MRI Manifestations of Breast Cancer Stroma and their Role in Predicting Molecular Subtype: A Case-control Study

OBJECTIVE

This study explored whether breast MRI manifestations could be used to predict the stroma distribution of breast cancer (BC) and the role of tumor stroma-based MRI manifestations in molecular subtype prediction.

METHODS

57 patients with pathologically confirmed invasive BC (non-special type) who had lumpy BC on MRI within one week before surgery were retrospectively collected in the study. Stroma distributions were classified according to their characteristics in the pathological sections. The stromal distribution patterns among molecular subtypes were compared with the MRI manifestations of BC with different stroma distribution types (SDTs).

RESULTS

SDTs were significantly different and depended on the BC hormone receptor (HR) (P<0.001). There were also significant differences among five SDTs on T2WI, ADC map, internal delayed enhanced features (IDEF), marginal delayed enhanced features (MDEF), and time signal intensity (TSI) curves. Spiculated margin and the absence of type-I TSI were independent predictors for BC with star grid type stroma. The appearance frequency of hypo-intensity on T2WI in HR- BCs was significantly lower (P=0.043) than in HR+ BCs. Star grid stroma and spiculated margin were key factors in predicting HR+ BCs, and the AUC was 0.927 (95% CI: 0.867-0.987).

CONCLUSION

Breast MRI can be used to predict BC's stromal distribution and molecular subtypes.

Burden of lung cancer attributed to particulate matter pollution in China: an epidemiological study from 1990 to 2019

OBJECTIVES

The aim of this study was to examine the disease burden of lung cancer attributable to particulate matter (PM2.5) pollution in China from 1990 to 2019.

STUDY DESIGN

Data from the Global Burden of Disease Study 2019 were used to estimate the disease burden of tracheal, bronchus and lung cancer attributed to PM2.5 over time in China.

METHODS

Joinpoint regression models were applied to disability-adjusted life years (DALYs) to assess the time trends and estimate the impact of PM2.5 on the overall disease burden of lung cancer. Furthermore, age-period-cohort models were conducted to assess the relationships between lung cancer DALYs attributed to PM2.5 exposure and age, calendar period and birth cohort trends in China from 1990 to 2019.

RESULTS

Lung cancer DALYs attributable to household air pollution from solid fuels decreased with an average annual percent change (AAPC) of 2.9 % per 100,000 population, while those attributable to ambient particular matter pollution (APE) increased (AAPC: -4.7 % per 100,000 population) over the past 30 years. The burden of lung cancer in terms of DALYs in males was higher than in females, and it demonstrated an age-dependent increase. The period and cohort effects also had significant impacts on the DALYs rates of lung cancer attributable to APE, indicating an overall increase in lung cancer DALYs for all age groups in each year.

CONCLUSIONS

This study highlights the need for effective strategies to reduce PM2.5 exposure in China, particularly from outdoor sources. Gender differences and age, period and cohort effects observed in the study provide valuable insights into long-term trends of lung cancer burden attributed to PM2.5.

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

Objective

To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm).

Methods

This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors).

Results

Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful.

Conclusion

T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.

[Pay attention to the infectious complications in the clinical application of biological agents]

Biological agents have been widely used in the treatment of many clinical diseases by targeting specific immune cells or cytokines. In the course of clinical use, biological agents can lead to secondary immune deficiency, which increases the risk of infection. At present, there are no evidence-based guidelines or management opinions on the differences of infections caused by various biological agents, how to identify infectious complications in the course of treatment with different biological agents at an early stage, and how to take effective and targeted prevention. This paper summarizes the infection complications and their characteristics that need to be paid attention to in the clinical introduction of biological agents, aiming to help clinicians make reasonable decisions for infection complications in the process of using biological agents, reduce the incidence of infection, and improve the success rate of diagnosis and treatment.

Shoulder MRI-based radiomics for diagnosis and severity staging assessment of surgically treated supraspinatus tendon tears

OBJECTIVE

To develop and validate MRI-based radiomics models capable of evaluating supraspinatus tendon tears within the shoulder joints by using arthroscopy as the reference standard.

METHODS

A total of 432 patients (332 in the training set and 100 in the external validation set) with intact supraspinatus tendon (n = 202) and supraspinatus tendon tear (n = 230, 130 full-thickness tears and 100 partial-thickness tears) were enrolled. Radiomics features were extracted from fat-saturated T2-weighted coronal images. Two radiomics signature models for detecting supraspinatus tendon abnormalities (tear or not), and stage lesion severity (full- or partial-thickness tear) and radiomics scores (Rad-score), were constructed and calculated using multivariate logistic regression analysis. The diagnostic performance of the two models was validated using ROC curves on the training and validation datasets.

RESULTS

For the radiomics model of no tears or tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.98 in the training set, 0.97 in the internal validation set, and 0.98 in the external validation set. For the radiomics model of full- or partial-thickness tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.79 in the training set, 0.69 in the internal validation set, and 0.77 in the external validation set.

CONCLUSION

The proposed radiomics models in this study can accurately rule out supraspinatus tendon tears and are capable of assessing the severity staging of tears with moderate accuracy based on shoulder MR images.

KEY POINTS

• The radiomics model of no tears or tears achieved a high overall accuracy of 93.6%, sensitivity of 91.6%, and specificity of 95.2% for supraspinatus tendon tears. • The radiomics model of full- or partial-thickness tears displayed moderate performance with an accuracy of 76.4%, a sensitivity of 79.2%, and a specificity of 74.3% for supraspinatus tendon tears severity staging.

Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer

The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions were studied. The included patients were divided into two cohorts by time (training/testing cohort, n = 366/122). Deep learning features were extracted from diffusion-weighted imaging-quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI (DCE-MRI) by a pretrained neural network of DenseNet121. After the selection of both radiomic and clinicopathological features, deep learning signature and a nomogram were built for independent validation. Twenty-three deep learning features were automatically selected in the training cohort to establish the deep learning signature of ALNM. Three clinicopathological factors, including LN palpability (odds ratio (OR) = 6.04; 95% confidence interval (CI) = 3.06-12.54, P = 0.004), tumor size in MRI (OR = 1.45, 95% CI = 1.18-1.80, P = 0.104), and Ki-67 (OR = 1.01; 95% CI = 1.00-1.02, P = 0.099), were selected and combined with radiomic signature to build a combined nomogram. The nomogram showed excellent predictive ability for ALNM (AUC 0.80 and 0.71 in training and testing cohorts, respectively). The sensitivity, specificity, and accuracy were 65%, 80%, and 75%, respectively, in the testing cohort. MRI-based deep learning radiomics in patients with breast cancer could be used to predict ALNM, providing a noninvasive approach to structuring the treatment strategy.

[Doublecortin-like kinase 1 activates Hippo pathway to promote migration, invasion and proliferation of pancreatic cancer cells]

Objective: To explore the mechanism of Doublecortin-like kinase 1 (DCLK1) in promoting cell migration, invasion and proliferation in pancreatic cancer. Methods: The correlation between DCLK1 and Hippo pathway was analyzed using TCGA and GTEx databases and confirmed by fluorescence staining of pancreatic cancer tissue microarrays. At the cellular level, immunofluorescence staining of cell crawls and western blot assays were performed to clarify whether DCLK1 regulates yes associated protein1 (YAP1), a downstream effector of the Hippo pathway. Reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) was used to analyze the expressions of YAP1 binding transcription factor TEA-DNA binding proteins (TEAD) and downstream malignant behavior-promoting molecules CYR61, EDN1, AREG, and CTGF. Transwell test of the DCLK1-overexpressing cells treated with the Hippo pathway inhibitor Verteporfin was used to examine whether the malignant behavior-promoting ability was blocked. Analysis of changes in the proliferation index of experimental cells used real-time label-free cells. Results: TCGA combined with GTEx data analysis showed that the expressions of DCLK1 and YAP1 molecules in pancreatic cancer tissues were significantly higher than those in adjacent tissues (P<0.05). Moreover, DCLK1was positively correlated with the expressions of many effectors in the Hippo pathway, including LATS1 (r=0.53, P<0.001), LATS2 (r=0.34, P<0.001), MOB1B (r=0.40, P<0.001). In addition, the tissue microarray of pancreatic cancer patients was stained with multicolor fluorescence, indicated that the high expression of DCLK1 in pancreatic cancer patients was accompanied by the up-regulated expression of YAP1. The expression of DCLK1 in pancreatic cancer cell lines was analyzed by the CCLE database. The results showed that the expression of DCLK1 in AsPC-1 and PANC-1 cells was low. Thus, we overexpressed DCLK1 in AsPC-1 and PANC-1 cell lines and found that DCLK1 overexpression in pancreatic cancer cell lines promoted YAP1 expression and accessible to the nucleus. In addition, DCLK1 up-regulated the expression of YAP1 binding transcription factor TEAD and increased the mRNA expression levels of downstream malignant behavior-promoting molecules. Finally, Verteporfin, an inhibitor of the Hippo pathway, could antagonize the cell's malignant behavior-promoting ability mediated by high expression of DCLK1. We found that the number of migrated cells with DCLK1 overexpressing AsPC-1 group was 68.33±7.09, which was significantly higher than 22.00±4.58 of DCLK1 overexpressing cells treated with Verteporfin (P<0.05). Similarly, the migration number of PANC-1 cells overexpressing DCLK1 was 65.66±8.73, which was significantly higher than 37.00±6.00 of the control group and 32.33±9.61 of Hippo pathway inhibitor-treated group (P<0.05). Meanwhile, the number of invasive cells in the DCLK1-overexpressed group was significantly higher than that in the DCLK1 wild-type group cells, while the Verteporfin-treated DCLK1-overexpressed cells showed a significant decrease. In addition, we monitored the cell proliferation index using the real-time cellular analysis (RTCA) assay, and the proliferation index of DCLK1-overexpressed AsPC-1 cells was 0.66±0.04, which was significantly higher than 0.38±0.01 of DCLK1 wild-type AsPC-1 cells (P<0.05) as well as 0.05±0.03 of DCLK1-overexpressed AsPC1 cells treated with Verteporfin (P<0.05). PANC-1 cells showed the same pattern, with a proliferation index of 0.77±0.04 for DCLK1-overexpressed PANC-1 cells, significantly higher than DCLK1-overexpressed PANC1 cells after Verteporfin treatment (0.14±0.05, P<0.05). Conclusion: The expression of DCLK1 is remarkably associated with the Hippo pathway, it promotes the migration, invasion, and proliferation of pancreatic cancer cells by activating the Hippo pathway.

Comparison of the clinical value of MRI and plasma markers for cognitive impairment in patients aged ≥75 years: a retrospective study

Background

Dementia has become the main cause of disability in older adults aged ≥75 years. Cerebral small vessel disease (CSVD) is involved in cognitive impairment (CI) and dementia and is a cause of vascular CI (VCI), which is manageable and its onset and progression can be delayed. Simple and effective markers will be beneficial to the early detection and intervention of CI. The aim of this study is to investigate the clinical application value of plasma amyloid β1-42 (Aβ42), phosphorylated tau 181 (p-tau181) and conventional structural magnetic resonance imaging (MRI) parameters for cognitive impairment (CI) in patients aged ≥75 years.

Methods

We retrospectively selected patients who visited the Affiliated Hospital of Xuzhou Medical University and were clinically diagnosed with or without cognitive dysfunction between May 2018 and November 2021. Plasma indicators (Aβ42 and p-tau181) and conventional structural MRI parameters were collected and analyzed. Multivariate logistic regression and receiver operator characteristic (ROC) curve were used to evaluate the diagnostic value.

Results

One hundred and eighty-four subjects were included, including 54 cases in CI group and 130 cases in noncognitive impairment (NCI) groups, respectively. Univariate logistic regression analysis revealed that the percentages of Aβ42+_, P-tau 181+, and Aβ42+/P-tau181+ showed no significant difference between the groups of CI and NCI (all P > 0.05). Multivariate logistic regression analysis showed that moderate/severe periventricular WMH (PVWMH) (OR 2.857, (1.365-5.983), P = 0.005), lateral ventricle body index (LVBI) (OR 0.413, (0.243-0.700), P = 0.001), and cortical atrophy (OR 1.304, (1.079-1.575), P = 0.006) were factors associated with CI. The combined model including PVWMH, LVBI, and cortical atrophy to detect CI and NCI showed an area under the ROC curve (AUROC) is 0.782, with the sensitivity and specificity 68.5% and 78.5%, respectively.

Conclusion

For individuals ≥75 years, plasma Aβ42 and P-tau181 might not be associated with cognitive impairment, and MRI parameters, including PVWMH, LVBI and cortical atrophy, are related to CI. The cognitive statuses of people over 75 years old were used as the endpoint event in this study. Therefore, it can be considered that these MRI markers might have more important clinical significance for early assessment and dynamic observation, but more studies are still needed to verify this hypothesis.

Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed

BACKGROUND

To investigate the value of preoperative computed tomography (CT) texture features, routine imaging features, and clinical features in the prognosis of non-small cell lung cancer (NSCLC) after radical resection.

METHODS

Demographic parameters and clinically features were analyzed in 107 patients with stage I-IIIB NSCLC, while 73 of these patients received CT scanning and radiomic characteristics for prognosis assessment. Texture analysis features include histogram, gray size area matrix and gray co-occurrence matrix features. The clinical risk features were identified using univariate and multivariate logistic analyses. By incorporating the radiomics score (Rad-score) and clinical risk features with multivariate cox regression, a combined nomogram was built. The nomogram performance was assessed by its calibration, clinical usefulness and Harrell's concordance index (C-index). The 5-year OS between the dichotomized subgroups was compared using Kaplan-Meier (KM) analysis and the log-rank test.

RESULTS

Consisting of 4 selected features, the radiomics signature showed a favorable discriminative performance for prognosis, with an AUC of 0.91 (95% CI: 0.84 ~ 0.97). The nomogram, consisting of the radiomics signature, N stage, and tumor size, showed good calibration. The nomogram also exhibited prognostic ability with a C-index of 0.91 (95% CI, 0.86-0.95) for OS. The decision curve analysis indicated that the nomogram was clinically useful. According to the KM survival curves, the low-risk group had higher 5-year survival rate compared to high-risk.

CONCLUSION

The as developed nomogram, combining with preoperative radiomics evidence, N stage, and tumor size, has potential to preoperatively predict the prognosis of NSCLC with a high accuracy and could assist to treatment for the NSCLC patients in the clinic.

Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors

PURPOSE

Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors.

METHODS

One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast-enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad-score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad-score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model.

RESULTS

The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80-0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77-1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96-1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88-1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88-0.99) in the training cohort (p < 0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model.

CONCLUSIONS

The CT-based radiomics nomogram combining Rad-score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.

[Efficacy and safety of daptomycin in the treatment of gram-positive infective endocarditis: a meta-analysis]

Objective: To assess the efficacy and safety of daptomycin in the treatment of gram-positive infective endocarditis (IE) systematically. Methods: China Biology Medicine Database (CBM), China National Knowledge Internet (CNKI), Wanfang Data, VIP Database, PubMed, Embase, the Cochrane Library, and Web of Science were searched from the time of establishing databases to April 2022 to obtain relevant controlled and uncontrolled studies of daptomycin for gram-positive infective endocarditis, using key search terms ("daptomycin","gram-positive bacterial infections","endocarditis"). We performed literature screening according to inclusion/exclusion criteria, data extraction, and quality assessment, and performed random-effects meta-analyses for pooled results data using R software. Results: A total of 11 studies (including 13 articles) were included. The findings in the three controlled studies showed that in the treatment of staphylococcus aureus endocarditis, there was no statistically significant differences in in-hospital death risk (RR=0.66, 95%CI: 0.24-1.84, P=0.427) and 6-month death risk (RR=1.27, 95%CI: 0.75-2.14, P=0.374) for daptomycin versus anti-staphylococcal penicillin or vancomycin; in the treatment of enterococcal endocarditis, there was no statistically significant difference in death risk (both P>0.05) for daptomycin versus ampicillin combined with ceftriaxone (RR=0.39, 95%CI: 0.06-2.49) and ampicillin or vancomycin plus or minus gentamicin (RR=0.42, 95%CI: 0.05-3.36); and for daptomycin versus ampicillin or vancomycin combined with an aminoglycoside antibiotic, the differences in in-hospital death risk (RR=0.80, 95%CI: 0.11-5.83) and 6-month death risk (RR=0.47, 95%CI: 0.07-3.21) were not statistically significant(both P>0.05). In a cost-effectiveness study, daptomycin as first-line treatment could save the medical cost of 4 037 pounds per patient compared with vancomycin over a longer period of patient treatment. The results of the meta-analysis of uncontrolled studies showed that the mean clinical success rate of daptomycin for left-side endocarditis was 77% (95%CI: 70% to 83%; I²=28%), for MSSA-infective right-side endocarditis was 87% (95%CI: 73%-95%), and for MRSA-infective right-side endocarditis was 78% (95%CI: 38%-95%; I²=49%); while the mortality rate [mean mortality rate for left-side endocarditis was 13% (95%CI: 11%-17%; I²=0); the mortality rate for right-side endocarditis was reported in only 2 studies, 3% and 27%, respectively] or the rate of daptomycin-related adverse events (4%) was within the acceptable ranges for clinical practice. Conclusions: The death risk in the treatment of infective endocarditis with dattomycin is comparable to that of other antibiotics, and the clinical success rate is higher. Some efficacy may be achieved with daptomycin while other treatments are not effective in treating IE.

Preoperative Cervical Lymph Node Metastasis Prediction in Papillary Thyroid Carcinoma: A Noninvasive Clinical Multimodal Radiomics (CMR) Nomogram Analysis

This study aimed to evaluate the feasibility of applying a clinical multimodal radiomics nomogram based on ultrasonography (US) and multiparametric magnetic resonance imaging (MRI) for the prediction of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively. We performed retrospective evaluations of 133 patients with pathologically confirmed PTC, who were assigned to the training cohort and validation cohort (7 : 3), and extracted radiomics features from the preoperative US, T2-weighted (T2WI),diffusion-weighted (DWI), and contrast-enhanced T1-weighted (CE-T1WI) images. Optimal subsets were selected using minimum redundancy, maximum relevance, and recursive feature elimination in the support vector machine (SVM). For LNM prediction, the radiomics model was constructed by SVM, and Multi-Omics Graph cOnvolutional NETworks (MOGONET) was used for the effective classification of multiradiomics data. Multivariable logistic regression incorporating multiradiomics signatures and clinical risk factors was used to generate a nomogram, whose performance and clinical utility were assessed. Results showed that the nine most predictive features were separately selected from US, T2WI, DWI, and CE-T1WI images, and 18 features were selected in the combined model. The combined radiomics model showed better performance than models based on US, T2WI, DWI, and CE-T1WI. In a comparison of the combined radiomics and MOGONET model, receiver operating curve analysis showed that the area under the curve (AUC) value (95% CI) was 0.84 (0.76-0.93) and 0.84 (0.71-0.96) for the MOGONET model in the training and validation cohorts, respectively. The corresponding values (95% CI) for the combined radiomics model were 0.82 (0.74-0.90) and 0.77 (0.61-0.94), respectively. The MOGONET model had better performance and better prediction specificity compared with the combined radiomics model. The nomogram including the MOGONET signature showed a better predictive value (AUC: 0.81 vs. 0.88) in the training and validation (AUC: 0.74vs. 0.87) cohorts, as compared with the clinical model. Calibration curves showed good agreement in both cohorts. The applicability of the clinical multimodal radiomics (CMR) nomogram in clinical settings was validated by decision curve analysis. In patients with PTC, the CMR nomogram could improve the prediction of cervical LNM preoperatively and may be helpful in clinical decision-making.

Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland

OBJECTIVE

To evaluate the diagnostic performance of computed tomography (CT) radiomics analysis for differentiating pleomorphic adenoma (PA), Warthin tumor (WT), and basal cell adenoma (BCA).

METHODS

A total of 189 patients with PA (n = 112), WT (n = 53) and BCA (n = 24) were divided into a training set (n = 133) and a test set (n = 56). The radiomics features were extracted from plain CT and contrast-enhanced CT images. After dimensionality reduction, plain CT, multiphase-enhanced CT, integrated radiomics signature models and radiomics score (Rad-score) were established and calculated. The receiver operating characteristic (ROC) curve analysis was taken for the assessment of the model performance, and then comparison was conducted among these models. Decision curve analysis (DCA) was adopted to assess the clinical benefits of the models. Diagnostic performances including sensitivity, specificity, and accuracy of the radiologists were evaluated.

RESULTS

Seven, nine, fourteen, and fourteen optimal features were used to constructed plain scan, arterial phase, venous phase, and integrated radiomics signature models, respectively. ROC analysis showed these four models were able to differentiate PA from BCA and WT, with the area under the ROC curve (AUC) values of 0.79, 0.90, 0.87, and 0.94 in the training set, and 0.79, 0.89, 0.86, and 0.94 in the test set, respectively. The integrated model had better diagnostic performance than single-phase radiomics model, but it had similar diagnostic performance to that of the radiomics model based on the arterial phase (p > 0.05). The sensitivity, specificity, and accuracy in the diagnosis of PA were 0.86, 0.46, and 0.70 for the non-subspecialized radiologist and 0.88, 0.77, and 0.84 for the subspecialized radiologist, respectively. Six venous phase parameters were finally selected in differentiating WT from BCA. The predictive effect of the model was favorable, with AUC value of 0.95, sensitivity of 0.96, specificity of 0.83, and accuracy of 0.92. The sensitivity, specificity, and accuracy in the diagnosis between WT and BCA were 0.26, 0.87, and 0.45 for the non-subspecialized radiologist and 0.85, 0.58, and 0.77 for the subspecialized radiologist, respectively.

CONCLUSION

The CT-based radiomics analysis showed favorable predictive performance for differentiating PA, WT, and BCA, thus may be helpful in the clinical decision-making.

Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma

BACKGROUND

To assess the potential of apparent diffusion coefficient (ADC) map in predicting aggressiveness of papillary thyroid carcinoma (PTC) based on whole-tumor histogram-based analysis.

METHODS

A total of 88 patients with PTC confirmed by pathology, who underwent neck magnetic resonance imaging, were enrolled in this retrospective study. Whole-lesion histogram features were extracted from ADC maps and compared between the aggressive and non-aggressive groups. Multivariable logistic regression analysis was performed for identifying independent predictive factors. Receiver operating characteristic curve analysis was used to evaluate the performances of significant factors, and an optimal predictive model for aggressiveness of PTC was developed.

RESULTS

The aggressive and non-aggressive groups comprised 67 (mean age, 44.03 ± 13.99 years) and 21 (mean age, 43.86 ± 12.16 years) patients, respectively. Five histogram features were included into the final predictive model. ADC_firstorder_TotalEnergy had the best performance (area under the curve [AUC] = 0.77). The final combined model showed an optimal performance, with AUC and accuracy of 0.88 and 0.75, respectively.

CONCLUSIONS

Whole-lesion histogram analysis based on ADC maps could be utilized for evaluating aggressiveness in PTC.

Computed Tomography-Based Radiomics Nomogram for Predicting the Postoperative Prognosis of Esophageal Squamous Cell Carcinoma: A Multicenter Study

OBJECTIVES

The aim of this study was to evaluate and identify the predictive value of combining CT radiomics features and clinical features to determine recurrence-free survival (RFS) and overall survival (OS) after surgery in patients with esophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

A total of 372 patients with surgically and pathologically confirmed ESCC from 2 institutions were retrospectively included. All patients from institution 1 were randomized at a 7:3 ratio into a training cohort (n=206) and an internal validation cohort (n=88), and patients from institution 2 were used as an external validation cohort (n=78). The association between the radiomics features and RFS and OS was assessed in the training cohort and verified in the validation cohort. Furthermore, the performance of the radiomics nomogram was evaluated by combining the radiomics score (rad-score) and clinical risk factors.

RESULTS

The radiomics nomogram that combined radiomics features and clinical risk factors was better than the clinical nomogram and radiomics model alone at predicting RFS and OS in ESCC patients. All calibration curves showed significant consistency between predicted survival and actual survival.

CONCLUSION

Radiomics features could be used to stratify patients with ESCC following radical resection into high- and low-risk groups. Furthermore, the radiomics nomograms provided better predictive accuracy than other predictive models and might serve as a therapeutic decision-making reference for clinicians and be used to monitor the risks of recurrence and death.

The role of booster vaccination and ongoing viral evolution in seasonal circulation of SARS-CoV-2

Periodic resurgences of COVID-19 in the coming years can be expected, while public health interventions may be able to reduce their intensity. We used a transmission model to assess how the use of booster doses and non-pharmaceutical interventions (NPIs) amid ongoing pathogen evolution might influence future transmission waves. We find that incidence is likely to increase as NPIs relax, with a second seasonally driven surge expected in autumn 2022. However, booster doses can greatly reduce the intensity of both waves and reduce cumulative deaths by 20% between 7 January 2022 and 7 January 2023. Reintroducing NPIs during the autumn as incidence begins to increase again could also be impactful. Combining boosters and NPIs results in a 30% decrease in cumulative deaths, with potential for greater impacts if variant-adapted boosters are used. Reintroducing these NPIs in autumn 2022 as transmission rates increase provides similar benefits to sustaining NPIs indefinitely (307 000 deaths with indefinite NPIs and boosters compared with 304 000 deaths with transient NPIs and boosters). If novel variants with increased transmissibility or immune escape emerge, deaths will be higher, but vaccination and NPIs are expected to remain effective tools to decrease both cumulative and peak health system burden, providing proportionally similar relative impacts.

Prognostic and predictive value of radiomic signature in stage I lung adenocarcinomas following complete lobectomy

BACKGROUND

The overall survival (OS) of stage I operable lung cancer is relatively low, and not all patients can benefit from adjuvant chemotherapy. This study aimed to develop and validate a radiomic signature (RS) for prediction of OS and adjuvant chemotherapy candidates in stage I lung adenocarcinoma.

METHODS

A total of 474 patients from 2 centers were divided into 1 training (n = 287), 1 internal validation (n = 122), and 1 external validation (n = 65) cohorts. We extracted 1218 radiomic features from preoperative CT images and constructed RS. We further investigated the prognostic value of the RS in survival analysis. Interaction between treatment and RS was assessed to evaluate its predictive value. Propensity score matching (PSM) was conducted.

RESULTS

Overall, 474 eligible patients with stage I lung adenocarcinoma (214 men [45.1%]; median age, 60 years) were identified. The RS was significantly associated with OS in the training and two validation cohorts (hazard ratios [HRs]  >  = 3.22). In multivariable analysis, the RS remained an independent prognostic factor adjusting for clinicopathologic variables (adjusted HRs >  = 2.63). The prognostic value of RS was also confirmed in PSM analysis. In stage I patients, the interaction between RS status and adjuvant chemotherapy was significant (interaction P = 0.020). Within the stratified analysis, good chemotherapy efficacy was only observed for patients with stage IB disease (interaction P < 0.001).

CONCLUSIONS

Our results suggested that the radiomic signature was associated with overall survival in patients with stage I lung adenocarcinoma and might predict adjuvant chemotherapy benefit, especially in stage IB patients. The potential of radiomic signature as a noninvasive predictor needed to be confirmed in future studies.

A novel clinical radiomics nomogram at baseline to predict mucosal healing in Crohn's disease patients treated with infliximab

OBJECTIVES

Mucosal healing (MH) is currently the gold standard in Crohn's disease (CD) management. Noninvasive assessment of MH in CD patients is increasingly a concern of clinicians.

METHODS

This retrospective study included 106 patients with confirmed CD who were divided into a training cohort (n = 73) and a testing cohort (n = 33). Patient demographics were evaluated to establish a clinical model. Radiomics features were extracted from computed tomography enterography (CTE) images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical radiomics nomogram was then built by incorporating the Rad-score and significant clinical features. The diagnostic performance of the three models was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Of the 106 patients with CD, 37 exhibited MH after 26 weeks of infliximab (IFX) treatment. The area under the ROC curve (AUC) of the clinical radiomics nomogram for distinguishing MH from non-MH, which was based on the disease duration and Rad-score, was 0.880 (95% confidence interval [CI]: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort. Decision curve analysis (DCA) confirmed the clinical utility of the clinical radiomics nomogram.

CONCLUSIONS

This is a preliminary study suggesting that this CTE-based radiomics model has potential value for predicting MH in CD patients. A nomogram constructed by combining radiomics signatures and clinical features can help clinicians select appropriate therapeutic strategies for CD patients.

KEY POINTS

• The disease duration (odds ratio (OR) = 0.969, 95% confidence interval (CI) = 0.943-0.995, p = 0.021) was an independent predictor of MH in the clinical model. • The AUC of the radiomics model constructed by the five radiomics features was 0.846 (95% CI: 0.759-0.921) in the training cohort and 0.817 (95% CI: 0.665-0.945) in the testing cohort for differentiating MH from non-MH. • The AUC of the clinical radiomics nomogram was 0.880 (95% CI: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort for distinguishing MH from non-MH.

MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke

Background

This study aims is to explore whether it is feasible to use magnetic resonance texture analysis (MRTA) in order to distinguish favorable from unfavorable function outcomes and determine the prognostic factors associated with favorable outcomes of stroke.

Methods

The retrospective study included 103 consecutive patients who confirmed unilateral anterior circulation subacute ischemic stroke by computed tomography angiography between January 2018 and September 2019. Patients were divided into favorable outcome (modified Rankin scale, mRS ≤ 2) and unfavorable outcome (mRS > 2) groups according to mRS scores at day 90. Two radiologists manually segmented the infarction lesions based on diffusion-weighted imaging and transferred the images to corresponding apparent diffusion coefficient (ADC) maps in order to extract texture features. The prediction models including clinical characteristics and texture features were built using multiple logistic regression. A univariate analysis was conducted to assess the performance of the mean ADC value of the infarction lesion. A Delong’s test was used to compare the predictive performance of models through the receiver operating characteristic curve.

Results

The mean ADC performance was moderate [AUC = 0.60, 95% confidence interval (CI) 0.49–0.71]. The texture feature model of the ADC map (tADC), contained seven texture features, and presented good prediction performance (AUC = 0.83, 95%CI 0.75–0.91). The energy obtained after wavelet transform, and the kurtosis and skewness obtained after Laplacian of Gaussian transformation were identified as independent prognostic factors for the favorable stroke outcomes. In addition, the combination of the tADC model and clinical characteristics (hypertension, diabetes mellitus, smoking, and atrial fibrillation) exhibited a subtly better performance (AUC = 0.86, 95%CI 0.79–0.93; P > 0.05, Delong’s).

Conclusion

The models based on MRTA on ADC maps are useful to evaluate the clinical function outcomes in patients with unilateral anterior circulation ischemic stroke. Energy obtained after wavelet transform, kurtosis obtained after Laplacian of Gaussian transform, and skewness obtained after Laplacian of Gaussian transform were identified as independent prognostic factors for favorable stroke outcomes.

A Radiomics Nomogram for Distinguishing Benign From Malignant Round-Like Breast Tumors

Objective

The objective of this study is to develop a radiomics nomogram for the presurgical distinction of benign and malignant round-like solid tumors.

Methods

This retrospective trial enrolled patients with round-like tumors who had received preoperative digital mammography (DM) no sooner than 20 days prior to surgery. Breast tumors were segmented manually on DM images in order to extract radiomic features. Four machine learning classification models were constructed, and their corresponding areas under the receiver operating characteristic (ROC) curves (AUCs) for differential tumor diagnosis were calculated. The optimal classifier was then selected for the validation set. After this, predictive machine learning models that employed radiomic features and/or patient features were applied for tumor assessment. The models' AUC, accuracy, negative (NPV) and positive (PPV) predictive values, sensitivity, and specificity were then derived.

Results

In total 129 cases with benign and malignant tumors confirmed by pathological analysis were enrolled in the study, including 91 and 38 in the training and test sets, respectively. The DM images yielded 1,370 features per patient. For the machine learning models, the Least Absolute Shrinkage and Selection Operator for Gradient Boosting Classifier turned out to be the optimal classifier (AUC=0.87, 95% CI 0.76-0.99), and ROC curves for the radiomics nomogram and the DM-only model were statistically different (P<0.001). The radiomics nomogram achieved an AUC of 0.90 (95% CI 0.80-1.00) in the test cohort and was statistically higher than the DM-based model (AUC=0.67, 95% CI 0.51-0.84). The radiomics nomogram was highly efficient in detecting malignancy, with accuracy, sensitivity, specificity, PPV, and NPV in the validation set of 0.868, 0.950, 0.778, 0.826, and 0.933, respectively.

Conclusions

This radiomics nomogram that combines radiomics signatures and clinical characteristics represents a noninvasive, cost-efficient presurgical prediction technique.

Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter

Background

To investigate the value of ¹⁸F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm).

Methods

A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA).

Results

According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93–0.97) in the training group and 0.94 (95% CI: 0.89–0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM.

Conclusions

The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00895-x.

Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Background

Due to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods

The preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI.

Results

We found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67–0.88), 0.75 (95% CI: 0.64–0.87), 0.79 (95% CI: 0.69–0.89), 0.73 (95% CI: 0.61–0.85), and 0.80 (95% CI: 0.70–0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74–0.93).

Conclusions

Models derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.

Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer

Objective

To investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC).

Methods

This study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness.

Results

The study cohort included 107 patients with PTC confirmed by pathology (cross-validation cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one modality, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modalities combined achieved 0.917.

Conclusion

Our study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

The predictive value of conventional MRI combined with radiomics in the immediate ablation rate of HIFU treatment for uterine fibroids

OBJECTIVE

This study aimed to assess the predictive value of conventional magnetic resonance imaging (MRI) combined with radiomics in determining the nonperfused volume ratio (NPVR) following high-intensity focused ultrasound (HIFU) ablation for uterine fibroids.

METHODS AND MATERIALS

A total of 216 symptomatic uterine fibroids in 216 women were subjected to HIFU ablation from October 2015 to March 2020. Baseline clinical and MR parameters acquired before and after HIFU ablation were analyzed, and the NPVR was calculated accordingly. Radiomics features were extracted using A.K. software on T2-weighted imaging (T2WI). The minimum redundancy and maximum relevancy (mRMR) method were used to refine the selected radiomics features. Then, multiple linear regression models, the Wilcoxon signed-rank test, and Spearman's rank correlation and Bland-Altman analyses were conducted.

RESULTS

Conventional MRI combined with radiomics revealed the signal intensity on T2WI (X₉), enhancement degree on T1-weighted imaging (T1WI) (X₁₁), uterine fibroid location (X₄), wavelet_glszm_SizeZoneNonUniformity first order (X₁₂) and wavelet_HHH_firstorder_Skewness (X₁₃) negatively affected the NPVR. The resulting regression equation was NPVR = 104.030 - 11.886 × X₉ - 5.459 × X₁₁ - 2.776 × X₄ - 0.20 × X₁₂ - 16.913 × X₁₃. The adjusted R² values of the conventional MRI model and combined model were 0.385 and 0.408, respectively, and the two fitted models were statistically significant (p < 0.05). No significant differences were observed between the predicted NPVR value [81 (71, 91) %] of the combined model and the actual NPVR value [89 (77, 97) %] (p > 0.05). In addition, the predicted NPVR was correlated with the actual NPVR (r = 0.655, p < 0.001).

CONCLUSIONS

The efficiency of the combined model was better than that of the conventional MRI model in predicting the NPVR following HIFU ablation for uterine fibroids. Radiomics is an important supplemental modality to conventional MRI.

727 BOOSTING BONE HEALTH: IMPROVING JUNIOR DOCTORS’ CONFIDENCE IN ASSESSING AND MANAGING FRAGILITY FRACTURES

Introduction

Fragility fractures are a major disease burden in the UK. With an ageing population and number of fragility fractures predicted to double in 50 years, prevention in this high-risk population needs to be addressed. This audit aimed to examine the assessment of fracture risk in patients presenting with fragility fractures and improve awareness amongst trainee doctors through education.

Methods

A retrospective study was conducted on patients over 65 years admitted with fragility fractures, excluding neck of femur, from January to March 2021 (n = 51). Data was collected on Fracture Risk Assessment Tool (FRAX) scores, dual energy X-ray absorptiometry (DEXA) scans, and risk factors including body mass index (BMI), previous fragility fracture, smoking, alcohol intake, and serum calcium and vitamin D. A teaching seminar for junior doctors was delivered to increase confidence in assessing and managing fragility fractures.

Results

The mean age of patients was 79, with most common presentations being proximal humerus, distal femur and ankle fractures. 46% of patients had a previous fragility fracture. Smoking and alcohol history were documented in 72% and 60% of patients respectively, and 29% had BMIs calculated. 68% had calcium and 45% had vitamin D checked. DEXA scans occurred in 12%, all of whom had osteopenia or osteoporosis. Over half of patients were already on bone protection and 28% were subsequently started on bisphosphonates. A teaching session was delivered to junior doctors (n = 10), leading to improved confidence in assessing fracture risk by 30%, and improved confidence in managing fragility fractures by 35%. Knowledge of FRAX score increased from 62% to 100%.

Conclusion

A significant proportion of the over-65 population are likely to present with fragility fractures. Improving awareness and confidence amongst junior doctors can lead to identification of risk factors and help better prevent and manage fragility fractures in this high-risk population.

A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma

Objective

To explore a new model to predict the prognosis of liver cancer based on MRI and CT imaging data.

Methods

A retrospective study of 103 patients with histologically proven hepatocellular carcinoma (HCC) was conducted. Patients were randomly divided into training (n = 73) and validation (n = 30) groups. A total of 1,217 radiomics features were extracted from regions of interest on CT and MR images of each patient. Univariate Cox regression, Spearman’s correlation analysis, Pearson’s correlation analysis, and least absolute shrinkage and selection operator Cox analysis were used for feature selection in the training set, multivariate Cox proportional risk models were established to predict disease-free survival (DFS) and overall survival (OS), and the models were validated using validation cohort data. Multimodal radiomics scores, integrating CT and MRI data, were applied, together with clinical risk factors, to construct nomograms for individualized survival assessment, and calibration curves were used to evaluate model consistency. Harrell’s concordance index (C-index) values were calculated to evaluate the prediction performance of the models.

Results

The radiomics score established using CT and MR data was an independent predictor of prognosis (DFS and OS) in patients with HCC (p &lt; 0.05). Prediction models illustrated by nomograms for predicting prognosis in liver cancer were established. Integrated CT and MRI and clinical multimodal data had the best predictive performance in the training and validation cohorts for both DFS [(C-index (95% CI): 0.858 (0.811–0.905) and 0.704 (0.563–0.845), respectively)] and OS [C-index (95% CI): 0.893 (0.846–0.940) and 0.738 (0.575–0.901), respectively]. The calibration curve showed that the multimodal radiomics model provides greater clinical benefits.

Conclusion

Multimodal (MRI/CT) radiomics models can serve as effective visual tools for predicting prognosis in patients with liver cancer. This approach has great potential to improve treatment decisions when applied for preoperative prediction in patients with HCC.

MRI-Based Radiomics Analysis for Intraoperative Risk Assessment in Gravid Patients at High Risk with Placenta Accreta Spectrum

Background: Gravid patients at high risk with placenta accreta spectrum (PAS) face life-threatening risk at delivery. Intraoperative risk assessment for patients is currently insufficient. We aimed to develop an assessment system of intraoperative risks through MRI-based radiomics. Methods: A total of 131 patients enrolled were randomly grouped according to a ratio of 7:3. Clinical data were analyzed retrospectively. Radiomic features were extracted from sagittal Fast Imaging Employing State-sate Acquisition images. Univariate and multivariate regression analyses were performed to build models using R software. A receiver operating characteristic curve and decision curve analysis (DCA) were performed to determine the predictive performance of models. Results: Six radiomic features and two clinical variables were used to construct the combined model for selection of removal protocols of the placenta, with an area under the curve (AUC) of 0.90 and 0.91 in the training and test cohorts, respectively. Nine radiomic features and two clinical variables were obtained to establish the combined model for prediction of intraoperative blood loss, with an AUC of 0.90 and 0.88 in the both cohorts, respectively. The DCA confirmed the clinical utility of the combined model. Conclusion: The analysis of combined MRI-based radiomics with clinics could be clinically beneficial for patients.

Correlative analysis of lung CT findings in patients with Birt–Hogg–Dubé Syndrome and the occurrence of spontaneous pneumothorax: a preliminary study

Background

The diagnosis of patients with Birt–Hogg–Dubé (BHD) syndrome is always delayed (even for more than 10 years). Improving the understanding and diagnosis of this disease is vital for clinicians and radiologists. In this study we presented the chest computed tomography (CT) findings of BHD syndrome and offered suggestions for BHD cases with spontaneous pneumothorax.

Methods

Twenty-six BHD patients from 11 families (10 men, 16 women; mean age: 46 ± 12 years, 20–68 years) were included. The clinical features of the patients included pneumothorax, renal lesions, and skin lesions. Twenty-three patients underwent chest CT imaging. The cyst condition of each patient derived from reconstructed chest CT imaging was recorded, including the cyst number, size, volume, pattern, and distribution.

Results

Pneumothorax occurred in 54% (14/26) of patients. Among them, 43% (6/14) had pneumothorax more than twice. However, typical skin and renal lesions were absent. Four patients had renal hamartoma. CT showed that 23 (100%) patients had lung cysts. Pulmonary cysts were bilateral and multiple, round, irregular, or willow-like. And 93.6% of the large cysts (long-axis diameter ≥ 20 mm) were under the pleura, and near the mediastinum and spine. The long-axis diameter, short-axis diameter and volume of the largest cysts were associated with the occurrence of pneumothorax (all P < 0.05).

Conclusions

Chest CT imaging can reveal some characteristic features of BHD syndrome. The occurrence of pneumothorax in BHD patients is closely related to their pulmonary cystic lesions.

Prediction and verification of survival in patients with non-small-cell lung cancer based on an integrated radiomics nomogram

AIM

To develop and validate a nomogram to predict 1-, 2-, and 5-year survival in patients with non-small-cell lung cancer (NSCLC) by combining optimised radiomics features, clinicopathological factors, and conventional image features extracted from three-dimensional (3D) computed tomography (CT) images.

MATERIALS AND METHODS

A total of 172 patients with NSCLC were selected to construct the model, and 74 and 72 patients were selected for internal validation and external testing, respectively. A total of 828 radiomics features were extracted from each patient's 3D CT images. Univariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to select features and generate a radiomics signature (radscore). The performance of the nomogram was evaluated by calibration curves, clinical practicability, and the c-index. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) between the two subgroups.

RESULT

The radiomics features of the NSCLC patients correlated significantly with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort, and external test cohort were 0.670, 0.658, and 0.660, respectively. The calibration curves showed that the predicted survival time was close to the actual survival time. Decision curve analysis shows that the nomogram could be useful in the clinic. According to KM analysis, the 1-, 2- and 5-year survival rates of the low-risk group were higher than those of the high-risk group.

CONCLUSION

The nomogram, combining the radscore, clinicopathological factors, and conventional CT parameters, can improve the accuracy of survival prediction in patients with NSCLC.

Clinical outcomes of keratinized mucosa augmentation in jaws reconstructed with fibula or iliac bone flaps

This prospective study was undertaken to evaluate the treatment outcomes of keratinized mucosa augmentation (KMA) on the buccal and palatal/lingual sides of implants in jaws reconstructed after oncological surgery. Forty-two implants in 12 patients whose jaws had been reconstructed with a fibula or iliac bone flap were included. KMA was performed at 3 months after implant placement; this included an apically displaced partial-thickness flap and a free gingival graft (FGG) around the implants to increase the keratinized mucosa width (KMW). Patients were followed up for at least 6 months post-surgery. KMW, shrinkage, and patient pain and discomfort measured on a visual analogue scale were analysed. A histological analysis was performed of tissue epithelium from two patients. The results showed that KMW was >2 mm on both the buccal and palatal/lingual sides during follow-up. Before surgery, histological analysis showed epithelium with no epithelial spikes; normal keratinized epithelial spikes were observed at 8 weeks after KMA. Greater KMW was observed around implants in reconstructed maxillae than around those in reconstructed mandibles (P < 0.001). Patients felt more pain at the donor site than at the recipient site during the first 3 days post-surgery. KMA with FGG was predictable in reconstructed jaws and may help maintain the long-term stability of implants.

A Novel Clinical Radiomics Nomogram to Identify Crohn's Disease from Intestinal Tuberculosis

Purpose

To establish a clinical radiomics nomogram to differentiate Crohn’s disease (CD) from intestinal tuberculosis (ITB).

Patients and Methods

Ninety-three patients with CD and 67 patients with ITB were recruited (111 in training cohort and 49 in test cohort). The region of interest (ROI) for the lesions in the ileocecal region was delineated on computed tomography enterography and radiomics features extracted. Radiomics features were filtered by the gradient boosting decision tree (GBDT), and a radiomics score was calculated by using the radiomics signature-based formula. We constructed a clinical radiomics model and nomogram combining clinical factors and radiomics score through multivariate logistic regression analysis, and the internal validation was undertaken by ten-fold cross validation. Analyses of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to evaluate the performance of the clinical, radiomics and combined model.

Results

The clinical radiomics nomogram, which was based on the 9 radiomics signature and two clinical factors, indicated that the clinical radiomics model had an area under the ROC curve (AUC) value of 0.96 (95% confidence interval [CI]: 0.93–0.99) in the training cohort and 0.93 (95% CI: 0.86–1.00) in validation cohort. The clinical radiomics model was superior to the clinical model and radiomics model, and the difference was significant (P = 0.006, 0.004) in the training cohort. DCA confirmed the clinical utility of clinical radiomics nomogram.

Conclusion

CTE-based radiomics model has a good performance in distinguishing CD from ITB. A nomogram constructed by combining radiomics and clinical factors can help clinicians accurately diagnose and select appropriate treatment strategies between CD and ITB.

A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer

OBJECTIVE

The status of lymph node metastasis (LNM) is highly correlated with the recurrence and survival outcomes of patients with lung cancer. Thus, a tool that predicts LNM could benefit patient treatment and prognosis. The present study established a new radiomic model by combining computed tomography (CT) radiomic features and clinical parameters to predict the LNM status in patients with non-small cell lung cancer (NSCLC).

METHODS

Demographic parameters and clinical laboratory values were analyzed in 217 patients with stage I-IIIB NSCLC; 107 of the patients received CT scanning and radiomic characteristics were used for LNM assessment (76 in the training cohort and 31 in the validation cohort). The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression model were used to select the most predictive features on the basis of the 76 patients in the training set. The value of the area under the receiver operator characteristic (ROC) curve (AUC) was adopted to determine the correlation between LN status and the radiomics signature in training cohorts and then validated in the 31 patients of validation set. The radiomics nomogram was analyzed using univariate and multivariate logistic regression. Decision curve analysis (DCA) was performed to evaluate the clinical utility of this model.

RESULTS

This was a retrospective study. Five radiomic characteristics were significantly correlated with LNM in the two cohorts (P < 0.05). The radiomic nomogram that incorporated the above radiomic characteristics, the RDW, and the CT-based LN status had satisfactory discrimination and calibration in the training (AUC, 0.79; 95% CI 0.69-0.89) and validation cohorts (AUC, 0.70; 95% CI 0.50-0.89).The DCA showed that the developed nomogram had promising clinical utility.

CONCLUSIONS

The developed nomogram, combined with preoperative radiomics evidence, the RDW, and the CT-based LN status, has the potential to preoperatively predict LNM with high accuracy and can facilitate the prediction of LN status for NSCLC patients.

The human-AI scoring system: A new method for CT-based assessment of COVID-19 severity

BACKGROUND

Chest computed tomography (CT) plays an important role in the diagnosis and assessment of coronavirus disease 2019 (COVID-19).

OBJECTIVE

To evaluate the value of an artificial intelligence (AI) scoring system for radiologically assessing the severity of COVID-19.

MATERIALS AND METHODS

Chest CT images of 81 patients (61 of normal type and 20 of severe type) with confirmed COVID-19 were used. The test data were anonymized. The scores achieved by four methods (junior radiologists; AI scoring system; human-AI segmentation system; human-AI scoring system) were compared with that by two experienced radiologists (reference score). The mean absolute errors (MAEs) between the four methods and experienced radiologists were calculated separately. The Wilcoxon test is used to predict the significance of the severity of COVID-19. Then use Spearman correlation analysis ROC analysis was used to evaluate the performance of different scores.

RESULTS

The AI score had a relatively low MAE (1.67-2.21). Score of human-AI scoring system had the lowest MAE (1.67), a diagnostic value almost equal to reference score (r= 0.97), and a strongest correlation with clinical severity (r= 0.59, p< 0.001). The AUCs of reference score, score of junior radiologists, AI score, score of human-AI segmentation system, and score of human-AI scoring system were 0.874, 0.841, 0.852, 0.857 and 0.865, respectively.

CONCLUSION

The human-AI scoring system can help radiologists to improve the accuracy of COVID-19 severity assessment.

Transcript expression profiling of fibromelanosis-related genes in black-bone chickens

1. The aim of the present study was to identify differentially expressed genes (DEGs) and metabolic pathways involved in this phenotype. Fibromelanosis is the most striking feature of black-bone chickens, such as the Silkie and Dongxiang indigenous breeds. Due to the accumulation of eumelanin in connective tissues, fibromelanosis manifests as black colouration of the skin, muscles, gut, and periosteum. Studies on fibromelanosis can provide useful information pertaining to human diseases and offer commercial value to the poultry industry. However, the genetic basis of fibromelanosis remains unclear.2. Digital gene expression analysis was performed on black and white skin samples collected from the HW1 black-bone chicken line to detect differences in genome-wide expression patterns. A total of >30 billion bp were sequenced, and 2,707,926,466 bp and 2,948,782,964 bp of clean data obtained for creation of libraries for black and white skin, respectively. In total, 252 DEGs from 15,508 mapped genes were identified with 83 up-regulated in white skin and 169 up-regulated in black skin.3. Gene ontology analysis highlighted that genes from the extracellular region and associated components were abundant among the DEGs. Pathway analysis revealed that many DEGs were linked to amino acid metabolism and the immune system. qRT-PCR validation using 14 genes showed good conformity with the sequence analysis of fibromelanosis-related genes.4. The results showed that L-dopachrometautomerase precursor (DCT), tyrosine aminotransferase (TAT), 4-hydroxyphenylpyruvate dioxygenase (HPD) from the tyrosine metabolism pathway, coagulation factor II (F2), fibrinogen beta chain (FGB), plasminogen (PLG) and complement component 7 (C7) from the complement and coagulation cascades were important genes in the fibromelanosis process in black-bone chickens. These candidate genes require further correlation analysis and functional verification.

Plasma miR-146a and miR-365 expression and inflammatory factors in patients with osteoarthritis

OBJECTIVE

To investigate the expression levels of micro-ribonucleic acid (miR)-146a and miR-365 in the plasma of osteoarthritis (OA) patients, to study their expression with the inflammatory factors and the severity of disease in patients and to analyse their diagnostic significance.

MATERIALS AND METHODS

A total of 42 OA patients diagnosed with OA and treated in our hospital from January 2017 to January 2018 were selected as the subjects, and 28 healthy people were enrolled as controls. The expressions of interleukin-1 beta (IL-1β) and IL-6 in the plasma of OA patients were detected via immunohistochemical staining. Moreover, the knee joint function of OA patients was evaluated by Lysholm score, Western Ontario and McMaster Universities (WOMAC) score and Visual Analogue Scale (VAS) score. The expression levels of plasma miR-146a and miR-365 in OA patients were measured through RT-PCR. Besides, the significance of the expression levels of miR-146a and miR-365 for the diagnosis of OA was analysed by ROC curves.

RESULTS

As compared with healthy people, OA patients had elevated expression levels of plasma IL-1β and IL-6, decreased Lysholm score, increased WOMAC and VAS scores as well as significantly up-regulated levels of plasma miR-146a and miR-365, which were of important significance for diagnosis.

CONCLUSION

The expression levels of plasma miR-146a, miR-365 and inflammatory factors are notably higher, the disease is more severe, and the function of knee joint movement is weaker in OA patients than those in healthy controls. It can be concluded that the levels of both miR-146a and miR-365 can serve as biomarkers of OA diagnosis.

Factors associated with Scrub Typhus infection: A case-control study from Luhe, China

INTRODUCTION

Scrub typhus (ST) is an acute febrile infection and remains a significant health problem globally. This study aimed to determine the factors associated with ST infection in Luhe District, China.

MATERIAL AND METHODS

The case-control study was conducted among 116 cases identified through passive surveillance systems over three years.The control subjects were 232 living in the same village for more than six months without any history of ST infection were selected by matching to the age (within 5-years) and identified through active surveillance. Statistical analyses were performed using SPSS v. 25.0 for Windows (IBM SPSS, Chicago, IL, USA).

RESULTS

The mean age of confirmed persons was 58.1(SD=10.15) years, while control subjects were 56.14 (11.57).There is no significant difference in gender, age, education, and occupations between case and control. Farmers had the most significant number of cases among occupational groups. The three factors that were significantly associated with an increased odds of having ST infection are bundling or moving waste straw (OR: 1.94, 95%CI; 0.99,381), morning exercise in the park or field (OR: 4.74 95%CI; 1.19, 18.95), and working as labourer in the vegetable field (OR:1.02, 95%CI:1.02,3.19).

CONCLUSIONS

Our findings suggested establishing a prevention and control strategy for these groups to lower ST development risk.