Classification of high-grade glioblastoma and single brain metastases using a new SCAT-inception model trained with MRI images.

Background and objectives: Glioblastoma (GBM) and brain metastasis (MET) are the two most common intracranial tumors. However, the different pathogenesis of the two tumors leads to completely different treatment options. In terms of magnetic resonance imaging (MRI), GBM and MET are extremely similar, which makes differentiation by imaging extremely challenging. Therefore, this study explores an improved deep learning algorithm to assist in the differentiation of GBM and MET. Materials and methods: For this study, axial contrast-enhanced T1 weight (ceT1W) MRI images from 321 cases of high-grade gliomas and solitary brain metastasis were collected. Among these, 251 out of 270 cases were selected for the experimental dataset (127 glioblastomas and 124 metastases), 207 cases were chosen as the training dataset, and 44 cases as the testing dataset. We designed a new deep learning algorithm called SCAT-inception (Spatial Convolutional Attention inception) and used five-fold cross-validation to verify the results. Results: By employing the newly designed SCAT-inception model to predict glioblastomas and brain metastasis, the prediction accuracy reached 92.3%, and the sensitivity and specificity reached 93.5 and 91.1%, respectively. On the external testing dataset, our model achieved an accuracy of 91.5%, which surpasses other model performances such as VGG, UNet, and GoogLeNet. Conclusion: This study demonstrated that the SCAT-inception architecture could extract more subtle features from ceT1W images, provide state-of-the-art performance in the differentiation of GBM and MET, and surpass most existing approaches.

An explainable machine learning based prediction model for Alzheimer's disease in China longitudinal aging study.

Alzheimer's disease (AD) is the most common cause of dementia. Accurate prediction and diagnosis of AD and its prodromal stage, i.e., mild cognitive impairment (MCI), is essential for the possible delay and early treatment for the disease. In this paper, we adopt the data from the China Longitudinal Aging Study (CLAS), which was launched in 2011, and includes a joint effort of 15 institutions all over the country. Four thousand four hundred and eleven people who are at least 60 years old participated in the project, where 3,514 people completed the baseline survey. The survey collected data including demographic information, daily lifestyle, medical history, and routine physical examination. In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection methods and nine machine learning classifiers are applied for comparison to find the most dominant features on AD/MCI prediction. The resulting model achieves accuracy of 89.2%, sensitivity of 87.7%, and specificity of 90.7% for MCI prediction, and accuracy of 99.2%, sensitivity of 99.7%, and specificity of 98.7% for AD prediction. We further utilize the SHapley Additive exPlanations (SHAP) algorithm to visualize the specific contribution of each feature to AD/MCI prediction at both global and individual levels. Consequently, our model not only provides the prediction outcome, but also helps to understand the relationship between lifestyle/physical disease history and cognitive function, and enables clinicians to make appropriate recommendations for the elderly. Therefore, our approach provides a new perspective for the design of a computer-aided diagnosis system for AD and MCI, and has potential high clinical application value.

Prediction of intraoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal pituitary surgery based on a deep neural network model trained with MRI images: a pilot study.

Objective: This study aimed to investigate the reliability of a deep neural network (DNN) model trained only on contrast-enhanced T1 (T1CE) images for predicting intraoperative cerebrospinal fluid (ioCSF) leaks in endoscopic transsphenoidal surgery (EETS). Methods: 396 pituitary adenoma (PA) cases were reviewed, only primary PAs with Hardy suprasellar Stages A, B, and C were included in this study. The T1CE images of these patients were collected, and sagittal and coronal T1CE slices were selected for training the DNN model. The model performance was evaluated and tested, and its interpretability was explored. Results: A total of 102 PA cases were enrolled in this study, 51 from the ioCSF leakage group, and 51 from the non-ioCSF leakage group. 306 sagittal and 306 coronal T1CE slices were collected as the original dataset, and data augmentation was applied before model training and testing. In the test dataset, the DNN model provided a single-slice prediction accuracy of 97.29%, a sensitivity of 98.25%, and a specificity of 96.35%. In clinical test, the accuracy of the DNN model in predicting ioCSF leaks in patients reached 84.6%. The feature maps of the model were visualized and the regions of interest for prediction were the tumor roof and suprasellar region. Conclusion: In this study, the DNN model could predict ioCSF leaks based on preoperative T1CE images, especially in PAs in Hardy Stages A, B, and C. The region of interest in the model prediction-making process is similar to that of humans. DNN models trained with preoperative MRI images may provide a novel tool for predicting ioCSF leak risk for PA patients.

Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study.

OBJECTIVES: Ki67 is an important biomarker of pituitary adenoma (PA) aggressiveness. In this study, PA invasion of surrounding structures is investigated and deep learning (DL) models are established for preoperative prediction of Ki67 labeling index (Ki67LI) status using conventional magnetic resonance (MR) images. METHODS: We reviewed 362 consecutive patients with PAs who underwent endoscopic transsphenoidal surgery, of which 246 patients with primary PA are selected for PA invasion analysis. MRI data from 234 of these PA patients are collected to develop DL models to predict Ki67LI status, and DL models were tested on 27 PA patients in the clinical setting. RESULTS: PA invasion is observed in 46.8% of cases in the Ki67 ≥ 3% group and 33.3% of cases in the Ki67 < 3% group. Three deep-learning models are developed using contrast-enhanced T1-weighted images (ceT1WI), T2-weighted images (T2WI), and multimodal images (ceT1WI+T2WI), respectively. On the validation dataset, the prediction accuracy of the ceT1WI model, T2WI model, and multimodal model were 87.4%, 89.4%, and 89.2%, respectively. In the clinical test, 27 MR slices with the largest tumors from 27 PA patients were tested using the ceT1WI model, T2WI model, and multimodal model, the average accuracy of Ki67LI status prediction was 63%, 77.8%, and 70.4%, respectively. CONCLUSION: Preoperative prediction of PA Ki67LI status in a noninvasive way was realized with the DL model by using MRI. T2WI model outperformed the ceT1WI model and multimodal model. This end-to-end model-based approach only requires a single slice of T2WI to predict Ki67LI status and provides a new tool to help clinicians make better PA treatment decisions.

Evaluation of Reliability of the Recomputed Nutrient Intake Data in the National Heart, Lung, and Blood Institute Twin Study.

The nutrient intake dataset is crucial in epidemiological studies. The latest version of the food composition database includes more types of nutrients than previous ones and can be used to obtain data on nutrient intake that could not be estimated before. Usual food consumption data were collected among 910 twins between 1969 and 1973 through dietary history interviews, and then used to calculate intake of eight types of nutrients (energy intake, carbohydrate, protein, cholesterol, total fat, and saturated, monounsaturated, and polyunsaturated fatty acids) in the National Heart, Lung, and Blood Institute Twin Study. We recalculated intakes using the food composition database updated in 2008. Several different statistical methods were used to evaluate the validity and the reliability of the recalculated intake data. Intra-class correlation coefficients between recalculated and original intake values were above 0.99 for all nutrients. R² values for regression models were above 0.90 for all nutrients except polyunsaturated fatty acids (R² = 0.63). In Bland⁻Altman plots, the percentage of scattering points that outlay the mean plus or minus two standard deviations lines was less than 5% for all nutrients. The arithmetic mean percentage of quintile agreement was 78.5% and that of the extreme quintile disagreement was 0.1% for all nutrients between the two datasets. Recalculated nutrient intake data is in strong agreement with the original one, supporting the reliability of the recalculated data. It is also implied that recalculation is a cost-efficient approach to obtain the intake of nutrients unavailable at baseline.

[Spectral Feature and Mg Number (Fo#) Analysis of Olivine in Sinus Iridium].

The research on the distribution and component of olivine is one of great significance to evaluate the geologic evolution of igneous planetary bodies such as the Moon. In this paper, the Sinus Iidium, as the survey region, was explored by Chang's serial satellite. Here we present an olivine survey of the Sinus Iridium by using Spectral Feature Fitting (SFF) method based on the M3 data. The exposures of olivine were located in the northern crater wall and at the foot of Montes Jura, which were associated with plagioclase and little anorthosite. The stratigraphic units of the located formation were the interior crater slopes and debris ejected from the impact-formed Iridium crater, and the geological age was relatively older. The Mg number of the lunar olivine samples was dependent variables, and the band center of the lunar olivine spectrums were independent, which derived from the fitting analysis using Modified Gaussian Model (MGM). The quantitative inversion models of Mg number (Fo#) of the lunar olivine is established with multiple linear regression analysis. On this basis, the Mg number of the olivine rich point in the Sinus Iridium are calculated with quantitative inversion models of Mg number (Fo#). The result shows that, the Fo# of olivine in the Sinus Iridium are relatively high. The mean value of Fo# is Fo~80.84. As mantle olivine would be expected to be quite Mg-rich, it is suggested that at the vast majority of the olivine detected in the Sinus Iridium come from upper mantle of the Moon.

Relationship Exploration of Chemical Content, Complex Dielectric Constant and Spectral Features of Rocks.

In this research, 97 pieces of rock in Xingcheng, Liaoning Province, China were collected to measure the spectral reflectance in 350~2 500 nm, chemical content, and complex dielectric constant of some samples. The absorption depths were calculated by using continuum- removal method. With correlation analysis method, two kinds of correlation curves were obtained based on the theory of spectral characteristics of chemical contents and the principle of dielectric constant. One described the relationship between chemical content and spectral absorption depth, and the other one represented the correlation of complex dielectric constant and reflectance. By summarizing curves morphological characteristics, several conclusions were drawn as follows: (1)There was a strong correlation between the chemical content (SiO2, Al2O3, CaO, K2O, MgO, burnt-loss) and spectral absorption depth in 1 900~2 500 nm, furthermore, at around 1 900, 2 200, 2 300 nm and other identifying characteristic bands, local extreme maximum / minimum values appeared. At Fe3+ characteristic band (400~550 nm), correlation coefficient reached -0.406 between Fe2O3 content and absorption in igneous rock samples collection. Exploring the relationship between rock spectral absorption features and its chemical contents had a positive effect on metallogenic prediction and lithology identification with remote sensing image. (2) Reflectance and complex dielectric constant were negatively correlated totally, compared with the imaginary part; the real part had a better relation reached -0.753 at around 1 900 nm. Curves showed that there were great correlations around 1 900 and 2 200 nm, so, our study adopted different models to simulate response relationships. Dielectric constant of media is one of the basic physical properties, and now most analyses of existing research between electromagnetic characteristics and dielectric constant are studied in microwave band, however, our research is conducted in visible and near infrared range. The conclusions will be useful for further exploration on dielectric characteristics and spectral features of rocks.

[Study on Recognition Model of Phyllosilicate of Martian Surface].

Phyllosilicate belongs to hydrated silica, which is a principal form of hydrous minerals on the martian surface. It's also an indicator in comparing different sediments and degree of aqueous alteration. Therefore, it's essential to establish its recognition model for studying the geologic evolution of the Mars. Short-wave infrared (SWIR) spectral bands and thermal infrared (TIR) spectral bands have distinct spectral response to the mineral groups and ions, so they have distinctive advantages in detecting minerals. However the method of combining SWIR and TIR to recognize phyllosilicate is rarely studied. Based on the USGS spectral library, facing Compact Reconnaissance Imaging Spectrometer for Mars(CRISM) and Thermal Emission Imaging System(THEMIS),we conducted the research on the mechanism of the spectral response of phyllosilicate, and established the SWIR and TIR identification model respectively, then combined the SWIR and TIR spectral features to build the combined recognition model of phyllosilicate with Fisher discriminant analysis. The results of cross validation show that the identification accuracy of combined model is the highest, which can correctly classify 90.6% of the mineral samples and improve the identification precision of phyllosilicate effectively.

Sandmeier model based topographic correction to lunar spectral profiler (SP) data from KAGUYA satellite.

The Moon may be considered as the frontier base for the deep space exploration. The spectral analysis is one of the key techniques to determine the lunar surface rock and mineral compositions. But the lunar topographic relief is more remarkable than that of the Earth. It is necessary to conduct the topographic correction for lunar spectral data before they are used to retrieve the compositions. In the present paper, a lunar Sandmeier model was proposed by considering the radiance effect from the macro and ambient topographic relief. And the reflectance correction model was also reduced based on the Sandmeier model. The Spectral Profile (SP) data from KAGUYA satellite in the Sinus Iridum quadrangle was taken as an example. And the digital elevation data from Lunar Orbiter Laser Altimeter are used to calculate the slope, aspect, incidence and emergence angles, and terrain-viewing factor for the topographic correction Thus, the lunar surface reflectance from the SP data was corrected by the proposed model after the direct component of irradiance on a horizontal surface was derived. As a result, the high spectral reflectance facing the sun is decreased and low spectral reflectance back to the sun is compensated. The statistical histogram of reflectance-corrected pixel numbers presents Gaussian distribution Therefore, the model is robust to correct lunar topographic effect and estimate lunar surface reflectance.

[An accurate approach to hyperspectral mineral identification based on naive bayesian classification model].

The spectral absorption features are very similar between some minerals, especially hydrothermal alteration minerals related to mineralization, and they are also influenced by other factors such as spectral mixture. As a result, many of the spectral identification approaches for the minerals with similar spectral absorption features are prone to confusion and misjudgment. Therefore, to solve the phenomenon of "same mineral has different spectrums, and same spectrum belongs to different minerals", this paper proposes an accurate approach to hyperspectral mineral identification based on naive bayesian classification model. By testing and analyzing muscovite and kaolinite, the two typical alteration minerals, and comparing this approach with spectral angle matching, binary encoding and spectral feature fitting, the three popular spectral identification approaches, the results show that this approach can make more obvious differences among different minerals having similar spectrums, and has higher classification accuracy, since it is based on the position of absorption feature, absorption depth and the slope of continuum.

[Study on hyperspectral response to surface alteration by oil and gas microseepage under loess area].

The present takes the Qingyang, Gansu as an example, based on the mechanism of oil-gas microseepage, the spectra of the loess samples were measured, and the contents of carbonate minerals, clay minerals, Fe2+ and Fe3+, were analyzed. From their contents, it was shown that the carbonate mineralization and the red fading phenomenon are obvious for the known oil-gas field. Meanwhile, the parameters of absorption spectra of the loess samples were identified after the continuum was removed. The multiple regression analysis between the spectrum parameters (independent variables) and the mineral contents (the dependent variables) was implemented. The results indicate that the absorption depth is more sensitive. Thus, by their spectrum absorption parameters, 14 spectra of loess samples were clustered into two groups: samples in the known oil-gas field and unknown oil-gas field.

[Vegetation stress spectra and their relations with the contents of metal elements within the plant leaves in metal mines in Heilongjiang].

In this paper, Duobao Mt. and Tong Mt. were taken as examples. The spectra of the crown or leaf of the vegetation were collected. Fourteen metal elements in the rock, soil (A, B, C) and vegetation (root, trunk, leaf), and biological chemical parameters were measured. It was indicated that different metal elements were selected and enriched in different vegetation. The red edge position (REP) and the absorbing depth are related to biological chemical parameters. A multivariable regression equation was built between the absorption depths and the contents of metal elements. The relative coefficients between the absorbing depths and chemical elements, including Co, Cu, N, Mo, Ag, Sb, W, Pb and As, are greater than 0.75. Thus, it is important to analyze and measure the contents of metal elements by hyper-spectral remote sensing of vegetation stress spectrum.

[Treatment of advanced metastatic breast cancer with exemestane, a multicenter randomized controlled study of 195 cases].

OBJECTIVE: To observe the efficacy of exemestane for postmenopausal advanced metastatic breast cancer, and to assess its side effects. METHODS: A randomized, double-blind, and parallel controlled study was conducted among 195 patients with postmenopausal advanced metastatic breast cancer from December 2001 to June 2002. Except for the 4 cases who were lost to follow-up, the remaining 191 patients were divided into two groups: study group (n = 96, treated with exemestane capsule 25 mg and one model tablet of letrozole orally q.d. for 8 weeks), and control group (n = 95, treated with letrozole 2.5 mg and one model capsule of exemestane orally q.d. for 8 weeks). Physical examination, roentgenography and CT were conducted to observe the outcome of the tumor and the level of estrogen was tested 2 weeks before and 4 and 8 weeks after the beginning of treatment. RESULTS: The effective rate was 44.8% in the study group and 45.3% in the control group (P = 0.971). The level of estradiol was 5.17 +/- 6.68 x 10(4) pg/L and 4.19 +/- 3.06 x 10(4) pg/L in the study group and control group respectively; and was 3.08 +/- 2.80 x 10(4) pg/L and 2.76 +/- 1.98 x 10(4) pg/L in the study group and control group respectively 8 weeks after the beginning of treatment, both decreased by 43.7% in comparison with those before treatment (both P < 0.001), however, there was no significant difference between the study group and control group (P = 0.141). The side effects of exemestane included thirst, giddiness, and nausea. CONCLUSION: An effective hormonal medicine, exemestane has good therapeutic efficacy in postmenopausal advanced metastatic breast cancer with only mild side effects.