Improved anaerobic sludge fermentation mediated by a tryptophan-degrading consortium: Effectiveness assessment and mechanism deciphering.

The hydrolysis of extracellular polymeric substances (EPS) represents a critical bottleneck in the anaerobic fermentation of waste activated sludge (WAS), while tryptophan is identified as an underestimated constituent of EPS. Herein, we harnessed a tryptophan-degrading microbial consortium (TDC) to enhance the hydrolysis efficiency of WAS. At TDC dosages of 5%, 10%, and 20%, a notable increase in SCOD was observed by factors of 1.13, 1.39, and 1.88, respectively. The introduction of TDC improved both the yield and quality of short chain fatty acids (SCFAs), the maximum SCFA yield increased from 590.6 to 1820.2, 1957.9 and 2194.9 mg COD/L, whilst the acetate ratio within SCFAs was raised from 34.1% to 61.2-70.9%. Furthermore, as TDC dosage increased, the relative activity of protease exhibited significant increments, reaching 116.3%, 168.0%, and 266.1%, respectively. This enhancement facilitated WAS solubilization and the release of organic substances from bound EPS into soluble EPS. Microbial analysis identified Tetrasphaera and Soehngenia as key participants in WAS solubilization and the breakdown of protein fraction. Metabolic analysis revealed that TDC triggered the secretion of enzymes associated with amino acid metabolism and fatty acid biosynthesis, thereby fostering the decomposition of proteins and production of SCFAs.

Preoperative MRI-Based Radiomics of Brain Metastasis to Assess T790M Resistance Mutation After EGFR-TKI Treatment in NSCLC.

BACKGROUND: Preoperative assessment of the acquired resistance T790M mutation in patients with metastatic non-small cell lung cancer (NSCLC) based on brain metastasis (BM) is important for early treatment decisions. PURPOSE: To investigate preoperative magnetic resonance imaging (MRI)-based radiomics for assessing T790M resistance mutation after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment in NSCLC patients with BM. STUDY TYPE: Retrospective. POPULATION: One hundred and ten primary NSCLC patients with pathologically confirmed BM and T790M mutation status assessment from two centers divided into primary training (N = 53), internal validation (N = 27), and external validation (N = 30) sets. FIELD STRENGTH/SEQUENCE: Contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) fast spin echo sequences at 3.0 T. ASSESSMENT: Forty-five (40.9%) patients were T790M-positive and 65 (59.1%) patients were T790M-negative. The tumor active area (TAA) and peritumoral edema area (POA) of BM were delineated on pre-treatment T1CE and T2W images. Radiomics signatures were built based on features selected from TAA (RS-TAA), POA (RS-POA), and their combination (RS-Com) to assess the T790M resistance mutation after EGFR-TKI treatment. STATISTICAL TESTS: Receiver operating characteristic (ROC) curves were used to assess the capabilities of the developed RSs. The area under the ROC curves (AUC), sensitivity, and specificity were generated as comparison metrics. RESULTS: We identified two features (from TAA) and three features (from POA) that are highly associated with the T790M mutation status. The developed RS-TAA, RS-POA, and RS-Com showed good performance, with AUCs of 0.807, 0.807, and 0.864 in the internal validation, and 0.783, 0.814, and 0.860 in the external validation sets, respectively. DATA CONCLUSION: Pretreatment brain MRI of NSCLC patients with BM might effectively detect the T790M resistance mutation, with both TAA and POA having important values. The multi-region combined radiomics signature may have potential to be a new biomarker for assessing T790M mutation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Brain-Tumor Interface-Based MRI Radiomics Models to Determine EGFR Mutation, Response to EGFR-TKI and T790M Resistance Mutation in Non-Small Cell Lung Carcinoma Brain Metastasis.

BACKGROUND: Preoperative assessment of epidermal growth factor receptor (EGFR) status, response to EGFR-tyrosine kinase inhibitors (TKI) and development of T790M mutation in non-small cell lung carcinoma (NSCLC) patients with brain metastases (BM) is important for clinical decision-making, while previous studies were only based on the whole BM. PURPOSE: To investigate values of brain-to-tumor interface (BTI) for determining the EGFR mutation, response to EGFR-TKI and T790M mutation. STUDY TYPE: Retrospective. POPULATION: Two hundred thirty patients from Hospital 1 (primary cohort) and 80 patients from Hospital 2 (external validation cohort) with BM and histological diagnosis of primary NSCLC, and with known EGFR status (biopsy) and T790M mutation status (gene sequencing). FIELD STRENGTH/SEQUENCE: Contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) fast spin echo sequences at 3.0T MRI. ASSESSMENT: Treatment response to EGFR-TKI therapy was determined by the Response Evaluation Criteria in Solid Tumors. Radiomics features were extracted from the 4 mm thickness BTI and selected by least shrinkage and selection operator regression. The selected BTI features and volume of peritumoral edema (VPE) were combined to construct models using logistic regression. STATISTICAL TESTS: The performance of each radiomics model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: A total of 7, 3, and 3 features were strongly associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status, respectively. The developed models combining BTI features and VPE can improve the performance than those based on BTI features alone, generating AUCs of 0.814, 0.730, and 0.774 for determining the EGFR mutation, response to EGFR-TKI and T790M mutation, respectively, in the external validation cohort. DATA CONCLUSION: BTI features and VPE were associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status in NSCLC patients with BM. EVIDENCE LEVEL: 3 Technical Efficacy: Stage 2.

Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC.

OBJECTIVES: To develop radiomics signatures from multiparametric magnetic resonance imaging (MRI) scans to detect epidermal growth factor receptor (EGFR) mutations and predict the response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM). METHODS: We included 230 NSCLC patients with BM treated at our hospital between January 2017 and December 2021 and 80 patients treated at another hospital between July 2014 and October 2021 to form the primary and external validation cohorts, respectively. All patients underwent contrast-enhanced T1-weighted (T1C) and T2-weighted (T2W) MRI, and radiomics features were extracted from both the tumor active area (TAA) and peritumoral edema area (POA) for each patient. The least absolute shrinkage and selection operator (LASSO) was used to identify the most predictive features. Radiomics signatures (RSs) were constructed using logistic regression analysis. RESULTS: For predicting the EGFR mutation status, the created RS-EGFR-TAA and RS-EGFR- POA showed similar performance. By combination of TAA and POA, the multi-region combined RS (RS-EGFR-Com) achieved the highest prediction performance, with AUCs of 0.896, 0.856, and 0.889 in the primary training, internal validation, and external validation cohort, respectively. For predicting response to EGFR-TKI, the multi-region combined RS (RS-TKI-Com) generated the highest AUCs in the primary training (AUC = 0.817), internal validation (AUC = 0.788), and external validation (AUC = 0.808) cohort, respectively. CONCLUSIONS: Our findings suggested values of multiregional radiomics of BM for predicting EGFR mutations and response to EGFR-TKI. CLINICAL RELEVANCE STATEMENT: The application of radiomic analysis of multiparametric brain MRI has proven to be a promising tool to stratify which patients can benefit from EGFR-TKI therapy and to facilitate the precise therapeutics of NSCLC patients with brain metastases. KEY POINTS: • Multiregional radiomics can improve efficacy in predicting therapeutic response to EGFR-TKI therapy in NSCLC patients with brain metastasis. • The tumor active area (TAA) and peritumoral edema area (POA) may hold complementary information related to the therapeutic response to EGFR-TKI. • The developed multi-region combined radiomics signature achieved the best predictive performance and may be considered as a potential tool for predicting response to EGFR-TKI.

Multiparametric MRI-based radiomics for the prediction of microvascular invasion in hepatocellular carcinoma.

BACKGROUND: Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is essential in obtaining a successful surgical treatment, in decreasing recurrence, and in improving survival. PURPOSE: To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics in the prediction of peritumoral MVI in HCC. MATERIAL AND METHODS: A total of 102 patient with pathologically proven HCC after surgical resection from June 2014 to March 2018 were enrolled in this retrospective study. Histological analysis of resected specimens confirmed positive MVI in 48 patients and negative MVI in 54 patients. Radiomics features were extracted from four MRI sequences and selected with the least absolute shrinkage and selection operator (LASSO) regression and used to analyze the tumoral and peritumoral regions for MVI. Univariate logistic regression was employed to identify the most important clinical factors, which were integrated with the radiomics signature to develop a nomogram. RESULTS: In total, 11 radiomics features were selected and used to build the radiomics signature. The serum level of alpha-fetoprotein was identified as the clinical factor with the highest predictive value. The developed nomogram achieved the highest AUC in predicting MVI status. The decision curve analysis confirmed the potential clinical utility of the proposed nomogram. CONCLUSION: The multiparametric MRI-based radiomics nomogram is a promising tool for the preoperative diagnosis of peritumoral MVI in HCCs and helps determine the appropriate medical or surgical therapy.

Simultaneous partial nitrification, denitrification, and phosphorus removal in sequencing batch reactors via controlled reduced aeration and short-term sludge retention time decrease.

Simultaneous bio-treatment processes of organic carbon (C)-, nitrogen (N)-, and phosphorus (P)-containing wastewater are challenged by insufficient carbon sources in the effluent. In the present study, two parallel anaerobic/aerobic sequencing batch reactors (R-1 and R-2) treating low C/N (≤4) wastewater were employed using different partial nitrification start-up strategies, controlled reduced aeration, and decreased sludge retention time. Advanced removal efficiencies for NH4+-N (≥96%), total nitrogen (TN, ≥86%), PO43--P (≥95%), and CODintra (≥91%) were realized, with TN and PO43--P effluent concentrations of 10.0 ± 3.5 and 0.11 ± 0.3 mg/L in R-1 and 9.28 ± 4.0 and 0.11 ± 0.1 mg/L in R-2, respectively. Higher nitrite accumulation rate (nearly 100%) and TN (121.1 ± 0.7 mg TN/g VSS·d) and P (12.5 ± 0.6 mg PO43--P/g VSS·d) removal loadings were obtained in R-2 by a thorough elimination of nitrite-oxidizing bacteria. Moreover, different microbial structures and nutrient removal pathways were identified. Denitrifying glycogen-accumulating organisms (Candidatus Competibacter) and phosphorus-accumulating organisms (PAOs) (Tetrasphaera) removed N and P with partial nitrification-endogenous denitrification pathways and aerobic P removal in R-1. In R-2, aerobic denitrifying bacteria (Psychrobacter) and PAOs ensured N and P removal through the partial nitrification-aerobic denitrification and aerobic P removal pathways. Compared to R-1, R-2 offers greater efficiency, convenience, and scope to further reduce carbon-source demand.

Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma.

OBJECTIVES: This study aims to explore values of multi-parametric MRI-based radiomics for detecting the epidermal growth factor receptor (EGFR) mutation and resistance (T790M) mutation in lung adenocarcinoma (LA) patients with spinal metastasis. METHODS: This study enrolled a group of 160 LA patients from our hospital (between Jan. 2017 and Feb. 2021) to build a primary cohort. An external cohort was developed with 32 patients from another hospital (between Jan. 2017 and Jan. 2021). All patients underwent spinal MRI (including T1-weighted (T1W) and T2-weighted fat-suppressed (T2FS)) scans. Radiomics features were extracted from the metastasis for each patient and selected to develop radiomics signatures (RSs) for detecting the EGFR and T790M mutations. The clinical-radiomics nomogram models were constructed with RSs and important clinical parameters. The receiver operating characteristics (ROC) curve was used to evaluate the predication capabilities of each model. Calibration and decision curve analyses (DCA) were constructed to verify the performance of the models. RESULTS: For detecting the EGFR and T790M mutation, the developed RSs comprised 9 and 4 most important features, respectively. The constructed nomogram models incorporating RSs and smoking status showed favorite prediction efficacy, with AUCs of 0.849 (Sen = 0.685, Spe = 0.885), 0.828 (Sen = 0.964, Spe = 0.692), and 0.778 (Sen = 0.611, Spe = 0.929) in the training, internal validation, and external validation sets for detecting the EGFR mutation, respectively, and with AUCs of 0.0.842 (Sen = 0.750, Spe = 0.867), 0.823 (Sen = 0.667, Spe = 0.938), and 0.800 (Sen = 0.875, Spe = 0.800) in the training, internal validation, and external validation sets for detecting the T790M mutation, respectively. CONCLUSIONS: Radiomics features from the spinal metastasis were predictive on both EGFR and T790M mutations. The constructed nomogram models can be potentially considered as new markers to guild treatment management in LA patients with spinal metastasis. KEY POINTS: • To our knowledge, this study was the first approach to detect the EGFR T790M mutation based on spinal metastasis in patients with lung adenocarcinoma. • We identified 13 MRI features that were strongly associated with the EGFR T790M mutation. • The proposed nomogram models can be considered as potential new markers for detecting EGFR and T790M mutations based on spinal metastasis.

Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface.

PURPOSE: To evaluate the potential of subregional radiomics as a novel tumor marker in predicting epidermal growth factor receptor (EGFR) mutation status and response to EGFR-tyrosine kinase inhibitor (TKI) therapy in NSCLC patients with brain metastasis (BM). MATERIALS AND METHODS: We included 230 patients from center 1, and 80 patients were included from center 2 to form a primary and external validation cohort, respectively. Patients underwent contrast-enhanced T1-weighted and T2-weighted MRI scans before treatment. The individual- and population-level clustering was used to partition the peritumoral edema area (POA) into phenotypically consistent subregions. Radiomics features were calculated and selected from the tumor active area (TAA), POA and subregions, and used to develop models. Prediction values of each region were investigated and compared with receiver operating characteristic curves and Delong test. RESULTS: For predicting EGFR mutations, a multi-region combined model (EGFR-Fusion) was developed based on joint of the partitioned metastasis/brain parenchyma (M/BP)-interface and TAA, and generated the highest prediction performance in the training (AUC = 0.945, SEN = 0.878, SPE = 0.937), internal validation (AUC = 0.880, SEN = 0.733, SPE = 0.969), and external validation (AUC = 0.895, SEN = 0.875, SPE = 0.800) cohorts. For predicting response to EGFR-TKI, the developed multi-region combined model (TKI-Fusion) yielded predictive AUCs of 0.869 (SEN = 0.717, SPE = 0.884), 0.786 (SEN = 0.708, SPE = 0.818), and 0.802 (SEN = 0.750, SPE = 0.800) in the training, internal validation and external validation cohort, respectively. CONCLUSION: Our study revealed that complementary information regarding the EGFR status and response to EGFR-TKI can be provided by subregional radiomics. The proposed radiomics models may be new markers to guide treatment plans for NSCLC patients with BM.

Volume computed tomography perfusion as a predictive marker for treatment response to concurrent chemoradiotherapy in cervical cancer: a prospective study.

BACKGROUND: Computed tomography perfusion (CTP) can provide information on blood perfusion as a reliable marker of tumor response to therapy. PURPOSE: To assess the role of volume CTP (vCTP) parameters in predicting treatment response to concurrent chemoradiotherapy (CCRT) for cervical cancer. MATERIAL AND METHODS: Thirty-three patients with cervical cancer underwent vCTP. Three CTP parameters of cervical cancer-including arterial flow (AF), blood volume (BV), and permeability surface (PS)-were measured in two different ways: the region of interest incorporating the "local hot" with the highest enhancement and "cold spot" with the lowest enhancement; and "whole-tumor" measurements. The patients were divided into non-residual and residual tumor groups according to the short-term response to treatment. The clinical and perfusion parameters were compared between the two groups. RESULTS: There was no significant difference in age, body mass index, FIGO stage, pathological grade, or pretreatment tumor size between the two groups (P > 0.05). The non-residual tumor group had higher pretreatment AF in high-perfusion and low-perfusion subregions than the residual tumor group (P <0.05), but the AF in whole-tumor regions was not different between the two groups (P > 0.05). There were no differences in BV and PS between the two groups (P > 0.05). The diagnostic potency of AF in the low-perfusion subregion was higher than that in the high-perfusion subregion. CONCLUSION: vCTP parameters are valuable for the prediction of short-term effects. The AF in the low-perfusion subregion was a more effective index for predicting treatment response to CCRT of cervical cancer.

The Role of Multiparametric Magnetic Resonance Imaging in the Study of Primary Tumor and Pelvic Lymph Node Metastasis in Stage IB1-IIA1 Cervical Cancer.

OBJECTIVE: The aim of this study was to investigate the value of multiparametric magnetic resonance imaging (MRI) in demonstrating the metastatic potential of primary tumor and differentiating metastatic lymph nodes (MLNs) from nonmetastatic lymph nodes (non-MLNs) in stage IB1-IIA1 cervical cancer. METHODS: Fifty-seven stage IB1-IIA1 subjects were included. The apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced MRI (DCE-MRI) parameters of primary tumors and lymph nodes and the conventional imaging features of the lymph nodes were measured and analyzed. Mann-Whitney test and χ test were used to analyze statistically significant parameters, logistic regression was used for multivariate analysis, and receiver operating characteristic analysis was used to compare the diagnostic performance of the MLNs. RESULTS: Nineteen subjects had lymph node metastasis. A total of 94 lymph nodes were evaluated, including 30 MLNs and 64 non-MLNs. There were no significant difference in ADC and DCE-MRI parameters between metastatic and nonmetastatic primary tumors. The heterogeneous signal was more commonly seen in MLNs than in non-MLNs (P = 0.001). The values of ADCmean, ADCmin, and ADCmax of MLNs were lower than those of non-MLNs (P < 0.001). The values of short-axis diameter, K, Kep, and Ve of MLNs were higher than those of non-MLNs (P < 0.05). Compared with individual MRI parameters, the combined evaluation of short-axis diameter, ADCmean, and K showed the highest area under the curve of 0.930. CONCLUSIONS: Diffusion-weighted imaging and DCE-MRI could not demonstrate the metastatic potential of primary tumor in stage IB1-IIA1 cervical cancer. Compared with individual MRI parameters, the combination of multiparametric MRI could improve the diagnostic performance of lymph node metastasis.

Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer.

BACKGROUND: Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM. PURPOSE: To evaluate a radiomic signature of LN involvement based on sagittal T1 contrast-enhanced (CE) and T2 MRI sequences. STUDY TYPE: Retrospective. POPULATION: In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort. FIELD STRENGTH/SEQUENCE: T1 CE and T2 MRI sequences at 3T. ASSESSMENT: The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups. STATISTICAL TESTS: A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature. RESULTS: The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort. DATA CONCLUSIONS: A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.

Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer.

OBJECTIVE: To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. METHODS: Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram. RESULTS: The radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79). CONCLUSIONS: We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients. KEY POINTS: • ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.

A Retrospective Study of the Imaging and Pathological Features of Metaplastic Breast Carcinoma and Review of the Literature.

BACKGROUND Metaplastic breast cancer (MBC) is a rare type of breast cancer, characterized histologically by the presence of two or more malignant cell types (epithelial and mesenchymal). This retrospective study aimed to review the imaging and histological features of MBC, with a review of the literature. MATERIAL AND METHODS Nineteen patients with MBC (age range, 28-75 years; mean, 55 years) underwent review of their clinical records, histopathology, immunohistochemistry, and imaging findings, which included mammography, sonography, and magnetic resonance imaging (MRI) with T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and diffusion restriction determined by the apparent diffusion coefficient (ADC) and a time-intensity curve (TIC) for signal intensity. RESULTS The mammographic features of MBC were oval shaped (54.5%), with indistinct margin (45.5%), and high tumor density (72.7%), and on sonography, they were oval shaped (57.1%), with hypo-echogenic areas (85.8%). On MRI, MBC showed moderate hyper-intensity with a high signal intensity in the center of the tumor on T2WI (100%), an indistinct margin (75.0%), and rim enhancement (58.3%). Using a TIC, the early phase showed rapid enhancement, and the delay phase showed a signal plateau (91.7%). DWI showed diffusion restriction in all cases determined by the ADC. Immunohistochemistry showed negative expression of estrogen receptor (ER) (91.0%), progesterone receptor (PR) (81%), and HER2 (erbB-2) (80.0%). CONCLUSIONS Imaging features of MBC on mammography and ultrasound were benign. The use of T2WI MRI showed characteristic features of signal intensity using TIC curve and ADC analysis, which may support biopsy and histological analysis for definitive diagnosis.

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.

Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists, where there is a notable inter- and intra-reader variability. In order to maintain consistency and accuracy in BI-RADS-based breast density assessment, gaining understanding on radiologists' reading behaviors will be educational. In this study, we proposed to leverage the newly emerged deep learning approach to investigate how the MLO and CC view images of a mammogram examination may have been clinically used by radiologists in coming up with a BI-RADS density category. We implemented a convolutional neural network (CNN)-based deep learning model, aimed at distinguishing the breast density categories using a large (15,415 images) set of real-world clinical mammogram images. Our results showed that the classification of density categories (in terms of area under the receiver operating characteristic curve) using MLO view images is significantly higher than that using the CC view. This indicates that most likely it is the MLO view that the radiologists have predominately used to determine the breast density BI-RADS categories. Our study holds a potential to further interpret radiologists' reading characteristics, enhance personalized clinical training to radiologists, and ultimately reduce reader variations in breast density assessment.

Sensitivity and Specificity of Diffusion-Weighted Magnetic Resonance Imaging in Diagnosis of Bladder Cancers.

PURPOSE: Our study is designed to examine the diagnostic performance of diffusion-weighted magnetic resonance imaging (DW-MRI) for bladder cancers (BC), and to determine whether DW-MRI can differentiate muscle invasive bladder cancer (MIBC) from non-MIBC (NMIBC). METHODS: A meta-analysis was performed of published studies that investigated the performance of DW-MRI for BC. These studies were retrieved from scientific literature databases using sensitive electronic search strategies. The STATA 12.0 and Meta-disc software were employed for statistical analyses of data extracted from selected studies. RESULTS: Our search initially returned 230 articles, of which 11 met the inclusion criteria and were enrolled into the final meta-analysis. Five of the included studies reported the diagnostic performance of DW-MRI for BC with a cumulative total of 243 BC patients and 82 healthy subjects. Eight studies investigated the diagnostic performance of DW-MRI for differentiating MIBC from NMIBC, involving 259 MIBC lesions and 515 NMIBC lesions. Meta-analysis results were as follows: the diagnostic performance of DW-MRI for BC (sensitivity: 0.95 [0.75-0.99]; specificity: 0.85 [0.74-0.92]; positive likelihood ratio: 6.45 [3.64-11.42]; negative likelihood ratio: 0.055 [0.009-0.333]; diagnostic odds ratio: 117.11 [19.37-708.05]; area under the curve (AUC): 0.91); the diagnostic performance of DW-MRI to differentiate MIBC from NMIBC (sensitivity: 0.85 [0.76 - 0.91]; specificity: 0.90 [0.87 - 0.93]; positive likelihood ratio:8.81[6.43 - 12.07]; negative likelihood ratio: 0.16 [0.10 - 0.28]; diagnostic odds ratio: 53.95 [25.68 - 113.33]; AUC: 0.92). CONCLUSION: DW-MRI has an outstanding diagnostic performance, with advanced sensitivity and specificity, for imaging of bladder cancers and for differentiating MIBC from NMIBC.

Prediction of T stage in gastric carcinoma by enhanced CT and oral contrast-enhanced ultrasonography.

BACKGROUND: The aim of this study is to explore the values of enhanced CT and oral contrast-enhanced ultrasonography on preoperative T stage in gastric carcinoma. METHODS: Forty patients with gastric carcinoma, including 27 males and 13 females, were confirmed by endoscopy, operation, and pathology. The median age of these patients was 49 years old (25 to 73 years). There were 19 cases of well-differentiated adenocarcinoma, 13 cases of poorly differentiated adenocarcinoma, 5 cases of signet ring cell carcinoma, and 4 cases of mucinous adenocarcinoma by pathology. All these patients were examined by both enhanced CT and ultrasound examination simultaneously 1 week before surgery. T staging in all these gastric carcinomas was carried out by enhanced CT or oral contrast-enhanced ultrasonography, respectively, or by both of them. The statistical difference between T stage by imaging and pathological T stage was analyzed. RESULTS: In this study, there were 5 cases with T1 stage, 9 cases with T2 stage, 20 cases with T3 stage, and 6 cases with T4 stage by pathology; 5 cases with T1 stage, 7 cases with T2 stage, 22 cases with T3 stage, and 6 cases with T4 stage by enhanced CT imaging with an accuracy of 75.00%; 6 cases with T1 stage, 7 cases with T2 stage, 22 cases with T3 stage, and 5 cases with T4 stage by ultrasonography examination, with an accuracy of 77.50%; and 4 cases with T1 stage, 10 cases with T2 stage, 19 cases with T3 stage, and 7 cases with T4 stage by both enhanced CT imaging and ultrasonography examination, with an accuracy of 85.00%. The accuracy of T staging in gastric carcinoma by both enhanced CT and ultrasound was higher than that either by enhanced CT or by ultrasound, respectively (P < 0.05). The anastomosis degree of the gastric carcinoma between enhanced CT and ultrasonography was κ = 0.404. CONCLUSIONS: Combination diagnosis of enhanced CT and oral contrast-enhanced ultrasonography is helpful to improve the accuracy of T staging of gastric carcinoma before operations.

Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study.

PURPOSE: To investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS). METHODS: In total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models. RESULTS: The margin was considered as an independent predictor (p < 0.05). A total of 4 MRI features were selected and used to develop the radiomics signature. By incorporating the margin and radiomics signature, the developed nomogram showed the best prediction performance in the training (AUCs, margin vs. radiomics signature vs. nomogram, 0.609 vs. 0.909 vs. 0.910) and validation (AUCs, margin vs. radiomics signature vs. nomogram, 0.666 vs. 0.841 vs. 0.894) cohorts. DCA indicated potential usefulness of the nomogram model. CONCLUSIONS: This feasibility study evaluated predictive values of multi-parametric MRI for the prediction of lung metastasis, and proposed a nomogram model to potentially facilitate the individualized treatment decision-making for STSs.

Construction of a high-density genetic map based on large-scale marker development in Coix lacryma-jobi L. using specific-locus amplified fragment sequencing (slaf-seq).

Coix lacryma-jobi L. is one of the most economically and medicinally important corns. This study constructed a high-density genetic linkage map of C. lacryma-jobi based on a cross between the parents 'Qianyi No. 2' × 'Wenyi No. 2' and their F2 progeny through high-throughput sequencing and the construction of a specific-locus amplified fragment (SLAF) library. After pre-processing, 325.49 GB of raw data containing 1628 M reads were obtained. A total of 22,944 high-quality SLAFs were identified, among which 3952 SLAFs and 3646 polymorphic markers met the requirements for the construction of a genetic linkage map. The integrated map contained 3605 high-quality SLAFs, which were grouped into ten genetic linkage groups. The total length of the map was 1620.39 cM, with an average distance of 0.45 cM and an average of 360.5 markers per linkage group. This report presents the first high-density genetic map of C. lacryma-jobi. This map was constructed using an F2 population and SLAF-seq approach, which allows the development of a large number of polymorphic markers in a short period. These results provide a platform for precise gene/quantitative trait locus (QTL) mapping, map-based gene separation, and molecular breeding in C. lacryma-jobi. They also help identify a target gene for tracking, splitting quantitative traits, and estimating the phenotypic effects of each QTL for QTL mapping. They are of great significance for improving the efficiency of discovering and utilizing excellent gene resources of C. lacryma-jobi.

GSTT1 null genotype contributes to increased risk of gastric cancer in Chinese population: evidence from a meta-analysis.

Previous studies suggested glutathione S-transferase T1 (GSTT1) null genotype might be a candidate genetic polymorphism with a role in the susceptibility to gastric cancer, but studies form Chinese population reported controversial findings. Thus, a meta-analysis was performed to clarify the effect of GSTT1 null genotype on gastric cancer risk in Chinese population. Eligible studies were searched in Medline, Embase, and China National Knowledge Infrastructure databases. Between-study heterogeneity was assessed using the I (2) statistic. Odds ratios (OR) with the corresponding 95 % confidence intervals (95 % CI) were pooled to assess the association. Twenty case-control studies involving a total of 3,204 gastric cancer cases and 5,462 controls were finally included in the meta-analysis. Meta-analysis of all 20 studies showed that GSTT1 null genotype was associated with an elevated risk of gastric cancer in Chinese population (OR=1.26, 95 % CI 1.09-1.46, P OR=0.002). The cumulative meta-analysis showed a trend of a more obvious association between GSTT1 null genotype and risk of gastric cancer in Chinese population as information accumulated gradually. Sensitivity analysis by omitting individual study, in turns, did not materially alter the pooled ORs. This meta-analysis provides a strong evidence for the significant association between GSTT1 null genotype and gastric cancer risk in Chinese population, and GSTT1 null genotype contributes to increased risk of gastric cancer.