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.

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.

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.

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.

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.

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.

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.

LWMA-Net: Light-weighted morphology attention learning for human embryo grading.

Visual inspection of embryo morphology is routinely used in embryo assessment and selection. However, due to the complexity of morphologies and large inter- and intra-observer variances among embryologists, manual evaluations remain to be subjective and time-consuming. Thus, we proposed a light-weighted morphology attention learning network (LWMA-Net) for automatic assistance on embryo grading. The LWMA-Net integrated a morphology attention module (MAM) to seek the informative features and their locations and a multiscale fusion module (MFM) to increase the features flowing in the model. The LWMA-Net was trained with a primary set of 3599 embryos from 2318 couples that were clinically enrolled between Sep. 2016 and Dec. 2018, and generated area under the receiver operating characteristic curves (AUCs) of 96.88% and 97.58% on 4- and 3-category gradings, respectively. An independent test set comprises 691 embryos from 321 couples between Jan. 2019 and Jan. 2021 were used to test the assisted fertility values on the embryo grading. Five experienced embryologists were invited to regrade the embryos in the independent set with and without the aid of the LWMA-Net three months apart. Embryologists aided by our LWMA-Net significantly improved their grading capabilities with average AUCs improved by 4.98%-5.32% on 4- and 3-category grading tasks, respectively, which suggests good potential of our LWMA-Net on assisted human reproduction.

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.

A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor.

Purpose: To propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis. Methods: We retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n = 60), breast cancer (BC, n = 60) and other tumor types (n = 20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2). Results: The brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set. Conclusion: Our proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.

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.

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.

MRI-based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma.

BACKGROUND: Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment. OBJECTIVE: To determine the predictive value of a magnetic resonance imaging (MRI)-based radiomics nomogram for the presence of DMI in the International Federation of Gynecology and Obstetrics (FIGO) stage I EC. METHODS: We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (Center 1) and a validation group (Center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator, and logistic regression analysis with Akaike information criterion to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULT: The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram. CONCLUSION: A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.

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.

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.

Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor.

PURPOSE: To develop and validate a multiparametric magnetic resonance imaging-based radiomics nomogram for differentiating malignant and benign soft-tissue tumors. METHODS: A total of 91 patients with pathologically confirmed soft-tissue tumors were enrolled between January 2017 and October 2020. Forty-eight patients were consecutively enrolled between November 2020 and March 2022, as a time-independent cohort. All patients underwent contrast-enhanced T1-weighted and T2-weighted fat-suppression magnetic resonance scans at 3.0 T. Radiomics features were extracted and selected from the two modalities to develop the radiomics signature. Significant clinical/morphological characteristics were identified using a multivariate logistic regression analysis. The least absolute shrinkage and selection operator regression were applied to identify discriminative features. A clinical-radiomics nomogram was constructed based on clinical/morphological characteristics and radiomics features. Finally, the performance of the nomogram was validated using the receiver operating characteristic and decision curve analysis (DCA). RESULTS: Six features were selected to establish the combined RS. Size, margin, and peritumoral edema were identified as the most important clinical and morphological factors, respectively. The radiomics signature outperformed the clinical model in terms of AUC and sensitivity. The nomogram integrating the combined RS, size, margin, and peritumoral edema achieved favorable predictive efficacy, generating AUCs of 0.954 (95% confidence interval [CI]: 0.907-1.000, Sen = 0.861, Spe = 0.917), 0.962 (95% CI: 0.901-1.000, Sen = 0.944, Spe = 0.923), and 0.935 (95% CI: 0.871-0.998, Sen = 0.815, Spe = 0.952) in the training (n = 60), validation (n = 31) and time-independent (n = 48) cohorts, respectively. The DCA curve indicated good clinical usefulness of the nomogram. CONCLUSIONS: Our study demonstrated the clinical potential of multiparametric MRI-based radiomics in distinguishing malignant from benign soft-tissue tumors, which can be considered as a noninvasive tool for individual treatment management.

Radiomics evaluates the EGFR mutation status from the brain metastasis: a multi-center study.

Objective.To develop and externally validate habitat-based MRI radiomics for preoperative prediction of the EGFR mutation status based on brain metastasis (BM) from primary lung adenocarcinoma (LA).Approach.We retrospectively reviewed 150 and 38 patients from hospital 1 and hospital 2 between January 2017 and December 2021 to form a primary and an external validation cohort, respectively. Radiomics features were calculated from the whole tumor (W), tumor active area (TAA) and peritumoral oedema area (POA) in the contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI image. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures (RSs) based on W (RS-W), TAA (RS-TAA), POA (RS-POA) and in combination (RS-Com). The area under receiver operating characteristic curve (AUC) and accuracy analysis were performed to assess the performance of radiomics models.Main results.RS-TAA and RS-POA outperformed RS-W in terms of AUC, ACC and sensitivity. The multi-region combined RS-Com showed the best prediction performance in the primary validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.901 versus 0.699 versus 0.812 versus 0.883) and external validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.900 versus 0.637 versus 0.814 versus 0.842) cohort.Significance.The developed habitat-based radiomics models can accurately detect the EGFR mutation in patients with BM from primary LA, and may provide a preoperative basis for personal treatment planning.

MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study.

PURPOSE: To evaluate intra- and preitumoral radiomics on the contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for potential clinical uses. METHODS: We enrolled 169 cervical cancer cases who underwent CE-T1 and T2W MR scans from two hospitals between Dec. 2015 and Sep. 2021. Intra- and peritumoral features were extracted separately and selected by the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures were built using the selected features from different regions. Clinical parameters were evaluated by statistical analysis. The nomogram was developed combining the multi-regional radiomics signature and the most predictive clinical parameters. RESULTS: Five radiomics features were finally selected from the peritumoral regions with 1 and 3 mm distances in the CE-T1 and T2W MRI, respectively. The nomogram incorporating multi-regional combined radiomics signature, MR-reported LN status and tumor diameter achieved the highest AUCs in the training (nomogram vs. combined radiomics signature vs. clinical model, 0.891 vs. 0.830 vs. 0.812), internal validation (nomogram vs. combined radiomics signature vs. clinical model, 0.863 vs. 0.853 vs. 0.816) and external validation (nomogram vs. combined radiomics signature vs. clinical model, 0.804 vs. 0.701 vs. 0.787) cohort. DCA suggested good clinical usefulness of our developed models. CONCLUSION: The current work suggested clinical potential for intra- and peritumoral radiomics with multi-modal MRI for preoperative predicting LNM.

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study.

PURPOSE: To non-invasively evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. METHODS: A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual- and population-level clustering. Handcrafted radiomics and deep learning-based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. RESULTS: Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429-0.727] vs. 0.559 [95% CI, 0.405-0.705], p-value: 0.920; deep RSs: 0.670 [95% CI, 0.516-0.802] vs. 0.551 [95% CI, 0.397-0.698], p-value: 0.776). The fusion RSs, combining handcrafted and deep learning-based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714-0.900) and 0.792 (95% CI, 0.647-0.897) for the internal and external validation cohorts, respectively. CONCLUSIONS: The subregional radiomics approach can accurately predict the Ki-67 level based on DBT data; thus, it may be used as a potential non-invasive tool for preoperative treatment planning in BC.