Assessment of lateral costal artery with CT angiography: determination of prevalence and vessel length in the general population and its potential impact for coronary artery bypass grafting

Objectives Standard treatment for severe coronary artery disease (CAD) is coronary artery bypass grafting (CABG). An underreported branch of the internal mammary artery, the lateral costal artery (LCA), can cause a steal phenomenon after CABG, resulting in angina. The aim of this study was to determine the prevalence and length of LCA based on CT angiography (CTA). Methods This retrospective study included adult patients undergoing a thoracic CTA between January 2016 and August 2018. Exclusion criteria were prior CABG, insufficient clinical information, or inadequate image quality. Two blinded, independent readers reviewed all studies for the prevalence of the LCA. Positive cases were reviewed by two readers (R1/R2) for side distribution and vessel length, measured in intercostal spaces (ICS). Study indication, aortic size, and coronary calcification were noted. Results LCA was present in up to 42/389 (11%) of studies (60.3 +/- 16.7 years, 30 males). The LCA was most commonly unilateral (n= 23, 55%). Median vessel length was 2 ICS (IQR 0; 3). Logistic regression was not significant in vessel distribution for sex (OR 0.6, 95% CI 0.28-1.15;p= 0.11). Inter-observer agreement in detecting LCA was substantial (kappa 0.71, 95% CI 0.59-0.83) and excellent for side/length distribution (kappa 0.94, 95% CI 0.82-1.0; ICC 0.96, 95% CI 0.93-0.98). Conclusion The LCA is uncommon and most often unilateral and extends the third rib. Radiologists should be aware of this vessel and its potential role in angina after CABG, particularly when large

Head and neck squamous cell carcinoma: evaluation of iodine overlay maps and low-energy virtual mono-energetic images acquired with spectral detector CT

AIM: To evaluate the diagnostic value of spectral detector computed tomography (SDCT)-derived iodine overlay maps and low-energy virtual mono-energetic images (VMI) for the initial locoregional assessment of primary, therapy-naive head and neck cancer. MATERIALS AND METHODS: Fifty-six patients with histologically confirmed untreated squamous cell carcinoma of the head and neck who underwent SDCT of the neck for staging purposes were included in this retrospective study. Attenuation, image noise as well as signal-and contrast-to-noise ratios (S-/CNR) in VMI40-70keV were obtained from region of interest (ROI)-based measurements in the tumour and important anatomical landmarks (sternocleidomastoid muscle, subcutaneous fat, thyroid gland, submandibular gland, carotid artery, and jugular vein). Tumour conspicuity and delineation, as well as subjective image quality, were rated for conventional images, VMI40-70keV, and iodine overlay maps using five-point Likert scales. RESULTS: The CNR of the tumour versus the floor of the mouth and the CNR of the tumour versus the sternocleidomastoid muscle was significantly higher in VMI40keV in comparison to conventional images (10.0 f 7.3 versus 3.8 f 3.3 and 11.3 f 7.6 versus 3.6 f 2.8; p<0.05 each). This was supported by qualitative results, as tumour conspicuity and delineation received superior ratings in iodine overlay maps and VMI40keV compared to conventional images (5 [3-5] and 5 [4-5] versus 3 [2-5]; 5 [2-5] and 5 [3-5] versus 3 [2-4], respectively, all p<0.05). VMI40keV yielded the highest score among all included image reconstructions for overall image quality (p<0.05 all). CONCLUSION: Iodine overlay maps and low-energy VMI derived from SDCT improve initial assessment of primary squamous cell carcinoma of the head and neck compared to conventional images. (c) 2022 Published by Elsevier Ltd on behalf of The Royal College of Radiologists

Chronic lung allograft dysfunction phenotype and prognosis by machine learning CT analysis

Background Chronic lung allograft dysfunction (CLAD) is the principal cause of graft failure in lung transplant recipients and prognosis depends on CLAD phenotype. We used a machine learning computed tomography (CT) lung texture analysis tool at CLAD diagnosis for phenotyping and prognostication compared with radiologist scoring. Methods This retrospective study included all adult first double lung transplant patients (January 2010-December 2015) with CLAD (censored December 2019) and inspiratory CT near CLAD diagnosis. The machine learning tool quantified ground-glass opacity, reticulation, hyperlucent lung and pulmonary vessel volume (PVV). Two radiologists scored for ground-glass opacity, reticulation, consolidation, pleural effusion, air trapping and bronchiectasis. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of machine learning and radiologist for CLAD phenotype. Multivariable Cox proportional hazards regression analysis for allograft survival controlled for age, sex, native lung disease, cytomegalovirus serostatus and CLAD phenotype. Results 88 patients were included (57 bronchiolitis obliterans syndrome (BOS), 20 restrictive allograft syndrome (RAS)/mixed and 11 unclassified/undefined) with CT a median 9.5 days from CLAD onset. Radiologist and machine learning parameters phenotyped RAS/mixed with PVV as the strongest indicator (area under the curve (AUC) 0.85). Machine learning hyperlucent lung phenotyped BOS using only inspiratory CT (AUC 0.76). Radiologist and machine learning parameters predicted graft failure in the multivariable analysis, best with PVV (hazard ratio 1.23, 95% CT 1.05-1.44; p=0.01). Conclusions Machine learning discriminated between CLAD phenotypes on CT. Both radiologist and machine learning scoring were associated with graft failure, independent of CLAD phenotype. PVV, unique to machine learning, was the strongest in phenotyping and prognostication

Diagnosis and Treatment of Endometriosis. Guideline of the DGGG, SGGG and OEGGG (S2k Level, AWMF Registry Number 015/045, August 2020)

Aims The aim of this official guideline published and coordinated by the German Society of Gynaecology and Obstetrics (DGGG) in cooperation with the Austrian Society for Gynaecology and Obstetrics (OEGGG) and the Swiss Society for Gynaecology and Obstetrics (SGGG) was to provide consensusbased recommendations for the diagnosis and treatment of endometriosis based on an evaluation of the relevant literature. Methods This S2k guideline represents the structured consensus of a representative panel of experts with different professional backgrounds commissioned by the Guideline Committee of the DGGG, OEGGG and SGGG. Recommendations Recommendations on the epidemiology, aetiology, classification, symptomatology, diagnosis and treatment of endometriosis are given and special situations are discussed