AIMC Topic: Early Detection of Cancer
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Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Mar 23, 2025
Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic methods. Near-infrared spectroscopy (NIRS) offers unique advantages in probing molecular vibrational i...
PLSKB: An Interactive Knowledge Base to Support Diagnosis, Treatment, and Screening of Lynch Syndrome on the Basis of Precision Oncology.
JCO clinical cancer informatics
Mar 14, 2025
PURPOSE: Understanding the genetic heterogeneity of Lynch syndrome (LS) cancers has led to significant scientific advancements. However, these findings are widely dispersed across various resources, making it difficult for clinicians and researchers ...
Nano biosensor unlocks tumor derived immune signals for the early detection of ovarian cancer.
Biosensors & bioelectronics
Mar 10, 2025
Ovarian cancer is a critical health issue for women nowadays. Its impact is significant because of its high mortality rate (324,603 worldwide), late-stage diagnosis and poor survival rate. Lack of screening tests, vague symptoms, misdiagnosis, and ag...
Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.
BMC gastroenterology
Mar 8, 2025
OBJECTIVES: Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligenc...
Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability.
Computers in biology and medicine
Mar 7, 2025
BACKGROUND AND OBJECTIVE: Early detection of Oral Squamous Cell Carcinoma (OSCC) improves survival rates, but traditional diagnostic methods often produce inconsistent results. This study introduces the Oral Cancer Attention Network (OCANet), a U-Net...
Economics of AI and human task sharing for decision making in screening mammography.
Nature communications
Mar 7, 2025
The rising global incidence of breast cancer and the persistent shortage of specialized radiologists have heightened the demand for innovative solutions in mammography screening. Artificial intelligence (AI) has emerged as a promising tool to bridge ...
AI integrations with lung cancer screening: Considerations in developing AI in a public health setting.
European journal of cancer (Oxford, England : 1990)
Mar 6, 2025
Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-exposed populations. Growing numbers of screening cases are also found to have CT-detectable emphysema or elevated levels of coronary calcium, indicating ...
Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.
Scientific reports
Mar 6, 2025
Cervical cancer (CC) is the leading cancer, which mainly affects women worldwide. It generally occurs from abnormal cell evolution in the cervix and a vital functional structure in the uterus. The importance of timely recognition cannot be overstated...
Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study.
Nature communications
Mar 6, 2025
Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-base...
Skin cancer detection using dermoscopic images with convolutional neural network.
Scientific reports
Mar 1, 2025
Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for a cure. Machine learning studies have focused on classifying melanoma tumors, but these methods are cumbersome and...
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