Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma

Gu, Yu; Huang, Hao; Qi, Tao; Cao, Meng; Ming, Wenlong; Zhang, Rongxin; Zhu, Wenyong; Wang, Yuqi; Sun, Xiao

doi:10.3390/cancers15082338

Cited by 6 publications

(4 citation statements)

References 76 publications

(95 reference statements)

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“…In line with previous studies showing the relevance of radiomic models to identify gene-expression-and methylome-based subtypes [56], with distinct immune molecular features [40], we built a radiomic model to classify hot and cold tumors, as defined by our score based on the expression of 27 genes. Thus, our study confirms, in patients with HNSCC, the relevance of a non-invasive imaging approach to identify different immune subtypes, as previously performed in other cancer types [63,64,[73][74][75].…”

Section: Discussionsupporting

confidence: 88%

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

Nguyen,

Bertolus,

Giraud

et al. 2023

Cancers

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Background: We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. Method: We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. Results: A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. Conclusions and Relevance: We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

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Section: Discussionsupporting

confidence: 88%

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

Nguyen,

Bertolus,

Giraud

et al. 2023

Cancers

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“…Overfitting may occur when using many radiomic features. Even if we utilized mRMR and LASSO for feature selection, collinearity between features and their high dimensionality may have impaired model performance ( 33 ). To address this issue, we designed a multi-view radiomics strategy that tried to determine optimal input feature subsets by subcategorizing and combining different features and could finally improve the performance of a radiomics model.…”

Section: Discussionmentioning

confidence: 99%

Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI

Deng

Zhong

et al. 2023

Front. Oncol.

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IntroductionThis study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia.MethodsA total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison.ResultsThe optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model’s predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics.DiscussionThe constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies.

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“…It describes the abstraction of parameters from diagnostic images, which are not recognizable to the human eye [24,25]. The potential of radiomics for the detection of tumor heterogeneity has been demonstrated several times for different tumor entities such as breast cancer [26][27][28] or hepatocellular carcinoma [29][30][31].…”

Section: Imaging Markers For Intralesional Heterogeneitymentioning

confidence: 99%

Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

Haag,

Hertel,

Tharmaseelan

et al. 2023

Rofo

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With personalized tumor therapy, understanding and addressing the heterogeneity of malignant tumors is becoming increasingly important. Heterogeneity can be found within one lesion (intralesional) and between several tumor lesions emerging from one primary tumor (interlesional). The heterogeneous tumor cells may show a different response to treatment due to their biology, which in turn influences the outcome of the affected patients and the choice of therapeutic agents. Therefore, both intra- and interlesional heterogeneity should be addressed at the diagnostic stage. While genetic and biological heterogeneity are important parameters in molecular tumor characterization and in histopathology, they are not yet addressed routinely in medical imaging. This article summarizes the recently established markers for tumor heterogeneity in imaging as well as heterogeneous/mixed response to therapy. Furthermore, a look at emerging markers is given. The ultimate goal of this overview is to provide comprehensive understanding of tumor heterogeneity and its implications for radiology and for communication with interdisciplinary teams in oncology. Key points:

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Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma

Cited by 6 publications

References 76 publications

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI

Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

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