Objective
Gray matter (GM) damage and meningeal inflammation have been associated with early disease onset and a more aggressive disease course in multiple sclerosis (MS), but can these changes be ...identified in the patient early in the disease course?
Methods
To identify possible biomarkers linking meningeal inflammation, GM damage, and disease severity, gene and protein expression were analyzed in meninges and cerebrospinal fluid (CSF) from 27 postmortem secondary progressive MS and 14 control cases. Combined cytokine/chemokine CSF profiling and 3T magnetic resonance imaging (MRI) were performed at diagnosis in 2 independent cohorts of MS patients (35 and 38 subjects) and in 26 non‐MS patients.
Results
Increased expression of proinflammatory cytokines (IFNγ, TNF, IL2, and IL22) and molecules related to sustained B‐cell activity and lymphoid‐neogenesis (CXCL13, CXCL10, LTα, IL6, and IL10) was detected in the meninges and CSF of postmortem MS cases with high levels of meningeal inflammation and GM demyelination. Similar proinflammatory patterns, including increased levels of CXCL13, TNF, IFNγ, CXCL12, IL6, IL8, and IL10, together with high levels of BAFF, APRIL, LIGHT, TWEAK, sTNFR1, sCD163, MMP2, and pentraxin III, were detected in the CSF of MS patients with higher levels of GM damage at diagnosis.
Interpretation
A common pattern of intrathecal (meninges and CSF) inflammatory profile strongly correlates with increased cortical pathology, both at the time of diagnosis and at death. These results suggest a role for detailed CSF analysis combined with MRI as a prognostic marker for more aggressive MS. Ann Neurol 2018 Ann Neurol 2018;83:739–755
Summary Background Digital breast tomosynthesis with 3D images might overcome some of the limitations of conventional 2D mammography for detection of breast cancer. We investigated the effect of ...integrated 2D and 3D mammography in population breast-cancer screening. Methods Screening with Tomosynthesis OR standard Mammography (STORM) was a prospective comparative study. We recruited asymptomatic women aged 48 years or older who attended population-based breast-cancer screening through the Trento and Verona screening services (Italy) from August, 2011, to June, 2012. We did screen-reading in two sequential phases—2D only and integrated 2D and 3D mammography—yielding paired data for each screen. Standard double-reading by breast radiologists determined whether to recall the participant based on positive mammography at either screen read. Outcomes were measured from final assessment or excision histology. Primary outcome measures were the number of detected cancers, the number of detected cancers per 1000 screens, the number and proportion of false positive recalls, and incremental cancer detection attributable to integrated 2D and 3D mammography. We compared paired binary data with McNemar's test. Findings 7292 women were screened (median age 58 years IQR 54–63). We detected 59 breast cancers (including 52 invasive cancers) in 57 women. Both 2D and integrated 2D and 3D screening detected 39 cancers. We detected 20 cancers with integrated 2D and 3D only versus none with 2D screening only (p<0·0001). Cancer detection rates were 5·3 cancers per 1000 screens (95% CI 3·8–7·3) for 2D only, and 8·1 cancers per 1000 screens (6·2–10·4) for integrated 2D and 3D screening. The incremental cancer detection rate attributable to integrated 2D and 3D mammography was 2·7 cancers per 1000 screens (1·7–4·2). 395 screens (5·5%; 95% CI 5·0–6·0) resulted in false positive recalls: 181 at both screen reads, and 141 with 2D only versus 73 with integrated 2D and 3D screening (p<0·0001). We estimated that conditional recall (positive integrated 2D and 3D mammography as a condition to recall) could have reduced false positive recalls by 17·2% (95% CI 13·6–21·3) without missing any of the cancers detected in the study population. Interpretation Integrated 2D and 3D mammography improves breast-cancer detection and has the potential to reduce false positive recalls. Randomised controlled trials are needed to compare integrated 2D and 3D mammography with 2D mammography for breast cancer screening. Funding National Breast Cancer Foundation, Australia; National Health and Medical Research Council, Australia; Hologic, USA; Technologic, Italy.
Purpose
To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features.
Material and Methods
CT data of 115 patients with respiratory symptoms suspected ...for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C−), respectively. C− patients, however, presented with interstitial lung involvement. A subgroup of C−, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set.
Results
The first model classified C + and C− pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C− (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81).
Conclusion
Whole lung ML models based on radiomics can classify C + and C− interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.
Objectives
To report and analyse the characteristics and performance of the first cohort of Italian radiologists completing the national mammography self-evaluation online test established by the ...Italian Society of Medical Radiology (SIRM).
Methods
A specifically-built dataset of 132 mammograms (24 with screen-detected cancers and 108 negative cases) was preliminarily tested on 48 radiologists to define pass thresholds (62% sensitivity and 86% specificity) and subsequently made available online to SIRM members during a 13-month timeframe between 2018 and 2019. Associations between participants’ characteristics, pass rates, and diagnostic accuracy were then investigated with descriptive statistics and univariate and multivariable regression analyses.
Results
A total of 342 radiologists completed the test, 151/342 (44.2%) with success. All individual variables, except gender, showed a significant correlation with pass rates and diagnostic sensitivity, confirmed by univariate logistic regression, while only involvement in organised screening programs and number of mammograms read per year showed a positive association with specificity at univariate logistic regression. In the multivariable regression analysis, fewer variables remained significant: > 3000 mammograms read per year for success rate; female gender, public practice setting, and higher experience self-judgement for sensitivity; no variables were significantly associated with specificity.
Conclusions
This national self-evaluation test effectively differentiated multiple aspects of mammographic reading experience, but specific breast imaging experience was shown not to strictly guarantee good diagnostic accuracy. Due to its easy use and the validity of obtained results, this test could be extended to all Italian breast radiologists, regardless of their experience, also as a Breast Unit accreditation criterion.
Key Points
•
This self-evaluation test was found to be able to differentiate various degrees of mammographic interpretation experience.
•
Breast cancer screening readers should undergo a self-assessment test, since experience parameters alone do not guarantee diagnostic ability.
Objective
It is reported that recovery from COVID-19 chemosensory deficit generally occurs in a few weeks, although olfactory dysfunction may persist longer. Here, we provide a detailed follow-up ...clinical investigation in a very young female patient (17-year-old) with a long-lasting anosmia after a mild infection, with partial recovery 15 months after the onset.
Methods
Neuroimaging and neurophysiologic assessments as well as olfactory mucosa swabbing for microbiological and immunocytochemical analyses were performed. Olfactory and gustatory evaluations were conducted through validated tests.
Results
Chemosensory evaluations were consistent with anosmia associated with parosmia phenomena and gustatory impairment, the latter less persistent. Brain MRI (3.0 T) showed no microvascular injury in olfactory bulbs and brain albeit we cannot rule out slight structural abnormalities during the acute phase, and a high-density EEG was negative. Immunocytochemistry of olfactory mucosa swabs showed high expression of ACE2 in sustentacular cells and lower dot-like cytoplasmic positivity in neuronal-shaped cells.
Discussion
The occurrence of long-term persistent olfactory deficit in spite of the absence of structural brain and olfactory bulb involvement supports the view of a possible persistent dysfunction of both sustentacular cells and olfactory neurons. The gustatory dysfunction even if less persisting for the described features could be related to a primary gustatory system involvement. Future longitudinal studies are needed to investigate the persistence of chemosensory impairment, which could have a relevant impact on the daily life.
Background:
Disease activity in the first years after a diagnosis of relapsing-remitting multiple sclerosis (RRMS) is a negative prognostic factor for long-term disability. Markers of both clinical ...and radiological responses to disease-modifying therapies (DMTs) are advocated.
Objective:
The objective of this study is to estimate the value of cerebrospinal fluid (CSF) inflammatory markers at the time of diagnosis in predicting the disease activity in treatment-naïve multiple sclerosis (MS) patients exposed to dimethyl fumarate (DMF).
Methods:
In total, 48 RRMS patients (31 females/17 males) treated with DMF after the diagnosis were included in this 2-year longitudinal study. All patients underwent a CSF examination, regular clinical and 3T magnetic resonance imaging (MRI) scans that included the assessment of white matter (WM) lesions, cortical lesions (CLs) and global cortical thickness. CSF levels of 10 pro-inflammatory markers – CXCL13 chemokine (C-X-C motif) ligand 13 or B lymphocyte chemoattractant, CXCL12 (stromal cell-derived factor or C-X-C motif chemokine 12), tumour necrosis factor (TNF), APRIL (a proliferation-inducing ligand, or tumour necrosis factor ligand superfamily member 13), LIGHT (tumour necrosis factor ligand superfamily member 14 or tumour necrosis factor superfamily member 14), interferon (IFN) gamma, interleukin 12 (IL-12), osteopontin, sCD163 soluble-CD163 (cluster of differentiation 163) and Chitinase3-like1 – were assessed using immune-assay multiplex techniques. The combined three-domain status of ‘no evidence of disease activity’ (NEDA-3) was defined by no relapses, no disability worsening and no MRI activity, including CLs.
Results:
Twenty patients (42%) reached the NEDA-3 status; patients with disease activity showed higher CSF TNF (p = 0.009), osteopontin (p = 0.005), CXCL12 (p = 0.037), CXCL13 (p = 0.040) and IFN gamma levels (p = 0.019) compared with NEDA-3 patients. After applying a random forest approach, TNF and osteopontin revealed the most important variables associated with the NEDA-3 status. Six molecules that emerged at the random forest approach were added in a multivariate regression model with demographic, clinical and MRI measures of WM and grey matter damage as independent variables. TNF levels confirmed to be associated with the absence of disease activity: odds ratio (OR) = 0.25, CI% = 0.04–0.77.
Conclusion:
CSF inflammatory markers may provide prognostic information in predicting disease activity in the first years after DMF initiation. CSF TNF levels are a possible candidate in predicting treatment response, in addition to clinical, demographic and MRI variables.
Background:
Data on the effect of dimethyl fumarate (DMF) on focal and diffuse gray matter (GM) damage, a relevant pathological substrate of multiple sclerosis (MS)-related disability are lacking.
...Objective:
To evaluate the DMF effect on cortical lesions (CLs) accumulation and global and regional GM atrophy in subjects with relapsing–remitting MS.
Methods:
A total of 148 patients (mean age 38.1 ± 9.7 years) treated with DMF ended a 2-year longitudinal study. All underwent regular Expanded Disability Status Scale (EDSS assessment), and at least two 3T-magnetic resonance imaging (MRI) at 3 and 24 months after DMF initiation. CLs and changes in global and regional atrophy of several brain regions were compared with 47 untreated age and sex-matched patients.
Results:
DMF-treated patients showed lower CLs accumulation (median 00–3 vs 20–7, p < 0.001) with respect to controls. Global cortical thickness (p < 0.001) and regional thickness and volume were lower in treated group (cerebellum, hippocampus, caudate, and putamen: p < 0.001; thalamus p = 0.03). Lower relapse rate (14% vs 40%, p < 0.001), EDSS change (0.2 ± 0.4 vs 0.4 ± 0.9, p < 0.001), and new WM lesions (median 00–5 vs 20–6, p < 0.001) were reported. No severe adverse drug reactions occurred.
Conclusions:
Beyond the well-known effect on disease activity, these results provide evidence of the effect of DMF through reduced progression of focal and diffuse GM damage.
Purpose
To evaluate the diagnostic role of a dedicated AI software in detecting anomalous breast findings on mammography and tomosynthesis images in the clinical setting, stand-alone and as aid of ...four readers.
Methods
A total of 210 patients with complete clinical and radiologic records were retrospectively analyzed. Pathology was used as the reference standard for patients undergoing surgery or biopsy, and a 1-year follow-up was used to confirm no change in the remaining patients.
The image evaluation was performed by four readers with different levels of experience (a junior and three senior breast radiologists) using a 5-point Likert scale moving from 1 (definitively no cancer) to 5 (definitively cancer).
The positivity of mammograms was assessed on the presence of any breast lesion (masses, architectural distortions, asymmetries, calcifications), including malignant and benign ones. A multi-reader multi-case analysis was performed. A
p
value < 0.05 was considered statistically significant.
Results
The stand-alone AI system achieved an accuracy of 71% (69% sensitivity and 73% specificity), which is overall lower than the value achieved by readers without AI. However, with the aid of AI, a significant increase of accuracy (
p
value = 0.004) and specificity (
p
value = 0.04) was achieved for the less experienced radiologist and a senior one.
Conclusion
The use of AI software as a second reader for breast lesions assessment could play a crucial role in the clinical setting, by increasing sensitivity and specificity, especially for less experienced radiologists.
Machine learning (ML) can extract high-throughput features of images to predict disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model to predict the risk of breast ...cancer.
The mpMRI included non-enhanced and enhanced T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC),
,
,
, and
. Regions of interest were annotated in an enhanced T1WI map and mapped to other maps in every slice. 1,132 features and top-10 principal components were extracted from every parameter map. Single-parametric and multi-parametric ML models were constructed
10 rounds of five-fold cross-validation. The model with the highest area under the curve (AUC) was considered as the optimal model and validated by calibration curve and decision curve. Nomogram was built with the optimal ML model and patients' characteristics.
This study involved 144 malignant lesions and 66 benign lesions. The average age of patients with benign and malignant lesions was 42.5 years old and 50.8 years old, respectively, which were statistically different. The sixth and fourth principal components of
had more importance than others. The AUCs of
,
,
and
, non-enhanced T1WI, enhanced T1WI, T2WI, and ADC models were 0.86, 0.81, 0.81, 0.83, 0.79, 0.81, 0.84, and 0.83 respectively. The model with an AUC of 0.90 was considered as the optimal model which was validated by calibration curve and decision curve. Nomogram for the prediction of breast cancer was built with the optimal ML models and patient age.
Nomogram could improve the ability of breast cancer prediction preoperatively.
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