Movements of Ancient Human Endogenous Retroviruses Detected in SOX2-Expressing Cells.

Human endogenous retroviruses (HERVs) occupy approximately 8% of the human genome. HERVs, transcribed in early embryos, are epigenetically silenced in somatic cells, except under pathological conditions. HERV-K is thought to protect embryos from exogenous viral infection. However, uncontrolled HERV-K expression in somatic cells has been implicated in several diseases. Here, we show that SOX2, which plays a key role in maintaining the pluripotency of stem cells, is critical for HERV-K LTR5Hs. HERV-K undergoes retrotransposition within producer cells in the absence of Env expression. Furthermore, we identified new HERV-K integration sites in long-term culture of induced pluripotent stem cells that express SOX2. These results suggest that the strict dependence of HERV-K on SOX2 has allowed HERV-K to protect early embryos during evolution while limiting the potentially harmful effects of HERV-K retrotransposition on host genome integrity in these early embryos. IMPORTANCE Human endogenous retroviruses (HERVs) account for approximately 8% of the human genome; however, the physiological role of HERV-K remains unknown. This study found that HERV-K LTR5Hs and LTR5B were transactivated by SOX2, which is essential for maintaining and reestablishing pluripotency. HERV-K can undergo retrotransposition within producer cells without env expression, and new integration sites may affect cell proliferation. In induced pluripotent stem cells (iPSCs), genomic impairment due to HERV-K retrotransposition has been identified, but it is a rare event. Considering the retention of SOX2-responsive elements in the HERV-K long terminal repeat (LTR) for over 20 million years, we conclude that HERV-K may play important physiological roles in SOX2-expressing cells.

[Selection of Radiomic Features for the Classification of Triple-negative Breast Cancer Based on Radiogenomics].

Subtype classification of breast cancer by analyzing the gene expression profile of cancer cells is becoming a standard procedure. Breast cancer subtype classification is more useful than the conventional method because the characteristics of subtype classification is directly connected with the treatment method. However, genetic testing is invasive, and a part of cancer cells may not represent the overall nature of the cancer. In the computer-aided diagnosis (CAD) scheme for differentiation of triple-negative breast cancer (TNBC) by estimating the genetic properties of cancer based on Radiogenomics, principal component analysis (PCA) and least absolute shrinkage and selection operator (Lasso) were used for reducing the dimension of radiomic features, and we compared usefulness of both. We collected 81 magnetic resonance (MR) images, which included 30 TNBC and 51 others, from the public database. From the MR slice images, we selected the slice containing the largest area of the cancer and manually marked the cancer region. We subsequently calculated 294 radiomic features in the cancer region, and reduced the dimension of radiomic features. Finally, linear discriminant analysis, with the dimensionally compressed 10 image features, was used for distinguishing between TNBC and others. Area under the curve (AUC) was 0.60 when we used PCA, whereas AUC was 0.70 when we used Lasso (p=0.0058). Therefore, Lasso is useful for the determination of radiomic features in Radiogenomics.

Apparent diffusion coefficient histogram analysis for prediction of prognosis in glioblastoma.

BACKGROUND: To investigate the potential to predict prognosis of glioblastoma (GBM) patients by analysis of the broader and lower values in the lower distribution of apparent diffusion coefficient (ADCL) (B&L-ADCL) values in the ADC histogram. BACKGROUND: Presurgical publicly available diffusion-weighted images (DWI) and contrast-enhanced T1-weighted images from 76 GBM patients were analyzed. With applied 2-mixture normal distribution in the ADC histogram of enhanced lesions on T1-weighted images, the mean and width of ADCL were analyzed. We dichotomized the lower mean of ADCL (L-ADCL) and the broader width of ADCL (B-ADCL) at their own average. B&L-ADCL was defined as B-ADCL with L-ADCL. Progression-free survival (PFS) and overall survival (OS) were determined by using Cox proportional hazards analysis and the Kaplan-Meier method with the log-rank test. The difference between PFS and OS was calculated. RESULTS: Six (7.89%) patients had B&L-ADCL values. B&L-ADCL was strongly associated with poor PFS (hazard risk: 5.747; P=0.002) and OS (hazard risk: 3.331; P=0.018). There were significant differences in PFS (median, 77 vs. 302 days; P<0.001) and OS (median, 199 vs. 472 days; P=0.004) between the patents with and without B&L-ADCL. There was no significant difference in the OS-PFS duration difference between the patients with (median, 79 days) and without B&L-ADCL (median, 132 days) (P=0.348). CONCLUSION: In this study, B&L-ADCL from pretreatment ADC analysis predicted poor PFS. B&L-ADCL may indicate higher cellularity and heterogeneity in GBM tumor tissue.

[Quantitation of Cerebral Atrophy due to Normal Aging: Principal Component Analysis with MR Images in Patients' Age Groups].

To evaluate the degree of cerebral atrophy, quantification methods of a difference from a standard normal brain are often used in clinical practice. However, these methods may not evaluate the cerebral atrophy accurately, because they do not take into account any cerebral atrophies due to normal aging. The purpose of this study is to develop a model for taking into account the cerebral atrophy due to normal aging. We obtained 60 normal magnetic resonance (MR) images from the Alzheimer's disease neuroimaging initiative database. These data included 20 images of each age group of 60's, 70's, and 80's, respectively. For anatomical standardization of the images, we used the statistical parametric mapping software and employed a linear grayscale transformation. The principal component (PC) analysis with voxel values of 60 normal MR images was subsequently performed to calculate eigenvectors and PC scores. All cases were projected onto the eigenspace formed by 2nd and 5th PC scores. The experimental result showed separated distributions corresponding to the age groups. In addition, the sites of cerebral atrophy could be recognized by displaying eigenimages. Our proposed method would be useful for the accurate evaluation of cerebral atrophy caused by Alzheimer's disease.

[Basic Investigation of a Cerebral Blood Flow Quantification Method without Blood Sampling Method (Improved Brain Uptake Ratio (IBUR)) Using a Fan-beam Collimator].

We performed a basic evaluation for measuring the input function using a fan-beam collimator. Furthermore, we examined the validity of the brain blood flow quantitative measurement from the input function. Using the fanbeam collimator, we imaged syringes of various diameters containing 99 mTc as well as a virtual aorta inside a thoracic phantom. We changed the collimator distance and angle in relation to the sources, and the syringe was placed in vertical and horizontal positions as well. For evaluation, we used region of interest (ROI) of various sizes and positions. Furthermore, we conducted clinical evaluation for 19 subjects and calculated whole-brain mean cerebral blood flow using improved brain uptake ratio method by examination of 99 mTc-ECD cerebral blood flow. For ROIs smaller in size than diameter of the syringes and virtual ascending aorta, amount of change in the ROI counts by fan-beam collimator became smaller as distance to the source became closer, with less than 5% at 175 mm. Also, change with respect to angle of the collimator was less than 5% at 20°. In a clinical study, aortas could be imaged without truncation and input-functions could be measured in all 19 patients. By using ROIs smaller than the aorta diameter and placing the collimator close to the source, it was suggested that fan-beam collimator can be used to determine the input function.

Image Data Mining for Pattern Classification and Visualization of Morphological Changes in Brain MR Images.

Hospital information systems (HISs) and picture archiving and communication systems (PACSs) are archiving large amounts of data (i.e., "big data") that are not being used. Therefore, many research projects in progress are trying to use "big data" for the development of early diagnosis, prediction of disease onset, and personalized therapies. In this study, we propose a new method for image data mining to identify regularities and abnormalities in the large image data sets. We used 70 archived magnetic resonance (MR) images that were acquired using three-dimensional magnetization-prepared rapid acquisition with gradient echo (3D MP-RAGE). These images were obtained from the Alzheimer's disease neuroimaging initiative (ADNI) database. For anatomical standardization of the data, we used the statistical parametric mapping (SPM) software. Using a similarity matrix based on cross-correlation coefficients (CCs) calculated from an anatomical region and a hierarchical clustering technique, we classified all the abnormal cases into five groups. The Z score map identified the difference between a standard normal brain and each of those from the Alzheimer's groups. In addition, the scatter plot obtained from two similarity matrixes visualized the regularities and abnormalities in the image data sets. Image features identified using our method could be useful for understanding of image findings associated with Alzheimer's disease.

Eigenspace template matching for detection of lacunar infarcts on MR images.

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification is often hindered by the difficulty in distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided detection (CAD) scheme for the detection of lacunar infarcts. Although our previous CAD method indicated a sensitivity of 96.8% with 0.71 false positives (FPs) per slice, further reduction of FPs remained an issue for the clinical application. Thus, the purpose of this study is to improve our CAD scheme by using template matching in the eigenspace. Conventional template matching is useful for the reduction of FPs, but it has the following two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the cross-correlation coefficient with these templates is time consuming. To solve these problems, we used template matching in the lower dimension space made by a principal component analysis. Our database comprised 1,143 T1- and T2-weighted images obtained from 132 patients. The proposed method was evaluated by using twofold cross-validation. By using this method, 34.1% of FPs was eliminated compared with our previous method. The final performance indicated that the sensitivity of the detection of lacunar infarcts was 96.8% with 0.47 FPs per slice. Therefore, the modified CAD scheme could improve FP rate without a significant reduction in the true positive rate.

Kernel eigenspace template matching for detection of lacunar infarcts on MR images.

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction and dementia. However, accurate identification of lacunar infarcts is often difficult for radiologists. Our previous computer-aided detection (CAD) scheme achieved a sensitivity of 96.8% with 0.76 false positives (FPs) per slice. However, further reduction of FPs remained an issue for the clinical application. The purpose of this study is to improve our CAD scheme by using kernel eigenspace template matching. First, we selected the regions of interest (ROIs) around the candidate regions detected in our previous method. A kernel eigenspace was then made by using kernel principal component analysis of the training data set. A test ROI was projected onto the same kernel eigenspace as the training data set. The cross-correlation coefficients between the test ROI and all the training ROIs were calculated on the kernel eigenspace. By comparing the two maxima of coefficients with a lacunar ROI and an FP ROI, the test ROI was classified. By using the proposed method, the quantity of the templates became 1.9% of that in template matching on the real space and 31. 9% of FPs could be eliminated while keeping the same sensitivity; nevertheless 30.3% of FPs were eliminated when we employed the eigenspace template matching under the same condition. Therefore, kernel eigenspace template matching could improve FP rate without a significant reduction in the true positive rate.

Development of an automated region-of-interest-setting method based on a deep neural network for brain perfusion single photon emission computed tomography quantification methods.

Objectives: A simple noninvasive microsphere (SIMS) method using 123I-IMP and an improved brain uptake ratio (IBUR) method using 99mTc-ECD for the quantitative measurement of regional cerebral blood flow have been recently reported. The input functions of these methods were determined using the administered dose, which was obtained by analyzing the time activity curve of the pulmonary artery (PA) for SIMS and the ascending aorta (AAo) for the IBUR methods for dynamic chest images. If the PA and AAo regions of interest (ROIs) can be determined using deep convolutional neural networks (DCNN) for segmentation, the accuracy of these ROI-setting methods can be improved through simple analytical operations to ensure repeatability and reproducibility. The purpose of this study was to develop new PA and AAo-ROI setting methods using a DCNN (DCNN-ROI method). Methods: A U-Net architecture based on convolutional neural networks was used to determine the PA and AAo candidate regions. Images of 290 patients who underwent 123I-IMP RI-angiography and 108 patients who underwent 99mTc-ECD RI-angiography were used. The PA and AAo-ROI results for the DCNN-ROI method were compared to those obtained using manual methods. The counts for the input function on the PA and AAo-ROI were determined by integrating the area under the curve (AUC) counts of the time-activity curve of PA and AAo-ROI, respectively. The effectiveness of the DCNN-ROI method was elucidated through a comparison with the integrated AUC counts of the DCNN-ROI and the manual ROI. Results: The coincidence ratio for the locations of the PA and AAo-ROI obtained using the DCNN method and that for the manual method was 100%. Strong correlations were observed between the AUC counts using the DCNN and manual methods. Conclusion: New ROI- setting programs were developed using a deep convolution neural network DCNN to determine the input functions for the SIMS and IBUR methods. The accuracy of these methods is comparable to that of the manual method.

Feasibility of direct brain 18F-fluorodeoxyglucose-positron emission tomography attenuation and high-resolution correction methods using deep learning.

Objectives: To develop the following three attenuation correction (AC) methods for brain 18F-fluorodeoxyglucose-positron emission tomography (PET), using deep learning, and to ascertain their precision levels: (i) indirect method; (ii) direct method; and (iii) direct and high-resolution correction (direct+HRC) method. Methods: We included 53 patients who underwent cranial magnetic resonance imaging (MRI) and computed tomography (CT) and 27 patients who underwent cranial MRI, CT, and PET. After fusion of the magnetic resonance, CT, and PET images, resampling was performed to standardize the field of view and matrix size and prepare the data set. In the indirect method, synthetic CT (SCT) images were generated, whereas in the direct and direct+HRC methods, a U-net structure was used to generate AC images. In the indirect method, attenuation correction was performed using SCT images generated from MRI findings using U-net instead of CT images. In the direct and direct+HRC methods, AC images were generated directly from non-AC images using U-net, followed by image evaluation. The precision levels of AC images generated using the indirect and direct methods were compared based on the normalized mean squared error (NMSE) and structural similarity (SSIM). Results: Visual inspection revealed no difference between the AC images prepared using CT-based attenuation correction and those prepared using the three methods. The NMSE increased in the order indirect, direct, and direct+HRC methods, with values of 0.281×10-3, 4.62×10-3, and 12.7×10-3, respectively. Moreover, the SSIM of the direct+HRC method was 0.975. Conclusion: The direct+HRC method enables accurate attenuation without CT exposure and high-resolution correction without dedicated correction programs.

A micro-disc-based multiplex method for monitoring emerging SARS-CoV-2 variants using the molecular diagnostic tool Intelli-OVI.

BACKGROUND: Highly transmissible viruses including SARS-CoV-2 frequently accumulate novel mutations that are detected via high-throughput sequencing. However, there is a need to develop an alternative rapid and non-expensive approach. Here we developed a novel multiplex DNA detection method Intelli-OVI for analysing existing and novel mutations of SARS-CoV-2. METHODS: We have developed Intelli-OVI that includes the micro-disc-based method IntelliPlex and computational algorithms of objective variant identification (OVI). More than 250 SARS-CoV-2 positive samples including wastewater ones were analysed to verify the efficiency of the method. RESULTS: IntelliPlex uses micro-discs printed with a unique pictorial pattern as a labelling conjugate for DNA probes, and OVI allows simultaneous identification of several variants using multidimensional data obtained by the IntelliPlex method. Importantly, de novo mutations can be identified by decreased signals, which indicates that there is an emergence of de novo variant virus as well as prompts the need to design additional primers and probes. We have upgraded probe panel according to the emergence of new variants and demonstrated that Intelli-OVI efficiently identified more than 20 different SARS-CoV-2 variants by using 35 different probes simultaneously. CONCLUSIONS: Intelli-OVI can be upgraded to keep up with rapidly evolving viruses as we showed in this study using SARS-CoV-2 as an example and may be suitable for other viruses but would need to be validated.

As the COVID-19 pandemic progresses, it is increasingly becoming important to be able to detect emerging new variants of concerns of SARS-CoV-2, the virus that causes COVID-19, for accurate surveillance and timely interventions. We developed a rapid diagnostic method for detecting multiple SARS-CoV-2 variants and tested it using various starting materials such as sputum, nasopharyngeal swabs and wastewater. The method could accurately detect multiple subvariants of Omicron and showed potential for rapid adaptability to detect the virus as it evolves. This technology could enable continuous monitoring of emerging SARS-CoV-2 variants and the opportunity to intercept transmission with timely interventions to prevent viral spread.

[Development of temporal subtraction method for chest radiographs by using pixel matching technique].

A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can enhance interval changes on a chest radiograph by removal of most normal structures. However, subtraction artifacts, which tend to reduce its effectiveness in the detection of interval changes, were still included in the conventional method. In this study, we have developed a pixel matching technique to reduce artifacts in the temporal subtraction images. With this technique, the pixel value in a nonlinearly warped previous image is replaced by a pixel value within a kernel, which is closest to the pixel value on a current image. For evaluation of the proposed method, one hundred temporal subtraction images with a simulated nodule were used. When the kernel size of 3×3 was employed in the pixel matching technique, the misregistration artifacts decreased by 72%, and the contrast-to-noise ratio of the simulated lung nodules was increased by 5% in comparison with the conventional method. However, the area of the simulated nodule on the subtraction image decreased by 6%. Our results indicated that the pixel matching technique can enhance simulated nodules, with a substantial reduction of misregistration artifacts in comparison with conventional subtraction images. Therefore, we believe that the temporal subtraction method with the pixel matching technique would assist radiologists' diagnoses for detection of lung nodules in digital chest radiography.

[Computer-aided detection of metastatic brain tumors in magnetic resonance images].

The fact that accurate detection of metastatic brain tumors is important for making decisions on the treatment course of patients prompted us to develop a computer-aided diagnostic scheme for detecting metastatic brain tumors. In this paper, we first describe how we extracted the cerebral parenchyma region using a standard deviation filter. Second, initial candidates for tumors were decided by sphericity and cross-correlation value with a simulated ring template. Third, we made true positive and false positive templates obtained from actual clinical images and applied the template matching technique to them. Finally, we detected metastatic tumors using these two characteristics. Our improved method was applied to 13 cases with 97 brain metastases. Sensitivity of detection of metastatic brain tumors was 80.4%, with 5.6 false positives per patient. Our proposed method has potential for detection of metastatic brain tumors in brain magnetic resonance (MR) images.

[Radioproteomics for Discriminating the Activity and Inactivity of Immune Checkpoint Molecules in Breast Cancer].

PURPOSE: Radioproteomics studies investigating the relationship between lesion phenotype and proteins have been progressed. The purpose of this study was to develop a radioproteomics method for discriminating between active and inactive immune checkpoint molecules based on lesion phenotype. METHODS: From the public database TCGA-BRCA, mRNA and fat suppression contrast-enhanced T1-weighted images of 49 patients with breast cancer were selected for the experiment. Using mRNA, we defined cases with active (10 cases) and inactive (39 cases) immune checkpoint molecules. To discriminate these cases using lesion phenotype, 275 radiomics features were measured from the tumor area. After selecting 3 radiomics features by using Lasso, logistic regression was employed to discriminate between active and inactive cases of immune checkpoint molecules. RESULTS: Evaluation of ROC analysis showed that the AUC was 0.81. CONCLUSION: Patients whose immune cell function is being braked by immune checkpoint molecules are likely to respond to immune checkpoint inhibitors when their activity is inhibited. Therefore, our results may be applied to predict the effects of immune checkpoint inhibitors in breast cancer treatment.

Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs.

INTRODUCTION: Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images. METHODS: In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance. RESULTS: The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90). CONCLUSIONS: We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.

Computer-aided diagnosis for detection of lacunar infarcts on MR images: ROC analysis of radiologists' performance.

The purpose of this study was to retrospectively evaluate radiologist performance in detection of lacunar infarcts on T1- and T2-weighted images, without and with the use of a computer-aided diagnosis (CAD) scheme. Thirty T1-weighted and 30 T2-weighted MR images obtained from 30 patients were used for assessing observer performance. These images were acquired using the fast spin-echo sequence with a 1.5-T MR imaging scanner. The group included 15 patients (age range, 48-83 years; mean age, 67.2 years; 10 men and five women) with a lacunar infarct and 15 patients (age range, 39-76 years; mean age, 64.0 years; eight men and seven women) without lacunar infarcts. Nine radiologists participated in the study. The radiologists initially interpreted the T1- and T2-weighted images without and then with the use of CAD, which indicated their confidence levels regarding the presence (or absence) of lacunar infarcts and the most likely position of a lesion on each MR scan. The observers' performance without and with the computer output was evaluated by performing receiver operating characteristic analysis. For the nine radiologists, the mean area under the best-fit binormal receiver operating characteristic curve plotted for unit square values of radiologists who interpreted the images without and with the scheme were 0.891 and 0.937, respectively. The performance of the radiologists improved significantly when they used the computer output (p=0.032). The CAD scheme has potential to improve the accuracy of radiologists' performance in detection of lacunar infarcts.

Prediction of pathological complete response using radiomics on MRI in patients with breast cancer undergoing neoadjuvant pharmacotherapy.

PURPOSE: Neoadjuvant pharmacotherapy is essential for patients with breast cancer who wish to preserve the breast by shrinking the malignant tumor, allowing breast-conserving surgery. It may eliminate cancer cells completely, which is known as pathologic complete response (pCR). Patients with pCR have a lower risk of recurrence. The purpose of this study was to develop a method for predicting patients who achieve pCR by neoadjuvant pharmacotherapy using radiomic features in MR images. METHODS: Fat-suppressed T2-weighted MR images of 64 cases were identified from the ISPY1 dataset. There were 26 cases of pCR and 38 cases of non-pCR. The image slice with the largest tumor diameter was selected from MR images, and the tumor region was manually segmented. A total of 371 radiomic features were calculated from the tumor region. We selected nine radiomic features using Lasso in this study. A support vector machine (SVM) with nine radiomic features was used for predicting patients with pCR. RESULTS: The result of the ROC analysis showed that the area under the curve of SVM was 0.92 for distinguishing between pCR and non-pCR. Although the input data contain data that were misclassified by SVM, the survival curve classified into the pCR group was at a higher position than the non-pCR group. However, the log-rank test was [Formula: see text]. CONCLUSIONS: We developed a method to predict patients with pCR by neoadjuvant pharmacotherapy using noninvasive MR images. The survival curve of patients classified as having pCR by the proposed method was higher than those classified as non-pCR. Since the proposed method predicts patients who achieve pCR by neoadjuvant pharmacotherapy, it enhances the value of preoperative image information.

A fast and fully automatic method for cerebrovascular segmentation on time-of-flight (TOF) MRA image.

The precise three-dimensional (3-D) segmentation of cerebral vessels from magnetic resonance angiography (MRA) images is essential for the detection of cerebrovascular diseases (e.g., occlusion, aneurysm). The complex 3-D structure of cerebral vessels and the low contrast of thin vessels in MRA images make precise segmentation difficult. We present a fast, fully automatic segmentation algorithm based on statistical model analysis and improved curve evolution for extracting the 3-D cerebral vessels from a time-of-flight (TOF) MRA dataset. Cerebral vessels and other tissue (brain tissue, CSF, and bone) in TOF MRA dataset are modeled by Gaussian distribution and combination of Rayleigh with several Gaussian distributions separately. The region distribution combined with gradient information is used in edge-strength of curve evolution as one novel mode. This edge-strength function is able to determine the boundary of thin vessels with low contrast around brain tissue accurately and robustly. Moreover, a fast level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Quantitative comparisons with 10 sets of manual segmentation results showed that the average volume sensitivity, the average branch sensitivity, and average mean absolute distance error are 93.6%, 95.98%, and 0.333 mm, respectively. By applying the algorithm to 200 clinical datasets from three hospitals, it is demonstrated that the proposed algorithm can provide good quality segmentation capable of extracting a vessel with a one-voxel diameter in less than 2 min. Its accuracy and speed make this novel algorithm more suitable for a clinical computer-aided diagnosis system.

[Radiomics for Estimating Recurrence Risk of Patients with Lung Cancer by Using Survival Analysis].

PURPOSE: Because of the promotion of cancer screening, the number of patients with lung cancer detected at the early stage has increased. However, it was reported that 30-40% of the lung cancer patients at stage I relapsed. If the recurrence risk can be accurately predicted, it is possible to give medical care for improving the prognosis of lung cancer patients. The purpose of this study was to develop a method for the prediction of recurrence risk of patients with lung cancer by using survival analysis of radiomics approach. METHOD: A public database was used in this study. Fifty patients (25 recurrences and 25 censored cases) classified as stage I or II were selected and their pretreatment computed tomography (CT) images were obtained. First, we selected one slice containing the largest tumor area and manually segmented the tumor regions. We subsequently calculated 367 radiomic features such as tumor size, shape, CT values, and texture. Radiomic features were selected by using least absolute shrinkage and selection (Lasso). Cox regression model and random survival forest (RSF) with the selected radiomic features were used for estimating the recurrence functions of fifty patients. RESULT: The experimental result showed that average area under the curve (AUC) values of Cox regression model and RSF for the prediction accuracy were 0.81 and 0.93, respectively. CONCLUSION: Since our scheme can predict recurrence risk of patients with lung cancer by using non-invasive image examinations, it would be useful for the selection of treatment and the follow-up after the treatment.