Biomedical subjects

Masahito Aoyama

Publications and source records attributed to Masahito Aoyama.

7 recordsLinked to original sources

PubMed2006

Improving radiologists' recommendations with computer-aided diagnosis for management of small nodules detected by CT.

RATIONALE AND OBJECTIVES: To evaluate how computer-aided diagnosis (CAD) can improve radiologists' recommendations for management of possible early lung cancers on CT. MATERIALS AND METHODS: Twenty-eight lung cancers and 28 benign lesions were employed. Each group of 28 lesions was classified into subgroups of two sizes (9 between 6 and 10 mm and 19 between 11 and 20 mm) and three patterns (8 with pure ground glass opacity [GGO], 12 with mixed GGO and 8 solid lesions). Sixteen radiologists participated in the observer study, first without and then with CAD. Radiologists' recommendations, including (1) follow-up in 12 months, (2) in 6 months, (3) in 3 months, or (4) biopsy, were compared at three levels of their malignancy probability ratings (low: 1%-33%; medium: 34%-66%; high: 67%-99%) for 896 observations (56 lesions by the 16 radiologists) in the two size subgroups and three patterns. RESULTS: The number of recommendations changed by radiologists by use of CAD was 163 (18%) among all 896 observations. Among these changed recommendations, the fraction showing a beneficial effect from CAD was 68% (111/163), and the fraction showing a beneficial effect regarding biopsy recommendations was 69% (48/70). With CAD, the radiologists' performance regarding biopsy recommendations was significantly improved for 43 lung cancers (31 changed to biopsy versus 12 changed away from biopsy; P = .003) and was also improved for 27 benign lesions (10 changed to biopsy versus 17 changed away from biopsy; P = .18). Most of the cancers with improved recommendations were solid lesions or mixed GGO and relatively large. CONCLUSION: CAD has the potential to improve the appropriateness of radiologists' recommendations for small malignant and benign lesions on CT scans.

Aged↗

PubMed2004

Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy.

OBJECTIVE: The purpose of our study was to evaluate whether a computer-aided diagnosis (CAD) scheme can assist radiologists in distinguishing small benign from malignant lung nodules on high-resolution CT (HRCT). MATERIALS AND METHODS: We developed an automated computerized scheme for determining the likelihood of malignancy of lung nodules on multiple HRCT slices; the likelihood estimate was obtained from various objective features of the nodules using linear discriminant analysis. The data set used in this observer study consisted of 28 primary lung cancers (6-20 mm) and 28 benign nodules. Cancer cases included nodules with pure ground-glass opacity, mixed ground-glass opacity, and solid opacity. Benign nodules were selected by matching their size and pattern to the malignant nodules. Consecutive region-of-interest images for each nodule on HRCT were displayed for interpretation in stacked mode on a cathode ray tube monitor. The images were presented to 16 radiologists-first without and then with the computer output-who were asked to indicate their confidence level regarding the malignancy of a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: The area under the ROC curve (Az value) of the CAD scheme alone was 0.831 for distinguishing benign from malignant nodules. The average Az value for radiologists was improved with the aid of the CAD scheme from 0.785 to 0.853 by a statistically significant level (p = 0.016). The radiologists' diagnostic performance with the CAD scheme was more accurate than that of the CAD scheme alone (p < 0.05) and also that of radiologists alone. CONCLUSION: CAD has the potential to improve radiologists' diagnostic accuracy in distinguishing small benign nodules from malignant ones on HRCT.

Diagnosis, Differential↗

PubMed2003

Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images.

An automated computerized scheme has been developed for determination of the likelihood measure of malignancy of pulmonary nodules on low-dose helical CT (LDCT) images. Our database consisted of 76 primary lung cancers (147 slices) and 413 benign nodules (576 slices). With this automated computerized scheme, the location of a nodule was first indicated by a radiologist. The outline of the nodule was segmented automatically by use of a dynamic programming technique. Various objective features on the nodules were determined by use of outline analysis and image analysis, and the likelihood measure of malignancy was determined by use of linear discriminant analysis (LDA). The effect of many different combinations of features and the performance of LDA in distinguishing benign nodules from malignant ones were evaluated by means of receiver operating characteristic (ROC) analysis. The Az value (area under the ROC curve) obtained by the computerized scheme in distinguishing benign nodules from malignant ones was 0.828 when a single slice was employed for each of the nodules. However, the Az value was improved to 0.846 when multiple slices were used for determination of the likelihood measure of malignancy. The Az values obtained by the computerized scheme on LDCT images were significantly greater than the Az value of 0.70, which was obtained from our previous observer studies by radiologists in distinguishing benign nodules from malignant ones on LDCT images. The automated computerized scheme for determination of the likelihood measure of malignancy would be useful in assisting radiologists to distinguish between benign and malignant pulmonary nodules on LDCT images.

Algorithms↗

PubMed2003

Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience.

PURPOSE: To evaluate radiologists' performance for determining a distinction between benign and malignant pulmonary nodules on chest radiographs without and with use of a computer-aided diagnosis scheme. MATERIALS AND METHODS: Fifty-three chest radiographs that depicted 31 primary lung cancers and 22 benign nodules were used. The likelihood measure of malignancy for each nodule was determined by using an automated computerized scheme. Sixteen radiologists (nine attending radiologists and seven radiology residents) participated in an observer study in which cases were interpreted first without and then with use of the scheme. The radiologists' performance was evaluated with receiver operating characteristic analysis. RESULTS: The mean area under the best-fit binormal receiver operating characteristic curve plotted in the unit square (Az) values of radiologists who interpreted images without and with the scheme were 0.743 and 0.817, respectively. The performance of radiologists was improved significantly when the scheme was used (P =.002). However, the performance (Az = 0.889) of the computer alone exceeded these results by a substantial margin. The average change in radiologists' confidence level for interpretation without and with the scheme was highly correlated (r = 0.845) with the likelihood measure of malignancy, which was presented as computer output. CONCLUSION: This scheme for computer-aided diagnosis has the potential to improve the accuracy of radiologists' performance in the classification of benign and malignant solitary pulmonary nodules.

Diagnosis, Computer-Assisted↗

PubMed2002

Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images.

A novel automated computerized scheme has been developed to assist radiologists for their distinction between benign and malignant solitary pulmonary nodules on chest images. Our database consisted of 55 chest radiographs (33 primary lung cancers and 22 benign nodules). In this method, the location of a nodule was indicated first by a radiologist. The difference image with a nodule was produced by use of filters and then represented in a polar coordinate system. The nodule was segmented automatically by analysis of contour lines of the gray-level distribution based on the polar-coordinate representation. Two clinical parameters (age and sex) and 75 image features were determined from the outline, the image, and histogram analysis for inside and outside regions of the segmented nodule. Linear discriminant analysis (LDA) and knowledge about benign and malignant nodules were used to select initial feature combinations. Many combinations for subgroups of 77 features were evaluated as input to artificial neural networks (ANNs). The performance of ANNs with the selected 7 features by use of the round-robin test showed Az = 0.872, which was greater than Az = 0.854 obtained previously with the manual method (P= 0.53). The performance of LDA (Az = 0.886) was slightly improved compared to that of ANNs (P = 0.59) and was greater than that of the manual method (Az = 0.854) reported previously (P = 0.40). The high level of its performance indicates the potential usefulness of this automated computerized scheme in assisting radiologists as a second opinion for distinction between benign and malignant solitary pulmonary nodules on chest images.

Adult↗

PubMed2002

[Development of an image processing scheme for chest radiographs using a dot printer].

The performance of dot printers has recently been improved. Images output by dot printers can provide simple, economical medical reference images if important diagnostic information is not lost. We developed an image processing scheme for chest radiographs that employed a dot printer. We used two types of pixel value conversions, a nonlinear pixel value correction using a lookup table and a linear pixel value conversion using histogram analysis. The density distribution of chest radiographs was analyzed and classified into high-density and low-density images. The two types of pixel value conversions were used depending on the density of chest radiographs. Converted pixel value had density characteristics that were adapted to the output image of the dot printer, and thus the density distributions of the lungs of radiographs became comparable. In addition, an adaptive unsharp masking technique with processing parameters optimized for each of the lungs and mediastinum was applied. An ROC study for the detection of lung nodules was carried out to evaluate the performance of dot printer images. The area under the ROC curve (A(z)) for dot printer images was 0.816, while sensitivity and specificity were 77.6% and 75.2%, respectively. The performance indicated the usefulness of our image processing scheme.

Humans↗

PubMedDate not supplied

Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies.

Since 1996, computer-aided diagnosis (CAD) schemes have been presented as interactive demonstrations on computer workstations at each scientific assembly of the Radiological Society of North America. The schemes involved (a) detection of pulmonary nodules, (b) temporal subtraction, (c) detection of interstitial lung disease, (d) differential diagnosis of interstitial lung disease, and (e) distinction between benign and malignant pulmonary nodules on chest radiographs. Large-scale observer tests were carried out to examine how radiologists can benefit from CAD systems. Observer performance was evaluated by analysis of receiver operating characteristic (ROC) curves. The statistical significance of the difference between the areas under the ROC curves without and with CAD was analyzed with the Student t test. In all of the tests, the diagnostic accuracy of the radiologists in total improved significantly when CAD was used. This result provides additional evidence that CAD has the potential to improve the performance of radiologists in their decision-making process in interpreting chest radiographs.

Diagnosis, Computer-Assisted↗