Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

Chakrabarty, N.; Mahajan, A.

doi:10.1016/j.clon.2023.09.013

Cited by 8 publications

(3 citation statements)

References 106 publications

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“…There has been an increase in research on the characterisation of quantitative imaging features reflecting tumour biology, physiology, and phenotype using artificial intelligence (AI)-based algorithms. Radiomics and deep-learning (DL)–AI-based models are extensively used with medical imaging [ 9 , 10 , 11 ]. Radiomics refers to the computerized extraction of data from radiologic images, and provides unique potential for making lung cancer screening more rapid and accurate by using machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Mahajan,

Kania,

Agarwal

et al. 2024

Cancers

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Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. Results: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model’s accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p < 0.03), the absence of peripheral emphysema (p < 0.03), the presence of pleural retraction (p = 0.004), the presence of fissure attachment (p = 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p = 0.001) and the non-tumour-containing lobe (p = 0.001), the presence of ipsilateral pleural effusion (p = 0.04), and average enhancement of the tumour mass above 54 HU (p < 0.001). Conclusions: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction.

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Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Mahajan,

Kania,

Agarwal

et al. 2024

Cancers

Self Cite

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“…The rapid development of highly flexible AI models is poised to revolutionize medicine. The Generalist Medical Artificial Intelligence (GMAI) is capable of diverse tasks with minimal task-specific data ( 6 ). The use of comprehensive structured synoptic templates in reporting promotes clear communication, reduces the omission of crucial information, and ensures the inclusion of essential details for optimal individualized management planning, contributing to precision oncology ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

Editorial: Editor’s challenge: Abhishek Mahajan - how can precision oncology be advanced with validated imaging-based nomograms?

Mahajan,

Shukla,

Vaish

et al. 2024

Front. Oncol.

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“…Deep learning significantly reduces costs in baseline imaging. Ethical issues related to the use of deep learning models were also studied in the review article ( 12 ). However, these review articles did not discuss the role of generative AI models and their applications in diagnosing ocular cancer.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT

Alotaibi,

Rehman,

Hasnain

2023

Front. Public Health

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This paper pioneers the exploration of ocular cancer, and its management with the help of Artificial Intelligence (AI) technology. Existing literature presents a significant increase in new eye cancer cases in 2023, experiencing a higher incidence rate. Extensive research was conducted using online databases such as PubMed, ACM Digital Library, ScienceDirect, and Springer. To conduct this review, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines are used. Of the collected 62 studies, only 20 documents met the inclusion criteria. The review study identifies seven ocular cancer types. Important challenges associated with ocular cancer are highlighted, including limited awareness about eye cancer, restricted healthcare access, financial barriers, and insufficient infrastructure support. Financial barriers is one of the widely examined ocular cancer challenges in the literature. The potential role and limitations of ChatGPT are discussed, emphasizing its usefulness in providing general information to physicians, noting its inability to deliver up-to-date information. The paper concludes by presenting the potential future applications of ChatGPT to advance research on ocular cancer globally.

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Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

Cited by 8 publications

References 106 publications

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

Editorial: Editor’s challenge: Abhishek Mahajan - how can precision oncology be advanced with validated imaging-based nomograms?

Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT

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