Cardiovascular parameters on computed tomography are independently associated with in-hospital complications and outcomes in level-1 trauma patients

Background: In-hospital complications after trauma may result in prolonged stays, higher costs, and adverse functional outcomes. Among reported risk factors for complications are pre-existing cardiopulmonary comorbidities. Objective and quick evaluation of cardiovascular risk would be beneficial for risk assessment in trauma patients. Studies in non-trauma patients suggested an independent association between cardiovascular abnormalities visible on routine computed tomography (CT) imaging and outcomes. However, whether this applies to trauma patients is unknown.Purpose: To assess the association between cardiopulmonary abnormalities visible on routine CT images and the development of in-hospital complications in patients in a level-1 trauma center.Methods: All trauma patients aged 16 years or older with CT imaging of the abdomen, thorax, or spine and admitted to the UMC Utrecht in 2017 were included. Patients with an active infection upon admission or severe neurological trauma were excluded. Routine trauma CT images were analyzed for visible abnormalities: pulmonary emphysema, coronary artery calcifications, and abdominal aorta calcification severity. Drug-treated complications were scored. The discharge condition was measured on the Glasgow Outcome Scale.Results: In total, 433 patients (median age 50 years, 67% male, 89% ASA 1–2) were analyzed. Median Injury Severity Score and Glasgow Coma Scale score were 9 and 15, respectively. Seventy-six patients suffered from at least one complication, mostly pneumonia (n = 39, 9%) or delirium (n = 19, 4%). Left main coronary artery calcification was independently associated with the development of any complication (OR 3.9, 95% CI 1.7–8.9). An increasing number of calcified coronary arteries showed a trend toward an association with complications (p = 0.07) and was significantly associated with an adverse discharge condition (p = 0.02). Pulmonary emphysema and aortic calcifications were not associated with complications.Conclusion: Coronary artery calcification, visible on routine CT imaging, is independently associated with in-hospital complications and an adverse discharge condition in level-1 trauma patients. The findings of this study may help to identify trauma patients quickly and objectively at risk for complications in an early stage without performing additional diagnostics or interventions.</p

Volumetric breast density affects performance of digital screening mammography

PURPOSE: To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM). METHODS: We collected a consecutive series of 111,898 DM examinations (2003-2011) from one screening unit of the Dutch biennial screening program (age 50-75 years). Volumetric mammographic density was automatically assessed using Volpara. We determined screening performance measures for four density categories comparable to the American College of Radiology (ACR) breast density categories. RESULTS: Of all the examinations, 21.6% were categorized as density category 1 ('almost entirely fatty') and 41.5, 28.9, and 8.0% as category 2-4 ('extremely dense'), respectively. We identified 667 screen-detected and 234 interval cancers. Interval cancer rates were 0.7, 1.9, 2.9, and 4.4‰ and false positive rates were 11.2, 15.1, 18.2, and 23.8‰ for categories 1-4, respectively (both p-trend < 0.001). The screening sensitivity, calculated as the proportion of screen-detected among the total of screen-detected and interval tumors, was lower in higher density categories: 85.7, 77.6, 69.5, and 61.0% for categories 1-4, respectively (p-trend < 0.001). CONCLUSIONS: Volumetric mammographic density, automatically measured on digital mammograms, impacts screening performance measures along the same patterns as established with ACR breast density categories. Since measuring breast density fully automatically has much higher reproducibility than visual assessment, this automatic method could help with implementing density-based supplemental screening

Towards personalised contrast injection: Artificial-intelligence-derived body composition and liver enhancement in computed tomography

In contrast-enhanced computed tomography, total body weight adapted contrast injection protocols have proven successful in achieving a homogeneous enhancement of vascular structures and liver parenchyma. However, because solid organs have greater perfusion than adipose tissue, the lean body weight (fat-free mass) rather than the total body weight is theorised to cause even more homogeneous enhancement. We included 102 consecutive patients who underwent a multiphase abdominal computed tomography between March 2016 and October 2019. Patients received contrast media (300 mgI/mL) according to bodyweight categories. Using regions of interest, we measured the Hounsfield unit (HU) increase in liver attenuation from unenhanced to contrast-enhanced computed tomography. Furthermore, subjective image quality was graded using a four-point Likert scale. An artificial intelligence algorithm automatically segmented and determined the body compositions and calculated the percentages of lean body weight. The hepatic enhancements were adjusted for iodine dose and iodine dose per total body weight, as well as percentage lean body weight. The associations between enhancement and total body weight, body mass index, and lean body weight were analysed using linear regression. Patients had a median age of 68 years (IQR: 58–74), a total body weight of 81 kg (IQR: 73 – 90), a body mass index of 26 kg/m2 (SD: ±4.2), and a lean body weight percentage of 50% (IQR: 36 – 55). Mean liver enhancements in the portal venous phase were 61 ± 12 HU (≤ 70 kg), 53 ± 10 HU (70 – 90 kg), and 53 ± 7 HU (≥ 90 kg). The majority (93%) of scans were rated as good or excellent. Regression analysis showed significant correlations between liver enhancement corrected for injected total iodine and total body weight (r = 0.53; p < 0.001) and between liver enhancement corrected for lean body weight and the percentage of lean body weight (r = 0.73; p < 0.001). Most benefits from personalising iodine injection using %LBW additive to total body weight would be achieved in patients under 90 kg. Liver enhancement is more strongly associated with the percentage of lean body weight than with the total body weight or body mass index. The observed variation in liver enhancement might be reduced by a personalised injection based on the artificial-intelligence-determined percentage of lean body weight

Computer-aided detection (CAD) for breast MRI: evaluation of efficacy at 3.0 T

OBJECTIVE: The purpose of the study was to evaluate the accuracy of 3.0-T breast MRI interpretation using manual and fully automated kinetic analyses. MATERIAL AND METHODS: Manual MRI interpretation was done on an Advantage Workstation. Retrospectively, all examinations were processed with a computer-aided detection (CAD) system. CAD data sets were interpreted by two experienced breast radiologists and two residents. For each lesion automated analysis of enhancement kinetics was evaluated at 50% and 100% thresholds. Forty-nine malignant and 22 benign lesions were evaluated. RESULTS: Using threshold enhancement alone, the sensitivity and specificity of CAD were 97.9% and 86.4%, respectively, for the 50% threshold, and 97.9% and 90%, respectively, for the 100% threshold. Manual interpretation by two breast radiologists showed a sensitivity of 84.6% and a specificity of 68.8%. For the same two radiologists the mean sensitivity and specificity for CAD-based interpretation was 90.4% (not significant) and 81.3% (significant at p < 0.05), respectively. With one-way ANOVA no significant differences were found between the two breast radiologists and the two residents together, or between any two readers separately. CONCLUSION: CAD-based analysis improved the specificity compared with manual analysis of enhancement. Automated analysis at 50% and 100% thresholds showed a high sensitivity and specificity for readers with varying levels of experience

Use of automated assessment for determining associations of low muscle mass and muscle loss with overall survival in patients with colorectal cancer – A validation study

Background: Low muscle mass and skeletal muscle mass (SMM) loss are associated with adverse patient outcomes, but the time-consuming nature of manual SMM quantification prohibits implementation of this metric in clinical practice. Therefore, we assessed the feasibility of automated SMM quantification compared to manual quantification. We evaluated both diagnostic accuracy for low muscle mass and associations of SMM (change) with survival in colorectal cancer (CRC) patients. Methods: Computed tomography (CT) images from CRC patients enrolled in two clinical studies were analyzed. We compared i) manual vs. automated segmentation of preselected slices at the third lumbar [L3] vertebra (“semi-automated”), and ii) manual L3-slice-selection + manual segmentation vs. automated L3-slice-selection + automated segmentation (“fully-automated”). Automated L3-selection and automated segmentation was performed with Quantib Body Composition v0.2.1. Bland–Altman analyses, within-subject coefficients of variation (WSCVs) and Intraclass Correlation Coefficients (ICCs) were used to evaluate the agreement between manual and automatic segmentation. Diagnostic accuracy for low muscle mass (defined by an established sarcopenia cut-off) was calculated with manual assessment as the “gold standard”. Using either manual or automated assessment, Cox proportional hazard ratios (HRs) were used to study the association between changes in SMM (>5% decrease yes/no) during first-line metastatic CRC treatment and mortality adjusted for prognostic factors. SMM change was also assessed separately in weight-stable (<5%, i.e. occult SMM loss) patients. Results: In total, 1580 CT scans were analyzed, while a subset of 307 scans were analyzed in the fully-automated comparison. Included patients (n = 553) had a mean age of 63 ± 9 years and 39% were female. The semi-automated comparison revealed a bias of −2.41 cm2, 95% limits of agreement [-9.02 to 4.20], a WSCV of 2.25%, and an ICC of 0.99 (95% confidence intervals (CI) 0.97 to 1.00). The fully-automated comparison method revealed a bias of −0.08 cm2 [-10.91 to 10.75], a WSCV of 2.85% and an ICC of 0.98 (95% CI 0.98 to 0.99). Sensitivity and specificity for low muscle mass were 0.99 and 0.89 for the semi-automated comparison and 0.96 and 0.90 for the fully-automated comparison. SMM decrease was associated with shorter survival in both manual and automated assessment (n = 78/280, HR 1.36 [95% CI 1.03 to 1.80] and n = 89/280, HR 1.38 [95% CI 1.05 to 1.81]). Occult SMM loss was associated with shorter survival in manual assessment, but not significantly in automated assessment (n = 44/263, HR 1.43 [95% CI 1.01 to 2.03] and n = 51/2639, HR 1.23 [95% CI 0.87 to 1.74]). Conclusion: Deep-learning based assessment of SMM at L3 shows reliable performance, enabling the use of CT measures to guide clinical decision making. Implementation in clinical practice helps to identify patients with low muscle mass or (occult) SMM loss who may benefit from lifestyle interventions

Prognostic value of radiological recurrence patterns in ovarian cancer

Objective: To study the prognostic value of CT assessed recurrence patterns on survival outcomes in women with epithelial ovarian cancer. Methods: CT scans were systematically re-evaluated on predefined anatomical sites for the presence of tumor in all 89 patients diagnosed with epithelial ovarian cancer between January 2008 and December 2013 who underwent cytoreductive surgery at our institution and developed a recurrence. A Cox proportional hazard analysis was used to test the effect of recurrence patterns on survival. Results: The median survival time for patients grouped as predominantly intraperitoneal (n = 62), hematogenous (n = 13) or lymphatic (n = 14) recurrence was 25.8 (95% CI 18.4–33.2), 27.6 (95% CI 18.5–36.6) and 52.9 months (95% CI 42.1–63.7), respectively. Univariate Cox regression analysis identified the following prognostic factors: lymphatic recurrence pattern (HR 0.42, 95% CI 0.21–0.85), ascites at diagnosis (HR 2.35, 95% CI 1.46–3.79), residual tumor at initial surgery (HR 2.16, 95% CI 1.36–3.44) and FIGO stage (I–IIIB: HR 0.59, 95% CI 0.33–1.06). The median time to recurrence was 19.5 month for patients after complete debulking surgery, 13.1 months for patients with residual disease ≤1 cm and 8.2 months for patients with residual disease >1 cm after surgery (P < 0.001). No differences in recurrence patterns between patients with complete and incomplete surgery were found. Conclusions: Prolonged survival rates were found in ovarian cancer patients with a predominantly lymphatic recurrence compared to patients with a predominantly peritoneal or hematogenous recurrence. Completeness of surgery was associated with time to recurrence. Classification of recurrence patterns can help counsel patients on their prognosis at the time of recurrence

Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses

OBJECTIVES: To reduce the number of false-positive diagnoses in the screening of women with extremely dense breasts using magnetic resonance imaging (MRI), we aimed to predict which BI-RADS 3 and BI-RADS 4 lesions are benign. For this purpose, we use computer-aided diagnosis (CAD) based on multiparametric assessment. MATERIALS AND METHODS: Consecutive data were used from the first screening round of the DENSE (Dense Tissue and Early Breast Neoplasm Screening) trial. In this trial, asymptomatic women with a negative screening mammography and extremely dense breasts were screened using multiparametric MRI. In total, 4783 women, aged 50 to 75 years, enrolled and were screened in 8 participating hospitals between December 2011 and January 2016. In total, 525 lesions in 454 women were given a BI-RADS 3 (n = 202), 4 (n = 304), or 5 score (n = 19). Of these lesions, 444 were benign and 81 were malignant on histologic examination.The MRI protocol consisted of 5 different MRI sequences: T1-weighted imaging without fat suppression, diffusion-weighted imaging, T1-weighted contrast-enhanced images at high spatial resolution, T1-weighted contrast-enhanced images at high temporal resolution, and T2-weighted imaging. A machine-learning method was developed to predict, without deterioration of sensitivity, which of the BI-RADS 3- and BI-RADS 4-scored lesions are actually benign and could be prevented from being recalled. BI-RADS 5 lesions were only used for training, because the gain in preventing false-positive diagnoses is expected to be low in this group. The CAD consists of 2 stages: feature extraction and lesion classification. Two groups of features were extracted: the first based on all multiparametric sequences, the second based only on sequences that are typically used in abbreviated MRI protocols. In the first group, 49 features were used as candidate predictors: 46 were automatically calculated from the MRI scans, supplemented with 3 clinical features (age, body mass index, and BI-RADS score). In the second group, 36 image features and the same 3 clinical features were used. Each group was considered separately in a machine-learning model to differentiate between benign and malignant lesions. We developed a Ridge regression model using 10-fold cross validation. Performance of the models was analyzed using an accuracy measure curve and receiver-operating characteristic analysis. RESULTS: Of the total number of BI-RADS 3 and BI-RADS 4 lesions referred to additional MRI or biopsy, 425/487 (87.3%) were false-positive. The full multiparametric model classified 176 (41.5%) and the abbreviated-protocol model classified 111 (26.2%) of the 425 false-positive BI-RADS 3- and BI-RADS 4-scored lesions as benign without missing a malignant lesion.If the full multiparametric CAD had been used to aid in referral, recall for biopsy or repeat MRI could have been reduced from 425/487 (87.3%) to 311/487 (63.9%) lesions. For the abbreviated protocol, it could have been 376/487 (77.2%). CONCLUSIONS: Dedicated multiparametric CAD of breast MRI for BI-RADS 3 and 4 lesions in screening of women with extremely dense breasts has the potential to reduce false-positive diagnoses and consequently to reduce the number of biopsies without missing cancers

The Predictive Value of Low Muscle Mass as Measured on CT Scans for Postoperative Complications and Mortality in Gastric Cancer Patients: A Systematic Review and Meta-Analysis

Risk assessment is relevant to predict outcomes in patients with gastric cancer. This systematic review aimed to investigate the predictive value of low muscle mass for postoperative complications in gastric cancer patients. A systematic literature search was performed to identify all articles reporting on muscle mass as measured on computed tomography (CT) scans in patients with gastric cancer. After full text screening, 15 articles reporting on 4887 patients were included. Meta-analysis demonstrated that patients with low muscle mass had significantly higher odds of postoperative complications (odds ratio (OR): 2.09, 95% confidence interval (CI): 1.55-2.83) and severe postoperative complications (Clavien-Dindo grade ≥III, OR: 1.73, 95% CI: 1.14-2.63). Moreover, patients with low muscle mass had a significantly higher overall mortality (hazard ratio (HR): 1.81, 95% CI: 1.52-2.14) and disease-specific mortality (HR: 1.58, 95% CI: 1.36-1.84). In conclusion, assessment of muscle mass on CT scans is a potential relevant clinical tool for risk prediction in gastric cancer patients. Considering the heterogeneity in definitions applied for low muscle mass on CT scans in the included studies, a universal cutoff value of CT-based low muscle mass is required for more reliable conclusions

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

Objectives: Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. Methods: This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. Results: The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). Conclusion: It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories