Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics.

OBJECTIVES: To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. METHODS: A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n1 = 407) and an independent validation dataset (n1 = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively. CONCLUSIONS: Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients. CLINICAL RELEVANCE STATEMENT: The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies. KEY POINTS: • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.

Application of logistic regression and machine learning methods for idiopathic inflammatory myopathies malignancy prediction.

OBJECTIVES: Malignancy is related to idiopathic inflammatory myopathies (IIM) and leads to a poor prognosis. Early prediction of malignancy is thought to improve the prognosis. However, predictive models have rarely been reported in IIM. Herein, we aimed to establish and use a machine learning (ML) algorithm to predict the possible risk factors for malignancy in IIM patients. METHODS: We retrospectively reviewed the medical records of 168 patients diagnosed with IIM in Shantou Central hospital, from 2013 to 2021. We randomly divided patients into two groups, the training sets (70%) for construction of the prediction model, and the validation sets (30%) for evaluation of model performance. We constructed six types of ML algorithms models and the AUC of ROC curves were used to describe the efficacy of the model. Finally, we set up a web version using the best prediction model to make it more generally available. RESULTS: According to the multi-variable regression analysis, three predictors were found to be the risk factors to establish the prediction model, including age, ALT<80U/L, and anti-TIF1-γ, and ILD was found to be a protective factor. Compared with five other ML algorithms models, the traditional algorithm logistic regression (LR) model was as good or better than the other models to predict malignancy in IIM. The AUC of the ROC using LR was 0.900 in the training set and 0.784 in the validation set. We selected the LR model as the final prediction model. Accordingly, a nomogram was constructed using the above four factors. A web version was built and can be visited on the website or acquired by scanning the QR code. CONCLUSIONS: The LR algorithm appears to be a good predictor of malignancy and may help clinicians screen, evaluate and follow up high-risk patients with IIM.

The value of quantitative plaque analysis based on coronary computed tomography angiography in predicting the percutaneous coronary intervention outcome of chronic total occlusion lesions.

Background: Due to the uncertainty of the success of percutaneous coronary intervention (PCI) and the complexity of selecting suitable treatment cases, the interventional outcome of coronary chronic total occlusion (CTO) remains challenging. The purpose of this study was to evaluate the role of quantitative plaque analysis based on coronary computed tomography angiography (CCTA) in predicting the CTO-PCI outcome. Methods: We retrospectively included 78 patients with CTO (80 lesions) confirmed by invasive coronary angiography from July 2016 to December 2018. All patients underwent PCI treatment according to standard practice. A total of 47 lesions in 47 patients were successfully treated with PCI. PCI failed in the remaining 33 lesions in 31 patients. The following conventional CCTA morphologic parameters were evaluated and compared between the PCI-success and PCI-failure groups: stump morphology; occlusion length, tortuous course; CTO lesion calcium; bridging collateral vessel; retrograde collateral vessel; the appearance of the occluded distal segment; and quantitative CTO plaque characteristics, including total plaque volume, calcified plaque (CP) volume, noncalcified plaque (NCP) volume, low-density noncalcified plaque (LDNCP) volume, and plaque length. Univariate and multivariate logistic regression analyses were performed to determine independent parameters predictive of CTO-PCI outcomes. The predictive performances were assessed using receiver operating characteristic curve analysis. Results: The blunt stump was the only independent CCTA morphologic parameter to predict the outcome of CTO-PCI [odds ratio (OR): 10.807; P<0.001]. NCP volume (OR: 1.018; P<0.001), CP volume (OR: 1.026; P=0.049), and plaque length (OR: 1.058; P=0.037) were independent quantitative CTO plaque characteristics predictive of CTO-PCI outcomes. The plaque-based model combining NCP volume with CP volume and plaque length had a higher area under the curve (AUC =0.96) than did the morphology-based model that included blunt stump (AUC 0.68) in predicting the outcomes of CTO-PCI (P<0.001). Conclusions: The CCTA-based plaque characteristics, including NCP volume, CP volume, and plaque length, outperformed morphologic parameters in predicting the CTO-PCI outcomes.

Predictive model of risk and severity of enteritis in systemic lupus erythematosus.

INTRODUCTION: To describe the clinical and laboratory features of systemic lupus erythematosus (SLE) enteritis and to establish a predictive model of risk and severity of lupus enteritis (LE). METHODS: Records of patients with SLE complaining about acute digestive symptoms were reviewed. The predictive nomogram for the diagnosis of LE was constructed by using R. The accuracy of the model was tested with correction curves. The receiver operating characteristic curve (ROC curve) program and a Decision curve analysis (DCA) were used for the verification of LE model. Receiver operating characteristic curve was also employed for evaluation of factors in the prediction of severity of LE. RESULTS: During the eight year period, 46 patients were in the LE group, while 32 were in the non-LE group. Abdominal pain, emesis, D-dimer >5 µg/mL, hypo-C3, and anti-SSA positive remained statistically significant and were included into the prediction model. Area under the curve (AUC) of ROC curve in this model was 0.909. Correction curve indicated consistency between the predicted rate and actual diagnostic rates. The DCA showed that the LE model was of benefit. Forty-four patients were included in developing the prediction model of LE severity. Infection, SLE disease activity index (SLEDAI), CT score, and new CT score were validated as risk factors for LE severity. The AUC of the combined SLEDAI, infection and new CT score were 0.870. CONCLUSION: The LE model exhibits good predictive ability to assess LE risk in SLE patients with acute digestive symptoms. The combination of SLEDAI, infection, and new CT score could improve the assessment of LE severity.

Serum B-cell activating factor and lung ultrasound B-lines in connective tissue disease related interstitial lung disease.

Objective: To investigate the role of serum B-cell activating factor (BAFF) and lung ultrasound (LUS) B-lines in connective tissue disease related interstitial lung disease (CTD-ILD), and their association with different ILD patterns on high resolution computed tomography (HRCT) of chest. Methods: We measured the levels of BAFF and KL-6 by ELISA in the sera of 63 CTD-ILD patients [26 with fibrotic ILD (F-ILD), 37 with non-fibrotic ILD (NF-ILD)], 30 CTD patients without ILD, and 26 healthy controls. All patients underwent chest HRCT and LUS examination. Results: Serum BAFF levels were significantly higher in CTD patients compared to healthy subjects (617.6 ± 288.1 pg/ml vs. 269.0 ± 60.4 pg/ml, p < 0.01). BAFF concentrations were significantly different between ILD group and non-ILD group (698.3 ± 627.4 pg/ml vs. 448.3 ± 188.6 pg/ml, p < 0.01). In patients with ILD, BAFF concentrations were significantly correlated with B-lines number (r = 0.37, 95% CI 0.13-0.56, p < 0.01), KL-6 level (r = 0.26, 95% CI 0.01-0.48, p < 0.05), and Warrick score (r = 0.33, 95% CI 0.09-0.53, p < 0.01), although all correlations were only low to moderate. B-lines number correlated with Warrick score (r = 0.65, 95% CI 0.48-0.78, p < 0.01), and KL-6 levels (r = 0.43, 95% CI 0.21-0.61, p < 0.01). Patients with F-ILD had higher serum BAFF concentrations (957.5 ± 811.0 pg/ml vs. 516.1 ± 357.5 pg/ml, p < 0.05), KL-6 levels (750.7 ± 759.0 U/ml vs. 432.5 ± 277.5 U/ml, p < 0.05), B-lines numbers (174.1 ± 82 vs. 52.3 ± 57.5, p < 0.01), and Warrick score (19.9 ± 4.6 vs. 13.6 ± 3.4, p < 0.01) vs. NF-ILD patients. The best cut-off values to separate F-ILD from NF-ILD using ROC curves were 408 pg/ml for BAFF (AUC = 0.73, p < 0.01), 367 U/ml for KL-6 (AUC = 0.72, p < 0.05), 122 for B-lines number (AUC = 0.89, p < 0.01), and 14 for Warrick score (AUC = 0.87, p < 0.01) respectively. Conclusion: Serum BAFF levels and LUS B-lines number could be useful supportive biomarkers for detecting and evaluating the severity and/or subsets of CTD-ILD. If corroborated, combining imaging, serological, and sonographic biomarkers might be beneficial and comprehensive in management of CTD-ILD.

Lung ultrasound correlates with radiographic severity and pattern in COVID-19 pneumonia: a preliminary study.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) was outbreaking in late 2019 and a proportion of patients developed to pneumonia. Although chest CT is a pivotal diagnostic tool for COVID-19 pneumonia, CT is expensive and also radiological burden for patients. There is urgent to investigate the role of lung ultrasound (LUS) in diagnosing and monitoring COVID-19 pneumonia. METHODS: A total of 8 patients with confirmed cases of COVID-19 pneumonia in Shantou Central Hospital from January 2020 to February 2020 were retrospectively studied. All participants underwent chest HRCT and LUS examination; both were independently performed within 1 day of the other. The radiological patterns were reviewed by 2 radiologists who were blind to the clinical information. A senior ultrasound physician, blind to HRCT results and clinical data, performed bedside LUS in the isolation ward. The CT score was used (a semi-quantitative scoring system) to assess radiographic severity and extent. A B-lines score denoting the extent and severity of sonographic lesion was calculated by summing the number of B-lines on 18 scanning sites. RESULTS: B-lines (100%), pleural irregularities (25%), consolidation (25%), and pleural effusion (25%) were the main findings of LUS examination. Interstitial abnormalities, ground-glass opacities (GGO), consolidations and local or bilateral patchy shadowing were the main findings of HRCT examination. The findings of LUS and HRCT were compared point to point and high consistency was found between the 2 measurements. A significant correlation was also found between the B-lines score and CT score [r=0.96, 95% confidence interval (CI): 0.81 to 0.99, P=0.0001]. CONCLUSIONS: Both LUS patterns and B-lines score are significantly correlated with HRCT findings and score, respectively, supporting its role in assessing COVID-19 pneumonia severity, screening, and following up dynamic changes of pneumonia.

The role of lung ultrasound B-lines and serum KL-6 in the screening and follow-up of rheumatoid arthritis patients for an identification of interstitial lung disease: review of the literature, proposal for a preliminary algorithm, and clinical application to cases.

Screening and follow-up of interstitial lung disease associated with rheumatoid arthritis (RA-ILD) is a challenge in clinical practice. In fact, the majority of RA-ILD patients are asymptomatic and optimal tools for early screening and regular follow-up are lacking. Furthermore, some patients may remain oligosymptomatic despite significant radiological abnormalities. In RA-ILD, usual interstitial pneumonia (UIP) is the most frequent radiological and pathological pattern, associated with a poor prognosis and a high risk to develop acute exacerbations and infections. If RA-ILD can be identified early, there may be an opportunity for an early treatment and close follow-up that might delay ILD progression and improve the long-term outcome.In connective tissue disease-associated interstitial lung disease (CTD-ILD), lung ultrasound (LUS) with the assessment of B-lines and serum Krebs von den Lungen-6 antigen (KL-6) has been recognized as sensitive biomarkers for the early detection of ILD. B-line number and serum KL-6 level were found to correlate with high-resolution computed tomography (HRCT), pulmonary function tests (PFTs), and other clinical parameters in systemic sclerosis-associated ILD (SSc-ILD). Recently, the significant correlation between B-lines and KL-6, two non-ionizing and non-invasive biomarkers, was demonstrated. Hence, the combined use of LUS and KL-6 to screen and follow up ILD in RA patients might be useful in clinical practice in addition to existing tools. Herein, we review relevant literature to support this concept, propose a preliminary screening algorithm, and present 2 cases where the algorithm was used.

Lung ultrasound B-lines and serum KL-6 correlate with the severity of idiopathic inflammatory myositis-associated interstitial lung disease.

OBJECTIVE: Idiopathic inflammatory myositis-associated interstitial lung disease (IIM-ILD) significantly increases morbidity and mortality. Lung ultrasound B-lines and Krebs von den Lungen-6 (KL-6) are identified as new sonographic and serum markers of ILD, respectively. The aim of our work was to assess the role of B-lines and KL-6 as markers of the severity of IIM-ILD. For this purpose, the correlation among B-lines score, serum KL-6 levels, high-resolution CT (HRCT) score, and pulmonary function tests were investigated in IIM-ILD patients. METHODS: Thirty-eight patients with IIM-ILD underwent chest HRCT scans, lung ultrasound and pulmonary function tests (independently performed within 1 week) examination. To assess severity and extent of ILD at HRCT, the Warrick score was used. The B-lines score denoting the extension of ILD was calculated by summing the number of B-lines on a total of 50 scanning sites. Serum KL-6 levels (U/ml) was measured by chemiluminescent enzyme immunoassay. RESULTS: A significant correlation was found between the B-lines score and serum KL-6 levels (r = 0.43, P < 0.01), and between the Warrick score and serum KL-6 levels (r = 0.45, P < 0.01). A positive correlation between B-lines score and the Warrick score (r = 0.87, P < 0.0001) was also confirmed. Both B-lines score and KL-6 levels inversely correlated to diffusion capacity for carbon monoxide (r = -0.77, P < 0.0001 and r = -0.42, P < 0.05, respectively) and total lung capacity (r = -0.73, P < 0.0001 and r = -0.36, P < 0.05, respectively). Moreover, B-lines correlated inversely with forced vital capacity (r = -0.73, P < 0.0001), forced expiratory volume in 1 s (r = -0.69, P < 0.0001). CONCLUSION: B-lines score and serum KL-6 levels correlate with HRCT findings and pulmonary function tests, supporting their use as measures of IIM-ILD severity.

Utilize lung ultrasound B-lines and KL-6 to monitor anti-MDA-5 antibody-positive clinically amyopathic dermatomyositis-associated interstitial lung disease: a case report and literature review.

In the past decade, lung ultrasound (LUS) B-lines and serum Krebs von den Lungen-6 (KL-6) antigen have been recognized as biomarkers of the connective tissue disease-associated interstitial lung diseases (CTD-ILDs). Robust data have demonstrated that B-lines total numbers and KL-6 levels are correlated with high-resolution computed tomography findings, pulmonary function test, and some clinical parameters in CTD-ILDs. However, limited data are available regarding the use of these two biomarkers to follow CTD-ILDs. Herein, we report a case with anti-melanoma differentiation-associated gene 5 antibody-positive clinically amyopathic dermatomyositis-associated ILD, successfully treated with high-dose methylprednisolone, cyclophosphamide, intravenous immunoglobulin, pirfenidone, and followed using lung ultrasound and KL-6.

Acute fibrinous and organizing pneumonia as initial presentation of primary Sjögren's syndrome: a case report and literature review.

Acute fibrinous and organizing pneumonia (AFOP) is a new histopathological pattern of acute lung injury first described by Beasley et al. in 2002. Hallmarks of pathological findings are characterized by the presence of intra-alveolar fibrin in the form of fibrin "balls" within the alveolar spaces and organizing pneumonia with a patchy distribution. Patients with AFOP may have an acute or subacute clinical presentation. Although the pathogenesis of AFOP is not fully elucidated, it may be associated with autoimmune diseases. Reported herein is a patient diagnosed of acute AFOP associated with primary Sjögren's syndrome. The patient's condition promptly improved after treatment with corticosteroid.

Matrix Metalloproteinase-9/Neutrophil Gelatinase-Associated Lipocalin Complex Activity in Human Glioma Samples Predicts Tumor Presence and Clinical Prognosis.

Matrix metalloproteinase-9/neutrophil gelatinase-associated lipocalin (MMP-9/NGAL) complex activity is elevated in brain tumors and may serve as a molecular marker for brain tumors. However, the relationship between MMP-9/NGAL activity in brain tumors and patient prognosis and treatment response remains unclear. Here, we compared the clinical characteristics of glioma patients with the MMP-9/NGAL activity measured in their respective tumor and urine samples. Using gelatin zymography assays, we found that MMP-9/NGAL activity was significantly increased in tumor tissues (TT) and preoperative urine samples (Preop-1d urine). Activity was reduced by seven days after surgery (Postop-1w urine) and elevated again in cases of tumor recurrence. The MMP-9/NGAL status correlated well with MRI-based tumor assessments. These findings suggest that MMP-9/NGAL activity could be a novel marker to detect gliomas and predict the clinical outcome of patients.