CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma

Feng, Zhichao; Li, Huiling; Liu, Qianyun; Duan, Junhong; Zhou, Wenming; Xiao-ping, YU; Chen, Qian; Liu, Zhenguo; Wang, Wei; Rong, Pengfei

doi:10.1148/radiol.221291

Cited by 50 publications

(32 citation statements)

References 34 publications

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“…16 Primary tumor radiomics reflect the tumor biological features but do not explain the systemic immune status. 27,28 Numerous studies have demonstrated the critical impact of systemic immune responses that drive tumor rejection. 16 The spleen is one of the largest SLOs and major extramedullary hematopoietic organs in the body and may function to modulate the immune system in patients with tumor.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, in addition to primary tumor regression, the systemic immune status is also important for patient outcomes, especially in patients receiving definitive treatment 16 . Primary tumor radiomics reflect the tumor biological features but do not explain the systemic immune status 27,28 . Numerous studies have demonstrated the critical impact of systemic immune responses that drive tumor rejection 16 .…”

Section: Discussionmentioning

confidence: 99%

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The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy

Guo,

Liu,

Geng

et al. 2024

Thoracic Cancer

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BackgroundThe spleen plays an important role in systemic antitumor immune response, but whether spleen imaging features have predictive effect for prognosis and immune status was unknown. The aim of this study was to investigate computed tomography (CT)‐based spleen radiomics to predict the prognosis of patients with esophageal squamous cell carcinoma (ESCC) underwent definitive radiotherapy (dRT) and to try to find its association with systemic immunity.MethodsThis retrospective study included 201 ESCC patients who received dRT. Patients were randomly divided into training (n = 142) and validation (n = 59) groups. The pre‐ and delta‐radiomic features were extracted from enhanced CT images. LASSO‐Cox regression was used to select the radiomics signatures most associated with progression‐free survival (PFS) and overall survival (OS). Independent prognostic factors were identified by univariate and multivariate Cox analyses. The ROC curve and C‐index were used to evaluate the predictive performance. Finally, the correlation between spleen radiomics and immune‐related hematological parameters was analyzed by spearman correlation analysis.ResultsIndependent prognostic factors involved TNM stage, treatment regimen, tumor location, pre‐ or delta‐Rad‐score. The AUC of the delta‐radiomics combined model was better than other models in the training and validation groups in predicting PFS (0.829 and 0.875, respectively) and OS (0.857 and 0.835, respectively). Furthermore, some spleen delta‐radiomic features are significantly correlated with delta‐ALC (absolute lymphocyte count) and delta‐NLR (neutrophil‐to‐lymphocyte ratio).ConclusionsSpleen radiomics is expected to be a useful noninvasive tool for predicting the prognosis and evaluating systemic immune status for ESCC patients underwent dRT.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy

Guo,

Liu,

Geng

et al. 2024

Thoracic Cancer

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“…82 Feng et al constructed an MTM radiomics model associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. 91 The latter study even utilized sequences of single-cell sequencing data to support its conclusions. Schniering et al constructed a CT-based radiomics model to predict prognosis in interstitial lung disease related to systemic sclerosis and validated the biological interpretability of the model by proteomics and immunohistochemistry in animal models.…”

Section: Interpretability and Future Of Radiomics And Deep Learningmentioning

confidence: 95%

“…Xia et al developed a radiomics model to predict MVI status and discovered that MVI‐positive status from model prediction may occur due to increased tumor glucose metabolism and decreased immune infiltration 82 . Feng et al constructed an MTM radiomics model associated with dysregulated humoral immunity involving B‐cell infiltration and immunoglobulin synthesis 91 . The latter study even utilized sequences of single‐cell sequencing data to support its conclusions.…”

Section: Interpretability and Future Of Radiomics And Deep Learningmentioning

confidence: 99%

MRI‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges

Xia,

Zhao,

et al. 2023

Magnetic Resonance Imaging

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Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer‐related death worldwide. HCC exhibits strong inter‐tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine‐learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI‐enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI‐enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI‐enabled models, including aspects such as domain knowledge, novel algorithms, and multi‐dimension data sources. Overcoming these challenges would allow AI‐enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use.Level of Evidence5Technical EfficacyStage 2

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“…Patients with high ANH and high SN scores were more likely to experience early recurrence compared with those with low SN scores, suggesting that the VETC pattern may be important for explaining the association between the MTM subtype and poor prognosis. Further analysis showed that CT radiomic models were able to predict both MTM-HCC and humoral immunodeficiency [36].…”

Section: Computed Tomographymentioning

confidence: 99%

Correlations behind macrotrabecular‐massive and vessels encapsulating tumor cluster patterns in hepatocellular carcinoma

et al. 2023

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Macrotrabecular‐massive hepatocellular carcinoma (MTM‐HCC) is a subtype of HCC with a very poor prognosis and exhibits biological behaviors and clinical presentations that distinguish it from conventional HCC. Vessels encapsulating tumor clusters (VETC) is a unique pattern of vascular growth that is more common in patients with MTM‐HCC. This unique interaction between the angiogenic system and tumor cells is of general interest. Early diagnosis and appropriate treatment of HCC with MTM and VETC patterns help prevent early recurrence and improve prognosis. This review summarizes the existing findings for HCC with MTM and VETC patterns.

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CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma

Cited by 50 publications

References 34 publications

The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy

The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy

MRI‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges

Correlations behind macrotrabecular‐massive and vessels encapsulating tumor cluster patterns in hepatocellular carcinoma

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