Differentiation Between Phyllodes Tumor and Fibroadenoma of the Breast: A Radiomics Prediction Model Based on Full-Field Digital Mammography & Digital Tomosynthesis.

OBJECTIVE: To assess the diagnostic performance of FFDM-based and DBT-based radiomics models to differentiate breast phyllodes tumors from fibroadenomas. METHODS: 192 patients (93 phyllodes tumors and 99 fibroadenomas) who underwent mammography were retrospectively enrolled. Radiomic features were respectively extracted from FFDM and the clearest slice of DBT images. A least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features. A combined model was constructed by radiomics and radiological signatures. Machine learning classification was done using logistic regression based on radiomics or radiological signatures (clinical model). Four radiologists were tested on phyllodes tumors and fibroadenomas with and without optimal model assistance. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model or radiologist. The Delong test and McNemar's test were performed to compare the performance. RESULTS: The combined model yielded the highest performance with an AUC of 0.948 (95%CI: 0.889-1.000) in the testing set, slightly higher than the FFDM-radiomics model (AUC of 0.937, 95%CI: 0.841-0.984) and the DBT-radiomics model (AUC of 0.860, 95%CI: 0.742-0.936) and significantly superior to the clinical model (AUC of 0.719, 95%CI: 0.585-0.829). With the combined model aid, the AUCs of four radiologists were improved from 0.808 to 0.914 (p=0.079), 0.759 to 0.888 (p=0.015), 0.717 to 0.846 (p=0.004), and 0.629 to 0.803 (p=0.001). CONCLUSION: Radiomics analysis based on FFDM and DBT shows promise in differentiating phyllodes tumors from fibroadenomas.

Breast masses with rim enhancement on contrast-enhanced mammography: morphological and enhancement features for diagnosis and differentiation of benign and malignant.

OBJECTIVES: To investigate the imaging characteristics and clinicopathological features of rim enhancement of breast masses demonstrated on contrast-enhanced mammography (CEM). METHODS: 67 cases of breast lesions confirmed by pathology and showing rim enhancement on CEM examinations were analyzed. The lesions were divided into benign and malignant groups, and the morphological and enhanced features were described. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated separately for each morphology descriptor to evaluate the diagnostic ability of each indicator. RESULTS: There were 35 (52.2%) malignant and 32 (47.8%) benign lesions. There are significant differences in the morphological and enhanced features between benign and malignant lesions. 29/35 (82.9%) malignant lesions exhibited irregular shapes, and 31/35 (88.6%) showed indistinct margins. 28/35 (80%) malignant lesions displayed strong enhancement on CEM, while 12/32 (37.5%) benign lesions exhibited weak enhancement (P = 0.001). Malignant lesions showed a higher incidence of unsmooth inner walls than benign lesions (28/35 vs 7/32; P <.001). Lesion margins showed high sensitivity of 88.57% and NPV of 81.8%. The presence of suspicious calcifications had the highest specificity of 100% and PPV of 100%. The diagnostic sensitivity, specificity, PPV, and NPV of the combined parameters were 97.14%, 93.15%, 94.44%, and 96.77%, respectively. CONCLUSIONS: The assessment of morphological and enhanced features of breast lesions exhibiting rim enhancement on CEM can improve the differentiation between benign and malignant breast lesions. ADVANCES IN KNOWLEDGE: This article provides a reference for the differential diagnosis of ring enhanced lesions on CEM.

Comparative Analysis of Chemical Composition of Zanthoxylum myriacanthum Branches and Leaves by GC-MS and UPLC-Q-Orbitrap HRMS, and Evaluation of Their Antioxidant Activities.

Zanthoxylum myriacanthum Wall. ex Hook. f., a plant belonging to the Rutaceae family and the Zanthoxylum genus, is extensively utilized for its medicinal properties and as a culinary seasoning in China and Southeast Asian countries. However, the chemical composition and biological activities of Z. myriacanthum branches and leaves remain insufficiently explored. In this study, the volatile and non-volatile components of Z. myriacanthum branches and leaves were analyzed using GC-MS and UPLC-Q-Orbitrap HRMS techniques. A total of 78 volatile compounds and 66 non-volatile compounds were identified. The volatile compounds were predominantly terpenoids and aliphatic compounds, while the non-volatile compounds were primarily flavonoids and alkaloids. The branches contained 52 volatile compounds and 33 non-volatile compounds, whereas the leaves contained 48 volatile compounds and 40 non-volatile compounds. The antioxidant activities of the methanol extracts from Z. myriacanthum branches and leaves were evaluated using ABTS and DPPH free-radical-scavenging assays, both of which demonstrated certain antioxidant activity. The methanol extract of leaves demonstrated significantly higher antioxidant activity compared to that of the branches, possibly due to the higher presence of flavonoids and phenols in the leaves, with IC50 values of 7.12 ± 0.257 µg/mL and 1.22 × 102 ± 5.01 µg/mL for ABTS and DPPH, respectively. These findings enhance our understanding of the chemical composition and antioxidant potential of Z. myriacanthum. The plant holds promise as a natural source of antioxidants for applications in pharmaceuticals, cosmetics, and functional foods. Further research can explore its broader biological activities and potential applications.

Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field.

Objective. In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult. Most existing computer-aided detection (CADe) models focus on the detection of typical ADs. This study focuses on atypical ADs and develops a deep learning-based CADe model with an adaptive receptive field in DBT.Approach. Our proposed model uses a Gabor filter and convergence measure to depict the distribution of fibroglandular tissues in DBT slices. Subsequently, two-dimensional (2D) detection is implemented using a deformable-convolution-based deep learning framework, in which an adaptive receptive field is introduced to extract global features in slices. Finally, 2D candidates are aggregated to form the three-dimensional AD detection results. The model is trained on 99 positive cases with ADs and evaluated on 120 AD-positive cases and 100 AD-negative cases.Main results. A convergence-measure-based model and deep-learning model without an adaptive receptive field are reproduced as controls. Their mean true positive fractions (MTPF) ranging from 0.05 to 4 false positives per volume are 0.3846 ± 0.0352 and 0.6501 ± 0.0380, respectively. Our proposed model achieves an MTPF of 0.7148 ± 0.0322, which is a significant improvement (p< 0.05) compared with the other two methods. In particular, our model detects more atypical ADs, primarily contributing to the performance improvement.Significance. The adaptive receptive field helps the model improve the atypical AD detection performance. It can help radiologists identify more ADs in breast cancer screening.

Performance of ImproGene cfDNA blood collection tubes for mutation analysis in cancer patients.

With the widely application of liquid biopsy and the development of detection technology, the standardization of pre-analysis procedures is necessary. For controlling pre-analysis variation of circulating tumor DNA (ctDNA) in blood samples, the blood collection tubes for ctDNA preservation particularly contribute a lot. The objective of this study was to investigate whether ImproGene® Cell Free DNA Tube (ImproGene tube) can be used in sample collection, preservation and NGS based mutation detection for ctDNA. We investigated hemolysis and cell free DNA (cfDNA) concentration of blood samples stored in ImproGene tubes and detected ß-actin, LINE1 and exogenous gene level by qPCR. We compared cfDNA and RNA quantity between samples in ImproGene tube and Streck Cell-Free DNA BCT® (Streck tube). And 10 gene mutations and three fusion mutations analysis were compared by sequencing. When stored at room temperature within 7 days in ImproGene tubes, blood samples had no visible hemolysis and the cfDNA concentration, levels of ß-actin, LINE1 and exogenous gene remained stable which means no genomic DNA release and cfDNA was protected. There was no significant difference in cfDNA and RNA quantity between ImproGene tubes and Streck tubes. Furthermore, based on this limited data set, ImproGene tubes showed increased detection rates of low-level mutations. Therefore, ImproGene Cell Free DNA Tubes may have promising applications in sample collection, preservation and NGS based mutation detection for ctDNA by its good preservation performance.

Architectural distortion detection based on superior-inferior directional context and anatomic prior knowledge in digital breast tomosynthesis.

BACKGROUND: In 2020, breast cancer becomes the most leading diagnosed cancer all over the world. The burden is increasing in the prevention and treatment of breast cancer. Accurately detecting breast lesions in screening images is important for early detection of cancer. Architectural distortion (AD) is one of the breast lesions that need to be detected. PURPOSE: To develop a deep-learning-based computer-aided detection (CADe) model for AD in digital breast tomosynthesis (DBT). This model uses the superior-inferior directional context of DBT and anatomic prior knowledge to reduce false positive (FP). It can identify some negative samples that cannot be distinguished by deep learning features. METHODS: The proposed CADe model consists of three steps. In the first step, a deep learning detection network detects two-dimensional (2D) candidates of ADs in DBT slices with the inputs preprocessed by Gabor filters and convergence measure. In the second step, three-dimensional (3D) candidates are obtained by stacking 2D candidates along superior-inferior direction. In the last step, FP reduction for 3D candidates is implemented based on superior-inferior directional context and anatomic prior knowledge of breast. DBT data from 99 cases with AD were used as the training set to train the CADe model, and data from 208 cases were used as an independent test set (including 108 cases with AD and 100 cases without AD as the control group). The free-response receiver operating characteristic and mean true positive fraction (MTPF) in the range of 0.05-2.0 FPs per volume are used to evaluate the model. RESULTS: Compared with the baseline model based on convergence measure, our proposed method demonstrates significant improvement (MTPF: 0.2826 ± 0.0321 vs. 0.6640 ± 0.0399). Results of an ablation study show that our proposed context- and anatomy-based FP reduction methods improve the detection performance. The number of FPs per DBT volume reduces from 2.47 to 1.66 at 80% sensitivity after employing these two schemes. CONCLUSIONS: The deep learning model demonstrates practical value for AD detection. The results indicate that introducing superior-inferior directional context and anatomic prior knowledge into model can indeed reduce FPs and improve the performance of CADe model.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.

OBJECTIVES: To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. METHODS: We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. RESULTS: The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. CONCLUSIONS: This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. KEY POINTS: • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

Performance of ImproGene Cell-Free DNA Tubes for Stabilization and Analysis of cfDNA in Blood Samples.

BACKGROUND: With the development of liquid biopsy technology, the demand for noninvasive prenatal testing (NIPT) is increasing rapidly. The aim of the study is to evaluate the effects of different blood collection tubes on plasma cfDNA and NIPT quality control. METHODS: We investigated hemolysis, cfDNA concentration, and fragment distribution within blood samples stored in EDTA, ImproGene, and Streck tubes. The effects of ImproGene and Streck tubes on NIPT quality control were evaluated. RESULTS: The ImproGene tubes prevented the time-dependent increase of cfDNA concentration and preserved the cfDNA fragment size distribution. For NIPT quality control, there is no significant difference in cfDNA, library concentration, and fetal fraction between ImproGene and Streck tubes samples. GC content of the samples in ImproGene tubes was closer to the human genome. CONCLUSION: The ImproGene cfDNA tube has excellent performance and is an effective choice for storing blood samples for NIPT testing or other cfDNA analysis.

Pure and Mixed Tubular Carcinoma of the Breast: Mammographic Features, Clinicopathological Characteristics and Prognostic Analysis.

Objective: To evaluate the mammographic features, clinicopathological characteristics, treatments, and prognosis of pure and mixed tubular carcinomas of the breast. Materials and methods: Twenty-five tubular carcinomas were pathologically confirmed at our hospital from January 2011 to May 2019. Twenty-one patients underwent preoperative mammography. A retrospective analysis of mammographic features, clinicopathological characteristics, treatment, and outcomes was performed. Results: Altogether, 95% of the pure tubular carcinomas (PTCs) and mixed tubular carcinomas (MTCs) showed the presence of a mass or structural distortions on mammography and the difference was not statistically significant (P = .373). MTCs exhibited a larger tumor size than PTCs (P = .033). Lymph node metastasis was more common (P = .005) in MTCs. Patients in our study showed high estrogen receptor and progesterone receptor positivity rates, but low human epidermal growth factor receptor 2 positivity rate. The overall survival rate was 100% in both PTC and MTC groups and the 5-year disease-free survival rates were 100% and 75%, respectively with no significant difference between the groups (P = .264). Conclusion: Tubular carcinoma of the breast is potentially malignant and has a favorable prognosis. Digital breast tomosynthesis may improve its detection. For patients with PTC, breast-conserving surgery and sentinel lymph node biopsy are recommended based on the low rate of lymph node metastasis and good prognosis. MTC has a relatively high rate of lymph node metastasis and a particular risk of metastasis. Axillary lymph node dissection should be performed for MTC even if the tumor is smaller than 2 cm.

The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT.

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients (p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs (p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.

Radiologists' diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists' diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.

A rolling circle amplification-assisted DNA walker triggered by multiple DNAzyme cores for highly sensitive electrochemical biosensing.

DNA walkers from monopodial to multipedal types have usually one cleavage site to power the walking system along with the track. Herein, a multipedal DNA walker (m-DNA walker) with multiple DNAzyme cores was constructed with the assistance of rolling circle amplification (RCA) for highly sensitive electrochemical biosensing. Firstly, a three-component DNA complex as a swing strand was prepared by integrating a padlock, an RCA primer and a block DNA as a recognition element in the DNA walker system. After ferrocene-labeled track DNA (trDNA) and capture DNA were fixed on a gold electrode, the three-component DNA complex was imported onto the electrode as a swing arm to form a m-DNA walker. In the presence of target DNA and a RCA kit, the block was displaced from the complex and RCA was initiated to form multiple DNAzyme strands. Upon hybridization with trDNA, the m-DNA walker was motivated by the cleavage of multiple DNAzyme cores in the presence of manganese ions to free signal molecules. Under the optimal circumstances, the electrochemical m-DNA walker showed a linear range from 1.0 fM to 1.0 nM with a detection limit of 0.28 fM. Moreover, the m-DNA walker demonstrated a rapid cleavage rate and a low ratio of the swing strand to the track, which is more excellent than a single foot walker and a bipedal DNA walker. The practicality of the proposed strategy was also confirmed by detecting target DNA in 10% human serum, showing promising applications in clinical diagnosis.

Identification of Mycobacterium tuberculosis PPE68-specific HLA-A*0201-restricted epitopes for tuberculosis diagnosis.

PPE68 is a Mycobacterium tuberculosis-specific protein which is absent from the vaccine strains of BCG. A panel of 14 PPE68-derived peptides predicted to bind to HLA-A*0201 was synthesized. The HLA-A*0201 restriction of these peptides was determined in T2 cell line and HLA-A*0201 transgenic mice. The specificity of peptides was assessed in pulmonary tuberculosis (TB) patients using IFN-γ enzyme-linked immunospot (ELISPOT) assay, and immunodominant peptides were further used to evaluate their diagnostic potential in HLA-A*0201-positive pulmonary TB patients. 13 out of 14 peptides were identified as high-affinity binders. Of these peptides, 12 peptides induced significant IFN-γ-secreting T cell response in transgenic mice and 9 peptides were efficiently recognized by peripheral blood mononuclear cells of 10 HLA-A*0201-positive TB patients. Four immunodominant HLA-A*0201-restricted epitopes (PPE68126-134, PPE68133-141, PPE68140-148, and PPE68148-156) were recognized by the most of 80 HLA-A*0201-positive TB patients (81, 86, 74, and 84 %, respectively). These epitopes may be used for a potential diagnosis of M. tuberculosis infection.

Identification of conserved and HLA-A*2402-restricted epitopes in Dengue virus serotype 2.

In this study, we set out to identify dengue virus serotype 2 (DENV-2)-specific HLA-A*2402-restricted epitopes and determine the characteristics of T cells generated to these epitopes. We screened the full-length amino-acid sequence of DENV-2 to find potential epitopes using the SYFPEITHI algorithm. Twelve putative HLA-A*2402-binding peptides conserved in hundreds of DENV-2 strains were synthesized, and the HLA restriction of peptides was tested in HLA-A*2402 transgenic mice. Nine peptides (NS4b(228-237), NS2a(73-81), E(298-306), M(141-149), NS4a(96-105), NS4b(159-168), NS5(475-484), NS1(162-171), and NS5(611-620)) induced high levels of peptide-specific IFN-γ-secreting cells in HLA-A*2402 transgenic mice. Apart from IFN-γ, NS4b(228-237-), NS2a(73-81-) and E(298-306)-specific CD8(+) cells produced TNF-α and IL-6 simultaneously, whereas M(141-149-) and NS5(475-484-) CD8(+) cells produced only IL-6. Moreover, splenic mononuclear cells (SMCs) efficiently recognized and killed peptide-pulsed splenocytes. Furthermore, each of nine peptides could be recognized by splenocytes from DENV-2-infected HLA-A*2402 transgenic mice. The SMCs from HLA-A*2402 transgenic mice immunized with nine immunogenic peptides efficiently killed DENV-2-infected splenic monocytes. The present identified epitopes have the potential to be new diagnostic tools for characterization of T-cell immunity in DENV infection and may serve as part of a universal epitope-based vaccine.

Two novel squamous cell carcinoma antigen-derived HLA-A*0201-binding peptides induce in vitro and in vivo CD8+ cytotoxic T lymphocyte responses.

Squamous cell carcinoma antigen (SCCA) is overexpressed in many squamous cell cancers and SCCA­derived peptide-specific CD8(+) cytotoxic T lymphocytes can display cytotoxicity against tumor cells. In the present study, we screened the SCCA amino acid sequence for potential HLA-A*0201-binding CD8(+) T­cell epitopes using two predictive computational algorithms. Seven epitope candidates were selected of which SCCA(246-254)(llpneidgl), SCCA(223-231)(sledvqakv), SCCA(328­336)(vlhkafvev) and SCCA(324­332)(vlsgvlhka) significantly stabilized HLA-A*0201 molecules on T2 cells. Both SCCA(328­336) and SCCA(324-332) induced CD8(+) IFN-γ(+) T­cell responses in HLA-A*0201-positive peripheral blood mononuclear cells as assessed by intracellular cytokine staining. Consistent with this, immunization with either SCCA(328-336) or SCCA(324­332) effectively elicited CD8(+) IFN-γ(+) T cells in HLA-A*0201 transgenic mice as visualized by IFN-γ ELISPOT assay and intracellular cytokine staining. Furthermore, CD8(+) T cells induced in vitro or in vivo by SCCA(328-336) or SCCA(324-332) demonstrated in vitro cytotoxicity against peptide-pulsed T2 cells and splenocytes, respectively. These novel SCCA­derived CD8(+) T­cell epitopes described, herein, may be potentially important components for diagnostic reagents and immunotherapeutic vaccines for the treatment of squamous cell carcinomas.