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Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study.

ABSTRACT: Purpose To test whether computer-aided diagnosis (CAD) approaches can increase the positive predictive value (PPV) and reduce the false-positive rate in lung cancer screening for small nodules compared with human reading by thoracic radiologists. Materials and Methods A matched case-control sample of low-dose computed tomography (CT) studies in 186 participants with 4-20-mm noncalcified lung nodules who underwent biopsy in the National Lung Screening Trial (NLST) was selected. Variables used for matching were age, sex, smoking status, chronic obstructive pulmonary disease status, body mass index, study year of the positive screening test, and screening results. Studies before lung biopsy were randomly split into a training set (70 cancers plus 70 benign controls) and a validation set (20 cancers plus 26 benign controls). Image features from within and outside dominant nodules were extracted. A CAD algorithm developed from the training set and a random forest classifier were applied to the validation set to predict biopsy outcomes. Receiver operating characteristic analysis was used to compare the prediction accuracy of CAD with the NLST investigator's diagnosis and readings from three experienced and board-certified thoracic radiologists who used contemporary clinical practice guidelines. Results In the validation cohort, the area under the receiver operating characteristic curve for CAD was 0.9154. By default, the sensitivity, specificity, and PPV of the NLST investigators were 1.00, 0.00, and 0.43, respectively. The sensitivity, specificity, PPV, and negative predictive value of CAD and the three radiologists' combined reading were 0.95, 0.88, 0.86, and 0.96 and 0.70, 0.69, 0.64, and 0.75, respectively. Conclusion CAD could increase PPV and reduce the false-positive rate in the early diagnosis of lung cancer. ^© RSNA, 2017 Online supplemental material is available for this article.

SUBMITTER: Huang P

PROVIDER: S-EPMC5779085 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAlthough miniscrews are widely used in orthodontic treatment as temporary anchorage devices, their correct and safe placement has attracted little attention. This study aimed to introduce a novel cone beam CT (CBCT) image-based computer aided design and computer aided manufacturing (CAD-CAM) template for orthodontic miniscrew implantation and to evaluate the effectiveness of miniscrews implanted under the guidance of this template.MethodsThe CBCT scans of ten patients requiring miniscrews as anchorage were analyzed in NNT software to predetermine the insertion sites of miniscrew implants. The DICOM data of the scans, along with virtual miniscrews acquired in Solidworks software, were imported into Mimics software to construct three-dimensional (3D) images of teeth and bone and to determine the virtual position of miniscrews and 3D virtual templates were designed following consideration of the virtual implantation plans. A STL (Stereolithography) file of the virtual template was output, and the resin template was then fabricated with a stereolithographic appliance (SLA). 24 Miniscrews were then implanted guided by the template and clinical evaluation of their safety and stability, as well as their placement deviations, were made. A dental casts model and cephalometric analysis before and after orthodontic treatment were made to assess the dentomaxillofacial changes.ResultsAll 24 miniscrews had no contact with adjacent roots. 18 miniscrews had a grade I safety score and six had a grade II. The miniscrews were stable at 1, 3, 6, and 9 months after implantation, although there was mild inflammation around two miniscrews. Implantation deviation of miniscrew in the crown was (1.03±0.65) and (1.26±0.72) mm in the apex, on average. Satisfactory dentomaxillofacial changes in 10 patients with these 24 miniscrews as anchorage were acquired.ConclusionsMiniscrews could be implanted in the targeted position safely and precisely when guided by the novel templates, and remained stable during orthodontic treatment. Patients treated with these miniscrews as anchorage in orthodontic treatment acquired satisfactory dentomaxillofacial changes.

Project description:IntroductionAnatomic characteristics of kidneys derived from ultrasound images are potential biomarkers of children with congenital abnormalities of the kidney and urinary tract (CAKUT), but current methods are limited by the lack of automated processes that accurately classify diseased and normal kidneys.ObjectiveThe objective of the study was to evaluate the diagnostic performance of deep transfer learning techniques to classify kidneys of normal children and those with CAKUT.Study designA transfer learning method was developed to extract features of kidneys from ultrasound images obtained during routine clinical care of 50 children with CAKUT and 50 controls. To classify diseased and normal kidneys, support vector machine classifiers were built on the extracted features using (1) transfer learning imaging features from a pretrained deep learning model, (2) conventional imaging features, and (3) their combination. These classifiers were compared, and their diagnosis performance was measured using area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity.ResultsThe AUC for classifiers built on the combination features were 0.92, 0.88, and 0.92 for discriminating the left, right, and bilateral abnormal kidney scans from controls with classification rates of 84%, 81%, and 87%; specificity of 84%, 74%, and 88%; and sensitivity of 85%, 88%, and 86%, respectively. These classifiers performed better than classifiers built on either the transfer learning features or the conventional features alone (p < 0.001).DiscussionThe present study validated transfer learning techniques for imaging feature extraction of ultrasound images to build classifiers for distinguishing children with CAKUT from controls. The experiments have demonstrated that the classifiers built on the transfer learning features and conventional image features could distinguish abnormal kidney images from controls with AUCs greater than 0.88, indicating that classification of ultrasound kidney scans has a great potential to aid kidney disease diagnosis. A limitation of the present study is the moderate number of patients that contributed data to the transfer learning approach.ConclusionsThe combination of transfer learning and conventional imaging features yielded the best classification performance for distinguishing children with CAKUT from controls based on ultrasound images of kidneys.

Project description:PurposeEffective diagnosis of tuberculosis (TB) relies on accurate interpretation of radiological patterns found in a chest radiograph (CXR). Lack of skilled radiologists and other resources, especially in developing countries, hinders its efficient diagnosis. Computer-aided diagnosis (CAD) methods provide second opinion to the radiologists for their findings and thereby assist in better diagnosis of cancer and other diseases including TB. However, existing CAD methods for TB are based on the extraction of textural features from manually or semi-automatically segmented CXRs. These methods are prone to errors and cannot be implemented in X-ray machines for automated classification.MethodsGabor, Gist, histogram of oriented gradients (HOG), and pyramid histogram of oriented gradients (PHOG) features extracted from the whole image can be implemented into existing X-ray machines to discriminate between TB and non-TB CXRs in an automated manner. Localized features were extracted for the above methods using various parameters, such as frequency range, blocks and region of interest. The performance of these features was evaluated against textural features. Two digital CXR image datasets (8-bit DA and 14-bit DB) were used for evaluating the performance of these features.ResultsGist (accuracy 94.2% for DA, 86.0% for DB) and PHOG (accuracy 92.3% for DA, 92.0% for DB) features provided better results for both the datasets. These features were implemented to develop a MATLAB toolbox, TB-Xpredict, which is freely available for academic use at http://sourceforge.net/projects/tbxpredict/. This toolbox provides both automated training and prediction modules and does not require expertise in image processing for operation.ConclusionSince the features used in TB-Xpredict do not require segmentation, the toolbox can easily be implemented in X-ray machines. This toolbox can effectively be used for the mass screening of TB in high-burden areas with improved efficiency.

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Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study.

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