: An ultra-low dose tin filter (Sn-filter) planning scan (100 kVp, maximum iterative reconstruction) was performed and used to plan the z-axis coverage on 48 consecutive CTCAs (62% men, 62 ± 13 years) compared with 47 CTCA planned using a localiser alone (46% men, 59 ± 12 years) between May and June 2019. Excess scanning beyond the ideal scan length was calculated for both groups. Estimations of radiation dose were also compared between the two groups. Results: Addition of an ultra-low dose unenhanced planning scan to CTCA protocol was associated with reduction in overscanning with no impact on image quality. There was no significant difference in total study effective dose with the addition of the planning scan, which had an average dose-length product of 3 mGy.cm. (total study dose: Protocol A 2.1 mSv vs Protocol B 2.2 mSv, p = 0.92). Conclusion: An ultra-low dose unenhanced planning scan facilitates optimal scan length for the diagnostic CTCA, reducing overscanning and preventing incomplete cardiac imaging with no significant dose penalty or impact on image quality. Advances in knowledge: An ultra-low dose CTCA planning is feasible and effective at optimising scan length.

Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19.

BACKGROUND: Direct evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19. METHODS: For this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes. FINDINGS: Patients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [<6·99] C19-RS; hazard ratio [HR] 3·31 [95% CI 1·49-7·33], p=0·0033; and 2·58 [1·10-6·05], p=0·028), adjusted for clinical factors, biochemical biomarkers of inflammation and myocardial injury, and technical parameters. The adjusted HR for in-hospital mortality was 8·24 (95% CI 2·16-31·36, p=0·0019) in patients who received no dexamethasone treatment, but 2·27 (0·69-7·55, p=0·18) in those who received dexamethasone after the scan, suggesting that vascular inflammation might have been a therapeutic target of dexamethasone in COVID-19. Finally, C19-RS was strongly associated (r=0·61, p=0·00031) with a whole blood transcriptional module representing dysregulation of coagulation and platelet aggregation pathways. INTERPRETATION: Radiotranscriptomic analysis of CT angiography scans introduces a potentially powerful new platform for the development of non-invasive imaging biomarkers. Application of this platform in routine CT pulmonary angiography scans done in patients with COVID-19 produced the radiotranscriptomic signature C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation and responsible for adverse clinical outcomes, which predicts in-hospital mortality and might allow targeted therapy. FUNDING: Engineering and Physical Sciences Research Council, British Heart Foundation, Oxford BHF Centre of Research Excellence, Innovate UK, NIHR Oxford Biomedical Research Centre, Wellcome Trust, Onassis Foundation.

Computed tomography appearances of the lung parenchyma in pulmonary hypertension.

Computed tomography (CT) is a valuable tool in the workup of patients under investigation for pulmonary hypertension (PH) and may be the first test to suggest the diagnosis. CT parenchymal lung changes can help to differentiate the aetiology of PH. CT can demonstrate interstitial lung disease, emphysema associated with chronic obstructive pulmonary disease, features of left heart failure (including interstitial oedema), and changes secondary to miscellaneous conditions such as sarcoidosis. CT also demonstrates parenchymal changes secondary to chronic thromboembolic disease and venous diseases such as pulmonary venous occlusive disease (PVOD) and pulmonary capillary haemangiomatosis (PCH). It is important for the radiologist to be aware of the various manifestations of PH in the lung, to help facilitate an accurate and timely diagnosis. This pictorial review illustrates the parenchymal lung changes that can be seen in the various conditions causing PH.