Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists.

PURPOSE: A wide array of benign and malignant lesions of the pancreas can be cystic and these cystic lesions can have overlapping imaging appearances. The purpose of this study is to compare the diagnostic accuracy of a radiomics-based pancreatic cyst classifier to an experienced academic radiologist. METHODS: In this IRB-approved retrospective single-institution study, patients with surgically resected pancreatic cysts who underwent preoperative abdominal CT from 2003 to 2016 were identified. Pancreatic cyst(s) and background pancreas were manually segmented, and 488 radiomics features were extracted. Random forest classification based on radiomics features, age, and gender was evaluated with fourfold cross-validation. An academic radiologist blinded to the final pathologic diagnosis reviewed each case and provided the most likely diagnosis. RESULTS: 214 patients were included (64 intraductal papillary mucinous neoplasms, 33 mucinous cystic neoplasms, 60 serous cystadenomas, 24 solid pseudopapillary neoplasms, and 33 cystic neuroendocrine tumors). The radiomics-based machine learning approach showed AUC of 0.940 in pancreatic cyst classification, compared with AUC of 0.895 for the radiologist. CONCLUSION: Radiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions.

Biphasic Regulation of Mitogen-Activated Protein Kinase Phosphatase 3 in Hypoxic Colon Cancer Cells.

Hypoxia, or low oxygen tension, is a hallmark of the tumor microenvironment. The hypoxia-inducible factor-1α (HIF-1α) subunit plays a critical role in the adaptive cellular response of hypoxic tumor cells to low oxygen tension by activating gene-expression programs that control cancer cell metabolism, angiogenesis, and therapy resistance. Phosphorylation is involved in the stabilization and regulation of HIF-1α transcriptional activity. HIF-1α is activated by several factors, including the mitogen-activated protein kinase (MAPK) superfamily. MAPK phosphatase 3 (MKP-3) is a cytoplasmic dual-specificity phosphatase specific for extracellular signal-regulated kinase 1/2 (Erk1/2). Recent evidence indicates that hypoxia increases the endogenous levels of both MKP-3 mRNA and protein. However, its role in the response of cells to hypoxia is poorly understood. Herein, we demonstrated that small-interfering RNA (siRNA)-mediated knockdown of MKP-3 enhanced HIF-1α (not HIF-2α) levels. Conversely, MKP-3 overexpression suppressed HIF-1α (not HIF-2α) levels, as well as the expression levels of hypoxia-responsive genes (LDHA, CA9, GLUT-1, and VEGF), in hypoxic colon cancer cells. These findings indicated that MKP-3, induced by HIF-1α in hypoxia, negatively regulates HIF-1α protein levels and hypoxia-responsive genes. However, we also found that long-term hypoxia (>12 h) induced proteasomal degradation of MKP-3 in a lactic acid-dependent manner. Taken together, MKP-3 expression is modulated by the hypoxic conditions prevailing in colon cancer, and plays a role in cellular adaptation to tumor hypoxia and tumor progression. Thus, MKP-3 may serve as a potential therapeutic target for colon cancer treatment.

CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.

OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predicting postoperative survival of patients with PDAC. MATERIALS AND METHODS. A total of 153 patients with surgically resected PDAC who underwent preoperative CT between 2011 and 2017 were retrospectively identified. Demographic, clinical, and survival information was collected from the medical records. Survival time after the surgical resection was used to stratify patients into a low-risk group (survival time > 3 years) and a high-risk group (survival time < 1 year). The 3D volume of the whole pancreatic tumor and background pancreas were manually segmented. A total of 478 radiomics features were extracted from tumors and 11 extra features were computed from pancreas boundaries. The 10 most relevant features were selected by feature reduction. Survival analysis was performed on the basis of clinical parameters both with and without the addition of the selected features. Survival status and time were estimated by a random survival forest algorithm. Concordance index (C-index) was used to evaluate performance of the survival prediction model. RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% accuracy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414. CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.

Current Status of Radiomics and Deep Learning in Liver Imaging.

ABSTRACT: Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.

Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade.

Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this "AstroPath" whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti-programmed cell death-1 (PD-1)-based therapy, including CD163+PD-L1- myeloid cells and CD8+FoxP3+PD-1low/mid T cells. These features were combined to stratify long-term survival after anti-PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution.

Pancreatic Cancer Imaging: A New Look at an Old Problem.

Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.

Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls.

PURPOSE: The purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls. MATERIALS AND METHODS: In this retrospective case-control study, 190 patients with PDAC (97 men, 93 women; 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. 3D volume of the pancreas was manually segmented from preoperative CT scans. Four hundred and seventy-eight radiomics features were extracted using in-house radiomics software. Eight hundred and fifty-four radiomics features were extracted using a commercially available research prototype. Random forest classifier was used for binary classification of PDAC vs. normal pancreas. Accuracy, sensitivity, and specificity of commercially available radiomics software were compared to in-house software. RESULTS: When 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house software decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged. CONCLUSION: Commercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research.

Deformable registration of x ray and MRI for postimplant dosimetry in low dose rate prostate brachytherapy.

PURPOSE: Dosimetric assessment following permanent prostate brachytherapy (PPB) commonly involves seed localization using CT and prostate delineation using coregistered MRI. However, pelvic CT leads to additional imaging dose and requires significant resources to acquire and process both CT and MRI. In this study, we propose an automatic postimplant dosimetry approach that retains MRI for soft-tissue contouring, but eliminates the need for CT and reduces imaging dose while overcoming the inconsistent appearance of seeds on MRI with three projection x rays acquired using a mobile C-arm. METHODS: Implanted seeds are reconstructed using x rays by solving a combinatorial optimization problem and deformably registered to MRI. Candidate seeds are located in MR images using local hypointensity identification. X ray-based seeds are registered to these candidate seeds in three steps: (a) rigid registration using a stochastic evolutionary optimizer, (b) affine registration using an iterative closest point optimizer, and (c) deformable registration using a local feature point search and nonrigid coherent point drift. The algorithm was evaluated using 20 PPB patients with x rays acquired immediately postimplant and T2-weighted MR images acquired the next day at 1.5 T with mean 0.8 × 0.8 × 3.0 mm 3 voxel dimensions. Target registration error (TRE) was computed based on the distance from algorithm results to manually identified seed locations using coregistered CT acquired the same day as the MRI. Dosimetric accuracy was determined by comparing prostate D90 determined using the algorithm and the ground truth CT-based seed locations. RESULTS: The mean ± standard deviation TREs across 20 patients including 1774 seeds were 2.23 ± 0.52 mm (rigid), 1.99 ± 0.49 mm (rigid + affine), and 1.76 ± 0.43 mm (rigid + affine + deformable). The corresponding mean ± standard deviation D90 errors were 5.8 ± 4.8%, 3.4 ± 3.4%, and 2.3 ± 1.9%, respectively. The mean computation time of the registration algorithm was 6.1 s. CONCLUSION: The registration algorithm accuracy and computation time are sufficient for clinical PPB postimplant dosimetry.

Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue.

OBJECTIVE. The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas. MATERIALS AND METHODS. In this retrospective case-control study, 190 patients with PDAC (97 men, 93 women; mean age ± SD, 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; mean age ± SD, 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. The 3D volume of the pancreas was manually segmented from the preoperative CT scans by four trained researchers and verified by three abdominal radiologists. Four hundred seventy-eight radiomics features were extracted to express the phenotype of the pancreas. Forty features were selected for analysis because of redundancy of computed features. The dataset was divided into 255 training cases (125 normal control cases and 130 PDAC cases) and 125 validation cases (65 normal control cases and 60 PDAC cases). A random forest classifier was used for binary classification of PDAC versus normal pancreas of control cases. Accuracy, sensitivity, and specificity were calculated. RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%. CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion.

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. More specifically, OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. Intuitively, OAN reduces the effect of the complex background by focusing attention so that each organ only needs to be discriminated from its local background. RCs are added to the first stage to give the lower layers more semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views (slices) enabling us to use holistic information and allowing efficient computation (compared to using 3D patches). To compensate for the limited cross-sectional information of the original 3D volumetric CT, e.g., the connectivity between neighbor slices, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm by 4-fold cross-validation and computed Dice-Sørensen similarity coefficients (DSC) and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach gives strong results and outperforms 2D- and 3D-patch based state-of-the-art methods in terms of DSC and mean surface distances.

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

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.

Simultaneous tumor and surrogate motion tracking with dynamic MRI for radiation therapy planning.

Respiration-induced tumor motion is a major obstacle for achieving high-precision radiotherapy of cancers in the thoracic and abdominal regions. Surrogate-based estimation and tracking methods are commonly used in radiotherapy, but with limited understanding of quantified correlation to tumor motion. In this study, we propose a method to simultaneously track the lung tumor and external surrogates to evaluate their spatial correlation in a quantitative way using dynamic MRI, which allows real-time acquisition without ionizing radiation exposure. To capture the lung and whole tumor, four MRI-compatible fiducials are placed on the patient's chest and upper abdomen. Two different types of acquisitions are performed in the sagittal orientation including multi-slice 2D cine MRIs to reconstruct 4D-MRI and two-slice 2D cine MRIs to simultaneously track the tumor and fiducials. A phase-binned 4D-MRI is first reconstructed from multi-slice MR images using body area as a respiratory surrogate and groupwise registration. The 4D-MRI provides 3D template volumes for different breathing phases. 3D tumor position is calculated by 3D-2D template matching in which 3D tumor templates in the 4D-MRI reconstruction and the 2D cine MRIs from the two-slice tracking dataset are registered. 3D trajectories of the external surrogates are derived via matching a 3D geometrical model of the fiducials to their segmentations on the 2D cine MRIs. We tested our method on ten lung cancer patients. Using a correlation analysis, the 3D tumor trajectory demonstrates a noticeable phase mismatch and significant cycle-to-cycle motion variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, there was significant phase mismatch between surrogate signals obtained from the fiducials at different locations.

Evaluation of lung tumor motion management in radiation therapy with dynamic MRI.

Surrogate-based tumor motion estimation and tracing methods are commonly used in radiotherapy despite the lack of continuous real time 3D tumor and surrogate data. In this study, we propose a method to simultaneously track the tumor and external surrogates with dynamic MRI, which allows us to evaluate their reproducible correlation. Four MRI-compatible fiducials are placed on the patient's chest and upper abdomen, and multi-slice 2D cine MRIs are acquired to capture the lung and whole tumor, followed by two-slice 2D cine MRIs to simultaneously track the tumor and fiducials, all in sagittal orientation. A phase-binned 4D-MRI is first reconstructed from multi-slice MR images using body area as a respiratory surrogate and group-wise registration. The 4D-MRI provides 3D template volumes for different breathing phases. 3D tumor position is calculated by 3D-2D template matching in which 3D tumor templates in 4D-MRI reconstruction and the 2D cine MRIs from the two-slice tracking dataset are registered. 3D trajectories of the external surrogates are derived via matching a 3D geometrical model to the fiducial segmentations on the 2D cine MRIs. We tested our method on five lung cancer patients. Internal target volume from 4D-CT showed average sensitivity of 86.5% compared to the actual tumor motion for 5 min. 3D tumor motion correlated with the external surrogate signal, but showed a noticeable phase mismatch. The 3D tumor trajectory showed significant cycle-to-cycle variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, there was significant phase mismatch between surrogate signals obtained from fiducials at different locations.

Deformable registration of CT and cone-beam CT with local intensity matching.

Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in an alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26 → 2.27 mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.

Technical Note: scuda: A software platform for cumulative dose assessment.

PURPOSE: Accurate tracking of anatomical changes and computation of actually delivered dose to the patient are critical for successful adaptive radiation therapy (ART). Additionally, efficient data management and fast processing are practically important for the adoption in clinic as ART involves a large amount of image and treatment data. The purpose of this study was to develop an accurate and efficient Software platform for CUmulative Dose Assessment (scuda) that can be seamlessly integrated into the clinical workflow. METHODS: scuda consists of deformable image registration (DIR), segmentation, dose computation modules, and a graphical user interface. It is connected to our image PACS and radiotherapy informatics databases from which it automatically queries/retrieves patient images, radiotherapy plan, beam data, and daily treatment information, thus providing an efficient and unified workflow. For accurate registration of the planning CT and daily CBCTs, the authors iteratively correct CBCT intensities by matching local intensity histograms during the DIR process. Contours of the target tumor and critical structures are then propagated from the planning CT to daily CBCTs using the computed deformations. The actual delivered daily dose is computed using the registered CT and patient setup information by a superposition/convolution algorithm, and accumulated using the computed deformation fields. Both DIR and dose computation modules are accelerated by a graphics processing unit. RESULTS: The cumulative dose computation process has been validated on 30 head and neck (HN) cancer cases, showing 3.5 ± 5.0 Gy (mean±STD) absolute mean dose differences between the planned and the actually delivered doses in the parotid glands. On average, DIR, dose computation, and segmentation take 20 s/fraction and 17 min for a 35-fraction treatment including additional computation for dose accumulation. CONCLUSIONS: The authors developed a unified software platform that provides accurate and efficient monitoring of anatomical changes and computation of actually delivered dose to the patient, thus realizing an efficient cumulative dose computation workflow. Evaluation on HN cases demonstrated the utility of our platform for monitoring the treatment quality and detecting significant dosimetric variations that are keys to successful ART.

Deformable registration of X-ray to MRI for post-implant dosimetry in prostate brachytherapy.

Post-implant dosimetric assessment in prostate brachytherapy is typically performed using CT as the standard imaging modality. However, poor soft tissue contrast in CT causes significant variability in target contouring, resulting in incorrect dose calculations for organs of interest. CT-MR fusion-based approach has been advocated taking advantage of the complementary capabilities of CT (seed identification) and MRI (soft tissue visibility), and has proved to provide more accurate dosimetry calculations. However, seed segmentation in CT requires manual review, and the accuracy is limited by the reconstructed voxel resolution. In addition, CT deposits considerable amount of radiation to the patient. In this paper, we propose an X-ray and MRI based post-implant dosimetry approach. Implanted seeds are localized using three X-ray images by solving a combinatorial optimization problem, and the identified seeds are registered to MR images by an intensity-based points-to-volume registration. We pre-process the MR images using geometric and Gaussian filtering. To accommodate potential soft tissue deformation, our registration is performed in two steps, an initial affine transformation and local deformable registration. An evolutionary optimizer in conjunction with a points-to-volume similarity metric is used for the affine registration. Local prostate deformation and seed migration are then adjusted by the deformable registration step with external and internal force constraints. We tested our algorithm on six patient data sets, achieving registration error of (1.2±0.8) mm in < 30 sec. Our proposed approach has the potential to be a fast and cost-effective solution for post-implant dosimetry with equivalent accuracy as the CT-MR fusion-based approach.

Automatic reconstruction of the arterial and venous trees on volumetric chest CT.

PURPOSE: This paper introduces a novel approach to classify pulmonary arteries and veins from volumetric chest computed tomography (CT) images. Although there is known to be a relationship between the alteration of vessel distributions and the progress of various pulmonary diseases, there has been relatively little research on the quantification of pulmonary vessels in vivo due to morphological difficulties. In particular, there have been few efforts to quantify the morphology and distribution of only arteries or veins through automated algorithms despite the clinical importance of such work. In this study, the authors classify different types of vessels by constructing a tree structure from vascular points while minimizing the construction cost using the vascular geometries and features of CT images. METHODS: First, a vascular point set is extracted from an input volume and the weights of the points are calculated using the intensity, distance from the boundaries, and the Laplacian of the distance field. The tree construction cost is then defined as the summation of edge connection costs depending on the vertex weights. As a solution, the authors can obtain a minimum spanning tree whose branches correspond to different vessels. By cutting the edges in the mediastinal region, branches can be separated. From the root points of each branch, the cut region is regrouped toward the entries of pulmonary vessels in the same framework of the initial tree construction. After merging branches with the same orientation as much as possible, it can be determined manually whether a given vessel is an artery or vein. Our approach can handle with noncontrast CT images as well as vascular contrast enhanced images. RESULTS: For the validation, mathematical virtual phantoms and ten chronic obstructive pulmonary disease (COPD) noncontrast volumetric chest CT scans with submillimeter thickness were used. Based on experimental findings, the suggested approach shows 9.18 ± 0.33 (mean ± SD) visual scores for ten datasets, 91% and 98% quantitative accuracies for two cases, a result which is clinically acceptable in terms of classification capability. CONCLUSIONS: This automatic classification approach with minimal user interactions may be useful in assessing many pulmonary disease, such as pulmonary hypertension, interstitial lung disease and COPD.

An engineering view on megatrends in radiology: digitization to quantitative tools of medicine.

Within six months of the discovery of X-ray in 1895, the technology was used to scan the interior of the human body, paving the way for many innovations in the field of medicine, including an ultrasound device in 1950, a CT scanner in 1972, and MRI in 1980. More recent decades have witnessed developments such as digital imaging using a picture archiving and communication system, computer-aided detection/diagnosis, organ-specific workstations, and molecular, functional, and quantitative imaging. One of the latest technical breakthrough in the field of radiology has been imaging genomics and robotic interventions for biopsy and theragnosis. This review provides an engineering perspective on these developments and several other megatrends in radiology.