Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram

BACKGROUND

Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics.

PURPOSE

This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS).

METHODS

CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram.

RESULTS

The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible.

CONCLUSION

The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer

BACKGROUND

Synchronous liver metastasis (SLM) is an indicator of poor prognosis for colorectal cancer (CRC). Nearly 50% of CRC patients develop hepatic metastasis, with 15%-25% of them presenting with SLM. The evaluation of SLM in CRC is crucial for precise and personalized treatment. It is beneficial to detect its response to chemotherapy and choose an optimal treatment method.

AIM

To construct prediction models based on magnetic resonance imaging (MRI)-radiomics and clinical parameters to evaluate the chemotherapy response in SLM of CRC.

METHODS

A total of 102 CRC patients with 223 SLM lesions were identified and divided into disease response (DR) and disease non-response (non-DR) to chemotherapy. After standardizing the MRI images, the volume of interest was delineated and radiomics features were calculated. The MRI-radiomics logistic model was constructed after methods of variance/Mann-Whitney U test, correlation analysis, and least absolute shrinkage and selection operator in feature selecting. The radiomics score was calculated. The receiver operating characteristics curves by the DeLong test were analyzed with MedCalc software to compare the validity of all models. Additionally, the area under curves (AUCs) of DWI, T2WI, and portal phase of contrast-enhanced sequences radiomics model (Ra-DWI, Ra-T2WI, and Ra-portal phase of contrast-enhanced sequences) were calculated. The radiomics-clinical nomogram was generated by combining radiomics features and clinical characteristics of CA19-9 and clinical N staging.

RESULTS

The AUCs of the MRI-radiomics model were 0.733 and 0.753 for the training (156 lesions with 68 non-DR and 88 DR) and the validation (67 lesions with 29 non-DR and 38 DR) set, respectively. Additionally, the AUCs of the training and the validation set of Ra-DWI were higher than those of Ra-T2WI and Ra-portal phase of contrast-enhanced sequences (training set: 0.652 vs 0.628 and 0.633, validation set: 0.661 vs 0.575 and 0.543). After chemotherapy, the top four of twelve delta-radiomics features of Ra-DWI in the DR group belonged to gray-level run-length matrices radiomics parameters. The radiomics-clinical nomogram containing radiomics score, CA19-9, and clinical N staging was built. This radiomics-clinical nomogram can effectively discriminate the patients with DR from non-DR with a higher AUC of 0.809 (95% confidence interval: 0.751-0.858).

CONCLUSION

MRI-radiomics is conducive to predict chemotherapeutic response in SLM patients of CRC. The radiomics-clinical nomogram, involving radiomics score, CA19-9, and clinical N staging is more effective in predicting chemotherapeutic response.

Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model

Objective:

This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD).

Methods:

357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T₁WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD.

Results:

Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798.

Conclusion:

The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.

MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors

Background

We conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).

Methods

Forty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal–Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.

Results

The high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).

Conclusions

Radiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.

Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis

This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross‐sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. Texture analysis extracted the top five most powerful texture features of MR images in each sequence quantitatively for detecting the MGMT promoter methylation status. The radiomic signature (Radscore) was generated by a linear combination of the five features and estimates in each sequence. The combined model based on each Radscore was established using multivariate logistic regression analysis. A receiver operating characteristic (ROC) curve, nomogram, calibration, and decision curve analysis (DCA) were used to evaluate the performance of the model. No significant differences were observed in any of the visual radiographic features or clinical factors between different MGMT methylated statuses. The top five most powerful features were selected from a total of 396 texture features of T1, contrast‐enhanced T1, T2, and T2 FLAIR. Each sequence’s Radscore can distinguish MGMT methylated status. A combined model based on Radscores showed differentiation between methylated MGMT and unmethylated MGMT both in the glioblastoma (GBM) dataset as well as the dataset for all other gliomas. The area under the ROC curve values for the combined model was 0.818, with 90.5% sensitivity and 72.7% specificity, in the GBM dataset, and 0.833, with 70.2% sensitivity and 90.6% specificity, in the overall gliomas dataset. Nomogram, calibration, and DCA also validated the performance of the combined model. The combined model based on texture features could be considered as a noninvasive imaging marker for detecting MGMT methylation status in glioma.

A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study

OBJECTIVE

This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection.

METHODS

The clinical and imaging data of 64 patients with confirmed diagnoses of COVID-19 were retrospectively selected and divided into a stable group and a progressive group according to the data obtained from the ongoing treatment process. Imaging features from whole-lung images from baseline computed tomography (CT) scans were extracted and dimensionality reduction was performed. Support vector machines were used to construct radiomics signatures and to compare differences between the 2 groups. We also compared the differences of signature scores in the clinical, laboratory, and CT image feature subgroups and finally analyzed the correlation between the radiomics features of the constructed signature and the other features including clinical, laboratory, and CT imaging features.

RESULTS

The signature has a good classification effect for the stable group and the progressive group, with area under curve, sensitivity, and specificity of 0.833, 80.95%, and 74.42%, respectively. Signature score differences in laboratory and CT imaging features between subgroups were not statistically significant (P>0.05); cough was negatively correlated with GLCM Entropy_angle 90_offset4 (r=-0.578), but was positively correlated with ShortRunEmphhasis_AllDirect_offset4_SD (r=0.454); C-reactive protein was positively correlated with Cluster Prominence_ AllDirect_offset 4_ SD (r=0.47).

CONCLUSION

The radiomics signature of the whole lung based on machine learning may reveal the changes of lung microstructure in the early stage and help to indicate the progression of the disease.

Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter

PURPOSE

This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD).

METHODS

PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T₁ -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility.

RESULTS

Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600.

CONCLUSION

Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.

A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer

Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve.

Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83–0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72–0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone.

Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.

[Application Values of Gadolinium-ethoxybenzyl Diethylenetriaminepentaacetic Acid Enhanced Magnetic Resonance Imaging-based Radiomics in the Quantitative Assessment of Liver Reserve Function of Patients with Liver Cirrhosis]

Objective To evaluate the correlation between the radiomics signature of hepatobiliary phase imaging of gadolinium-ethoxybenzyl diethylenetriaminepentaacetic acid(Gd-EOB-DTPA)enhanced magnetic resonance imaging(MRI)and Child-Pugh of liver cirrhosis,establish nomogram prediction model,and assess the predictive value of quantitative assessment of liver reserve function of patients with liver cirrhosis. Methods One hundred patients with liver cirrhosis who met the inclusion criteria were divided into 52 patients with Child-Pugh grade A and 48 patients with Child-Pugh grade B+C according to Child-Pugh classification criteria,and were randomly divided into training set and test set at a proportion of 7∶3.The AK software was used to extract the imaging features of the Gd-EOB-DTPA-enhanced MRI hepatobiliary images of the patients in the training set,and the least absolute shrinkage and selection operator feature selection algorithm was used to reduce the dimension of the data,select the features,and construct the radiomics tags.According to the radiomics label Rad-score,a line chart(nomogram)prediction model was established to predict the Child-Pugh B+C level of liver reserve function.The model was applied to the training set and test set respectively,and the diagnostic efficiency was quantitatively evaluated by receiver operating characteristic(ROC)curve. Results After dimension reduction and screening of 396 texture feature parameters extracted by AK software,7 image feature parameters were obtained.According to the above characteristics,the radiomics tag Rad-score was constructed and the nomogram prediction model was created.The differences of Rad-score scores between Child-Pugh A and Child-Pugh B+C groups in training set and test set were statistically analyzed by Wilcoxon rank sum test(P=0.000, P=0.001).The diagnostic efficacy of nomogram prediction model for predicting Child-Pugh B+C grade of liver reserve function in the ROC curve of training set and test set was 0.88 and 0.86 respectively. Conclusions The nomogram prediction model created according to the radiomics tag Rad-score of patients with liver cirrhosis with different liver reserve functions can be used as a more accurate and reliable auxiliary detection tool for liver reserve function.It provides a new means for clinicians to evaluate liver reserve function more accurately.

Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer

Background

In pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required.

Purpose

To develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice.

Study Type

Retrospective.

Population

We enrolled 303 patients from two medical centers. Patients with a disease‐free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126).

Field Strength/Sequence

3.0T axial T₁‐weighted (T₁‐w), T₂‐weighted (T₂‐w), contrast‐enhanced T₁‐weighted (CET₁‐w).

Assessment

ER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19‐9, and radiomic‐related features of ER were assessed. In addition, to determine the intra‐ and interobserver reproducibility of radiomic features extraction, the intra‐ and interclass correlation coefficients (ICC) were calculated.

Statistical Tests

The area under the receiver‐operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit.

Results

The AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19‐9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19‐9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort).

Data Conclusion

The radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer.

Level of Evidence: 4

Technical Efficacy: Stage 4

J. Magn. Reson. Imaging 2020;52:231–245.

Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method

Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building the classification models in aMCI diagnosis. Methods: For this target, we recruited 42 subjects with aMCI and 44 normal controls (NC). The right and left hippocampi were segmented for each subject using an efficient learning-based method. Then, the radiomic analysis was applied to calculate and select the radiomic features. Finally, two logistic regression models were built based on the selected features obtained from the right and left hippocampi. Results: There were 385 features derived after calculation, and four features remained after feature selection from each group of data. The area under the receiver operating characteristic (ROC) curve, specificity, sensitivity, positive predictive value, negative predictive value, precision, recall, and F-score of the classification evaluation index of the right hippocampus logistic regression model were 0.76, 0.71, 0.69, 0.69, 0.71, 0.69, 0.69, and 0.69, and those of the left hippocampus model were 0.79, 0.71, 0.54, 0.64, 0.63, 0.64, 0.54, and 0.58, respectively. Conclusion: Results demonstrate the potential hippocampal radiomic biomarkers are valid for the aMCI diagnosis. The MRI-based radiomic analysis, with further improvement and validation, can be used to identify patients with aMCI and guide the individual treatment.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults

BACKGROUND

White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need.

PURPOSE

To develop and validate a radiomics nomogram for the prediction of WMH progression.

STUDY TYPE

Retrospective.

POPULATION

Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old.

FIELD STRENGTH/SEQUENCE

T₁ -fluid attenuated inversion recovery images were acquired using a 3T scanner.

ASSESSMENT

WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction.

STATISTICAL TESTS

A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed.

RESULTS

The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively.

DATA CONCLUSION

This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Whole-tumor perfusion CT using texture analysis in unresectable stage IIIA/B non-small cell lung cancer treated with recombinant human endostatin

Background

To observe the dynamic changes of blood perfusion with whole-tumor computed tomography (CT) perfusion imaging using texture analysis in patients with unresectable stage IIIA/B non-small cell lung cancer (NSCLC) treated with recombinant human endostatin (Endostar).

Methods

This phase II clinical trial recruited 11 patients diagnosed with stage IIIA/B NSCLC. Histological examination prior to treatment revealed squamous cell carcinoma in 4 cases and adenocarcinoma in 7 cases. All patients underwent contrast-enhanced perfusion CT at baseline and a second CT scan 1 week after treatment initiation with Endostar. CT perfusion images including blood flow (BF), blood volume (BV), and permeability (PMB) were imported into OmniKinetics software to quantitatively assess the texture features. Skewness, kurtosis, and entropy were calculated at baseline and after anti-angiogenic therapy. Changes in tumor were analyzed using Wilcoxon signed-rank test. The association of parameters with survival was evaluated using Cox proportional hazards regression model.

Results

There were no statistical differences in the mean values of BF, BV, and PMB before and after treatment (P=0.594, 0.477 and 0.328, respectively). The skewness on BF images demonstrated significant differences at baseline and after treatment (0.6±2.7 vs. 1.0±2.6, P=0.010), while skewness of BV and PMB showed no significant variation (P=0.477 and 0.213, respectively). The kurtosis and entropy for BF, BV and PMB showed no significant differences (all P>0.05). In adenocarcinoma, the mean BF showed no significant differences at baseline and after treatment (76.5±25.7 vs. 101.2±46.4, P=0.398), while skewness for BF was significantly higher after treatment than at baseline (-0.19±3.3 vs. 0.59±3.2, P=0.028). No significant associations were found between perfusion CT imaging parameters and progression-free survival.

Conclusions

These results suggested that blood perfusion showed improvement with whole-tumor perfusion CT using texture analysis in patients with stage IIIA/B NSCLC treated by Endostar.

Application evaluation of DCE-MRI combined with quantitative analysis of DWI for the diagnosis of prostate cancer

The present study aimed to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with quantitative analysis of diffusion weighted imaging (DWI) for the diagnosis of prostate cancer (PCa). A total of 81 patients with prostatic diseases, including PCa (n=44) and benign prostatic hyperplasia (BPH, n=37), were imaged with T1 weighted imaging (T1WI), T2 weighted imaging (T2WI), DWI and DCE-MRI. The blood vessel permeability parameters volume transfer rate constant (K^trans), back flow rate constant (K_ep), extravascular extracellular space volume fraction (V_e), plasma volume fraction (V_p) and apparent diffusion coefficient (ADC) were measured, and compared between the two groups. The efficiency of these tools for the diagnosis of PCa was analyzed by receiver operating characteristic curve analysis. The efficiency of ADC combined with blood vessel permeability parameters in the diagnosis of PCa was analyzed by logistic regression. The correlation between these parameters and the Gleason score was evaluated by Spearman correlation analysis in the PCa group. The results demonstrated that, compared with the BPH group, K^trans, K_ep, V_e and V_p were higher, and ADC was lower in the PCa group (P<0.05). The combination of K_ep and ADC offered the highest diagnosis efficiency [area under the curve (AUC=0.939)]. However, the combination of three parameters did not significantly improve the diagnostic efficiency. A subtle improvement in diagnostic efficiency was observed when four parameters (K^trans + K_ep + V_e + ADC) were combined (AUC=0.940), which was significantly higher than with one parameter. The ADC value of the PCa group was negatively correlated with the primary Gleason pattern, secondary Gleason pattern and the total Gleason score in PCa (r=−0.665, −0.456 and −0.714, respectively; P<0.001). The V_p in the PCa group was slightly negatively correlated with the primary Gleason pattern of PCa (r=−0.385; P<0.05); however, no significant correlation was found with secondary Gleason pattern and the total Gleason score. The present study revealed that the combination of DCE-MRI quantitative analysis and DWI was efficient for PCa diagnosis. This may be because DCE-MRI and DWI can noninvasively detect water motility in tumor tissues and alterations in permeability during tumor neovascularization. The present study demonstrated that K_ep and ADC values may be used as predictive parameters for PCa diagnosis, which may help differentiate benign from malignant prostate lesions.

Molecular cloning, genomic organization, expression and evolution of 12S seed storage protein genes of Arabidopsis thaliana

We have identified a number of genes of the flowering plant Arabidopsis thaliana that are abundantly expressed during embryogenesis. In this paper we discuss four of these genes, which comprise a gene family: complete genomic nucleotide sequence of two of the genes and partial sequence of the other two shows that they are all homologous to the 12S globulin seed storage protein genes of other angiosperms. The four genes fall into three subfamilies, as defined by cross-hybridization. One subfamily contains two genes in the Landsberg erecta strain, but only a single gene in the Columbia strain of Arabidopsis. The other two of these 12S gene subfamilies contain only single genes in both strains. Thus, the seed storage protein gene family in Arabidopsis appears much simpler than that in other higher plants.These genes are expressed during the latter half of embryogenesis, a period in which abscisic acid (ABA) is thought to play a role in gene regulation, and known to play a role in seed physiology. We observed no significant difference in the expression profiles of these four genes in ABA-deficient and ABA-insensitive mutants of Arabidopsis, except that the onset of detectable expression of all of the transcripts is slightly delayed in both types of mutants.

Nucleotide sequence of the Salmonella typhimurium mutS gene required for mismatch repair: homology of MutS and HexA of Streptococcus pneumoniae

The mutS gene product of Escherichia coli and Salmonella typhimurium is one of at least four proteins required for methyl-directed mismatch repair in these organisms. A functionally similar repair system in Streptococcus pneumoniae requires the hex genes. We have sequenced the S. typhimurium mutS gene, showing that it encodes a 96-kilodalton protein. Amino-terminal amino acid sequencing of purified S. typhimurium MutS protein confirmed the initial portion of the deduced amino acid sequence. The S. typhimurium MutS protein is homologous to the S. pneumoniae HexA protein, suggesting that they arose from a common ancestor before the gram-negative and gram-positive bacteria diverged. Overall, approximately 36% of the amino acids of the two proteins are identical when the sequences are optimally aligned, including regions of stronger homology which are of particular interest. One such region is close to the amino terminus. Another, located closer to the carboxy terminus, includes homology to a consensus sequence thought to be diagnostic of nucleotide-binding sites. A third one, adjacent to the second, is homologous to the consensus sequence for the helix-turn-helix motif found in many DNA-binding proteins. We found that the S. typhimurium MutS protein can substitute for the E. coli MutS protein in vitro as it can in vivo, but we have not yet been able to demonstrate a similar in vitro complementation by the S. pneumoniae HexA protein.

The Salmonella typhimurium LT2 uvrD gene is regulated by the lexA gene product

The uvrD gene product apparently plays a role in the repair of UV damage, in mismatch repair, and in genetic recombination. A lower level of expression of the Salmonella typhimurium LT2 uvrD gene was observed in maxicells prepared from an Escherichia coli strain that contained a lexA+ plasmid than in maxicells prepared from an E. coli strain that lacked functional LexA protein. These results suggest that the uvrD+ gene is repressed by the LexA protein and is thus a member of the set of genes whose expression is increased by "SOS"-inducing treatments.

Identification of the uvrD gene product of Salmonella typhimurium LT2

The product of the uvrD gene of Salmonella typhimurium LT2 and Escherichia coli K-12 is thought to play a role in both the correction of mismatched bases and the repair of DNA damage, since insertion mutations in the uvrD gene increase the spontaneous mutation frequency and make the cells more sensitive to killing by UV irradiation. To clone the uvrD gene of S. typhimurium, we first generated a uvrD-specific probe by using DNA from an S. typhimurium uvrD421::Tn5 mutant. This probe was used to screen a lambda library of S. typhimurium DNA. Bacteriophage carrying intact uvrD+ genes were subsequently identified, and the uvrD+ gene was subcloned onto a low-copy-number vector. By using a combination of Tn1000 insertion mutagenesis and the maxicell technique, the product of the uvrD gene was shown to be a 75,000-dalton protein, and the relative direction of transcription of this protein was determined. Introduction of a low-copy-number plasmid carrying the S. typhimurium uvrD+ gene into uvrD insertion mutants of either S. typhimurium or E. coli restored the spontaneous mutation frequency and degree of UV sensitivity to the levels in the corresponding uvrD+ strains.

Self-association and base pairing of guanosine, cytidine, adenosine, and uridine in dimethyl sulfoxide solution measured by 15N nuclear magnetic resonance spectroscopy

The self-association of guanosine, cytidine, and adenosine and base pairing between guanosine, cytidine, adenosine, and uridine in dimethyl sulfoxide have been investigated by the variation of their 15N NMR chemical shifts with concentration and temperature. Guanosine, cytidine, and adenosine all showed evidence of self-association by hydrogen bonding. In guanosine/cytidine mixtures, a hydrogen-bonded dimer is formed; however, no base pairing could be detected with adenosine/cytidine or adenosine/uridine mixtures.