Image analysis – Applications – Biomedical applications
Reexamination Certificate
2001-07-02
2004-05-18
Patel, Jayanti K. (Department: 2625)
Image analysis
Applications
Biomedical applications
C382S159000
Reexamination Certificate
active
06738499
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention relates generally to a method and system for the computerized analysis of radiographic images, and more specifically, to the determination of the likelihood of malignancy in pulmonary nodules using artificial neural networks (ANNs).
The present invention generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,740,268; 5,790,690; and 5,832,103; as well as U.S. patent applications Ser. No. 08/158,388 (PCT Publication WO 95/14431); Ser. Nos. 08/173,935; 08/220,917 (PCT Publication WO 95/26682); Ser. No. 08/398,307 (PCT Publication WO 96/27846); Ser. No. 08/523,210 (PCT Publication WO 95/15537); Ser. Nos. 08/536,149; 08/562,087; 08/757,611; 08/758,438; 08/900,191; 08/900,361; 08/900,362; 08/900,188; 08/900,189, 08/900,192; 08/979,623; 08/979,639; 08/982,282; 09/027,468; 09/027,685; 09/028,518; 09/053,798; 09/092,004; 09/098,504; 09/121,719; 09/131,162; 09/141,535; 09/156,413; No. 60/107,095 (Attorney Docket No. 0730-0060-20PROV, filed Nov. 5, 1998) all of which are incorporated herein by reference.
The present invention includes the use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the appended APPENDIX and cross-referenced throughout the specification by reference to the corresponding number, in brackets, of the respective references listed in the APPENDIX, the entire contents of which, including the related patents and applications listed above and the references listed in the APPENDIX, are incorporated herein by reference.
2. Discussion of the Background
Although a solitary pulmonary nodule (SPN) is a common finding on a chest radiograph, the differential diagnosis of a solitary pulmonary nodule or chest radiograph is often a difficult task for radiologists [1-8]. Since a solitary pulmonary nodule may be the first sign of lung cancer, especially in its early stage, most patients undergo a further diagnostic evaluation that may include an imaging study with computed tomography (CT) [1]. Malignant diseases are estimated to occur in about 20% of patients with solitary pulmonary nodules in the population [9]. The majority of radiographically detected pulmonary nodules, however, are benign [3,8,10-14].
Although CT has become a major diagnostic method to differentiate pulmonary nodules in recent years, a large number of CT examinations have been performed for benign cases that were suspected of being malignant. A survey was conducted to obtain estimates for the relative numbers (percentages) of malignant and benign cases that were performed for chest CT study under the investigation of a solitary pulmonary nodule. The survey was performed at the University of Chicago Hospital and at four Hospitals in Japan (University of Occupational and Environmental Health Hospital, Fukuoka; Nagasaki University Hospital, Nagasaki; Iwate Prefectural Central Hospital, Morioka; and Tokyo Metropolitan Hospital, Tokyo). At each institution, patients who underwent chest CT examinations for suspicious pulmonary nodules on chest radiographs were assessed regarding pre-CT clinical diagnosis, the final diagnosis, patient's age, and patient's gender. Final diagnosis was established by a pathologic examination or clinical follow-up.
One hundred thirty-three patients (83 male, 50 female) who ranged in age from 25 to 85 (mean, 62.9 years) were identified at the five hospitals. Pre-CT clinical diagnosis consisted of “suspicion of lung cancer” for 43 of the patients, “lung nodule/lung mass” for 70 of the patients, “abnormal shadow” for 10 of the patients, “suspicion of pulmonary metastasis” for 6 of the patients, and benign diseases for four of the patients (“suspicion of pulmonary tuberculosis” for one patient, “suspicion of pulmonary abscess” for two patients, and “suspicion of pulmonary aspergillosis” for one patient). In the cases in which pre-CT diagnosis included a lung nodule/lung mass, some of the cases may have involved suspected benign diseases; however, it was assumed that most of these cases involved suspected malignancy.
Table 1 shows the summary of the survey on the final diagnosis of solitary pulmonary nodules which underwent chest CT. Fifty-five out of 133 cases (or 41.4%) indicated malignant nodules including primary lung cancer and pulmonary metastases. Sixty-four cases (48.1%) indicated benign conditions including benign diseases and “negative” cases that had no apparent lung abnormality as a result of CT examination. Fourteen cases had inconclusive final diagnoses. The results obtained in this survey show that a large fraction of patients who underwent chest CT examination were ultimately identified as having benign conditions. Accordingly, some of the CT examinations may have been avoided if these benign conditions were diagnosed accurately and/or confidently on the initial chest radiographs.
TABLE 1
Benign
Institution
Malignant
Benign
Lesion
Negative
Unknown
Total
A
9 (32.0%)
15 (53.6%)
11
4
4
28 (100%)
B
8 (25.8%)
23 (74.2%)
—
—
0
31 (100%)
C
11 (40.7%)
11 (40.7%)
3
8
5
27 (100%)
D
6 (35.3%)
10 (58.8%)
—
—
1
17 (100%)
E
21 (70.0%)
5 (16.7%)
—
—
4
30 (100%)
Total
55 (41.4%)
64 (48.1%)
14
133 (100%)
Computer schemes capable of providing objective information on the nature of pulmonary nodules may aid radiologists in their classification of pulmonary nodules. Various computerized schemes have been investigated for characterizing pulmonary nodules.
In most of these studies, however, radiographic features were manually extracted, and the computer was used only to determine the likelihood of malignancy by merging image features using rule-based or discriminant analysis. Swensen et al. [15] estimated the probability of malignancy in radiologically indeterminate SPNs by using multivariate logistic regression. They concluded that three clinical parameters (age, cigarette-smoking status, and history of cancer) and 3 radiological features (diameter, spiculation, and upper lobe location) were independent predictors of malignancy. Cummings et al. [16] estimated the probability of malignancy of pulmonary nodules by using Bayesian analysis based on the diameter of an SPN, the patient's age, history of cigarette smoking, and the prevalence of malignancy in SPNs. Gurney [17,18] also used Bayesian analysis to calculate the probability of malignancy, which was compared with the performance of radiologists.
Other investigators have used computer-extracted features to differentiate between malignant and benign lung nodules. Sherrier et al. [19] applied a gradient analysis for distinguishing benign nodules from malignant nodules, and they presented that benign calcified granuloma showed greater gradient number than malignant nodules. Sasaoka et al. [20] extracted nodule features using a computerized method. However, the extracted features, such as density gradient and density entropy, were not directly correlated with specific radiological findings, and thus the meaning of these features is inconclusive. Recently, artificial neural networks (ANNs) have been used in the field of diagnostic radiology to provide a potentially powerful classification tool [12-27]. Gurney et al. [28] reported that the Bayesian method was better than the neural network in the prediction of the probability of malignancy in pulmonary nodules. Despite these considerable efforts, a computerized scheme has not been applied in clinical situations to assist radiologists in their interpretation of malignancy of pulmonary nodules.
SUMMARY OF THE INVENTION
Doi Kunio
Nakamura Katsumi
Arch Development Corporation
Oblon & Spivak, McClelland, Maier & Neustadt P.C.
Patel Jayanti K.
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