Incidental Breast Lesions Detected on Computed Thorax Tomography

Poyraz, Necdet; Emlik, Ganime Dilek; Keskin, Suat; Kalkan, Havva

doi:10.5152/tjbh.2015.2656

Cited by 10 publications

(7 citation statements)

References 13 publications

(7 reference statements)

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“…Past studies of breast tumor detection by chest CT, and, in particular, Hussain [ 47 ], Bach [ 48 ], and Poyraz [ 49 ], have explored the importance of breast lesions accidentally found in chest CT and the characteristics of malignant and benign breast lesions. Kuo [ 50 ] and Caballo [ 51 ] only discussed the possibility of automatic segmentation of breast tumors in chest CT.…”

Section: Discussionmentioning

confidence: 99%

Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing

Kuo

Chen

Barman

et al. 2023

JCM

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Breast cancer is the most common type of cancer in women, and early detection is important to significantly reduce its mortality rate. This study introduces a detection and diagnosis system that automatically detects and classifies breast tumors in CT scan images. First, the contours of the chest wall are extracted from computed chest tomography images, and two-dimensional image characteristics and three-dimensional image features, together with the application of active contours without edge and geodesic active contours methods, are used to detect, locate, and circle the tumor. Then, the computer-assisted diagnostic system extracts features, quantifying and classifying benign and malignant breast tumors using a greedy algorithm and a support vector machine. The study used 174 breast tumors for experiment and training and performed cross-validation 10 times (k-fold cross-validation) to evaluate performance of the system. The accuracy, sensitivity, specificity, and positive and negative predictive values of the system were 99.43%, 98.82%, 100%, 100%, and 98.89% respectively. This system supports the rapid extraction and classification of breast tumors as either benign or malignant, helping physicians to improve clinical diagnosis.

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Section: Discussionmentioning

confidence: 99%

Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing

Kuo

Chen

Barman

et al. 2023

JCM

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“…In our current literature review, only a few researchers identified the CT features of incidental breast lesions (7,8,11,14,15). Very limitted researchers investigate breast lesion's imaging features with CT and have attempted to compare other imaging modalities (3,16,17).…”

Section: Discussionmentioning

confidence: 99%

Tomografi ve Mamografinin Karşılaştırılması, Bilgisayarlı Tomografide Meme Kanserinin Tanısal Özellikleri

Çakır¹,

Korkmaz²,

Bulut³

et al. 2020

Bozok Tıp Dergisi

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Bu çalışmanın amacı, meme kanserinin bilgisayarlı tomografi (BT)'de tanısal özelliklerini belirlemek ve meme kanserinin tomografi görülebilirlik derecesini araştırmaktır. Gereç ve Yöntem: Mayıs 2010-Ocak 2016 tarihleri arasında histopatolojik tanısı bulunan 1000 meme lezyonu değerlendirildi. Bu lezyonların 604 (% 60,4)'ünde meme kanseri tanısı vardı. 604 meme kanseri olgusunun 161 (% 16.1)'inin mamografi ve toraks BT görüntüleri vardı. Çalışmaya dahil edilen bu 161 lezyonun BT ve mamografi (MG)'de lezyonların tanısal özellikleri, görülebilirlik oranları, malign ek odak ve patolojik LAP varlığı lezyonların tipi, boyutu ve lokalizasyonundan haberi olmayan iki radyolog tarafından değerlendirildi. Bulgular: Lezyon boyutları BT'de 7-110 (25.82) mm ve MG'de 6-92 (24.97) mm arasında değişmekteydi. BT ve MG'de kitle şekilleri ve kontürleri büyük oranda düzensiz idi. İki görüntüleme yöntemi arasında istatistiksel olarak anlamlı bir fark bulunmadı (p <0,001). Cerrahi patolojisi mevcut olan 133 hastanın 38'inde (% 28) tümörden ayrı malign ek odak saptanmış olup, MG'de lezyonların 18'i (% 47), BT'de ise 32'si (% 84) tespit edildi. Cerrahi patolojide 133 hastanın 55'inde (%41) patolojik lenfadenopati (LAP) tespit edilmiş olup MG'de 17 (% 30,1), BT'de ise 45 (% 81,9) patolojik LAP tespit edildi. Lezyonların yaklaşık % 70'inde hem BT hem de MG'de oldukça iyi görülebilirlik oranları vardı. İki gözlemci arasında, gözlemciler arası uyum anlamlıydı. Sonuç: Tomografi kullanımındaki artıştan dolayı, toraks BT'de meme dokusuna dikkat edilmesi ve meme kitlelerinin BT görüntüleme özellikleri bilinmesi meme kanserinin erken teşhisinde hayati öneme sahiptir.

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“…There are studies about incidentally detected breast lesions by tomography (2,(6)(7)(8)(9)(10)(11)(12)(13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

Value of tomography in detecting breast masses and discriminating malign and benign lesions

et al. 2019

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Objective: The main purpose of the present study was to determine the effectivity of computerized tomography (CT) in detecting breast masses and discriminating masses as malignant or benign. Material and Methods: After having received the institutional local ethics committee approval, an experienced radiologist who did not participate in the study created a patient pool by searching our health center's Pathology department database between 2010 and 2018. The group created consisted of dense and non-dense breast types equally and included approximately similar percentages of benign and malignant breast mass sizes. Finally, 70 subjects were included: 30 females with definite malign, 20 with definite benign breast masses, and 20 without any breast pathology based on mammography and ultrasonography results, who were considered as the control group. Three experienced Radiologists (R1, R2, R3) who were not aware of the final diagnosis evaluated all images independently. Radiologist performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and interobserver reliability values were estimated by intraclass correlation coefficient (ICC) analysis. Results: The diagnostic accuracy suitability of CT according to BI-RADS scores for R1, R2 and R3 were found as p< 0.001, p< 0.001 and p< 0.001, respectively. There were significant interobserver reliability rates between all investigators (p= 0.0001). Conclusion: CT may be used as a valuable diagnostic tool in discriminating breast masses with further training in widely varying appearances of normal breast tissues leading to false positive findings.

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Incidental Breast Lesions Detected on Computed Thorax Tomography

Cited by 10 publications

References 13 publications

Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing

Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing

Tomografi ve Mamografinin Karşılaştırılması, Bilgisayarlı Tomografide Meme Kanserinin Tanısal Özellikleri

Value of tomography in detecting breast masses and discriminating malign and benign lesions

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