Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Verma, Suraj; Magazzù, Giuseppe; Eftekhari, Noushin; Lou, Thai; Gilhespy, Alex; Occhipinti, Annalisa; Angione, Claudio

doi:10.1016/j.crmeth.2024.100817

Cell Reports Methods

2024

DOI: 10.1016/j.crmeth.2024.100817

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Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Suraj Verma,

Giuseppe Magazzù,

Noushin Eftekhari

et al.

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Cited by 2 publications

References 110 publications

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Radiogenomics: bridging the gap between imaging and genomics for precision oncology

He,

Huang,

Zhang

et al. 2024

MedComm

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Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker‐guided clinical decision‐making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high‐throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross‐scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

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Radiogenomics: bridging the gap between imaging and genomics for precision oncology

He,

Huang,

Zhang

et al. 2024

MedComm

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Enhancing deep learning for demand forecasting to address large data gaps

Riachy,

He,

Joneidy

et al. 2025

Expert Systems with Applications

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Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Cited by 2 publications

References 110 publications

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Enhancing deep learning for demand forecasting to address large data gaps

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