ReviewInternational journal of biological sciences2022
The progress of multimodal imaging combination and subregion based radiomics research of cancers.
Review in International journal of biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Medical Image-Based Deep Learning for Detecting Microvascular Invasion in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Predicting early recurrence of craniopharyngioma using multi-omics radiomic modeling: a retrospective cohort study.Neurosurgical review · 2026Article
- Application advances of habitat imaging based on CT or PET/CT: a systematic review.La Radiologia medica · 2026Review
- Anticipating femoral shaft nonunion at 1-3 months: a multimodal framework with automated X‑ray segmentation despite metal implants.International orthopaedics · 2026Article
- Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Development of a clinical-CT-radiomics nomogram for predicting endoscopic red color sign in cirrhotic patients with esophageal varices.Abdominal radiology (New York) · 2026Article
- Grayscale ultrasound radiomics for characterizing subpleural pulmonary lesions: a multicenter prospective study.Insights into imaging · 2026Article
- Peritumoral habitat radiomics predicts axillary lymph node metastasis in breast cancer.iScience · 2026Article
- Article
- Artificial intelligence and radiomics on computed tomography for differentiating hepatocellular carcinoma and intrahepatic cholangiocarcinoma: a multimodal integration approach.BMC medical imaging · 2026Article
- Can 3D T1 Post-Contrast MRI in A Radiomics-Machine Learning Model Distinguish Infective from Neoplastic Ring-Enhancing Brain Lesions? An Exploratory Study.Diagnostics (Basel, Switzerland) · 2026Article
- DENND2D serves as a novel prognostic biomarker with paradoxical protumorigenic effects in glioma.Discover oncology · 2026Article
- Explainable PET-Based Habitat Modeling for Predicting Postoperative Recurrence Risk in Invasive Lung Adenocarcinoma.Annals of surgical oncology · 2026Article
- Development and validation of a CT-based habitat radiomics model for predicting pathological grading in non-small cell lung cancer.Frontiers in medicine · 2026Article
- The predictive value ofFrontiers in medicine · 2026Article
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- Intratumoral and peritumoral heterogeneity based on CT to predict the pathological response after neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.International journal of surgery (London, England) · 2026Article
- Machine Learning Applied to CT Radiomics Identifies Symptomatic Carotid Plaques.Current medical imaging · 2026Article
- MRI-based habitat radiomics for predicting WHO/ISUP nuclear grade in clear cell renal cell carcinoma.Frontiers in oncology · 2026Article
- An integrated prognostic model incorporating clinical characteristics, multiparametric MRI radiomics, deep learning features, and intratumoral heterogeneity for glioma patients.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, with the standardization of radiomics methods; development of tools; and popularization of the concept, radiomics has been widely used in all aspects of tumor diagnosis; treatment; and prognosis. As the study of radiomics in cancer has become more advanced, the currently used methods have revealed their shortcomings. The performance of cancer radiomics based on single-modality medical images, which based on their imaging principles, only partially reflects tumor information, has been necessarily compromised. Using the whole tumor as a region of interest to extract radiomic features inevitably leads to the loss of intra-tumoral heterogeneity of, which also affects the performance of radiomics. Radiomics of multimodal images extracts various aspects of information from images of each modality and then integrates them together for model construction; thus, avoiding missing information. Subregional segmentation based on multimodal medical image combinations allows radiomics features acquired from subregions to retain tumor heterogeneity, further improving the performance of radiomics. In this review, we provide a detailed summary of the current research on the radiomics of multimodal images of cancer and tumor subregion-based radiomics, and then raised some of the research problems and also provide a thorough discussion on these issues.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.