Application of artificial intelligence in predicting lymph node metastasis in breast cancer

Windsor, Gabrielle O.; Bai, Harrison X.; Lourenço, Ana P.; Jiao, Zhicheng

doi:10.3389/fradi.2023.928639

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Recent advancements in medical imaging, particularly the integration of artificial intelligence (AI), offer promising prospects for enhancing the diagnostic performance of both [ 18 F]FDG-PET/CT and CE-CT. AI-driven algorithms have shown the potential to improve the accuracy of diagnosing metastatic spread, especially in scenarios like axillary lymph node metastasis in breast cancer [49,50]. These AI models, powered by deep learning techniques, can aid clinicians in making more precise and efficient diagnostic decisions, potentially reducing the need for unnecessary invasive procedures.…”

Section: Discussionmentioning

confidence: 99%

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [18F]FDG-PET/CT and CE-CT

Gram-Nielsen,

Christensen,

Naghavi-Behzad

et al. 2023

J. Imaging

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The study aimed to compare the metastatic pattern of breast cancer and the intermodality proportion of agreement between [18F]FDG-PET/CT and CE-CT. Women with metastatic breast cancer (MBC) were enrolled prospectively and underwent a combined [18F]FDG-PET/CT and CE-CT scan to diagnose MBC. Experienced nuclear medicine and radiology physicians evaluated the scans blinded to the opposite scan results. Descriptive statistics were applied, and the intermodality proportion of agreement was used to compare [18F]FDG-PET/CT and CE-CT. In total, 76 women with verified MBC were enrolled in the study. The reported number of site-specific metastases for [18F]FDG-PET/CT vs. CE-CT was 53 (69.7%) vs. 44 (57.9%) for bone lesions, 31 (40.8%) vs. 43 (56.6%) for lung lesions, and 16 (21.1%) vs. 23 (30.3%) for liver lesions, respectively. The proportion of agreement between imaging modalities was 76.3% (95% CI 65.2–85.3) for bone lesions; 82.9% (95% CI 72.5–90.6) for liver lesions; 57.9% (95% CI 46.0–69.1) for lung lesions; and 59.2% (95% CI 47.3–70.4) for lymph nodes. In conclusion, bone and distant lymph node metastases were reported more often by [18F]FDG-PET/CT than CE-CT, while liver and lung metastases were reported more often by CE-CT than [18F]FDG-PET/CT. Agreement between scans was highest for bone and liver lesions and lowest for lymph node metastases.

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Section: Discussionmentioning

confidence: 99%

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [18F]FDG-PET/CT and CE-CT

Gram-Nielsen,

Christensen,

Naghavi-Behzad

et al. 2023

J. Imaging

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“…The success rate of ultrasonography in the detection of BC has significantly increased with the newest invention of ultrasound elastography, and the semiquantitative evaluation of lesion arduousness provides an improved distinction between swelling benignity and malignancy. In order to determine the condition of axillary lymph node metastases in clinical T12 cancer, ultrasound elastography, and breast ultrasound imaging characteristics can be combined 19 21 . This method can effectively segment breast ultrasound images and handle a variety of tumors with different sizes and shapes.…”

Section: Ultrasoundmentioning

confidence: 99%

Artificial Intelligence - A Primer for Diagnosis and Interpretation of Breast Cancer

Mohan Jha,

Prasada Kumar Mahapatra,

Abraham

et al. 2024

Int J Tre Onc Sci

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Breast Cancer (BC) is a major universal health problem. Early detection and precise diagnosis are vital for enlightening outcomes. Artificial Intelligence (AI) technologies can potentially revolutionize the field of BC by providing quantitative representations of medical images to assist in segmentation, diagnosis, and prognosis. AI can improve image quality, detect and segment breast lesions, classify cancer and predict its behavior, and integrate data from multiple sources to predict clinical outcomes. It can lead to more personalized and effective treatment for BC patients. Challenges faced by AI in real-life solicitations include data curation, model interpretability, and run-through guidelines. However, the clinical implementation of AI is expected to deliver vital guidance for patient-tailored management. BC is a major global health problem; early detection and treatment are crucial for improving outcomes. Imaging detection is a key screening, diagnosis, and treatment effectiveness assessment tool. However, the irresistible number of images creates a heavy capacity for radiologists and delays reporting. AI has the potential to revolutionize BC imaging by improving efficiency and accuracy. AI can recognize, segment, and diagnose tumor lesions automatically and analyze tumor images on a molecular level. It could lead to more personalized treatment strategies. However, AI-assisted imaging diagnosis is still in its early stages of development, and more research is needed to validate its clinical effectiveness. Therefore, AI is a promising new technology that has the potential to progress the diagnosis and treatment of BC, and AI-assisted imaging diagnosis is a promising new technology for improving the early detection and diagnosis of BC. More research is needed to bring this technology to clinical practice.

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“…We chose to use deep learning for our algorithm because of its potential to revolutionize medical imaging for disease diagnosis [35,41,42]. Furthermore, we needed our algorithm to be rigorous and dynamic, so that, regardless of the section of the TM that was being imaged, the TM could be automatically segmented.…”

Section: Deep Learning and Model Limitationsmentioning

confidence: 99%

Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning

Oghalai,

Long,

Kim

et al. 2023

Algorithms

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Optical Coherence Tomography (OCT) is a light-based imaging modality that is used widely in the diagnosis and management of eye disease, and it is starting to become used to evaluate for ear disease. However, manual image analysis to interpret the anatomical and pathological findings in the images it provides is complicated and time-consuming. To streamline data analysis and image processing, we applied a machine learning algorithm to identify and segment the key anatomical structure of interest for medical diagnostics, the tympanic membrane. Using 3D volumes of the human tympanic membrane, we used thresholding and contour finding to locate a series of objects. We then applied TensorFlow deep learning algorithms to identify the tympanic membrane within the objects using a convolutional neural network. Finally, we reconstructed the 3D volume to selectively display the tympanic membrane. The algorithm was able to correctly identify the tympanic membrane properly with an accuracy of ~98% while removing most of the artifacts within the images, caused by reflections and signal saturations. Thus, the algorithm significantly improved visualization of the tympanic membrane, which was our primary objective. Machine learning approaches, such as this one, will be critical to allowing OCT medical imaging to become a convenient and viable diagnostic tool within the field of otolaryngology.

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Application of artificial intelligence in predicting lymph node metastasis in breast cancer

Cited by 10 publications

References 26 publications

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [18F]FDG-PET/CT and CE-CT

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [18F]FDG-PET/CT and CE-CT

Artificial Intelligence - A Primer for Diagnosis and Interpretation of Breast Cancer

Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning

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