Mammoscintigraphy and Breast Cancer

Villanueva-Mever, J.; Leonard, Morton H.; Briscoe, E. G.; Cesani, Fernando; Ali, Simak; Hove, Maike vom; Cowan, David P.

doi:10.1097/00003072-199501000-00037

Cited by 2 publications

(4 citation statements)

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“…так, появление нового поколения специализированных маммосцинтиграфических гамма-камер с высокой (2-4 мм) разрешающей способностью обеспечило существенный рост информативности мСГ. на сегодняшний день имеются доказательства того, что чувствительность метода при скрининговой диагностике рмЖ с помощью мСГ может быть увеличена до 86%-90% [21]. Проводимые в настоящее время многоцентровые исследования показали, что у женщин с плотной тканью молочной железы дополнительное использование мСГ позволяет почти в 4 раза увеличить выявляемость рмЖ [19].…”

Section: материалы и методыunclassified

“…Полученные результаты показали, что сочетание ммГ с мСГ позволяет существенно (до 93,4%) повысить чувствительность диагностики минимальных форм рмЖ, что совпадает с заключениями, сделанными другими авторами [13,14,20,21].…”

Section: материалы и методыunclassified

“…анализ литературы показывает, что возможными причинами Ло результатов при мСГ могут быть гистологические и генетические особенности опухоли: сверхэкспрессия опухолью гена множественной лекарственной резистентности, наличие в ней очагов поражения с низкой десмопластической активностью или целлюлярной пролиферацией [21]. Возможными причинами ЛП заключений могут стать гиперпролиферативные изменения в мЖ, в первую очередь, гиперплазия на фоне атипии [22], а также фиброаденомы с высокой митотической активностью.…”

Section: материалы и методыunclassified

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Diagnostic Accuracy of Mammography and Mammoscintigraphy With 99m Tc-Mibi in Detection of Minimal Breast Cancer

Chernaya¹,

Kanaev²,

Novikov³

et al. 2017

Problems in oncology

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In 437 primary patients with breast cancer (BC) we compared diagnostic accuracy of mammography (MG) and mammoscintigraphy (MCG) with 99mTc-MIBI. All women, included in the study, had lesions suspicious for BC. In 108 suspicious lesions were less than 10 mm in diameter. On histology 33 (30,5%) patients had invasive BC, 20 (18,5%) - noninvasive BC, 6 (5,6%) - lobular carcinoma, 2 (1,9%) - mucinous BC, 8 (7,4%) - fibroadenoma and in remained 39 cases (36,1%) - localised fibrosis. In the group of women with lesions below 10 mm sensitivity, specificity and accuracy of MG in diagnosis of BC were 72%, 74%, 73%, for MCG - 62%, 51%, 57%. Combination of MG and MCG helped to increase sensitivity to 93,4%. In 53 women with dense breasts sensitivity, specificity and accuracy of MG were 40%, 71,4%, 64%; for mCg two times higher (82%) with specificity of 55% and 60% accuracy. Combination of MG and MCG allowed increasing the sensitivity of diagnosis of minimal BC 2 times more (up to 91%).

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Section: материалы и методыunclassified

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Diagnostic Accuracy of Mammography and Mammoscintigraphy With 99m Tc-Mibi in Detection of Minimal Breast Cancer

Chernaya¹,

Kanaev²,

Novikov³

et al. 2017

Problems in oncology

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“…In BSGI, a radiotracer such as Technetium-99 m Sestamibi is injected into the patient’s bloodstream and the breast is visualized using a special camera. The radiotracer uptake is commensurate with blood flow and mitochondrial activity within tumour cells [ 13 ], which enables us to diagnose breast cancer by distinguishing the biological behaviour between tumour cells and normal cells. In addition, The BSGI presents an excellent specificity for the diagnosis of ALN metastasis [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features

Cai,

Deng,

Zhang

et al. 2024

Radiat Oncol

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Background The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status. Materials and methods Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model. Results Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines’ model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients’ risk of ALN metastasis (https://wuqian.shinyapps.io/shinybsgi/). The result in ROC showed the model could benefit from incorporating BSGI feature. Conclusion This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

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Mammoscintigraphy and Breast Cancer

Cited by 2 publications

References 0 publications

Diagnostic Accuracy of Mammography and Mammoscintigraphy With 99m Tc-Mibi in Detection of Minimal Breast Cancer

Diagnostic Accuracy of Mammography and Mammoscintigraphy With 99m Tc-Mibi in Detection of Minimal Breast Cancer

A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features

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