MATERIALS AND METHODS: The clinical characteristics, presenting symptoms and survival of RCC patients (n=151) treated at UMMC from 2003-2012 were analysed. Symptoms evaluated were macrohaematuria, flank pain, palpable abdominal mass, fever, lethargy, loss of weight, anaemia, elevated ALP, hypoalbuminemia and thrombocytosis. Univariate and multivariate Cox regression analyses were performed to determine the prognostic significance of these presenting symptoms. Kaplan Meier and log rank tests were employed for survival analysis.
RESULTS: The 2002 TNM staging was a prognostic factor (p<0.001) but Fuhrman grading was not significantly correlated with survival (p=0.088). At presentation, 76.8% of the patients were symptomatic. Generally, symptomatic tumours had a worse survival prognosis compared to asymptomatic cases (p=0.009; HR 4.74). All symptoms significantly affect disease specific survival except frank haematuria and loin pain on univariate Cox regression analysis. On multivariate analysis adjusted for stage, only clinically palpable abdominal mass remained statistically significant (p=0.027). The mean tumour size of palpable abdominal masses, 9.5±4.3cm, was larger than non palpable masses, 5.3±2.7cm (p<0.001).
CONCLUSIONS: This is the first report which includes survival information of RCC patients from Malaysia. Here the TNM stage and a palpable abdominal mass were independent predictors for survival. Further investigations using a multicentre cohort to analyse mortality and survival rates may aid in improving management of these patients.
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.
METHODS: Pooled urine samples of patients with BTG (n=10), patients with PTC (n=9) and healthy controls (n=10) were subjected to iTRAQ analysis and immunoblotting.
RESULTS: The ITRAQ analysis of the urine samples detected 646 proteins, 18 of which showed significant altered levels (p<0.01; fold-change>1.5) between patients and controls. Whilst four urinary proteins were commonly altered in both BTG and PTC patients, 14 were unique to either BTG or PTC. Amongst these, four proteins were further chosen for validation using immunoblotting, and the enhanced levels of osteopontin in BTG patients and increased levels of a truncated gelsolin fragment in PTC patients, relative to controls, appeared to corroborate the findings of the iTRAQ analysis.
CONCLUSION: The data of the present study is suggestive of the potential application of urinary osteopontin and gelsolin to discriminate patients with BTG from those with PTC non-invasively. However, this needs to be further validated in studies of individual urine samples.
Case Report: Three cases that had been initially presented as a cystic neck lesion in which a benign etiology was considered primarily were compiled in this study. PTC was only diagnosed after surgical excision of these cystic neck lesions in the first two cases, and after performing fine needle aspiration cytology (FNAC) and an 18fluorine-fluorodeoxyglucose positron emission tomography computed tomography (18F-FDG-PET CT) scan in the latter case.
Conclusion: PTC can sometimes present as a cystic neck mass; a presentation which is usually related to a benign lesion. This case series emphasizes that patients who appear to have a solitary cystic neck mass must be treated with a high index of clinical suspicion. Although not a first-line imaging modality, 18F-FDG-PET can be extremely useful in assessing patients with a cystic neck lesion, where diagnosis is still uncertain after standard investigations such as ultrasonography and FNAC have been performed.