Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia

Background: Breast cysts are generally categorized as simple, complicated and complex breast cyst. Complex cystic breast lesions are classified as BI-RADS 4 as there are 23 to 31 % of chance being malignant. Breast ultrasonography is the first-line examination in detection and characterization of br...

पूर्ण विवरण

ग्रंथसूची विवरण
मुख्य लेखक: Pei, Foo Chiao
स्वरूप: थीसिस
भाषा:अंग्रेज़ी
प्रकाशित: 2023
विषय:
ऑनलाइन पहुंच:http://eprints.usm.my/62178/
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author Pei, Foo Chiao
author_facet Pei, Foo Chiao
author_sort Pei, Foo Chiao
description Background: Breast cysts are generally categorized as simple, complicated and complex breast cyst. Complex cystic breast lesions are classified as BI-RADS 4 as there are 23 to 31 % of chance being malignant. Breast ultrasonography is the first-line examination in detection and characterization of breast lesions. In view of few limitations of handheld ultrasound (HHUS), automated breast volume scanner (ABVS) was introduced to overcome the limitation. The purpose of this study is to focus on agreement between HHUS and ABVS in detecting characteristic of complex breast cysts which are thick septa, thick wall and solid component as there is no similar study are done before. Methods: A cross-sectional study was conducted in Hospital Universiti Sains Malaysia (HUSM), Kubang Kerian, Kelantan from 3rd October 2021 to 2nd October 2023. Data was collected retrospectively and prospectively. Retrospectively, the data was taken from pool of patients with complex breast cysts who underwent ABVS and HHUS in Radiology Department HUSM from Picture Archiving and Communication System (PACS). Prospectively, complementary ABVS was performed in patients who had complex breast cyst after performed HHUS by radiology medical officers with at least 2 years of experience in radiology service after obtaining patient consent. Agreement between HHUS and ABVS was estimated using Kappa statistic (unweighted Kappa). Kappa statistics is a measurement of the agreement between two raters. The estimated Kappa was presented as the Kappa value, accompanied by its 95% confidence interval. Kappa value < 0 as indicating poor agreement and 0.01–0.20 as slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement Results: The overall mean (SD) age for the patient was 45.9 (9.4) years old. Total 40 patients with 61 breast lesions were included in this study. The average size of the lesion was 0.6cm in anterior-posterior diameter and 1.1cm in width. There was substantial agreement between HHUS and ABVS in detecting thick septa and solid component which showed kappa agreement of 0.701 (95% CI: 0.525, 0.877) and 0.758 (95% CI :0.589,0.927) respectively. There was almost perfect agreement between HHUS and ABVS in detecting thick wall which showed kappa agreement of 0.880 (95% CI: 0.649, 1.0). Conclusion: Since there is good agreement between HHUS and ABVS in detecting features of complex breast cysts, complimentary HHUS can be exempted in further characterization of the complex breast cyst. Thus, it may help in reducing scanning time. However, larger cohort with more numbers of complex breast cysts is needed to yield favourable results.
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spelling usm-621782025-05-25T02:42:30Z http://eprints.usm.my/62178/ Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia Pei, Foo Chiao RA440-440.87 Study and teaching. Research RC254-282 Neoplasms. Tumors. Oncology (including Cancer) Background: Breast cysts are generally categorized as simple, complicated and complex breast cyst. Complex cystic breast lesions are classified as BI-RADS 4 as there are 23 to 31 % of chance being malignant. Breast ultrasonography is the first-line examination in detection and characterization of breast lesions. In view of few limitations of handheld ultrasound (HHUS), automated breast volume scanner (ABVS) was introduced to overcome the limitation. The purpose of this study is to focus on agreement between HHUS and ABVS in detecting characteristic of complex breast cysts which are thick septa, thick wall and solid component as there is no similar study are done before. Methods: A cross-sectional study was conducted in Hospital Universiti Sains Malaysia (HUSM), Kubang Kerian, Kelantan from 3rd October 2021 to 2nd October 2023. Data was collected retrospectively and prospectively. Retrospectively, the data was taken from pool of patients with complex breast cysts who underwent ABVS and HHUS in Radiology Department HUSM from Picture Archiving and Communication System (PACS). Prospectively, complementary ABVS was performed in patients who had complex breast cyst after performed HHUS by radiology medical officers with at least 2 years of experience in radiology service after obtaining patient consent. Agreement between HHUS and ABVS was estimated using Kappa statistic (unweighted Kappa). Kappa statistics is a measurement of the agreement between two raters. The estimated Kappa was presented as the Kappa value, accompanied by its 95% confidence interval. Kappa value < 0 as indicating poor agreement and 0.01–0.20 as slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement Results: The overall mean (SD) age for the patient was 45.9 (9.4) years old. Total 40 patients with 61 breast lesions were included in this study. The average size of the lesion was 0.6cm in anterior-posterior diameter and 1.1cm in width. There was substantial agreement between HHUS and ABVS in detecting thick septa and solid component which showed kappa agreement of 0.701 (95% CI: 0.525, 0.877) and 0.758 (95% CI :0.589,0.927) respectively. There was almost perfect agreement between HHUS and ABVS in detecting thick wall which showed kappa agreement of 0.880 (95% CI: 0.649, 1.0). Conclusion: Since there is good agreement between HHUS and ABVS in detecting features of complex breast cysts, complimentary HHUS can be exempted in further characterization of the complex breast cyst. Thus, it may help in reducing scanning time. However, larger cohort with more numbers of complex breast cysts is needed to yield favourable results. 2023 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/62178/1/Foo%20Chiao%20Pei-E.pdf Pei, Foo Chiao (2023) Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia. Masters thesis, Universiti Sains Malaysia.
spellingShingle RA440-440.87 Study and teaching. Research
RC254-282 Neoplasms. Tumors. Oncology (including Cancer)
Pei, Foo Chiao
Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia
title Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia
title_full Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia
title_fullStr Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia
title_full_unstemmed Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia
title_short Agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in Hospital Universiti Sains Malaysia
title_sort agreement between handheld ultrasound and automated breast volume scanner in detecting characteristic of complex breast cysts in hospital universiti sains malaysia
topic RA440-440.87 Study and teaching. Research
RC254-282 Neoplasms. Tumors. Oncology (including Cancer)
url http://eprints.usm.my/62178/
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