AI-Enhanced PET and MR Imaging for Patients with Breast Cancer

Romeo, Valeria; Moy, Linda; Pinker, Katja

doi:10.1016/j.cpet.2023.05.002

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…Despite this study being retrospective, it has a substantial degree of MRI scan heterogeneity and a little follow-up time, and its results offer important guidance aimed at the practice of MRI-based imaging radiomics in the detection of BC. To deliver more detailed and customized radiological and genetic features, radiogenomics performs the joint examination of radiomics and genomics 32 . As a result, precision healthcare and customized treatment are no longer restricted to genetic or proteomics research projects that rely on tissue or blood samples.…”

Section: Radiomics In Breast Cancermentioning

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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Section: Radiomics In Breast Cancermentioning

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

View full text Add to dashboard Cite

show abstract

“…For instance, radiomic characteristics could indicate temporal and geographical variability, which is well established to be a significant factor in tumor behavior and therapeutic resistance ( 3 ). As a result, radiomics has the potential to serve as a “virtual biopsy” and, in contrast to traditional biopsies, uses noninvasive imaging that allows analysis of the entire tumor (rather than a focal sample) and can be applied more easily at multiple time points for disease monitoring, offering potentially significant diagnostic information related to disease evolution ( 4 , 5 ).…”

mentioning

confidence: 99%