Research advances in microfluidic collection and detection of virus, bacterial, and fungal bioaerosols.

Bioaerosols are airborne suspensions of fine solid or liquid particles containing biological substances such as viruses, bacteria, cellular debris, fungal spores, mycelium, and byproducts of microbial metabolism. The global Coronavirus disease 2019 (COVID-19) pandemic and the previous emergence of severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and influenza have increased the need for reliable and effective monitoring tools for bioaerosols. Bioaerosol collection and detection have aroused considerable attention. Current bioaerosol sampling and detection techniques suffer from long response time, low sensitivity, and high costs, and these drawbacks have forced the development of novel monitoring strategies. Microfluidic technique is considered a breakthrough for high performance analysis of bioaerosols. In recent years, several emerging methods based on microfluidics have been developed and reported for collection and detection of bioaerosols. The unique advantages of microfluidic technique have enabled the integration of bioaerosol collection and detection, which has a higher efficiency over conventional methods. This review focused on the research progress of bioaerosol collection and detection methods based on microfluidic techniques, with special attention on virus aerosols and bacterial aerosols. Different from the existing reviews, this work took a unique perspective of the targets to be collected and detected in bioaerosols, which would provide a direct index of bioaerosol categories readers may be interested in. We also discussed integrated microfluidic monitoring system for bioaerosols. Additionally, the application of bioaerosol detection in biomedicine was presented. Finally, the current challenges in the field of bioaerosol monitoring are presented and an outlook given of future developments.

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.

Development and validation of MRI-based radiomics signatures as new markers for preoperative assessment of EGFR mutation and subtypes from bone metastases.

BACKGROUND: This study aimed to develop and externally validate contrast-enhanced (CE) T1-weighted MRI-based radiomics for the identification of epidermal growth factor receptor (EGFR) mutation, exon-19 deletion and exon-21 L858R mutation from MR imaging of spinal bone metastasis from primary lung adenocarcinoma. METHODS: A total of 159 patients from our hospital between January 2017 and September 2021 formed a primary set, and 24 patients from another center between January 2017 and October 2021 formed an independent validation set. Radiomics features were extracted from the CET1 MRI using the Pyradiomics method. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting the most predictive features. Radiomics signatures (RSs) were developed based on the primary training set to predict EGFR mutations and differentiate between exon-19 deletion and exon-21 L858R. The RSs were validated on the internal and external validation sets using the Receiver Operating Characteristic (ROC) curve analysis. RESULTS: Eight, three, and five most predictive features were selected to build RS-EGFR, RS-19, and RS-21 for predicting EGFR mutation, exon-19 deletion and exon-21 L858R, respectively. The RSs generated favorable prediction efficacies for the primary (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.851 vs. 0.816 vs. 0.814) and external validation (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.807 vs. 0.742 vs. 0.792) sets. CONCLUSIONS: Radiomics features from the CE MRI could be used to detect the EGFR mutation, increasing the certainty of identifying exon-19 deletion and exon-21 L858R mutations based on spinal metastasis MR imaging.

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.

Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma.

BACKGROUND: Preoperative prediction of epidermal growth factor receptor (EGFR) mutation status in patients with spinal bone metastases (SBM) from primary lung adenocarcinoma is potentially important for treatment decisions. PURPOSE: To develop and validate multiparametric magnetic resonance imaging (MRI)-based radiomics methods for preoperative prediction of EGFR mutation based on MRI of SBM. STUDY TYPE: Retrospective. POPULATION: A total of 97 preoperative patients with lumbar SBM from lung adenocarcinoma (77 in training set and 20 in validation set). FIELD STRENGTH/SEQUENCE: T1-weighted, T2-weighted, and T2-weighted fat-suppressed fast spin echo sequences at 3.0 T. ASSESSMENT: Radiomics handcrafted and deep learning-based features were extracted and selected from each MRI sequence. The abilities of the features to predict EGFR mutation status were analyzed and compared. A radiomics nomogram was constructed integrating the selected features. STATISTICAL TESTS: The Mann-Whitney U test and χ2 test were employed for evaluating associations between clinical characteristics and EGFR mutation status for continuous and discrete variables, respectively. Least absolute shrinkage and selection operator was used for selection of predictive features. Sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC) were used to evaluate the ability of radiomics models to predict the EGFR mutation. Calibration and decision curve analysis (DCA) were performed to assess and validate nomogram results. RESULTS: The radiomics signature comprised five handcrafted and one deep learning-based features and achieved good performance for predicting EGFR mutation status, with AUCs of 0.891 (95% confidence interval [CI], 0.820-0.962, SEN = 0.913, SPE = 0.710) in the training group and 0.771 (95% CI, 0.551-0.991, SEN = 0.750, SPE = 0.875) in the validation group. DCA confirmed the potential clinical usefulness of the radiomics models. DATA CONCLUSION: Multiparametric MRI-based radiomics is potentially clinical valuable for predicting EGFR mutation status in patients with SBM from lung adenocarcinoma. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: 2.

Associations of psychological distress with positive psychological variables and activities of daily living among stroke patients: a cross-sectional study.

BACKGROUND: Depression and anxiety result in psychological distress, which can further affect mental status and quality of life in stroke patients. Exploring the associations between positive psychological variables and symptoms of psychological distress following stroke is of great significance for further psychological interventions. METHODS: A total of 710 stroke patients from the five largest cities in Liaoning Province in China were enrolled into the present study in July 2014. All patients independently completed the questionnaires with respect to psychological distress and positive psychological variables. Depressive and anxiety symptoms were evaluated using Center for Epidemiologic Studies Depression Scale (CES-D) and Self-Rating Anxiety Scale, respectively. Positive psychological variables were evaluated using Perceived Social Support Scale, Adult Hope Scale (AHS), General Perceived Self-Efficacy Scale and Resilience Scale-14 (RS-14). Activities of Daily Living (ADL) was measured using Barthel Index. Factors associated with psychological variables and depressive and anxiety symptoms were identified using t-test, ANOVA, correlation and hierarchical linear regression analysis. RESULTS: Depressive and anxiety symptoms were present in 600 of 710 (84.51%) and 537 of 710 (75.63%) stroke patients enrolled, respectively. Social support (ß = - 0.111, p < 0.001) and hope (ß = - 0.120, p < 0.001) were negatively associated with both depressive and anxiety symptoms. Resilience (ß = - 0.179, p < 0.001) was negatively associated with depressive symptoms. Self-efficacy (ß = - 0.135, p < 0.001) was negatively associated with anxiety symptoms. Hierarchical regression analyses indicated that ADL accounted for 10.0 and 6.0% of the variance of depressive and anxiety symptoms, respectively. Social support, resilience, self-efficacy and hope as a whole accounted for 7.5 and 5.3% of the variance of depressive and anxiety symptoms. CONCLUSIONS: The high frequency of depressive and anxiety symptoms among Chinese stroke survivors should receive attentions from all stakeholders. Findings suggested that intervention strategies on ADL, social support, hope, resilience and self-efficacy could be developed to improve psychosocial outcomes for stroke survivors.

Microfluidic platform for direct capture and analysis of airborne Mycobacterium tuberculosis.

Airborne Mycobacterium tuberculosis is the main source of tuberculosis infection, which is known as one of the worldwide infectious diseases. Direct capture and analysis of airborne Mycobacterium tuberculosis is essential for disease prevention and control. At present, low concentration of pathogens directly collected from the air is the major drawback for rapid analysis. Herein an integrated microfluidic system capable of airborne Mycobacterium tuberculosis capture, enrichment, and rapid bacteriological immunoassay was developed. The whole detection time was decreased to less than 50 min including 20 min of enrichment and 30 min of immunoreaction analysis. It had the advantages of low detection limit, fast detection speed, and low reagent consumption compared with conventional techniques, showing the potential to become a new airborne pathogen analysis platform.

Structural and functional characterization of the actin-1 gene promoter from the Antheraea pernyi (Lepidoptera: Saturniidae).

The Chinese oak silkworm, Antheraea pernyi, is an economically important insect of the Saturniidae family. In this study, genome walking was performed to obtain an A. pernyi actin promoter, which can be employed in transgenic or stable cell line expression systems. The putative promoter was analyzed by the online promoter analysis programs at the Berkeley Drosophila Genome Project and the Web Promoter Scan Service, which led to the recognition of several functional elements. With respect to these elements, a series of actin A1 promoter fragments with 5'-deletions were generated that were then used to construct different vectors expressing Green Fluorescent Protein (GFP). The plasmids were transfected into Sf9 cells and GFP expression was determined by observing GFP fluorescence in cells and by measuring GFP mRNA levels with real-time polymerase chain reaction. Sequence comparisons indicated that the sequence cloned from A. pernyi was the actin A1 promoter. The basic function of the promoter was verified by constructing expression vectors and observing GFP expression. In addition, real-time polymerase chain reaction revealed a strong inhibitory element may exist upstream of the TATA box, which downregulated gene expression. The actin A1 promoter is an ideal candidate for use in A. pernyi transgenic systems.

Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (EGFR) mutation and subtypes in metastatic non-small cell lung cancer.

Background: The preoperative identification of epidermal growth factor receptor (EGFR) mutations and subtypes based on magnetic resonance imaging (MRI) of brain metastases (BM) is necessary to facilitate individualized therapy. This study aimed to develop a deep learning model to preoperatively detect EGFR mutations and identify the location of EGFR mutations in patients with non-small cell lung cancer (NSCLC) and BM. Methods: We included 160 and 72 patients who underwent contrast-enhanced T1-weighted (T1w-CE) and T2-weighted (T2W) MRI at Liaoning Cancer Hospital and Institute (center 1) and Shengjing Hospital of China Medical University (center 2) to form a training cohort and an external validation cohort, respectively. A multiscale feature fusion network (MSF-Net) was developed by adaptively integrating features based on different stages of residual network (ResNet) 50 and by introducing channel and spatial attention modules. The external validation set from center 2 was used to assess the performance of MSF-Net and to compare it with that of handcrafted radiomics features. Receiver operating characteristic (ROC) curves, accuracy, precision, recall, and F1-score were used to evaluate the effectiveness of the models. Gradient-weighted class activation mapping (Grad-CAM) was used to demonstrate the attention of the MSF-Net model. Results: The developed MSF-Net generated a better diagnostic performance than did the handcrafted radiomics in terms of the microaveraged area under the curve (AUC) (MSF-Net: 0.91; radiomics: 0.80) and macroaveraged AUC (MSF-Net: 0.90; radiomics: 0.81) for predicting EGFR mutations and subtypes. Conclusions: This study provides an end-to-end and noninvasive imaging tool for the preoperative prediction of EGFR mutation status and subtypes based on BM, which may be helpful for facilitating individualized clinical treatment plans.

A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.

Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges. A self-supervised rotation loss was designed to mitigate discriminator forgetting. The proposed model achieved Dice coefficients of 70.92, 73.55, and 68.52% on multi-center pneumonia, lung nodule, and tuberculosis test datasets, respectively. No significant decrease in accuracy was observed (p > 0.10) when a small training sample size was used. The cyclic training of the discriminator was reduced with self-supervised rotation loss (p < 0.01). The proposed approach is promising for segmenting multiple lung lesion types in CT images.

Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer.

This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's t-test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.

Digital breast tomosynthesis-based peritumoral radiomics approaches in the differentiation of benign and malignant breast lesions.

PURPOSE We aimed to evaluate digital breast tomosynthesis (DBT)-based radiomics in the differentiation of benign and malignant breast lesions in women. METHODS A total of 185 patients who underwent DBT scans were enrolled between December 2017 and June 2019. The features of handcrafted and deep learning-based radiomics were extracted from the tumoral and peritumoral regions with different radial dilation distances outside the tumor. A 3-step method was used to select discriminative features and develop the radiomics signature. Discriminative clinical factors were identified by univariate logistic regression. The clinical fac- tors with P < .05 were used to build a clinical model with multivariate logistic regression. The radiomics nomogram was developed by integrating the radiomics signature and discriminative clinical factors. Discriminative performance of the radiomics signature, clinical model, nomo- gram, and breast imaging reporting and data system assessment were evaluated and compared with the receiver operating characteristic and decision curves analysis (DCA). RESULTS A total of 2 handcrafted and 2 deep features were identified as the most discriminative features from the peritumoral regions with 2 mm dilation distances and used to develop the radiomics signature. The nomogram incorporating the radiomics signature, age, and menstruation status showed the best discriminative performance with area under the curve (AUC) values of 0.980 (95% CI, 0.960 to 1.000; sensitivity =0.970, specificity =0.946) in the training cohort and 0.985 (95% CI, 0.960 to 1.000; sensitivity = 0.909, specificity = 0.966) in the validation cohort. DCA con- firmed the potential clinical usefulness of our nomogram. CONCLUSION Our results illustrate that the radiomics nomogram integrating the DBT imaging features and clinical factors (age and menstruation status) can be considered as a useful tool in aiding the clinical diagnosis of breast cancer.

Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI.

PURPOSE: This study aimed to investigate the efficacy of digital mammography (DM), digital breast tomosynthesis (DBT), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI separately and combined in the prediction of molecular subtypes of breast cancer. METHODS: A total of 241 patients were enrolled and underwent breast MD, DBT, DW and DCE scans. Radiomics features were calculated from intra- and peritumoral regions, and selected with least absolute shrinkage and selection operator (LASSO) regression to develop radiomics signatures (RSs). Prediction performance of intra- and peritumoral regions in the four modalities were evaluated and compared with area under the receiver-operating characteristic (ROC) curve (AUC), specificity and sensitivity as comparison metrics. RESULTS: The RSs derived from combined intra- and peritumoral regions improved prediction AUCs compared with those from intra- or peritumoral regions alone. DM plus DBT generated better AUCs than the DW plus DCE on predicting Luminal A and Luminal B in the training (Luminal A: 0.859 and 0.805; Luminal B: 0.773 and 0.747) and validation (Luminal A: 0.906 and 0.853; Luminal B: 0.807 and 0.784) cohort. For the prediction of HER2-enriched and TN, the DW plus DCE yielded better AUCs than the DM plus DBT in the training (HER2-enriched: 0.954 and 0.857; TN: 0.877 and 0.802) and validation (HER2-enriched: 0.974 and 0.907; TN: 0.938 and 0.874) cohort. CONCLUSIONS: Peritumoral regions can provide complementary information to intratumoral regions for the prediction of molecular subtypes. Compared with MRI, the mammography showed higher AUCs for the prediction of Luminal A and B, but lower AUCs for the prediction of HER2-enriched and TN.

Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer.

PURPOSE This retrospective study aims to evaluate the use of multi-parametric magnetic resonance imaging (MRI) in predicting lymph-vascular space invasion (LVSI) in early-stage cervical cancer using radiomics methods. METHODS A total of 163 patients who underwent contrast-enhanced T1-weighted (CE T1W) and T2-weighted (T2W) MRI scans at 3.0T were enrolled between January 2014 and September 2019. Radiomics features were extracted and selected from the tumoral and peritumoral regions at different dilation distances outside the tumor. Mann-Whitney U test, the least absolute shrinkage and selection operator logistic regression, and logistic regression was applied to select the predictive features and develop the radiomics signature. Univariate analysis was performed on the clinical characteristics. The radiomics nomogram was constructed incorporating the radiomics signature and the selected important clinical predictor. Prediction performance of the radiomics signature, clinical model, and nomogram was evaluated with the area under the curve (AUC), specificity, sensitivity, calibration, and decision curve analysis (DCA). RESULTS A total of 5 features that were selected from the peritumoral regions with 3- and 7-mm dilation distances outside tumors in CE T1W and T2W MRI, respectively, showed optimal discriminative performance. The radiomics signature comprising the selected features was significantly associated with the LVSI status. The radiomics nomogram integrating the radiomics signature and degree of cellular differentiation exhibited the best predictability with AUCs of 0.771 (specificity (SPE)=0.831 and sensitivity (SEN)=0.581) in the training cohort and 0.788 (SPE=0.727, SEN=0.773) in the validation cohort. DCA confirmed the clinical usefulness of our model. CONCLUSION Our results illustrate that the radiomics nomogram based on MRI features from peritumoral regions and the degree of cellular differentiation can be used as a noninvasive tool for predicting LVSI in cervical cancer.

Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study.

PURPOSE: To noninvasively evaluate the use of intratumoral and peritumoral regions from full-field digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) magnetic resonance imaging (MRI) images separately and combined to predict the Ki-67 level based on radiomics. PROCEDURES: A total of 209 patients with pathologically confirmed breast cancer were consecutively enrolled from September 2017 to March 2021, who underwent DM, DBT, DCE-MRI, and DW MRI scans. Radiomics features were calculated from intratumoral and peritumoral regions in each modality and selected with the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures (RSs) were built based on intratumoral, peritumoral, and combined intra- and peritumoral regions. The prediction performance of the RSs was evaluated using the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity as comparison metrics. A nomogram was constructed by integrating the multi-model RS and important clinical predictors and assessed by calibration and decision curve analysis. RESULTS: The combined intra- and peritumoral RSs improved the AUC compared with intra- or peritumoral RSs in each modality. The DCE plus DW MRI yielded higher AUC and specificity but lower sensitivity compared with the DM plus DBT. The nomogram incorporating the multi-model RS, age, and lymph node metastasis status achieved the best prediction performance in the training (AUC, nomogram vs. fusion RS vs. clinical model, 0.922 vs. 0.917 vs. 0.672) and validation (AUCs, nomogram vs. fusion RS vs. clinical model, 0.866 vs. 0.838 vs. 0.661) cohorts. DCA analysis confirmed the potential clinical utility of the nomogram. CONCLUSIONS: Peritumoral regions can provide complementary information to intratumoral regions in mammography and MRI for the prediction of Ki-67 levels. The MRI performed better than mammography in terms of AUC and specificity but weaker in sensitivity. The nomogram has a predictive advantage over each modality and could be a potential tool for predicting Ki-67 levels in breast cancer.