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Quinazolinedione Derivatives as Potential Anticancer Agents Through Apoptosis Induction in MCF-7
(2025-07-01)
Limboonreung, Tanapol
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Suansilpong, Teetat
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Jumjitvi, Panitan
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Lohawittayanan, Duangporn
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Krobthong, Sucheewin
Breast cancer remains a leading cause of mortality among women worldwide. Surgery, radiation therapy, chemotherapy, and hormone-based treatments are standard therapeutic approaches, but drug resistance and adverse effects necessitate the search for novel anticancer agents. Quinazolinedione derivatives have emerged as potential anticancer compounds due to their cytotoxic and apoptosis-inducing properties. This study aimed to evaluate the apoptotic induction of previously reported quinazolinedione derivatives on MCF-7 breast cancer cells. The cytotoxic effect was assessed using the MTT assay, apoptosis was quantified by Annexin V-PE/7AAD staining and flow cytometry, and apoptosis-related protein expression was analyzed via multiplexed bead-based immunoassays. These findings indicate that two derivatives in the series significantly reduced the cell viability in a dose-dependent manner. Apoptosis was induced primarily through the intrinsic apoptotic pathway as evidenced by the upregulation of caspase-9 and p53 and the downregulation of Bcl-2 and p-Akt. These results highlight quinazolinedione derivatives as promising candidates for breast cancer therapy prompting further investigation into their molecular mechanisms and potential clinical applications.
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Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward
(2024-09-01)
Mathur, Archana
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Arya, Nikhilanand
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Pasupa, Kitsuchart
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Saha, Sriparna
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Roy Dey, Sudeepa
We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority.
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Explainable AI (XAI) for Breast Cancer Diagnosis
(2024-01-01)
Ariyametkul, Awika
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Tamang, Sudarshan
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Paing, May Phu
Breast cancer is the leading cause of mortality and incidence among women worldwide. Mammography, an essential imaging technique, plays a pivotal role in both screening and diagnostic processes by facilitating early detection, which helps improve survival rates. Despite its effectiveness, interpreting mammographic images presents considerable challenges, necessitating the expertise of highly trained radiologists. Artificial intelligence (AI) is a powerful tool for managing large amounts of data and is increasingly used across numerous sectors, including medical applications. This research focuses on applying Convolutional Neural Networks (CNNs) to classify breast cancer from mammograms. We explored six different CNN models including simple ConvNet, AlexNet, VGG-16, GoogLeNet, XceptionNet, and DenseNet201. Our results indicate that DenseNet201 is the most suitable model for this task, achieving 99% accuracy. However, a limitation of AI is the lack of transparency and explanation, often referred to as the 'black box' problem. This vulnerability can be addressed through explainable artificial intelligence (XAI), which elucidates the processes behind AI's decision-making. We employed three different XAI methodologies, including LIME, GradCAM, and GradCAM++, to visualize the model's decision-making process.
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Optimizing the Hyperparameter Tuning of YOLOv5 for Breast Cancer Detection
(2023-01-01)
Intasam, Anongnat
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Promworn, Yuttachon
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Juhong, Aniwat
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Thanasitthichai, Somchai
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Khwayotha, Sirihattaya
This research aims to find the best use optimizer for the task while reducing training time. We optimized the YOLOv5s model and focused on three optimizers, including the Stochastic Gradient Descent (SGD) optimizer, Adaptive Moment Estimation (Adam) optimizer, and Adam with Weight Decay Regularization (AdamW) optimizer. This research utilized 1,471 mammogram images from National Cancer Institute and Udonthani Cancer Hospital, Thailand. A dataset of mammograms was labeled into six classes, including Masses Benign, Masses Malignant, Calcifications Benign, Calcifications Malignant, Associated Features Benign, and Associated Features Malignant, to classify the results accurately. We found that the SGD optimizer outperformed the others, with a mean average precision (mAP) of 0.91, a precision of 0.92, a recall of 0.85, and the shortest training time of about 5.453 hr.
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A comparative study of convolutional neural networks for mammogram diagnosis
(2022-01-01)
Intasam, Anongnat
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Promworn, Yuttachon
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Thanasitthichai, Somchai
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Piyawattanametha, Wibool
This work evaluates and compares the architectures: Inceptionv4, InceptionResnetV2, and Resnet152, to classify benign and malignant. We evaluate the architectures with a statistical analysis base on the received operational characteristics (ROC), accuracy, precision, recall, and F1 score. We generate the best results with the CNN InceptionResnetV2 trained with two classes on a balanced mammogram database. The results for benign cases have a ROC of 0.93, a precision of 0.8319, a recall of 0.9216, and an F1-score of 0.8744. The results for malignant cases have a ROC of 0.91, a precision of 0.9121, a recall of 0.8137, and an F1-score of 0.8601.

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