Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study

Yan, Jing; Sun, Qinjun; Tan, Xiangliang; Liang, Chaofeng; Bai, Hao; Duan, Wenchao; Mu, Tingzhen; Guo, Yang; Qiu, Yuning; Wang, Weiwei; Yao, Qiaoli; Pei, Dongling; Zhao, Yuanshen; Liu, Danni; Duan, Jingxian; Chen, Shifu; Sun, Chen; Wang, Wenqing; Liu, Zhen; Hong, Xuanke; Wang, Xiangxiang; Guo, Yu; Xu, Yikai; Liu, Xianzhi; Cheng, Jingliang; Li, Zhicheng; Zhang, Zhenyu

doi:10.1007/s00330-022-09066-x

Cited by 8 publications

(5 citation statements)

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“…Moreover, most of the previous studies tried to explore the biological meaning of deep features with transcriptomic data 20,21 . Transcriptomics measure rates of change due to ongoing transcription from mRNA, which is a proxy of protein concentration.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, most of the previous studies tried to explore the biological meaning of deep features with transcriptomic data. 20 , 21 Transcriptomics measure rates of change due to ongoing transcription from mRNA, which is a proxy of protein concentration. However, proteomic data are the more reliable representation of the effector proteins because they are the end products of the transcription‐translation cascade that could reflect alterations caused by pre‐ and posttranslational modification.…”

Section: Discussionmentioning

confidence: 99%

“…However, the data‐driven nature of deep learning algorithm makes it a black box and thusly hindered its applications in clinical practice. A few pioneer studies managed to map radiomic features to transcriptomic expression patterns and biological functions, 19 , 20 , 21 offering a biologically interpretable artificial intelligence (AI) algorithm for the prediction of patient survival. Notably, most existing radiogenomic studies were based on transcriptomic analysis, which is the middle step of the transcription‐translation cascade.…”

Section: Introductionmentioning

confidence: 99%

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Imaging‐proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer

Duan,

Zhao,

Sun

et al. 2023

Cancer Medicine

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BackgroundOptimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data.MethodsMRI‐based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis.ResultsThe DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin‐like growth factor binding, protein localization to membranes, and cytoskeleton‐dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05).ConclusionsOur study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Imaging‐proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer

Duan,

Zhao,

Sun

et al. 2023

Cancer Medicine

Self Cite

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“…It is hypothesized that medical images reflect the underlying pathophysiological characteristics of cancer, and radiomic features may, therefore function as a surrogate biomarker of the tumor 8 . Several studies have shown that radiomic features have incremental prognostic value over clinicopathological factors in gliomas 9–11 . Recently, radiogenomic studies have demonstrated that prognostic radiomic features derived from conventional magnetic resonance (MR) sequences are correlated with specific biological pathways 10,12,13 .…”

Section: Introductionmentioning

confidence: 99%

“… 8 Several studies have shown that radiomic features have incremental prognostic value over clinicopathological factors in gliomas. 9 , 10 , 11 Recently, radiogenomic studies have demonstrated that prognostic radiomic features derived from conventional magnetic resonance (MR) sequences are correlated with specific biological pathways. 10 , 12 , 13 Notably, these studies used either gene set enrichment analysis (GSEA) or weighted gene co‐expression network analysis (WGCNA) to identify biological pathways in radiogenomic analysis.…”

Section: Introductionmentioning

confidence: 99%

Diffusion tensor imaging‐based machine learning for IDH wild‐type glioblastoma stratification to reveal the biological underpinning of radiomic features

et al. 2023

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IntroductionThis study addresses the lack of systematic investigation into the prognostic value of hand‐crafted radiomic features derived from diffusion tensor imaging (DTI) in isocitrate dehydrogenase (IDH) wild‐type glioblastoma (GBM), as well as the limited understanding of the biological interpretation of individual DTI radiomic features and metrics.AimsTo develop and validate a DTI‐based radiomic model for predicting prognosis in patients with IDH wild‐type GBM and reveal the biological underpinning of individual DTI radiomic features and metrics.ResultsThe DTI‐based radiomic signature was an independent prognostic factor (p < 0.001). Incorporating the radiomic signature into a clinical model resulted in a radiomic‐clinical nomogram that predicted survival better than either the radiomic model or clinical model alone, with a better calibration and classification accuracy. Four categories of pathways (synapse, proliferation, DNA damage response, and complex cellular functions) were significantly correlated with the DTI‐based radiomic features and DTI metrics.ConclusionThe prognostic radiomic features derived from DTI are driven by distinct pathways involved in synapse, proliferation, DNA damage response, and complex cellular functions of GBM.

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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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Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study

Cited by 8 publications

References 40 publications

Imaging‐proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer

Imaging‐proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer

Diffusion tensor imaging‐based machine learning for IDH wild‐type glioblastoma stratification to reveal the biological underpinning of radiomic features

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

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