AIMC Topic: Early Detection of Cancer

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Machine learning model for early diagnosis of breast cancer based on PiRNA expression with CA153.

Scientific reports
PIWI-interacting RNAs (piRNAs) have been implicated in the biological processes of various cancers. This study aimed to investigate the diagnostic potential of circulating piRNAs in breast cancer (BC) using machine learning (ML) frameworks. A serum t...

Multimodal analysis of cell-free DNA enhances differentiation of early-stage breast cancer from benign lesions and healthy individuals.

BMC biology
BACKGROUND: Breast cancer (BC) remains the second leading cause of cancer-related mortality among women worldwide. Liquid biopsy based on circulating tumor DNA (ctDNA) offers a promising noninvasive approach for early detection; however, differentiat...

AI-assisted cervical cytology precancerous screening for high-risk population in resource-limited regions using a compact microscope.

Nature communications
Insufficient coverage of cervical cytology screening in resource-limited areas remains a major bottleneck for women's health, as traditional centralized methods require significant investment and many qualified pathologists. Using consumer-grade elec...

Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

European journal of cancer (Oxford, England : 1990)
IMPORTANCE: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learnin...

Deep learning model for early acute lymphoblastic leukemia detection using microscopic images.

Scientific reports
Cancer of bone marrow is classified as Acute Lymphoblastic Leukemia (ALL), an abnormal growth of lymphoid progenitor cells. It affects both children and adults and is the most predominant form of infantile cancer. Currently, there has been significan...

Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer.

eLife
Pancreatic cancer (PC) is a highly aggressive malignancy in humans, where early diagnosis significantly improves patient outcomes. However, effective methods for accurate and early detection remain limited. In this multiethnic study involving human s...

Colonoscopy in obese patients: challenges and emerging solutions.

Current opinion in gastroenterology
PURPOSE OF REVIEW: The rising prevalence of obesity, now affecting over 40% of U.S. adults, poses critical implications for colorectal cancer screening, as obesity increases the risk of both colorectal adenomas and cancer. Despite these elevated risk...

Socioeconomic impact of artificial intelligence-driven point-of-care testing devices for liquid biopsy in the OncoCheck system.

Cancer metastasis reviews
Cancer disparities in low- and middle-income countries (LMICs) persist because of socioeconomic inequalities and limited access to screening infrastructure, which requires equitable diagnostic solutions. As researchers, we need to develop interventio...

Non-invasive breath testing to detect colorectal cancer: protocol for a multicentre, case-control development and validation study (COBRA2 study).

BMC cancer
BACKGROUND: Colorectal cancer (CRC) is the fourth most common cancer in the United Kingdom. The five-year survival rate from CRC is only 10% when discovered at a late stage, but can exceed 90% if diagnosed early. Symptoms related to CRC can be non-sp...

Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China.

BMC pulmonary medicine
BACKGROUND: Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed t...