MATERIALS AND METHODS: Twenty-four patients with clinically node-negative breast cancer were recruited. Combined radiotracer and blue dye methods were used for identification of SLNs. The nodes were thinly sliced and embedded. Serial sectioning and immunohistochemical (IHC) staining against AE1/AE3 were performed if initial HandE sections of the blocks were negative.
RESULTS: SLNs were successfully identified in all patients. Ten cases had nodal metastases with 7 detected in SLNs and 3 detected only in axillary nodes (false negative rate, FNR=30%). Some 5 out of 7 metastatic lesions in the SLNs (71.4%) were detected in initial sections of the thinly sliced tissue. Serial sectioning detected the remaining two cases with either micrometastases or isolated tumour cells (ITC).
CONCLUSIONS: Thin slicing of tissue to 3-5mm thickness and serial sectioning improved the detection of micro and macro-metastases but the additional burden of serial sectioning gave low yield of micrometastases or ITC and may not be cost effective. IHC validation did not further increase sensitivity of detection. Therefore its use should only be limited to confirmation of suspicious lesions. False negative cases where SLNs were not involved could be due to skipped metastases to non-sentinel nodes or poor technique during procurement, resulting in missed detection of actual SLNs.
OBJECTIVE: This study aimed to determine the survival rate of breast cancer among the women of Malaysia and characteristics of the survivors.
METHOD: A retrospective cohort study was conducted on secondary data obtained from the Breast Cancer Registry and medical records of breast cancer patients admitted to Hospital Kuala Lumpur from 2005 to 2009. Survival data were validated with National Birth and Death Registry. Statistical analysis applied logistic regression, the Cox proportional hazard model, the Kaplan-Meier method and log rank test.
RESULTS: A total of 868 women were diagnosed with breast cancer between January 2005 and December 2009, comprising 58%, 25% and 17% Malays, Chinese and Indians, respectively. The overall survival rate was 43.5% (CI 0.573-0.597), with Chinese, Indians and Malays having 5 year survival rates of 48.2% (CI 0.444-0.520), 47.2% (CI 0.432-0.512) and 39.7% (CI 0.373-0.421), respectively (p<0.05). The survival rate was lower as the stages increased, with the late stages were mostly seen among the Malays (46%), followed by Chinese (36%) and Indians (34%). Size of tumor>3.0cm; lymph node involvement, ERPR, and HER 2 status, delayed presentation and involvement of both breasts were among other factors that were associated with poor survival.
CONCLUSIONS: The overall survival rate of Malaysian women with breast cancer was lower than the western figures with Malays having the lowest because they presented at late stage, after a long duration of symptoms, had larger tumor size, and had more lymph nodes affected. There is an urgent need to conduct studies on why there is delay in diagnosis and treatment of breast cancer women in Malaysia.
METHODS: Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy.
RESULTS: The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively.
CONCLUSION: Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models' accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.