Freehand 3D Ultrasound Imaging Based on Probe-mounted Vision and IMU System.

OBJECTIVES: Freehand three-dimensional (3D) ultrasound (US) is of great significance for clinical diagnosis and treatment, it is often achieved with the aid of external devices (optical and/or electromagnetic, etc.) that monitor the location and orientation of the US probe. However, this external monitoring is often impacted by imaging environment such as optical occlusions and/or electromagnetic (EM) interference. METHODS: To address the above issues, we integrated a binocular camera and an inertial measurement unit (IMU) on a US probe. Subsequently, we built a tight coupling model utilizing the unscented Kalman algorithm based on Lie groups (UKF-LG), combining vision and inertial information to infer the probe's movement, through which the position and orientation of the US image frame are calculated. Finally, the volume data was reconstructed with the voxel-based hole-filling method. RESULTS: The experiments including calibration experiments, tracking performance evaluation, phantom scans, and real scenarios scans have been conducted. The results show that the proposed system achieved the accumulated frame position error of 3.78 mm and the orientation error of 0.36° and reconstructed 3D US images with high quality in both phantom and real scenarios. CONCLUSIONS: The proposed method has been demonstrated to enhance the robustness and effectiveness of freehand 3D US. Follow-up research will focus on improving the accuracy and stability of multi-sensor fusion to make the system more practical in clinical environments.

Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance.

Aims: To investigate the characteristics and diagnostic performance of quantitative computed tomography (QCT) parameters in eosinophilic chronic obstructive pulmonary disease (COPD) patients. Methods: High-resolution CT scans of COPD patients were retrospectively analyzed, and various emphysematous parenchyma measurements, including lung volume (LC), lung mean density (LMD), lung standard deviation (LSD), full-width half maximum (FWHM), and lung relative voxel number (LRVN) were performed. The QCT parameters were compared between eosinophilic and noneosinophilic COPD patients, using a definition of eosinophilic COPD as blood eosinophil values ≥ 300 cells·µL-1 on at least three times. Receiver operating characteristic curves and area under the curve (ROC-AUC) and python were used to evaluate discriminative efficacy of QCT. Results: Noneosinophilic COPD patients had a significantly lower TLMD (-846.3 ± 47.9 Hounsfield Unit [HU]) and TFWHM(162.5 ± 30.6 HU) compared to eosinophilic COPD patients (-817.8 ± 54.4, 177.3 ± 33.1 HU, respectively) (p = 0.018, 0.03, respectively). Moreover, the total LC (TLC) and TLSD were significantly lower in eosinophilic COPD group (3234.4 ± 1145.8, 183.8 ± 33.9 HU, respectively) than the noneosinophilic COPD group (5600.2 ± 1248.4, 203.5 ± 20.4 HU, respectively) (p = 0.009, 0.002, respectively). The ROC-AUC values for TLC, TLMD, TLSD, and TFWHM were 0.91 (95% confidence interval [CI], 0.828-0.936), 0.66 (95% CI, 0.546-0.761), 0.64 (95% CI, 0.524-0.742), and 0.63 (95% CI, 0.511-0.731), respectively. When the TLC value was 4110 mL, the sensitivity was 90.7% (95% CI, 79.7-96.9), specificity was 77.8% (95% CI, 57.7-91.4) and accuracy was 86.4%. Notably, TLC demonstrated the highest discriminative efficiency with an F1 Score of 0.79, diagnostic Odds Ratio of 34.3 and Matthews Correlation Coefficient of 0.69, surpassing TLMD (0.55, 3.66, 0.25), TLSD (0.56, 3.95, 0.26), and TFWHM (0.56, 4.16, 0.33). Conclusion: Eosinophilic COPD patients exhibit lower levels of emphysema and a more uniform density distribution throughout the lungs compared to noneosinophilic COPD patients. Furthermore, TLC demonstrated the highest diagnostic efficiency and may serve as a valuable diagnostic marker for distinguishing between the two groups.

Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population.

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Investigation of rare earth-based magnetic nanocomposites for specific enrichment of exosomes from human plasma.

Exosomes, also known as small extracellular vesicles, are widely present in a variety of body fluids (e.g., blood, urine, and saliva). Exosomes are becoming an alternative promising source of diagnostic markers for disease rich in cargo of metabolites, proteins, and nucleic acids. However, due to the low abundance and structure similarity with protein complex, the efficient isolation of exosomes is one of the most important issues for biomedical applications. With a higher order of f-orbitals in rare earth element, it will have strong adsorption toward the phosphate group on the surface of the phospholipid bilayer of exosomes. In this study, we systematically investigated the ability of various rare earths interacting with phosphate-containing molecules and plasma exosomes. One of the best binding europium was selected and used to synthesize core-shell magnetic nanomaterials (Fe3O4@SiO2@Eu2O3) for the enrichment of exosomes from human plasma. The developed nanomaterials exhibited higher enrichment capacity, less time consumption and more convenient handling compared to commonly used ultracentrifugation method. The nanomaterials were applied to separate exosomes from the plasma of patients with hepatocellular carcinoma and healthy controls for metabolomics study with high-resolution mass spectrometry, where 70 differentially expressed metabolites were identified, involving amino acid and lipid metabolic pathway. We anticipated the rare earth-based materials to be an alternative approach on exosome isolation for disease diagnosis or postoperative clinical monitoring.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.

RATIONALE AND OBJECTIVES: To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules. MATERIALS AND METHODS: The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. RESULTS: The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules. CONCLUSION: Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.

Highly active antiretroviral therapy-related effects on morphological connectivity in HIV.

OBJECTIVE: Suboptimal concentration of the antiretroviral drug is insufficient to inhibit HIV destruction on brain structure and function due to the resistance of blood brain barrier. We aimed to investigate highly active antiretroviral therapy (HAART)-related effects on the morphological connectivity in people with HIV (PWH). DESIGN: Case-control study. METHODS: Fifty-five HAART-treated for more than 3 months and 54 untreated PWH, as well as 66 demographically matched healthy controls underwent a high-resolution 3D T1-weighted MRI. Individual-level morphological brain network based on gray matter volume of 90 brain regions was constructed and network topological properties were analyzed. Network-based statistics (NBS) was performed to identify sub-networks showing significant differences in morphological connectivity. Correlation and mediation analyses were employed to evaluate associations between the morphological properties and clinical variables of PWH. RESULTS: Although PWH exhibited small-world architecture in their morphological brain networks, untreated PWH demonstrated altered network properties while HAART-treated PWH showed relatively similar network properties compared to healthy controls. Furthermore, HAART-related effects were mainly involved the bilateral putamen and left thalamus. The findings of NBS further indicated the cortico-striatum-thalamic-cortical loop was involved in the therapeutic-associated morphological network. The positive correlations between the HAART treatment and nodal degree and efficiency of the putamen were mediated by the number of CD4 + T lymphocytes. CONCLUSIONS: The topological properties are recovered to normal in PWH after HAART and the effects induced by HAART are mostly within the cortical-subcortical circuit.

Comparative genomics reveals evolutionary drivers of the dietary shift in Hemiptera.

Hemiptera insects exhibit a close relationship to plants and demonstrate a diverse range of dietary preferences, encompassing phytophagy as the predominant feeding habit while a minority engages in carnivorous or haematophagous behaviour. To counteract the challenges posed by phytophagous insects, plants have developed an array of toxic compounds, causing significant evolutionary selection pressure on these insects. In this study, we employed a comparative genomics approach to analyse the expansion and contraction of gene families specific to phytophagous insect lineages, along with their adaptive evolutionary traits, utilising representative species from the Hemiptera order. Our investigation revealed substantial expansions of gene families within the phytophagous lineages, especially in the Pentatomomorpha branch represented by Oncopeltus fasciatus and Riptortus pedestris. Notably, these expansions of gene families encoding enzymes are potentially involved in hemipteran-plant interactions. Moreover, the adaptive evolutionary analysis of these lineages revealed a higher prevalence of adaptively evolved genes in the Pentatomomorpha branch. The observed branch-specific gene expansions and adaptive evolution likely contribute significantly to the diversification of species within Hemiptera. These results help enhance our understanding of the genomic characteristics of the evolution of different feeding habits in hemipteran insects.

Diffusely Charged Polymeric Zwitterions as Loosely Hydrated Marine Antifouling Coatings.

Polymeric zwitterions exhibit exceptional fouling resistance through the formation of a strongly hydrated surface of immobilized water molecules. While being extensively tested for their performance in biomedical, membrane, and, to a lesser extent, marine environments, few studies have investigated how the molecular design of the zwitterion may enhance its performance. Furthermore, while theories of zwitterion antifouling mechanisms exist for molecular-scale foulant species (e.g., proteins and small molecules), it remains unclear how molecular-scale mechanisms influence the micro- and macroscopic interactions of relevance for marine applications. The present study addresses these gaps through the use of a modular zwitterion chemistry platform, which is characterized by a combination of surface-sensitive sum frequency generation (SFG) vibrational spectroscopy and marine assays. Zwitterions with increasingly delocalized cations demonstrate improved fouling resistance against the green alga Ulva linza. SFG spectra correlate well with the assay results, suggesting that the more diffuse charges exhibit greater surface hydration with more bound water molecules. Hence, the number of bound interfacial water molecules appears to be more influential in determining the marine antifouling activities of zwitterionic polymers than the binding strength of individual water molecules at the interface.

CUDC­101 is a potential target inhibitor for the EGFR­overexpression bladder cancer cells.

Bladder cancer is one of the most common urological malignancies worldwide. The molecular mechanism underlying its development is complex, but its carcinogenesis has been proposed to occur with cell proliferation and resistance to apoptosis, driven by the signaling activity of abundant EGFR and receptor tyrosine­protein kinase erbB­2. In the present study, T24 bladder cancer cell lines with EGFR­overexpression were constructed, before the multi­target inhibitor CUDC­101 was used to investigate its potential as a targeted therapeutic agent for bladder cancer using chemosensitivity methods. The results showed that CUDC­101 induced cytotoxic effects, inhibited growth vitality and proliferation in a dose­dependent manner. CUDC­101 also altered the skeletal morphology and microfilament structure, while blocking cell cycle progression and causing apoptosis. These results supported the proposed cytotoxic effects of CUDC­101, in addition to its inhibitory effects on cell division and proliferation in EGFR­overexpressing bladder cancer cells. Therefore CUDC­101 may to be a potential therapeutic option for the treatment of bladder cancer.

Hydration behaviors of nonfouling zwitterionic materials.

Zwitterionic materials have emerged as highly effective ultralow fouling materials for many applications, however the underlying mechanism of fouling resistance remains unclear. Using ab initio molecular dynamics simulations and surface-sensitive sum frequency generation vibrational spectroscopy, we studied the hydration behaviors of zwitterionic materials, including trimethylamine-N-oxide (TMAO) and carboxybetaines of different charge-separation distances, to understand their fouling-resistant mechanism and provide a design principle for improved performance. Our study reveals that the interplay among hydrogen bonding, net charge, and dipole moment is crucial to the fouling-resistant capabilities of zwitterionic materials. Shortening of the zwitterionic spacing strengthens hydrogen bonding with water against biomolecule attachment due to the increased electrostatic and induction interactions, charge transfer, and improved structural stability. Moreover, the shortened charge separation reduces the dipole moment of zwitterionic materials with an intrinsic near-neutral net charge, decreasing their electrostatic and dipole-dipole interactions with biofoulers, and increasing their resistance to fouling. Compared to carboxybetaine compounds, TMAO has the shortest zwitterionic spacing and exhibits the strongest hydrogen bonding, the smallest net charge, and the minimum dipole moment, making it an excellent nonfouling material.

Mutually communicated model based on multi-parametric MRI for automated segmentation and classification of prostate cancer.

BACKGROUND: Multiparametric magnetic resonance imaging (mp-MRI) is introduced and established as a noninvasive alternative for prostate cancer (PCa) detection and characterization. PURPOSE: To develop and evaluate a mutually communicated deep learning segmentation and classification network (MC-DSCN) based on mp-MRI for prostate segmentation and PCa diagnosis. METHODS: The proposed MC-DSCN can transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way. For classification task, the MC-DSCN can transfer the masks produced by the coarse segmentation component to the classification component to exclude irrelevant regions and facilitate classification. For segmentation task, this model can transfer the high-quality localization information learned by the classification component to the fine segmentation component to mitigate the impact of inaccurate localization on segmentation results. Consecutive MRI exams of patients were retrospectively collected from two medical centers (referred to as center A and B). Two experienced radiologists segmented the prostate regions, and the ground truth of the classification refers to the prostate biopsy results. MC-DSCN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted and apparent diffusion coefficient) and the effect of different architectures on the network's performance was tested and discussed. Data from center A were used for training, validation, and internal testing, while another center's data were used for external testing. The statistical analysis is performed to evaluate the performance of the MC-DSCN. The DeLong test and paired t-test were used to assess the performance of classification and segmentation, respectively. RESULTS: In total, 134 patients were included. The proposed MC-DSCN outperforms the networks that were designed solely for segmentation or classification. Regarding the segmentation task, the classification localization information helped to improve the IOU in center A: from 84.5% to 87.8% (p < 0.01) and in center B: from 83.8% to 87.1% (p < 0.01), while the area under curve (AUC) of PCa classification was improved in center A: from 0.946 to 0.991 (p < 0.02) and in center B: from 0.926 to 0.955 (p < 0.01) as a result of the additional information provided by the prostate segmentation. CONCLUSION: The proposed architecture could effectively transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way, thus outperforming the networks designed to perform only one task.

Six first-line tyrosine kinase inhibitors reveal novel inhibition potential for the EGFR S768I mutation.

Lung cancer, the leading cause of cancer-related mortality, is the most commonly diagnosed cancer. Tyrosine kinase inhibitors (TKIs) are considered a drug-targeted therapy for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, limited data are available involving the activity of EGFR TKIs against rare EGFR mutations. Here, based on an endogenous EGFR-depleted cell Line H3255 by CRISPR, H3255 cells with rare mutant EGFRS768I and compound mutations EGFRS768I+L858R were tested using cell proliferation assay, cytotoxicity, membrane potential, flow cytometry and Western blot analysis. We conducted cytotoxicity screening of EGFR mutations on six front-line TKIs based on first-, second-, and third-generation TKIs (afatinib, dacomitinib, osimertinib, erlotinib, gefitinib, and icotinib). The results showed that the sensitivity of these mutants containing rare variants EGFRS768I to six front-line TKIs was enriched in the irreversible TKI cytotoxicity assays by determining their change in cytotoxicity, apoptosis, cell proliferation and signal pathway factors. Importantly, the variants harboring EGFRL858R (H3255), EGFRS768I (H3255S768I) and EGFRS768I+L858R (H3255S768I+L858R) were sensitive to six TKIs and induced cytotoxicity through different pathways. Moreover, the compound mutations EGFRS768I+L858R showed more TKI resistance than EGFRS768I mutation and EGFRL858R mutation. We present a comprehensive reference for the sensitivity of EGFRS768I variants to six front-line TKIs. For patients with the EGFR S768I mutation and compound mutations EGFRS768I+L858R, six first-line TKIs appear to be reasonable therapeutic options.

Defining the sensitivity landscape of EGFR variants to tyrosine kinase inhibitors.

Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs.

Sensitivity analysis of EGFR L861Q mutation to six tyrosine kinase inhibitors

Purpose: Lung cancer is one of the most common carcinomas with the highest mortality in the world. Non-small cell lung carcinoma has a large proportion of epidermal growth factor receptor (EGFR) mutations, of which rare EGFR mutations account for about 10%–20%. Currently, tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with non-small cell lung carcinoma with EGFR mutations. To date, the toxicological effects of the EGFR L861Q variant (less than 2%) have been rarely reported, so further investigation of its sensitivity to six first-in-class TKIs is of great clinical interest.MethodsIn this study, two EGFR L861Q variants cell lines (EGFR L861Q variant and EGFR L861Q + exon 19 deletion variant) were established by CRISPR-Cas9 gene-editing technology. The steady-state plasma concentrations of six TKIs (gefitinib/erlotinib/icotinib, the first generation; dacomitinib/afatinib, the second generation; and osimertinib, the third generation) were tested, respectively. The change of cell viability, proliferation, cloning ability, mitochondrial membrane potential and apoptosis were detected by MTT assay, EdU staining assay, colony formation assay, mitochondrial membrane potential and apoptosis test. TUNEL and Annexin V / PI staining were used to detect cell apoptosis, and flow cytometry was employed to explore the sensitivity of two variants to six TKIs.ResultsOur study indicated that the six TKIs inhibited the viability of the two cell lines in a time-dependent manner, and the inhibitory time of six TKIs on proliferation was different between the two cell lines. The proliferation and cloning ability of two cell lines were inhibited by six TKIs. The cytoskeleton morphology, microfilament structure and distribution of the two cell lines were changed by six TKIs. (AU)

Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients.

Background: Early and accurate diagnosis of invasive fungal infection (IFI) is pivotal for the initiation of effective antifungal therapy for patients with hematologic malignancies. Methods: This retrospective study involved 235 patients with hematologic malignancies and pulmonary infections diagnosed as IFIs (n=118) or bacterial pneumonia (n=117). Patients were randomly divided into training (n=188) and validation (n=47) datasets. Four feature selection methods with nine classifiers were implemented to select the optimal machine learning (ML) model using five-fold cross-validation. A radiomic signature was constructed using a linear ML algorithm, and a radiomic score (Radscore) was calculated. The combined model was developed with the Radscore, the significant clinical and radiologic factors were selected using multivariable logistic regression, and the results were presented as a clinical radiomic nomogram. A prospective pilot study was also conducted to compare the classification performance of the combined nomogram with practicing radiologists. Results: Significant differences were found in the Radscore between IFI and bacterial pneumonia patients in the training (0.683 vs. -0.724, P<0.001) and validation set (0.353 vs. -0.717, P=0.002). The combined model showed good discrimination performance in the validation cohort [area under the curve (AUC) =0.844] and outperformed the clinical (AUC =0.696) and radiomics (AUC =0.767) model alone (both P<0.05). Conclusions: The clinical radiomic nomogram can serve as a promising predictive tool for IFI in patients with hematologic malignancies.

Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning.

Purpose: To develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution computed tomography (HRCT) scans. Material and Methods: In this retrospective study, 474 HRCT scans were included (mean age, 64.10 years ± 9.57 [SD]). Five-fold cross-validation was performed on 365 HRCT scans. Furthermore, an external dataset comprising 109 patients was used as a test set. An HCR model, a DL model, and an ensemble of HCR and DL model were developed. A virtual in-silico trial was conducted with two radiologists and one pulmonologist on the same external test set for performance comparison. The performance was compared using DeLong method and McNemar test. Shapley Additive exPlanations (SHAP) plots and Grad-CAM heatmaps were used for the post-hoc interpretability of HCR and DL models, respectively. Results: In five-fold cross-validation, the HCR model, DL model, and the ensemble of HCR and DL models achieved accuracies of 76.2 ± 6.8, 77.9 ± 4.6, and 85.2 ± 2.7%, respectively. For the diagnosis of IPF and non-IPF ILDs on the external test set, the HCR, DL, and the ensemble of HCR and DL models achieved accuracies of 76.1, 77.9, and 85.3%, respectively. The ensemble model outperformed the diagnostic performance of clinicians who achieved a mean accuracy of 66.3 ± 6.7% (p < 0.05) during the in-silico trial. The area under the receiver operating characteristic curve (AUC) for the ensemble model on the test set was 0.917 which was significantly higher than the HCR model (0.817, p = 0.02) and the DL model (0.823, p = 0.005). The agreement between HCR and DL models was 61.4%, and the accuracy and specificity for the predictions when both the models agree were 93 and 97%, respectively. SHAP analysis showed the texture features as the most important features for IPF diagnosis and Grad-CAM showed that the model focused on the clinically relevant part of the image. Conclusion: Deep learning and HCR models can complement each other and serve as useful clinical aids for the diagnosis of IPF and non-IPF ILDs.

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Sensitivity analysis of EGFR L861Q mutation to six tyrosine kinase inhibitors.

PURPOSE: Lung cancer is one of the most common carcinomas with the highest mortality in the world. Non-small cell lung carcinoma has a large proportion of epidermal growth factor receptor (EGFR) mutations, of which rare EGFR mutations account for about 10%-20%. Currently, tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with non-small cell lung carcinoma with EGFR mutations. To date, the toxicological effects of the EGFR L861Q variant (less than 2%) have been rarely reported, so further investigation of its sensitivity to six first-in-class TKIs is of great clinical interest. METHODS: In this study, two EGFR L861Q variants cell lines (EGFR L861Q variant and EGFR L861Q + exon 19 deletion variant) were established by CRISPR-Cas9 gene-editing technology. The steady-state plasma concentrations of six TKIs (gefitinib/erlotinib/icotinib, the first generation; dacomitinib/afatinib, the second generation; and osimertinib, the third generation) were tested, respectively. The change of cell viability, proliferation, cloning ability, mitochondrial membrane potential and apoptosis were detected by MTT assay, EdU staining assay, colony formation assay, mitochondrial membrane potential and apoptosis test. TUNEL and Annexin V / PI staining were used to detect cell apoptosis, and flow cytometry was employed to explore the sensitivity of two variants to six TKIs. RESULTS: Our study indicated that the six TKIs inhibited the viability of the two cell lines in a time-dependent manner, and the inhibitory time of six TKIs on proliferation was different between the two cell lines. The proliferation and cloning ability of two cell lines were inhibited by six TKIs. The cytoskeleton morphology, microfilament structure and distribution of the two cell lines were changed by six TKIs. Compared with the control, the mitochondrial membrane potential decreased while the apoptosis increased of the two of variants after treatment with the six TKIs, and the associated mechanisms were elucidated. CONCLUSIONS: Based on the above results, EGFR L861Q + 19del variant and EGFR L861Q variant showed significant sensitivity to six first-in-class TKIs. Among the six TKIs, the first generation TKIs (gefitinib/erlotinib/icotinib), showed stronger inhibition ability to the EGFR L861Q + 19del variant and EGFR L861Q variant, among which gefitinib showed the strongest inhibition.