Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors' evaluation in lung cancer screening.

OBJECTIVE: To evaluate the consistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulmonary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules. MATERIALS AND METHODS: This retrospective study analysed participants aged 40-74 in the local area from 2011 to 2013. Pulmonary nodules were examined radiologically using a low-dose chest CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic(CAD) system based on the three-dimensional(3D) convolutional neural network (CNN) with DenseNet architecture(InferRead CT Lung, IRCL). Consistency tests were employed to assess the uniformity of the radiological characteristics of the pulmonary nodules. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules. RESULTS: A total of 570 subjects were included in this retrospective study. The AI software demonstrated high consistency with the panel's evaluation in determining the position and diameter of the pulmonary nodules (kappa = 0.883, concordance correlation coefficient (CCC) = 0.809, p = 0.000). The comparison of the solid nodules' attenuation characteristics also showed acceptable consistency (kappa = 0.503). In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0.829-0.909) and 0.921 (95%CI: 0.884-0.949), respectively. However, there was no significant difference (p = 0.0950). The maximum diameter, solid nodules, subsolid nodules were the crucial factors for interpreting carcinomatous nodules in the analysis of expert panel and IRCL pulmonary nodule characteristics. CONCLUSION: AI software can assist doctors in diagnosing nodules and is consistent with doctors' evaluations and diagnosis of pulmonary nodules.

Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases.

Background: Kirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction. Methods: This retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models. Results: The four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort). Conclusions: Radiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.

CT-based nomogram for early identification of T790M resistance in metastatic non-small cell lung cancer before first-line epidermal growth factor receptor-tyrosine kinase inhibitors therapy.

BACKGROUND: To evaluate the value of computed tomography (CT) radiomics in predicting the risk of developing epidermal growth factor receptor (EGFR) T790M resistance mutation for metastatic non-small lung cancer (NSCLC) patients before first-line EGFR-tyrosine kinase inhibitors (EGFR-TKIs) therapy. METHODS: A total of 162 metastatic NSCLC patients were recruited and split into training and testing cohort. Radiomics features were extracted from tumor lesions on nonenhanced CT (NECT) and contrast-enhanced CT (CECT). Radiomics score (rad-score) of two CT scans was calculated respectively. A nomogram combining two CT scans was developed to evaluate T790M resistance within up to 14 months. Patients were followed up to calculate the time of T790M occurrence. Models were evaluated by area under the curve at receiver operating characteristic analysis (ROC-AUC), calibration curve, and decision curve analysis (DCA). The association of the nomogram with the time of T790M occurrence was evaluated by Kaplan-Meier survival analysis. RESULTS: The nomogram constructed with the rad-score of NECT and CECT for predicting T790M resistance within 14 months achieved the highest ROC-AUCs of 0.828 and 0.853 in training and testing cohorts, respectively. The DCA showed that the nomogram was clinically useful. The Kaplan-Meier analysis showed that the occurrence time of T790M difference between the high- and low-risk groups distinguished by the rad-score was significant (p < 0.001). CONCLUSIONS: The CT-based radiomics signature may provide prognostic information and improve pretreatment risk stratification in EGFR NSCLC patients before EGFR-TKIs therapy. The multimodal radiomics nomogram further improved the capability. RELEVANCE STATEMENT: Radiomics based on NECT and CECT images can effectively identify and stratify the risk of T790M resistance before the first-line TKIs treatment in metastatic non-small cell lung cancer patients. KEY POINTS: • Early identification of the risk of T790M resistance before TKIs treatment is clinically relevant. • Multimodel radiomics nomogram holds potential to be a diagnostic tool. • It provided an imaging surrogate for identifying the pretreatment risk of T790M.

Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study.

OBJECTIVES: Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB. METHODS: We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (n = 295, 102), nodules (n = 302, 97), and their combination (n = 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves. RESULTS: Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877, p > 0.05) and testing cohort (0.820 versus 0.786, p < 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933, p > 0.05) and testing cohort (0.820 versus 0.855, p > 0.05). CONCLUSIONS: The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB. CLINICAL RELEVANCE STATEMENT: Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients. KEY POINTS: • This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB. • The radiomics model showed a favorable performance for the identification of MDR-TB. • The combined model holds potential to be used as a diagnostic tool in routine clinical practice.

Regional homogeneity alterations in multifrequency bands in patients with extracranial multi-organ tuberculosis: a prospective cross-sectional study.

Background: This study aimed to clarify the spontaneous neural activity in the conventional frequency band (0.01-0.08 Hz) and 2 subfrequency bands (slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz) in patients with extracranial multi-organ tuberculosis (EMTB) through regional homogeneity (ReHo) analysis. Methods: In all, 32 patients with EMTB and 31 healthy controls (HCs) were assessed by resting-state functional magnetic resonance imaging (rs-fMRI) scans to clarify the abnormal spontaneous neural activity through ReHo analysis in the conventional frequency band and 2 subfrequency bands. Results: Compared with the HCs, the patients with EMTB exhibited decreased ReHo in the left postcentral gyrus [t=-4.79; 95% confidence interval (CI): -0.79 to -0.31] and the left superior cerebellum (t=-4.45; 95% CI: -0.54 to -0.21) in the conventional band. Conversely, increased ReHo was observed in the right middle occipital gyrus (t=3.94; 95% CI: 0.18-0.53). In the slow-4 band, patients with EMTB only exhibited decreased ReHo in the superior cerebellum (t=-4.69; 95% CI: -0.54 to -0.22); meanwhile, in the slow-5 band, these patients exhibited decreased ReHo in the right postcentral gyrus (t=-3.76; 95% CI: -0.74 to -0.21) and the left superior cerebellum (t=-5.20, 95% CI: -0.72 to -0.31). After Bonferroni correction, no significant correlation was observed between the ReHo values in clusters showing significant between-group differences and cognitive test scores. Conclusions: ReHo showed abnormal synchronous neural activity in patients with EMTB in different frequency bands, which provides a novel understanding of the pathological mechanism of EMTB.

Understanding the hydrological response of a headwater-dominated catchment by analysis of distributed surface-subsurface interactions.

We computationally explore the relationship between surface-subsurface exchange and hydrological response in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to isolate the effect of surface-subsurface exchange on the hydrological response, we compare three model variations that differ only in soil permeability. Traditional methods of hydrograph analysis that have been developed for headwater catchments may fail to properly characterize catchments, where catchment response is tightly coupled to headwater inflow. Analyzing the spatially distributed hydrological response of such catchments gives additional information on the catchment functioning. Thus, we compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the catchment. Our results show that changes in the surface-subsurface exchange fluxes trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Further, changes in surface-subsurface exchange rates lead to a nonlinear change in the degree of connectivity-quantified through the number of disconnected clusters of ponding water-in the catchment. Although the runoff formation in the catchment changes significantly, these changes do not significantly alter the aggregated streamflow hydrograph. This hints at a crucial gap in our ability to infer catchment function from aggregated signatures. We show that while these changes in distributed hydrological response may not always be observable through aggregated hydrological signatures, they can be quantified through the use of indices of connectivity.

Predicting EGFR T790M Mutation in Brain Metastases Using Multisequence MRI-Based Radiomics Signature.

RATIONALE AND OBJECTIVES: Timely identifying T790M mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM) is essential to adjust targeted treatment strategies. To develop and validate radiomics models based on multisequence MRI for differentiating patients with T790M resistance from no T790M mutation in BM and explore the optimal sequence for prediction. MATERIALS AND METHODS: This retrospective study enrolled 233 patients with proven of BM in NSCLC which included 95 with T790M and 138 without T790M from two hospitals as the training cohort and testing cohort separately. Radiomics features extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequence respectively. The most predictable features were selected based on the maximal information coefficient and Boruta method. Then four radiomics models were built to characterize T790M mutation by random forest classifier. ROC curves, F1 score and DCA curves were constructed to validate the capability and verify the performance of four models. RESULTS: The DWI model showed best performance with AUC and F1 score of 0.886 and 0.789 in the training cohort, 0.850 and 0.743 in the testing cohort. DCA curves also showed higher overall net benefit from the DWI model than from the remaining three models in the testing cohort. Other three models also had some classification power whether in the training or testing cohort, especially T2-FLAIR model. CONCLUSION: Multisequence MRI-based radiomics has potential to predict the emergence of EGFR T790M resistance mutations especially the radiomics signature based on DWI sequence.

Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis.

PURPOSE: More and more small brain metastases (BMs) in asymptomatic patients can be detected even prior to their primary lung cancer with the development of MRI. The aim of this study was to develop a predictive radiomics model to identify epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation status in BM and explore the optimal MR sequence for predication. METHODS: This retrospective study included 186 patients with proven BM of lung cancer (training cohort: 70 patients with EGFR mutations and 65 patients with ALK rearrangements; testing cohort: 26 patients with EGFR mutations and 25 patients with ALK rearrangements). Radiomics features were separately extracted from contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR) and T2WI sequences. The model for three MR sequences were constructed using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts. RESULTS: The AUCs of the T2-FLAIR model were significantly higher than those of the T1-CE model in training cohort (0.991 versus 0.954) and testing cohort (0.950 versus 0.867) and much higher than those of the T2WI model in training cohort (0.991 versus 0.880) and testing cohort (0.950 versus 0.731). Besides, the F1 scores of the T1-CE model were slightly higher than the T2-FLAIR model and much higher than the T2WI model in two cohorts. CONCLUSION: T2-FLAIR and T1-CE radiomics models that can be used as noninvasive tools for identifying EGFR and ALK mutation status are helpful to guide therapeutic strategies.

Numerical simulation of seawater intrusion to coastal aquifers and brine water/freshwater interaction in south coast of Laizhou Bay, China.

Seawater intrusion and brine water/freshwater interaction have significantly affected agriculture, industry and public water supply at Laizhou Bay, Shandong Province, China. In this study, a two-dimensional SEAWAT model is developed to simulate the seawater intrusion to coastal aquifers and brine water/fresh water interaction in the south of Laizhou Bay. This model is applied to predict the seawater intrusion and brine water/freshwater interface development in the coming years. The model profile is perpendicular to the coastal line with two interfaces, freshwater-saline water interface near the shore and inland brine water-saline water-seawater interface. The hydrogeological parameters in the SEAWAT-2000 model are calibrated by the head and salinity measurements. The precipitation infiltration coefficient, boundary conditions and thicknesses of aquifers are studied in a sensitivity analysis. The predicted results indicate that equivalent freshwater head in shallow freshwater-saline water area will decline 2.0 m by the end of the forecasting period, caused by groundwater over-pumping for farmland irrigation. The groundwater head in the brine-saline water area will also decrease about 1.8 m by the end of forecasting period, caused by excessive brine mining. Salinity finally decreases below 105 g/L in the brine area, but increases in other areas and contaminates fresh groundwater resources.

Long distance seawater intrusion through a karst conduit network in the Woodville Karst Plain, Florida.

Five periods of increased electrical conductivity have been found in the karst conduits supplying one of the largest first magnitude springs in Florida with water. Numerous well-developed conduit networks are distributed in the Woodville Karst Plain (WKP), Florida and connected to the Gulf of Mexico. A composite analysis of precipitation and electrical conductivity data provides strong evidence that the increases in conductivity are directly tied to seawater intrusion moving inland and traveling 11 miles against the prevailing regional hydraulic gradient from from Spring Creek Spring Complex (SCSC), a group of submarine springs at the Gulf Coast. A geochemical analysis of samples from the spring vent rules out anthropogenic contamination and upwelling regional recharge from the deep aquifer as sources of the rising conductivity. The interpretation is supported by the conceptual model established by prior researchers working to characterize the study area. This paper documents the first and longest case of seawater intrusion in the WKP, and also indicates significant possibility of seawater contamination through subsurface conduit networks in a coastal karst aquifer.

Numerical study of groundwater flow cycling controlled by seawater/freshwater interaction in a coastal karst aquifer through conduit network using CFPv2.

In this study, a groundwater flow cycling in a karst springshed and an interaction between two springs, Spring Creek Springs and Wakulla Springs, through a subground conduit network are numerically simulated using CFPv2, the latest research version of MODFLOW-CFP (Conduit Flow Process). The Spring Creek Springs and Wakulla Springs, located in a marine estuary and 11 miles inland, respectively, are two major groundwater discharge spots in the Woodville Karst Plain (WKP), North Florida, USA. A three-phase conceptual model of groundwater flow cycling between the two springs and surface water recharge from a major surface creek (Lost Creek) was proposed in various rainfall conditions. A high permeable subground karst conduit network connecting the two springs was found by tracer tests and cave diving. Flow rate of discharge, salinity, sea level and tide height at Spring Creek Springs could significantly affect groundwater discharge and water stage at Wakulla Springs simultaneously. Based on the conceptual model, a numerical hybrid discrete-continuum groundwater flow model is developed using CFPv2 and calibrated by field measurements. Non-laminar flows in conduits and flow exchange between conduits and porous medium are implemented in the hybrid coupling numerical model. Time-variable salinity and equivalent freshwater head boundary conditions at the submarine spring as well as changing recharges have significant impacts on seawater/freshwater interaction and springs' discharges. The developed numerical model is used to simulate the dynamic hydrological process and quantitatively represent the three-phase conceptual model from June 2007 to June 2010. Simulated results of two springs' discharges match reasonably well to measurements with correlation coefficients 0.891 and 0.866 at Spring Creeks Springs and Wakulla Springs, respectively. The impacts of sea level rise on regional groundwater flow field and relationship between the inland springs and submarine springs are evaluated as well in this study.