Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Zhang, Chaoyi; Xu, Jin; Tang, Rong; Yang, Jianhui; Wang, Wei; Yu, Xianjun; Shi, Si

doi:10.1186/s13045-023-01514-5

Cited by 23 publications

(4 citation statements)

References 261 publications

(219 reference statements)

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“…Below, we first describe computational studies in NDDs. While we do not dedicate a separate section to cancer-related computational studies, we encourage interested readers to explore recent reviews that focus on cancer and machine learning ( Zhang et al, 2023 ; Ma et al, 2024 ; Unger and Kather, 2024 ) in addition to the ones mentioned earlier. We then proceed to the connection between NDDs and cancer.…”

Section: Ai ML Dl and Other Computational Methods In Tackling Nddsmentioning

confidence: 99%

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Nussinov,

Yavuz,

Demirel

et al. 2024

Front. Cell Dev. Biol.

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The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

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Section: Ai ML Dl and Other Computational Methods In Tackling Nddsmentioning

confidence: 99%

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Nussinov,

Yavuz,

Demirel

et al. 2024

Front. Cell Dev. Biol.

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“…The evaluation of hormone receptor (HR) status is both a prognostic and predictive factor in BC and a crucial step in tailoring therapy in BC patients [ 73 , 74 , 75 ]. The implication of DIA in ER and PgR receptors’ evaluation by IHC image analysis is plausible after the massive success of Ki67 automated scoring, given that all these markers exhibit nuclear expression [ 76 , 77 ].…”

Section: Prognostic and Predictive Models On Immunohistochemistry-sta...mentioning

confidence: 99%

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

Ivanova,

Pescia,

Trapani

et al. 2024

Cancers

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Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

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“…Gilson et al analyzed ChatGPT's ability to answer applied knowledge test questions related to primary care, particularly those involving histological image analysis [155] . These investigations unfold the diverse utility of AI technology in healthcare paradigms [155] , [156] , [157] . Given the rapid and generative advancement of AI technology, image interpretation capabilities have now been introduced into large-scale language models such as ChatGPT-4.0 and Google Bard, which can provide new insights into healthcare practices [158] .…”

Section: Future Directions and Perspectivesmentioning

confidence: 99%

Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment

Xu,

Yang,

Tan

et al. 2024

Computational and Structural Biotechnology Journal

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Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Cited by 23 publications

References 261 publications

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment

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