The effectiveness of pharmacotherapy has been improved through the development of drugs that incorporate the knowledge of the molecular biology of breast cancer. Therefore, neoadjuvant chemotherapy (NAC) is actively administered to patients who wish to undergo breast-conserving surgery. During NAC, some patients have a pathological complete response (pCR). This study aims to develop a method for predicting patients with pCR during NAC. This creates new value for preoperative imaging. Breast ultrasound images were collected from 43 patients with breast cancer who received NAC at the Kumamoto University Hospital. The tumor area on the breast ultrasound image was manually marked. From the marked tumor regions, 379 radiomics features related to size, shape, density, and texture were measured. We employed the least absolute shrinkage and selection operator to select the useful radiomic features. Linear discriminant analysis (LDA) with eight selected radiomic features was used to distinguish between pCR and non-pCR. Leave-one-out was used for training and testing LDA. The sensitivity, specificity, and AUC were 89.5 % (17/19), 83.3% (19/24), and 0.920, respectively. Because the LDA is the simplest classifier, the phenotype of the lesion in the breast ultrasound image may contain information that predicts the therapeutic effect. Our proposed method could provide a new value for preoperative imaging.
The aim of this study was to investigate the diagnostic performance of radiological technologists (RTs) in the detection of malignant microcalcifications and to evaluate how much computer-aided detection (CADe) improved their performances compared with those by expert breast radiologists (BRs). Six board-certified breast RTs and four board-certified BRs participated in a free-response receiver operating characteristic observer study. The dataset consisted of 75 cases (25 malignant, 25 benign, and 25 normal cases) of digital mammograms, selected from the digital database for screening mammography provided by the University of South Florida. Average figure of merit (FOM) of the RTs’ performances was statistically analyzed using jack-knife free-response receiver operating characteristic and compared with that of expert BRs. The detection performance of RTs was significantly improved by using CADe; average sensitivity was increased from 46.7% to 56.7%, with a decrease in the average number of false positives per case from 0.19 to 0.13. Detection accuracy of an average FOM was improved from 0.680 to 0.816 (p=0.001) and the difference in FOMs between RTs and radiologists failed to reach statistical significance. RTs’ performances for the identification of malignant microcalcifications on digital mammography were sufficiently high and comparable to those of radiologists by using CADe.
Abnormal accumulation of brain iron has been detected in various neurodegenerative diseases. Quantitative susceptibility mapping (QSM) is a novel contrast mechanism in magnetic resonance (MR) imaging and enables the quantitative analysis of local tissue susceptibility property. Therefore, automatic segmentation tools of brain regions on QSM images would be helpful for radiologists’ quantitative analysis in various neurodegenerative diseases. The purpose of this study was to develop an automatic segmentation and classification method of striatum regions on QSM images. Our image database consisted of 22 QSM images obtained from healthy volunteers. These images were acquired on a 3.0 T MR scanner. The voxel size was 0.9×0.9×2 mm. The matrix size of each slice image was 256×256 pixels. In our computerized method, a template mating technique was first used for the detection of a slice image containing striatum regions. An image registration technique was subsequently employed for the classification of striatum regions in consideration of the anatomical knowledge. After the image registration, the voxels in the target image which correspond with striatum regions in the reference image were classified into three striatum regions, i.e., head of the caudate nucleus, putamen, and globus pallidus. The experimental results indicated that 100% (21/21) of the slice images containing striatum regions were detected accurately. The subjective evaluation of the classification results indicated that 20 (95.2%) of 21 showed good or adequate quality. Our computerized method would be useful for the quantitative analysis of Parkinson diseases in QSM images.
The detection of cerebrovascular diseases such as unruptured aneurysm, stenosis, and occlusion is a major application of magnetic resonance angiography (MRA). However, their accurate detection is often difficult for radiologists. Therefore, several computer-aided diagnosis (CAD) schemes have been developed in order to assist radiologists with image interpretation. The purpose of this study was to develop a computerized method for segmenting cerebral arteries, which is an essential component of CAD schemes. For the segmentation of vessel regions, we first used a gray level transformation to calibrate voxel values. To adjust for variations in the positioning of patients, registration was subsequently employed to maximize the overlapping of the vessel regions in the target image and reference image. The vessel regions were then segmented from the background using gray-level thresholding and region growing techniques. Finally, rule-based schemes with features such as size, shape, and anatomical location were employed to distinguish between vessel regions and false positives. Our method was applied to 854 clinical cases obtained from two different hospitals. The segmentation of cerebral arteries in 97.1%(829/854) of the MRA studies was attained as an acceptable result. Therefore, our computerized method would be useful in CAD schemes for the detection of cerebrovascular diseases in MRA images.
The detection of unruptured aneurysms is a major subject in magnetic resonance angiography (MRA). However, their accurate detection is often difficult because of the overlapping between the aneurysm and the adjacent vessels on maximum intensity projection images. The purpose of this study is to develop a computerized method for the detection of unruptured aneurysms in order to assist radiologists in image interpretation. The vessel regions were first segmented using gray-level thresholding and a region growing technique. The gradient concentration (GC) filter was then employed for the enhancement of the aneurysms. The initial candidates were identified in the GC image using a gray-level threshold. For the elimination of false positives (FPs), we determined shape features and an anatomical location feature. Finally, rule-based schemes and quadratic discriminant analysis were employed along with these features for distinguishing between the aneurysms and the FPs. The sensitivity for the detection of unruptured aneurysms was 90.0% with 1.52 FPs per patient. Our computerized scheme can be useful in assisting the radiologists in the detection of unruptured aneurysms in MRA images.
Cerebrovascular diseases are the third leading cause of death in Japan. Therefore, a screening system for the early detection of asymptomatic brain diseases is widely used. In this screening system, leukoaraiosis is often detected in magnetic resonance (MR) images. The quantitative analysis of leukoaraiosis is important because its presence and extension is associated with an increased risk of severe stroke. However, thus far, the diagnosis of leukoaraiosis has generally been limited to subjective judgments by radiologists. Therefore, the purpose of this study was to develop a computerized method for the segmentation of leukoaraiosis, and provide an objective measurement of the lesion volume. Our database comprised of T1- and T2-weighted images obtained from 73 patients. The locations of leukoaraiosis regions were determined by an experienced neuroradiologist. We first segment cerebral parenchymal regions in T1-weighted images by using a region growing technique. For determining the initial candidate regions for leukoaraiosis, the k-means clustering of pixel values in the T1- and T2-weighted images was applied to the segmented cerebral region. For the elimination of false positives (FPs), we determined features such as the location, size, and circularity from each of the initial candidates. Finally, rule-based schemes and a quadratic discriminant analysis with these features were employed for distinguishing between the leukoaraiosis regions and the FPs. The results indicated that the sensitivity for the detection of leukoaraiosis was 100% with 5.84 FPs per image. Our computerized scheme can be useful in assisting radiologists for the quantitative analysis of leukoaraiosis in T1- and T2-weighted images.
Magnetic resonance angiography (MRA) is routinely employed in the diagnosis of cerebrovascular disease. Unruptured
aneurysms and arterial occlusions can be detected in examinations using MRA. This paper describes a computerized
detection method of arterial occlusion in MRA studies. Our database consists of 100 MRA studies, including 85 normal
cases and 15 abnormal cases with arterial occlusion. Detection of abnormality is based on comparison with a reference
(normal) MRA study with all the vessel known. Vessel regions in a 3D target MRA study is first segmented by using
thresholding and region growing techniques. Image registration is then performed so as to maximize the overlapping of
the vessel regions in the target image and the reference image. The segmented vessel regions are then classified into
eight arteries based on comparison of the target image and the reference image. Relative lengths of the eight arteries are
used as eight features in classifying the normal and arterial occlusion cases. Classifier based on the distance of a case
from the center of distribution of normal cases is employed for distinguishing between normal cases and abnormal cases.
The sensitivity and specificity for the detection of abnormal cases with arterial occlusion is 80.0% (12/15) and 95.3%
(81/85), respectively. The potential of our proposed method in detecting arterial occlusion is demonstrated.
Cirrhosis of the liver is characterized by the presence of widespread nodules and fibrosis in the liver. The fibrosis
and nodules formation causes distortion of the normal liver architecture, resulting in characteristic texture patterns.
Texture patterns are commonly analyzed with the use of co-occurrence matrix based features measured on regions-of-interest (ROIs). A classifier is subsequently used for the classification of cirrhotic or non-cirrhotic livers.
Problem arises if the classifier employed falls into the category of supervised classifier which is a popular choice.
This is because the 'true disease states' of the ROIs are required for the training of the classifier but is, generally, not
available. A common approach is to adopt the 'true disease state' of the liver as the 'true disease state' of all ROIs in
that liver. This paper investigates the use of a nonsupervised classifier, the k-means clustering method in classifying
livers as cirrhotic or non-cirrhotic using unlabelled ROI data. A preliminary result with a sensitivity and specificity
of 72% and 60%, respectively, demonstrates the feasibility of using the k-means non-supervised clustering method
in generating a characteristic cluster structure that could facilitate the classification of cirrhotic and non-cirrhotic
livers.
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