Purpose To compare digital mammography (DM) plus digital breast tomosynthesis (DBT) versus DM alone for breast cancer screening in the Reggio Emilia Tomosynthesis trial, a two-arm test-and-treat ...randomized controlled trial. Materials and Methods For this trial, eligible women (45-70 years old) who previously participated in the Reggio Emilia screening program were invited for mammography. Consenting women were randomly assigned 1:1 to undergo DBT+DM or DM (both of which involved two projections and double reading). Women were treated according to the decision at DBT+DM. Sensitivity, recall rate, and positive predictive value (PPV) at baseline were determined; the ratios of these rates for DBT+DM relative to DM alone were determined. Results From March 2014 to March 2016, 9777 women were recruited to the DM+DBT arm of the study, and 9783 women were recruited to the DM arm (mean age, 56.2 vs 56.3 years). Recall was 3.5% in both arms; detection was 4.5 per 1000 (44 of 9783) and 8.6 per 1000 (83 of 9777), respectively (+89%; 95% confidence interval CI: 31, 72). PPV of the recall was 13.0% and 24.1%, respectively (P = .0002); 72 of 80 cancers found in the DBT+DM arm and with complete DBT imaging were positive at least at one DBT-alone reading. The greater detection rate for DM+DBT was stronger for ductal carcinoma in situ (+180%, 95% CI: 1, 665); it was notable for small and medium invasive cancers, but not for large ones (+94 95% CI: 6, 254; +122 95% CI: 18, 316; -12 95% CI: -68, 141; for invasive cancers < 10 mm, 10-19 mm, and ≥ 20 mm, respectively). Conclusion DBT+DM depicts 90% more cancers in a population previously screened with DM, with similar recall rates.
Neoadjuvant-chemotherapy (NAC) is considered the standard treatment for locally advanced breast carcinomas. Accurate assessment of disease response is fundamental to increase the chances of ...successful breast-conserving surgery and to avoid local recurrence. The purpose of this study was to compare contrast-enhanced spectral mammography (CESM) and contrast-enhanced-MRI (MRI) in the evaluation of tumor response to NAC.
This prospective study was approved by the institutional review board and written informed consent was obtained. Fifty-four consenting women with breast cancer and indication of NAC were consecutively enrolled between October 2012 and December 2014. Patients underwent both CESM and MRI before, during and after NAC. MRI was performed first, followed by CESM within 3 days. Response to therapy was evaluated for each patient, comparing the size of the residual lesion measured on CESM and MRI performed after NAC to the pathological response on surgical specimens (gold standard), independently of and blinded to the results of the other test. The agreement between measurements was evaluated using Lin's coefficient. The agreement between measurements using CESM and MRI was tested at each step of the study, before, during and after NAC. And last of all, the variation in the largest dimension of the tumor on CESM and MRI was assessed according to the parameters set in RECIST 1.1 criteria, focusing on pathological complete response (pCR).
A total of 46 patients (85%) completed the study. CESM predicted pCR better than MRI (Lin's coefficient 0.81 and 0.59, respectively). Both methods tend to underestimate the real extent of residual tumor (mean 4.1mm in CESM, 7.5mm in MRI). The agreement between measurements using CESM and MRI was 0.96, 0.94 and 0.76 before, during and after NAC respectively. The distinction between responders and non-responders with CESM and MRI was identical for 45/46 patients. In the assessment of CR, sensitivity and specificity were 100% and 84%, respectively, for CESM, and 87% and 60% for MRI.
CESM and MRI lesion size measurements were highly correlated. CESM seems at least as reliable as MRI in assessing the response to NAC, and may be an alternative if MRI is contraindicated or its availability is limited.
We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on ...radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5-4.4) and a 3.6 (95% CI 1.4-9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists' visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580-0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623-0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists' visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity.
Objective
To assess sensitivity/specificity of CT vs RT-PCR for the diagnosis of COVID-19 pneumonia in a prospective Italian cohort of symptomatic patients during the outbreak peak.
Methods
In this ...cross-sectional study, we included all consecutive patients who presented to the ER between March 13 and 23 for suspected COVID-19 and underwent CT and RT-PCR within 3 days. Using a structured report, radiologists prospectively classified CTs in highly suggestive, suggestive, and non-suggestive of COVID-19 pneumonia. Ground-glass, consolidation, and visual extension of parenchymal changes were collected. Three different RT-PCR-based reference standard definitions were used. Oxygen saturation level, CRP, LDH, and blood cell counts were collected and compared between CT/RT-PCR classes.
Results
The study included 696 patients (41.4% women; age 59 ± 15.8 years): 423/454 (93%) patients with highly suggestive CT, 97/127 (76%) with suggestive CT, and 31/115 (27%) with non-suggestive CT had positive RT-PCR. CT sensitivity ranged from 73 to 77% and from 90 to 94% for high and low positivity threshold, respectively. Specificity ranged from 79 to 84% for high positivity threshold and was about 58% for low positivity threshold. PPV remained ≥ 90% in all cases. Ground-glass was more frequent in patients with positive RT-PCR in all CT classes. Blood tests were significantly associated with RT-PCR and CT classes. Leukocytes, lymphocytes, neutrophils, and platelets decreased, CRP and LDH increased from non-suggestive to suggestive CT classes.
Conclusions
During the outbreak peak (in a high-prevalence setting), CT presented high PPV and may be considered a good reference to recognize COVID-19 patients while waiting for RT-PCR confirmation.
Key Points
•
During the epidemic peak, CT showed high positive predictive value and sensitivity for COVID-19 pneumonia when compared with RT-PCR.
•
Blood tests were significantly associated with RT-PCR and CT classes.
Objective
The aims of this study were to develop a multiparametric prognostic model for death in COVID-19 patients and to assess the incremental value of CT disease extension over clinical ...parameters.
Methods
Consecutive patients who presented to all five of the emergency rooms of the Reggio Emilia province between February 27 and March 23, 2020, for suspected COVID-19, underwent chest CT, and had a positive swab within 10 days were included in this retrospective study. Age, sex, comorbidities, days from symptom onset, and laboratory data were retrieved from institutional information systems. CT disease extension was visually graded as < 20%, 20–39%, 40–59%, or ≥ 60%. The association between clinical and CT variables with death was estimated with univariable and multivariable Cox proportional hazards models; model performance was assessed using
k
-fold cross-validation for the area under the ROC curve (cvAUC).
Results
Of the 866 included patients (median age 59.8, women 39.2%), 93 (10.74%) died. Clinical variables significantly associated with death in multivariable model were age, male sex, HDL cholesterol, dementia, heart failure, vascular diseases, time from symptom onset, neutrophils, LDH, and oxygen saturation level. CT disease extension was also independently associated with death (HR = 7.56, 95% CI = 3.49; 16.38 for ≥ 60% extension). cvAUCs were 0.927 (bootstrap bias-corrected 95% CI = 0.899–0.947) for the clinical model and 0.936 (bootstrap bias-corrected 95% CI = 0.912–0.953) when adding CT extension.
Conclusions
A prognostic model based on clinical variables is highly accurate in predicting death in COVID-19 patients. Adding CT disease extension to the model scarcely improves its accuracy.
Key Points
• Early identification of COVID-19 patients at higher risk of disease progression and death is crucial; the role of CT scan in defining prognosis is unclear.
• A clinical model based on age, sex, comorbidities, days from symptom onset, and laboratory results was highly accurate in predicting death in COVID-19 patients presenting to the emergency room.
• Disease extension assessed with CT was independently associated with death when added to the model but did not produce a valuable increase in accuracy.
Inflammatory burden is associated with COVID-19 severity and outcomes. Residual computed tomography (CT) lung abnormalities have been reported after COVID-19. The aim was to evaluate the association ...between inflammatory burden during COVID-19 and residual lung CT abnormalities collected on follow-up CT scans performed 2-3 and 6-7 months after COVID-19, in severe COVID-19 pneumonia survivors. C-reactive protein (CRP) curves describing inflammatory burden during the clinical course were built, and CRP peaks, velocities of increase, and integrals were calculated. Other putative determinants were age, sex, mechanical ventilation, lowest PaO2/FiO2 ratio, D-dimer peak, and length of hospital stay (LOS). Of the 259 included patients (median age 65 years; 30.5% females), 202 (78%) and 100 (38.6%) had residual, predominantly non-fibrotic, abnormalities at 2-3 and 6-7 months, respectively. In age- and sex-adjusted models, best CRP predictors for residual abnormalities were CRP peak (odds ratio OR for one standard deviation SD increase = 1.79; 95% confidence interval CI = 1.23-2.62) at 2-3 months and CRP integral (OR for one SD increase = 2.24; 95%CI = 1.53-3.28) at 6-7 months. Hence, inflammation is associated with short- and medium-term lung damage in COVID-19. Other severity measures, including mechanical ventilation and LOS, but not D-dimer, were mediators of the relationship between CRP and residual abnormalities.
Purpose:
A characterization of a clinical unit for digital radiography (FUJIFILM FDR D-EVO) is presented. This system is based on the irradiation side sampling (ISS) technology and can be equipped ...with two different scintillators: one traditional gadolinium-oxysulphide phosphor (GOS) and a needle structured cesium iodide (CsI) phosphor panel.
Methods:
The characterization was achieved in terms of response curve, modulation transfer function (MTF), noise power spectra (NPS), detective quantum efficiency (DQE), and psychophysical parameters (contrast-detail analysis with an automatic reading of CDRAD images). For both scintillation screens the authors accomplished the measurements with four standard beam conditions: RAQ3, RQA5, RQA7, and RQA9.
Results:
At the Nyquist frequency (3.33 lp/mm) the MTF is about 35% and 25% for CsI and GOS detectors, respectively. The CsI scintillator has better noise properties than the GOS screen in almost all the conditions. This is particularly true for low-energy beams, where the noise for the GOS system can go up to a factor 2 greater than that found for CsI. The DQE of the CsI detector reaches a peak of 60%, 60%, 58%, and 50% for the RQA3, RQA5, RQA7, and RQA9 beams, respectively, whereas for the GOS screen the maximum DQE is 40%, 44%, 44%, and 35%. The contrast-detail analysis confirms that in the majority of cases the CsI scintillator is able to provide improved outcomes to those obtained with the GOS screen.
Conclusions:
The limited diffusion of light produced by the ISS reading makes possible the achievement of very good spatial resolution. In fact, the MTF of the unit with the CsI panel is only slightly lower to that achieved with direct conversion detectors. The combination of very good spatial resolution, together with the good noise properties reached with the CsI screen, allows achieving DQE on average about 1.5 times greater than that obtained with GOS. In fact, the DQE of unit equipped with CsI is comparable to the best alternative methods available which are based on the same technology, and similar to others based on an a-Se direct conversion detectors.
Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network ...features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.
The purpose of this study was to perform a complete evaluation of three pieces of clinical digital mammography equipment. Image quality was assessed by performing physical characterization and ...contrast-detail (CD) analysis. We considered three different FFDM systems: a computed radiography unit (Fuji “FCR 5000 MA”) and two flat-panel units, the indirect conversion a-Si based GE “Senographe 2000D” and the direct conversion a-Se based IMS “Giotto Image MD.” The physical characterization was estimated by measuring the MTF, NNPS, and DQE of the detectors with no antiscatter grid and over the clinical range of exposures. The CD analysis was performed using a CDMAM 3.4 phantom and custom software designed for automatic computation of the contrast-detail curves. The physical characterization of the three digital systems confirms the excellent MTF properties of the direct conversion flat-panel detector (FPD). We performed a relative standard deviation (RSD) analysis, for investigating the different components of the noise presented by the three systems. It turned out that the two FPDs show a significant additive component, whereas for the CR system the statistical noise is dominant. The multiplicative factor is a minor constituent for all the systems. The two FPDs demonstrate better DQE, with respect to the CR system, for exposures higher than
70
μ
Gy
. The CD analysis indicated that the three systems are not statistically different for detail objects with a diameter greater than
0.3
mm
. However, the IMS system showed a statistically significant different response for details smaller than
0.3
mm
. In this case, the poor response of the a-Se detector could be attributed to its high-frequency noise characteristics, since its MTF, NEQ, and DQE are not inferior to those of the other systems. The CD results were independent of exposure level, within the investigated clinical range. We observed slight variations in the CD results, due to the changes in the visualization parameters (window/level and magnification factor). This suggests that radiologists would benefit from viewing images using varied window/level and magnification.
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