Abstract Objective Complication rates after pancreatic resections remain high despite improvement in perioperative management. The effects of body composition and the relationship among different ...body compartments on surgical morbidity are not comprehensively investigated. The aim of this study was to assess whether the evaluation of different body compartments and their relationship was associated with the development of major postoperative complications after pancreatoduodenectomy (PD) for cancer. Methods We retrospectively analyzed 124 patients who underwent PD and had a staging computed tomography (CT) scan at our center. CT scan was used to measure abdominal skeletal muscle area and volume, as well as visceral fat area (VFA) and volume. The total abdominal muscle area (TAMA) was then normalized for height. The severity of complications was assessed. Univariate and multivariate analyses were performed to investigate correlations between the above variables and postoperative complications. The receiver operating characteristic curve methodology was used to investigate the predictive ability of each parameter. Results Major complications occurred in 42 patients (33.9%). The prevalence of sarcopenia was 24.2%. Regression analyses revealed no correlation between abdominal muscular and adipose tissue areas. Univariate analysis showed that the depletion of muscle area normalized for height was not per se predictive of complications ( P = 0.318). Multivariate logistic regression showed that the VFA/TAMA was the only determinant of major complications (odds ratio, 3.20; 95% confidence interval, 1.35–7.60; P = 0.008). The model predictive performance was 0.735 (area under the curve) with a sensitivity of 64.3% and a specificity of 74.4%. Conclusion Sarcopenic obesity is a strong predictor of major complications after PD for cancer.
Objectives
To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were ...compared with clinical and functional parameters and outcomes.
Methods
All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated.
Results
A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (
r
= 0.369), neutrophils (
r
= 0.446), platelets (
r
= 0.182), CRP (
r
= 0.190), PaCO
2
(
r
= 0.176), HCO
3
−
(
r
= 0.284), and PaO2/FiO2
(P
/
F
) values (
r
= − 0.344). CONS-V/TLV significantly correlated with WBC (
r
= 0.294), neutrophils (
r
= 0.300), lymphocytes (
r
= −0.225), CRP (
r
= 0.306), PaCO
2
(
r
= 0.227), pH (r = 0.162), HCO
3
−
(
r
= 0.394), and
P
/
F
(
r
= − 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all
p
< 0.05).
Conclusion
The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation.
Key Points
• Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia.
• All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count.
• All lung volumes correlate with patient’s outcome, in particular concerning invasive ventilation.
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed ...tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of Formula: see text mm and Dice coefficient of Formula: see text. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.
Respiratory failure due to COVID-19 pneumonia is associated with high mortality and may overwhelm health care systems, due to the surge of patients requiring advanced respiratory support. Shortage of ...intensive care unit (ICU) beds required many patients to be treated outside the ICU despite severe gas exchange impairment. Helmet is an effective interface to provide continuous positive airway pressure (CPAP) noninvasively. We report data about the usefulness of helmet CPAP during pandemic, either as treatment, a bridge to intubation or a rescue therapy for patients with care limitations (DNI).
In this observational study we collected data regarding patients failing standard oxygen therapy (i.e., non-rebreathing mask) due to COVID-19 pneumonia treated with a free flow helmet CPAP system. Patients' data were recorded before, at initiation of CPAP treatment and once a day, thereafter. CPAP failure was defined as a composite outcome of intubation or death.
A total of 306 patients were included; 42% were deemed as DNI. Helmet CPAP treatment was successful in 69% of the full treatment and 28% of the DNI patients (P < 0.001). With helmet CPAP, PaO
/FiO
ratio doubled from about 100 to 200 mmHg (P < 0.001); respiratory rate decreased from 28 22-32 to 24 20-29 breaths per minute, P < 0.001). C-reactive protein, time to oxygen mask failure, age, PaO
/FiO
during CPAP, number of comorbidities were independently associated with CPAP failure. Helmet CPAP was maintained for 6 3-9 days, almost continuously during the first two days. None of the full treatment patients died before intubation in the wards.
Helmet CPAP treatment is feasible for several days outside the ICU, despite persistent impairment in gas exchange. It was used, without escalating to intubation, in the majority of full treatment patients after standard oxygen therapy failed. DNI patients could benefit from helmet CPAP as rescue therapy to improve survival.
NCT04424992.
Background
We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in ...Lombardy, Italy.
Methods
We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard.
Results
At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval CI 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2.
Conclusions
This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
Objectives
Enlarged main pulmonary artery diameter (MPAD) resulted to be associated with pulmonary hypertension and mortality in a non-COVID-19 setting. The aim was to investigate and validate the ...association between MPAD enlargement and overall survival in COVID-19 patients.
Methods
This is a cohort study on 1469 consecutive COVID-19 patients submitted to chest CT within 72 h from admission in seven tertiary level hospitals in Northern Italy, between March 1 and April 20, 2020. Derivation cohort (
n
= 761) included patients from the first three participating hospitals; validation cohort (
n
= 633) included patients from the remaining hospitals. CT images were centrally analyzed in a core-lab blinded to clinical data. The prognostic value of MPAD on overall survival was evaluated at adjusted and multivariable Cox’s regression analysis on the derivation cohort. The final multivariable model was tested on the validation cohort.
Results
In the derivation cohort, the median age was 69 (IQR, 58–77) years and 537 (70.6%) were males. In the validation cohort, the median age was 69 (IQR, 59–77) years with 421 (66.5%) males. Enlarged MPAD (≥ 31 mm) was a predictor of mortality at adjusted (hazard ratio, HR 95%CI: 1.741 1.253–2.418,
p
< 0.001) and multivariable regression analysis (HR 95%CI: 1.592 1.154–2.196,
p
= 0.005), together with male gender, old age, high creatinine, low well-aerated lung volume, and high pneumonia extension (c-index 95%CI = 0.826 0.796–0.851). Model discrimination was confirmed on the validation cohort (c-index 95%CI = 0.789 0.758–0.823), also using CT measurements from a second reader (c-index 95%CI = 0.790 0.753;0.825).
Conclusion
Enlarged MPAD (≥ 31 mm) at admitting chest CT is an independent predictor of mortality in COVID-19.
Key Points
•
Enlargement of main pulmonary artery diameter at chest CT performed within 72 h from the admission was associated with a higher rate of in-hospital mortality in COVID-19 patients.
•
Enlargement of main pulmonary artery diameter (≥ 31 mm) was an independent predictor of death in COVID-19 patients at adjusted and multivariable regression analysis.
•
The combined evaluation of clinical findings, lung CT features, and main pulmonary artery diameter may be useful for risk stratification in COVID-19 patients.
Purpose
In overwhelmed emergency departments (EDs) facing COVID-19 outbreak, a swift diagnosis is imperative. CT role was widely debated for its limited specificity. Here we report the diagnostic ...role of CT in two EDs in Lombardy, epicenter of Italian outbreak.
Material and methods
Admitting chest CT from 142 consecutive patients with suspected COVID-19 were retrospectively analyzed. CT scans were classified in “highly likely,” “likely,” and “unlikely” COVID-19 pneumonia according to the presence of typical, indeterminate, and atypical findings, or “negative” in the absence of findings, or “alternative diagnosis” when a different diagnosis was found. Nasopharyngeal swab results, turnaround time, and time to positive results were collected. CT diagnostic performances were assessed considering RT-PCR as reference standard.
Results
Most of cases (96/142, 68%) were classified as “highly likely” COVID-19 pneumonia. Ten (7%) and seven (5%) patients were classified as “likely” and “unlikely” COVID-19 pneumonia, respectively. In 21 (15%) patients a differential diagnosis was provided, including typical pneumonia, pulmonary edema, neoplasia, and pulmonary embolism. CT was negative in 8/142 (6%) patients. Mean turnaround time for the first COVID-19 RT-PCR was 30 ± 13 h. CT diagnostic accuracy in respect of the first test swab was 79% and increased to 91.5% after repeated swabs and/or BAL, for 18 false-negative first swab. CT performance was good with 76% specificity, 99% sensitivity, 90% positive predictive value and 97% negative predictive value.
Conclusion
Chest CT was useful to streamline patients’ triage while waiting for RT-PCR in the ED, supporting the clinical suspicion of COVID-19 or providing alternative diagnosis.
Purpose
To evaluate the features of arterial enhancement pattern of focal nodular hyperplasia (FNH) and hepatocellular carcinoma (HCC) by triple-phase arterial magnetic resonance imaging (MRI).
...Methods
Data were retrospectively collected from 52 consecutive patients who underwent triple-phase arterial MRI using hepatocyte-specific contrast agents (Gd-EOB-DTPA) from January 2017 to October 2017, with a MR imaging diagnosis of HCC or FNH. The images were independently assessed by two blinded readers. Contrast enhancement ratio (CER) and liver-to-lesion contrast ratio (LLCR) were calculated. The lesions were classified visually and also based on the peak of LLCR into the following groups: (1) early arterial, (2) middle arterial and (3) late arterial. Data were eventually analysed using nonparametric tests.
Results
The CER analysis showed no significant difference between HCC and FNH patients (
p
> 0.05). LLCR
FNH
were significantly higher than LLCR
HCC
in the early arterial (
p
= 0.01), but not in the middle and late arterial phases (
p
= 0.20 and
p
= 0.82, respectively). LLCR
HCC
presented a meaningful increase from early to middle arterial phase (
p
= 0.009), whereas LLCR
FNH
showed a decrease from middle to late arterial phase (
p
= 0.004). Based on the peak of LLCR, 17 (55%) FNHs were classified into early, 11 (35%) in middle and only 3 (10%) in late arterial phase groups. Similarly, 14 (34%) HCCs were categorized into early, 13 (32%) in middle and 14 (33%) in late arterial phase groups. There was a good agreement between qualitative analysis and LLCR in 85% of cases.
Conclusion
The optimal visualization of FNH has been detected in early and middle arterial phases while HCC has been best observed during middle and late arterial phases.
To evaluate the imaging features of routine admission chest X-ray in patients referred for novel Coronavirus 2019 infection.
All patients referred to the emergency departments, RT-PCR positive for ...SARS-CoV-2 infection were evaluated. Demographic and clinical data were recorded. Two radiologists (8 and 15 years of experience) reviewed all the X-ray images and evaluated the following findings: interstitial opacities, alveolar opacities (AO), AO associated with consolidation, consolidation and/or pleural effusion. We stratified patients in groups according to the time interval between symptoms onset (cut-off 5 days) and X-ray imaging and according to age (cut-off 60 years old). Computed tomography was performed in case of a discrepancy between clinical symptoms, laboratory and X-ray findings, and/or suspicion of complications.
A total of 468 patients were tested positive for SARS-CoV-2. Lung lesions primarily manifested as interstitial opacities (71.7%) and AO opacities (60.5%), more frequently bilateral (64.5%) and with a peripheral predominance (62.5%). Patients admitted to the emergency radiology department after 5 days from symptoms onset, more frequently had interstitial and AO opacities, in comparison to those admitted within 5 days, and lung lesions were more frequently bilateral and peripheral. Older patients more frequently presented interstitial and AO opacities in comparison to younger ones. Sixty-eight patients underwent CT that principally showed the presence of ground-glass opacities and consolidations.
The most common X-ray pattern is multifocal and peripheral, associated with interstitial and alveolar opacities. Chest X-ray, compared to CT, can be considered a reliable diagnostic tool, especially in the Emergency setting.
•SARS-CoV-2 pulmonary findings are typically bilateral and subpleural.•Symptoms onset time of 5 days is a reliable cut-off.•Pulmonary findings are age-related.
The aim of this article is to assess whether measures of abdominal fat distribution, visceral density, and antropometric parameters obtained from computed tomography (CT) may predict postoperative ...pancreatic fistula (POPF) occurrence.We analyzed 117 patients who underwent pancreatoduodenectomy (PD) and had a preoperative CT scan as staging in our center. CT images were processed to obtain measures of total fat volume (TFV), visceral fat volume (VFV), density of spleen, and pancreas, and diameter of pancreatic duct. The predictive ability of each parameter was investigated by receiver-operating characteristic (ROC) curves methodology and assessing optimal cutoff thresholds. A stepwise selection method was used to determine the best predictive model.Clinically relevant (grades B and C) POPF occurred in 24 patients (20.5%). Areas under ROC-curves showed that none of the parameters was per se significantly predictive. The multivariate analysis revealed that a VFV >2334 cm, TFV >4408 cm, pancreas/spleen density ratio <0.707, and pancreatic duct diameter <5 mm were predictive of POPF. The risk of POPF progressively increased with the number of factors involved and age.It is possible to deduce objective information on the risk of POPF from a simple and routine preoperative radiologic workup.
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