Correlation of NIS mRNA levels with radioiodide uptake in mammary tumors and non-tumor mammary glands of MMTV-infected mice

We have developed an in vivo non-invasive gamma camera imaging system which uses 125I to detect functional iodide metabolism in mammary tumors. Iodide metabolism in these tumors is mediated by the sodium iodide symporter. The quantity and pattern of radioiodide uptake varies between mammary tumors. We have previously shown that localization of NIS protein expression reflects the radioiodide uptake in gamma camera images. In this study, we investigate whether expression levels of NIS mRNA in mammary tumors correlate with 125I uptake pattern shown in gamma camera images. Our hypothesis is that NIS function in mammary tumors and non-tumor mammary glands is regulated primarily at the transcriptional level. To test this hypothesis, we quantified NIS mRNA levels using TaqMan real-time RT-PCR, and constructed a cRNA standard curve for quantification. The ratio of NIS to the housekeeping gene β-actin was compared to the intensity and pattern of mammary tumor radioiodide uptake as imaged by the gamma camera. In MMTV tumors, our results suggest that NIS is under both transcriptional and post-transcriptional control in this model for breast cancer. In separate tumors, we observed both positive correlation and no correlation between NIS mRNA level and radioiodide uptake. We also found that NIS mRNA levels were increased in non-palpable tumors in correlation with increases in radioiodide uptake, suggesting that an upregulation of NIS mRNA occurs in early tumor development

Effective Feature Engineering and Classification of Breast Cancer Diagnosis: A Comparative Study

: Breast cancer is among the most common cancers found in women, causing cancer-related deaths and making it a severe public health issue. Early prediction of breast cancer can increase the chances of survival and promote early medical treatment. Moreover, the accurate classification of benign cases can prevent cancer patients from undergoing unnecessary treatments. Therefore, the accurate and early diagnosis of breast cancer and the classification into benign or malignant classes are much-needed research topics. This paper presents an effective feature engineering method to extract and modify features from data and the effects on different classifiers using the Wisconsin Breast Cancer Diagnosis Dataset. We then use the feature to compare six popular machine-learning models for classification. The models compared were Logistic Regression, Random Forest, Decision Tree, K-Neighbors, Multi-Layer Perception (MLP), and XGBoost. The results showed that the Decision Tree model, when applied to the proposed feature engineering, was the best performing, achieving an average accuracy of 98.64%

Using Morphological Operation and Watershed Techniques for Breast Cancer Detection

Breast cancer is one of the leading causes of mortality between women, with one in eight women diagnosed with the disease, but early detection can reduce death rates. Therefore, continuous effort is being made to advance more effective methods for the early and effective diagnosis of breast cancer with high accuracy without human intervention. Classical attempts were manual, time- consuming and ineffective in many situations. The purpose of this work is to detect and locate the presence of malignant tissues in the breast using the morphological technique in mammogram images to diagnose breast cancer because morphology is one of the most reliable methods for early detection of breast cancer. The proposed algorithm is developed using watershed segmentation after the preprocessing is completed by the median filter to eliminate any expected noise, and contouring the tumor by morphological techniques to take the best diagnostic for breast cancer in a mammogram image. Good results are obtained for the measurements used like MSE, PSNR, SNR, entropy for the mammogram images.  

Using Morphological Operation and Watershed Techniques for Breast Cancer Detection

Breast cancer is one of the leading causes of mortality between women, with one in eight women diagnosed with the disease, but early detection can reduce death rates. Therefore, continuous effort is being made to advance more effective methods for the early and effective diagnosis of breast cancer with high accuracy without human intervention. Classical attempts were manual, time- consuming and ineffective in many situations. The purpose of this work is to detect and locate the presence of malignant tissues in the breast using the morphological technique in mammogram images to diagnose breast cancer because morphology is one of the most reliable methods for early detection of breast cancer. The proposed algorithm is developed using watershed segmentation after the preprocessing is completed by the median filter to eliminate any expected noise, and contouring the tumor by morphological techniques to take the best diagnostic for breast cancer in a mammogram image. Good results are obtained for the measurements used like MSE, PSNR, SNR, entropy for the mammogram images.    </pre

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis