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Titolo:
Computer-aided diagnosis for surgical office-based breast ultrasound
Autore:
Chang, RF; Kuo, WJ; Chen, DR; Huang, YL; Lee, JH; Chou, YH;
Indirizzi:
China Med Coll & Hosp, Dept Gen Surg, Taichung, Taiwan China Med Coll & Hosp Taichung Taiwan , Dept Gen Surg, Taichung, Taiwan Natl Chung Cheng Univ, Dept Comp Sci & Informat Engn, Chiayi, Taiwan Natl Chung Cheng Univ Chiayi Taiwan Sci & Informat Engn, Chiayi, Taiwan Vet Gen Hosp, Dept Radiol, Taipei, Taiwan Vet Gen Hosp Taipei TaiwanVet Gen Hosp, Dept Radiol, Taipei, Taiwan
Titolo Testata:
ARCHIVES OF SURGERY
fascicolo: 6, volume: 135, anno: 2000,
pagine: 696 - 699
SICI:
0004-0010(200006)135:6<696:CDFSOB>2.0.ZU;2-5
Fonte:
ISI
Lingua:
ENG
Soggetto:
ARTIFICIAL NEURAL-NETWORK; SELF-ORGANIZING MAP; CROSSROADS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Life Sciences
Citazioni:
16
Recensione:
Indirizzi per estratti:
Indirizzo: Chen, DR China Med Coll & Hosp, Dept Gen Surg, 2 Yer Der Rd, Taichung, Taiwan China Med Coll & Hosp 2 Yer Der Rd Taichung Taiwan hung, Taiwan
Citazione:
R.F. Chang et al., "Computer-aided diagnosis for surgical office-based breast ultrasound", ARCH SURG, 135(6), 2000, pp. 696-699

Abstract

Hypothesis: The computer-aided diagnostic system is an intelligent system with great potential for categorizing solid breast nodules. It can be used conveniently for surgical office-based digital ultrasonography (US) of the breast. Design: Retrospective, nonrandomized study. Setting: University teaching hospital. Patients: We retrospectively reviewed 243 medical records of digital US images of the breast of pathologically proved, benign breast tumors from 161 patients (ie, 136 fibroadenomas and 25 fibrocystic nodules), and carcinomasfrom 82 patients (ie, 73 invasive duct carcinomas, 5 invasive lobular carcinomas, and 4 intraductal carcinomas). The digital US images were consecutively recorded from January 1, 1997, to December 31, 1998. Intervention: The physician selected the region of interest on the digitalUS image. Then a learning vector quantization model with 24 autocorrelation texture features is used to classify the tumor as benign or malignant. Inthe experiment, 153 cases were arbitrarily selected:to be the training setof the learning vector quantization model and 90 cases were selected to evaluate the performance. One experienced radiologist who was completely blind to these cases was asked to classify these tumors in the test set. Main Outcome Measure: Contribution of breast US to diagnosis. Results: The performance comparison results illustrated the following: accuracy, 90%: sensitivity, 96.67%;specificity, 86.67%; positive predictive value, 78.38%; and negative predictive value, 98.11% for the computer-aided diagnostic (CAD) system and accuracy, 86.67%; sensitivity, 86.67%; specificity, 86.67%; positive predictive value, 76.47%; and negative predictive value, 92.86% for the radiologist. Conclusion: The proposed CAD system provides an immediate second opinion. All accurate preoperative diagnosis can be routinely established for surgical office-based digital US of the breast. The diagnostic rate was even better than the results of an experienced radiologist. The high negative predictive rate by the CAD system can avert benign biopsies. It call be easily implemented on exisiting commercial diagnostic digital US machines. For most available diagnostic digital US machines, all that would be required for the CAD system is only a personal computer loaded with CAD software.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 19/01/20 alle ore 00:22:30