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Heber MacMahon

Publications and source records attributed to Heber MacMahon.

13 recordsLinked to original sources
PubMed2004

Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease: results of a simulation test with actual clinical cases.

RATIONALE AND OBJECTIVES: To evaluate the performance of an artificial neural network (ANN) scheme with use of consecutive clinical cases and its effect on radiologists with an observer test. MATERIALS AND METHODS: Artificial neural networks were designed to distinguish among 11 interstitial lung diseases on the basis of 26 inputs (16 radiologic findings, 10 clinical parameters). Chest radiographs of 96 consecutive cases with interstitial lung disease were used. Five radiologists independently rated their radiologic findings on the 96 chest radiographs. Based on their ratings of radiologic findings and clinical parameters obtained from the hospital information system, the output values indicating the likelihood of each of the 11 interstitial lung diseases were determined. Subsequently, 30 cases were selected from these 96 cases for an observer test. Five radiologists marked their confidence levels for diagnosis of 11 possible diseases in each case without and with ANN output. The performance of ANNs and radiologists was evaluated by receiver operating characteristic analysis based on their outputs and on confidence levels, respectively. RESULTS; The average Az value (area under the receiver operating characteristic curve) indicating ANN performance for the 96 consecutive cases was 0.85 +/- 0.03. The average Az values indicating radiologists' performance without and with ANN outputs were 0.81 +/- 0.11 and 0.87 +/- 0.06, respectively. The diagnostic accuracy was improved significantly when radiologists read chest radiographs with ANN outputs (P < .05). CONCLUSION: Artificial neural networks for differential diagnosis of interstitial lung disease may be useful in clinical situations, and radiologists may be able to utilize the ANN output to their advantage in the differential diagnosis of interstitial lung disease on chest radiographs.

Adult↗
PubMed2004

Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques.

Our purpose in this study was to evaluate the variability of manual mesothelioma tumor thickness measurements in computed tomography (CT) scans and to assess the relative performance of six computerized measurement algorithms. The CT scans of 22 patients with malignant pleural mesothelioma were collected. In each scan, an initial observer identified up to three sites in each of three CT sections at which tumor thickness measurements were to be made. At each site, five observers manually measured tumor thickness through a computer interface. Three observers repeated these measurements during three separate sessions. Inter- and intra-observer variability in the manual measurement of tumor thickness was assessed. Six automated measurement algorithms were developed based on the geometric relationship between a specified measurement site and the automatically extracted lung regions. Computer-generated measurements were compared with manual measurements. The tumor thickness measurements of different observers were highly correlated (r > or = 0.99); however, the 95% limits of agreement for relative inter-observer difference spanned a range of 30%. Tumor thickness measurements generated by the computer algorithms also correlated highly with the average of observer measurements (r > or = 0.93). We have developed computerized techniques for the measurement of mesothelioma tumor thickness in CT scans. These techniques achieved varying levels of agreement with measurements made by human observers.

Adult↗
PubMed2004

Lung image database consortium: developing a resource for the medical imaging research community.

To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues.

Biomedical Research↗
PubMed2003

Low-dose computed tomography screening for lung cancer in a general population: characteristics of cancer in non-smokers versus smokers.

RATIONALE AND OBJECTIVES: To report the detection rate for lung cancers in computed tomography (CT) screening in Japanese adults, and to analyze differences in the appearance of the cancers in non-smokers versus smokers. MATERIALS AND METHODS: Subjects consisted of 7,847 Japanese adults who received low-dose CT screening at least once in a 3-year period. The detection rate of lung cancers and the correlation of imaging, clinical, and pathologic findings of cancers in non-smokers versus smokers were examined. RESULTS: The detection rate for lung cancer was 1.1% for both non-smokers (45 of 4,251) and smokers (39 of 3,596). The prevalence of well-differentiated adenocarcinomas was greater in non-smokers (88%; 22 of 25) than in smokers (29%; 4 of 14) (P < .001). The prevalence and incidence of pathologic stage IA disease were greater in non-smokers than in smokers (92%; [22 of 24] vs 58% [7 of 12], and 100% [19 of 19] vs 70% [14 of 20]) (both P < .05). The mean size of the tumors in the non-smokers (12.4 mm) was smaller than that in smokers (18.2 mm) (P < .001). The percentage of cancers categorized as pure or mixed ground-glass opacity (86%; 38 of 44) on CT was greater in non-smokers than in smokers (46%; 16 of 35) (P < .001). CONCLUSION: Most of the lung cancers in non-smokers were slow-growing adenocarcinomas appearing as faint ground-glass opacities on CT, whereas rapidly growing cancers appearing as solid nodules were more commonly seen in smokers.

Adenocarcinoma↗
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

Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographs.

RATIONALE AND OBJECTIVES: The authors investigated the diagnostic value of each of multiple clinical parameters and radiologic findings in differentiating between various interstitial lung diseases by using an artificial neural network (ANN). MATERIALS AND METHODS: The ANN was designed to differentiate between 11 interstitial lung diseases. The authors employed 10 clinical parameters and 16 radiologic findings that were divided into three groups (location, general appearance, specific findings). The performance of the ANN was evaluated with receiver operating characteristic analysis with amodified round-robin (leave-one-out) method and 370 cases (150 actual cases, 110 published cases, and 110 hypothetical cases). The Az values of ANNs were evaluated with various combinations of 10 clinical parameters and 16 radiologic findings. RESULTS: The Az value obtained with the complete set of clinical parameters and radiologic findings was 0.947. The Az value obtained with the 10 clinical parameters alone was 0.900, which was greater than 0.843 obtained with the 16 radiologic findings alone. There were statistically significant differences among Az values for some diseases when certain clinical parameters were removed from the input. Omission of specific findings among the three groups of radiologic findings decreased the Az value significantly. CONCLUSION: These results appear to confirm that clinical parameters can be equally as or more important than radiologic findings in the diagnosis of interstitial lung diseases. Among radiologic findings, certain specific findings can be more important than the location or general appearance of abnormal findings.

Humans↗
PubMed2002

Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis.

A fully automated method for computerized detection of pulmonary embolism in spiral computed tomography angiography was developed based on volumetric image analysis. The detection method is based on segmentation of pulmonary vessels to limit the search space, and analysis of several three-dimensional features inside segmented vessel volume. The features utilized are vascular size, local contrast based on mathematical morphology, degree of curvilinearity based on second derivatives, and geometric features such as volume and length. Detection results were obtained for 19 clinical data sets and the performance of the method was evaluated. Using the number and locations of thrombi diagnosed by radiologists as the gold standard, 100% sensitivity was achieved with 7.7 false positives per case, and 85% sensitivity was obtained with 2.6 false positives. For identification of all the positive cases as positive, i.e., detection of at least one thrombus per positive case, 1.9 false positives per case were obtained. These preliminary results suggest that the method has potential for fully automated detection of pulmonary embolism.

Algorithms↗
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

Detection of lung nodules on digital chest radiographs: potential usefulness of a new contralateral subtraction technique.

PURPOSE: To evaluate the potential usefulness of a contralateral subtraction technique developed for radiologists' performance in the detection of subtle lung nodules on chest radiographs. MATERIALS AND METHODS: Fifty chest radiographs (25 normal and 25 abnormal with a subtle lung nodule) that were digitized with a 0.175-mm pixel size and 4,096 gray levels were used. Twelve radiologists (10 attending and two residents) participated in observer tests and read both original and contralateral subtraction images with a sequential testing method. Radiologists' performance was evaluated by means of receiver operating characteristic analysis with use of a continuous rating scale. The beneficial and detrimental effects of the contralateral subtraction technique on the radiologists' performance were also evaluated. RESULTS: The area under the receiver operating characteristic curve values obtained without and with contralateral subtraction images were 0.926 and 0.962, respectively. Results indicated that the contralateral subtraction images significantly (P <.05) improved diagnostic accuracy, particularly for radiologists with limited experience. CONCLUSION: The contralateral subtraction technique can assist radiologists in the correct identification of subtle lung nodules on chest radiographs.

Humans↗
PubMed2002

Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings.

PURPOSE: To compare clinical, histopathologic, and imaging features of lung cancers missed at low-radiation-dose helical computed tomography (CT). MATERIALS AND METHODS: Eighty-three primary lung cancers were found during an annual low-dose CT screening program and confirmed histopathologically at either surgery or biopsy. Thirty-two of these lung cancers were missed on 39 CT scans: on 23 scans owing to detection errors and on 16 owing to interpretation errors. The clinical characteristics, CT features, and histopathologic findings of these missed lung cancers were correlated. RESULTS: All missed cancers were intrapulmonary, and 28 (88%) were stage IA. All 20 detection errors occurred in cases of adenocarcinoma, 17 (85%) of which were well-differentiated tumors and 11 (55%) of which were in nonsmoking women. The mean size of cancers missed owing to detection error, 9.8 mm, was smaller than that of cancers missed owing to interpretation error, 15.9 mm (P <.001). In the detection error group, the percentages of nodules with ground-glass opacity (91%) or judged to be subtle (91%) were greater than those of nodules in the interpretation error group (38% and 25%, respectively) (P <.001). In the detection error group, 83% (19/23) of cancers were overlapped with, obscured by, or similar in appearance to normal structures such as pulmonary vessels. On 14 of the 16 CT scans with which there were interpretation errors, the CT findings mimicked benign disease, and the patients also had underlying lung disease, such as tuberculosis, emphysema, or lung fibrosis. CONCLUSION: The lung cancers missed at low-dose CT screening in this series generally were very subtle and appeared as small faint nodules, overlapping normal structures, or opacities in a complex background of other disease.

Adenocarcinoma↗
PubMed2002

Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program.

PURPOSE: To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation. MATERIALS AND METHODS: A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis. RESULTS: At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section. CONCLUSION: With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.

Aged↗
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↗