Integrating radiomics into multi<b>-</b>omics research: unveiling new perspectives on precision oncology_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

HUANG Yanqi ^1,2 ,  LIU Zaiyi ^1,2

1. Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, P.R. China;
2. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, P.R. China;

Corresponding author：

LIU Zaiyi, Email: liuzaiyi@gdph.org.cn

Keywords：

radiomics; multi-omics research; artificial intelligence; precision oncology

DOI：

10.7507/1007-9424.202312050

Video：

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Abstract Full text Figures/Tables Video References Cited by

Cancer presents a significant global public health challenge, impacting human health on a broad scale. In recent years, the rapid advancement of big data-based bioinformatics has unveiled crucial potential in precision oncology through various omics research methods. The advent of radiomics has notably expanded the application scope of medical imaging in the field. However, due to the multi-level and multifactorial nature of tumor initiation and progression, a single omics information remains insufficient to meet the demands for advancing precision oncology strategies. Multi-omics research has become an emerging trend. The research paradigm integrating radiomics with other omics offers a novel perspective for personalized decision-making in oncology. Nevertheless, there persists a need to introduce more integrated new technologies and theories to expedite the progress of this field.

Citation： HUANG Yanqi, LIU Zaiyi. Integrating radiomics into multi-omics research: unveiling new perspectives on precision oncology. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2024, 31(3): 257-264. doi: 10.7507/1007-9424.202312050 Copy

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15. Le Large TYS, Bijlsma MF, Kazemier G, et al. Key biological processes driving metastatic spread of pancreatic cancer as identified by multi-omics studies. Semin Cancer Biol, 2017 Jun: 44: 153-169.
16. Jing Y, Yang J, Johnson DB, et al. Harnessing big data to characterize immune-related adverse events. Nat Rev Clin Oncol, 2022, 19(4): 269-280.
17. Aldea M, Friboulet L, Apcher S, et al. Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision? ESMO Open, 2023, 8(5): 101642. doi: 10.1016/ j. esmoop.2023.101642.
18. Migliozzi S, Oh YT, Hasanain M, et al. Integrative multi-omics networks identify PKCδ and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy. Nat Cancer, 2023, 4(2): 181-202.
19. Yang J, Chen Y, Jing Y, et al. Advancing CAR T cell therapy through the use of multidimensional omics data. Nat Rev Clin Oncol, 2023, 20(4): 211-228.
20. Gao Q, Zhu H, Dong L, et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell, 2019, 179(2): 561-577.
21. Liu Z, Zhao Y, Kong P, et al. Integrated multi-omics profiling yields a clinically relevant molecular classification for esophageal squamous cell carcinoma. Cancer Cell, 2023, 41(1): 181-195.
22. Kang W, Qiu X, Luo Y, et al. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med, 2023, 21(1): 598.
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30. Gryglewski G, Seiger R, James GM, et al. Spatial analysis and high resolution mapping of the human whole-brain transcriptome for integrative analysis in neuroimaging. Neuroimage, 2018 Aug 1: 176: 259-267.
31. Ritchie J, Pantazatos SP, French L. Transcriptomic characterization of MRI contrast with focus on the T1-w/T2-w ratio in the cerebral cortex. Neuroimage, 2018, 174: 504-517.
32. Katrib A, Hsu W, Bui A, et al. “RADIOTRANSCRIPTOMICS”: A synergy of imaging and transcriptomics in clinical assessment. Quant Biol, 2016, 4(1): 1-12.
33. Huang C, Cintra M, Brennan K, et al. Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes. EBioMedicine, 2019, 45: 70-80.
34. Fan L, Cao Q, Ding X, et al. Radiotranscriptomics signature-based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels. Cancer Med, 2020, 9(14): 5065-5074.
35. Le NQK, Hung TNK, Do DT, et al. Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Comput Biol Med, 2021, 132: 104320.
36. Crombé A, Bertolo F, Fadli D, et al. Distinct patterns of the natural evolution of soft tissue sarcomas on pre-treatment MRIs captured with delta-radiomics correlate with gene expression profiles. Eur Radiol, 2023, 33(2): 1205-1218.
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38. Alvarez-Jimenez C, Sandino AA, Prasanna P, et al. Identifying cross-scale associations between radiomic and pathomic signatures of non-small cell lung cancer subtypes: Preliminary results. Cancers (Basel), 2020, 12(12): 3663.
39. Brancato V, Cavaliere C, Garbino N, et al. The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study. Front Oncol, 2022, 12: 1005805.
40. Feng L, Liu Z, Li C, et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health, 2022, 4(1): e8-e17.
41. Wan L, Sun Z, Peng W, et al. Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics. J Magn Reson Imaging, 2022, 56(4): 1130-1142.
42. Wang R, Dai W, Gong J, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol, 2022, 15(1): 11.
43. Steyaert S, Pizurica M, Nagaraj D, et al. Multimodal data fusion for cancer biomarker discovery with deep learning. Nat Mach Intell, 2023, 5(4): 351-362.
44. Vaidya P, Bera K, Gupta A, et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in StageⅠ, Ⅱ resectable non-small cell lung cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health, 2020, 2(3): e116-e128.
45. Wang X, Xie T, Luo J, et al. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment. Breast Cancer Res, 2022, 24(1): 20.
46. Su GH, Xiao Y, You C, et al. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets. Sci Adv, 2023, 9(40): eadf0837.
47. Boehm KM, Aherne EA, Ellenson L, et al. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer, 2022, 3(6): 723-733.
48. Vanguri RS, Luo J, Aukerman AT, et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer, 2022, 3(10): 1151-1164.
49. Luo Y. Evaluating the state of the art in missing data imputation for clinical data. Brief Bioinform, 2022, 23(1): bbab489.
50. Yoon J, Zame WR, van der Schaar M. Estimating missing data in temporal data streams using multi- directional recurrent neural networks. IEEE Trans Biomed Eng, 2019, 66(5): 1477-1490.
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52. Cheerla A, Gevaert O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics, 2019, 35(14): i446-i454.
53. Ning Z, Du D, Tu C, et al. Relation-aware shared representation learning for cancer prognosis analysis with auxiliary clinical variables and incomplete multi-modality data. IEEE Trans Med Imaging, 2022, 41(1): 186-198.
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