THE CLINICAL SIGNIFICANCE OF ^(99m)Tc-MIBI BREAST IMAGING IN THE DIAGNOSIS OF EARLY BREAST CANCER

Objective: To find an effective, sensitive, specific and noninvasive diagnostic method of breast cancer. Methods: 109 masses of 102 patients with breast lesions smaller than 2 cm in diameter were divided into three groups to undergo 99mTc-MIBI imaging and compared with the results of pathology examination. 20 cases without breast lesions were selected as control. Abnormal condensation of 99mTc-MIBI in the breast reaching 10% higher than that in the counterpart of the healthy breast was regarded as positive. Results: Of 32 breast cancers, positive imaging appeared in 25. Negative imaging were found in 31 of 38 benign breast lesions. Of 39 occult breast lesions, positive imaging appeared in 6 and 3 of them were breast cancer, 2 of 3 patients with slightly increased 99mTc-MIBI imaging threshold were breast cancer also. No positive imaging was found in the control group. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value of 99mTc-MIBI was 88.4%, 89.2%, 88.0%, 75.0% and 95.3%, respectively. Conclusion: 99mTc-MIBI imaging had higher sensitivity and accuracy in the diagnosis of breast cancer and differentiation between benign and malignant breast lesions. It could provide useful information for the diagnosis of clinically suspected breast cancer.

A Swin Transformer and Residualnetwork Combined Model for Breast Cancer Disease Multi-Classification Using Histopathological Images

Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.

The accuracy of magnetic resonance imaging and ultrasound in evaluating the size of early-stage breast neoplasms

Objective Breast cancer is the most frequently diagnosed cancer in women. Accurate evaluation of the size and extent of the tumor is crucial in selecting a suitable surgical method for patients with breast cancer. Both overestimation and underestimation have important adverse effects on patient care. This study aimed to evaluate the accuracy of breast magnetic resonance imaging(MRI) and ultrasound(US) examination for measuring the size and extent of early-stage breast neoplasms.Methods The longest diameter of breast tumors in patients with T_(1–2)N_(0–1)M_0 invasive breast cancer preparing for breast-conserving surgery(BCS) was measured preoperatively by using both MRI and US and their accuracy was compared with that of postoperative pathologic examination. If the diameter difference was within 2 mm, it was considered to be consistent with pathologic examination.Results A total of 36 patients were imaged using both MRI and US. The mean longest diameter of the tumors on MRI, US, and postoperative pathologic examination was 20.86 mm ± 4.09 mm(range: 11–27 mm), 16.14 mm ± 4.91 mm(range: 6–26 mm), and 18.36 mm ± 3.88 mm(range: 9–24 mm). US examination underestimated the size of the tumor compared to that determined using pathologic examination(t = 3.49, P < 0.01), while MRI overestimated it(t =-6.35, P < 0.01). The linear correlation coefficients between the image measurements and pathologic tumor size were r = 0.826(P < 0.01) for MRI and r = 0.645(P < 0.01) for US. The rate of consistency of MRI and US compared to that with pathologic examination was 88.89% and 80.65%, respectively, and there was no statistically significant difference between them(χ~2 = 0.80, P > 0.05).Conclusion MRI and US are both effective methods to assess the size of breast tumors, and they maintain good consistency with pathologic examination. MRI has a better correlation with pathology. However, we should be careful about the risk of inaccurate size estimation.

Evaluation of Clinical and Radiological Tumour Response during Neo-Adjuvant Breast Cancer Chemotherapy at Yalgado Ouedraogo University Hospital

Background: Neoadjuvant chemotherapy (NAC) is one of the treatment options for breast cancer. Its aim is to significantly reduce the size of the tumour in preparation for surgery. The aim of this work is to analyze the conditions of clinical and radiological evaluation of NAC at the Yalgado Ouédraogo University Hospital (CHUYO). Patients and Methods: This was a descriptive cross-sectional study based on the medical records of patients followed up in the cancer department of the CHUYO from 1 January 2013 to 31 December 2021. All patients followed for histologically proven, non-metastatic breast cancer and having received at least one course of NAC were included in this study. The variables were related to the socio-demographic characteristics of the patients, the indications, the protocols of NAC and the sequences of evaluation of the tumour response (clinical, radiological and anatomopathological). Results: We collected 105 cases. The average age of the patients concerned was 44 years. The most frequent histological type was non-specific invasive carcinoma in 97.1% of cases. Immunohistochemically, triple-negative patients accounted for 51.4%. At the initial stage, all patients underwent clinical exploration. Clinical measurement of the tumour was performed in 70.5% of cases. The radiological size of the tumour was determined by ultrasound in 59.1% of cases. One patient had a breast MRI. Thirty-one patients were lost to follow-up after the initial evaluation. At mid-term and at the end of treatment, clinical tumour size was performed in 38.6% and 45.6% of cases respectively. There was no breast imaging performed at mid- and end-of-treatment. CT scans were performed in all cases at baseline, mid-term and end of treatment for extension assessment but did not mention the breast tumour. The tumour response rate was not recorded. Conclusion: Clinical assessment of tumour response is almost always empirical and not quantified. Medical imaging examinations are prescribed sparingly so as not to compromise the regularity of treatment and patient assessment.

Diagnostic performance of contrast-enhanced ultrasound and enhanced magnetic resonance for breast nodules

In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound（US）, contrastenhanced US（CEUS）, combined US and CEUS and magnetic resonance imaging（MRI） in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve.The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them（kappa 〈0.20）. In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.

Biomarker-targeted fluorescent probes for breast cancer imaging

Breast cancer has become a common tumor worldwide which seriously endangers people＇s health. Earlydiagnosis and treatment are particularly urgent in order to reduce the onset risk, mortality, and prolongthe five-year survival rate. Therefore, we need a kind of diagnosis and treatment technology with highspecificity, sensitivity and selectivity. In recent years, because of its unique properties in biologicalapplications, fluorescence imaging has become an attractive research subject. Fluorescence imagingoffers innovative ideas of targetable recognition of breast cancer cells, breast cancer imaging in vivoanimal models, anticancer drugs delivery for guiding the mammary surgery via a noninvasive way withhigh sensitively and specifically. In this review, we summarized the recent advances of fluorescent probesfor breast cancer imaging, which were classified according to different biomarkers the probes recognized.Moreover, we discussed the strengths, built-in problems as well as the challenges about the fluorescentprobe as a unique potential method for the better application in breast cancer diagnosis and treatment.~ 2018 Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.