Application Of Radiomics Research Articles

From pixels to prognosis: radiomics and AI in Alzheimer’s disease management

Alzheimer’s disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD pathology. Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. This review explores the application of radiomics and AI in AD, focusing on key imaging modalities such as PET and MRI, as well as multimodal approaches combining structural and functional data. We discuss the potential of these technologies to identify disease-specific biomarkers, predict disease progression, and guide personalized interventions. Additionally, the review addresses critical challenges, including data standardization, model interpretability, and the integration of AI into clinical workflows. By highlighting current achievements and identifying future directions, this article underscores the transformative potential of AI-driven radiomics in reshaping AD diagnostics and care.

Application of radiomics in acute and severe non-neoplastic diseases: A literature review.

Radiomics involves the integration of computer technology, big data analysis, and clinical medicine. Currently, there have been initial advancements in the fields of acute cerebrovascular disease and cardiovascular disease. The objective of radiomics is to extract quantitative features from medical images for analysis to predict the risk or treatment outcome, help in differential diagnosis, and guide clinical decisions and management. Radiomics applied research has reached a more advanced stage yet encounters several obstacles, including the need for standardization of radiomics features and alignment with treatment requirements for acute and severe illnesses. Future research should aim to seamlessly incorporate radiomics with various disciplines, leverage big data and artificial intelligence advancements, cater to the requirements of acute and critical medicine, and enhance the effectiveness of technological innovation and application in diagnosing and treating acute and critical illnesses.

The application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer: A systematic review.

To study the application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer. Different electronic databases were considered. Articles published in the last five years were analyzed (January 2019 and December 2023). Papers were selected by two investigators with over 15years of experience in Radiomics analysis in cancer imaging. The methodological quality of each radiomics study was performed using the Radiomic Quality Score (RQS) by two different readers in consensus and then by a third operator to solve disagreements between the two readers. 19 articles are included in the review. Among the analyzed studies, only one study achieved an RQS of 18 reporting multivariable analyzes with also non-radiomics features and using the validation phase considering two datasets from two distinct institutes and open science and data domain. This informative review has brought attention to the increasingly consolidated potential of Radiomics, although there are still several aspects to be evaluated before the transition to routine clinical practice. There are several challenges to address, including the need for standardization at all stages of the workflow and the potential for cross-site validation using heterogeneous real-world datasets. It will be necessary to establish and promote an imaging data acquisition protocol, conduct multicenter prospective quality control studies, add scanner differences and vendor-dependent characteristics; to collect images of individuals at additional time points, to report calibration statistics.

Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation.

To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation. A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had undergone silicone breast augmentation. The ultrasound data were collected between 1 January 2006 and 1 September 2023. The nodules were allocated into a training set (n = 69) and a validation set (n = 30). Regions of interest (ROIs) were manually delineated using 3D Slicer software, and radiomic features were extracted and selected using Python programming. Eight machine learning algorithms were applied to build predictive models, and their performance was assessed using sensitivity, specificity, area under the ROC curve (AUC), accuracy, Brier score, and log loss. Model performance was further evaluated using ROC curves and calibration curves, while clinical utility was assessed via decision curve analysis (DCA). The random forest model exhibited superior performance in differentiating benign from malignant nodules in the validation set, achieving sensitivity of 0.765, specificity of 0.838, and an AUC of 0.787 (95% CI: 0.561-0.960). The model's accuracy, Brier score, and log loss were 0.796, 0.197, and 0.599, respectively. DCA suggested potential clinical utility of the model. Ultrasound radiomics demonstrates promising diagnostic accuracy in differentiating benign from malignant breast nodules in women with silicone breast prostheses. This approach has the potential to serve as an additional diagnostic tool for patients following silicone breast augmentation.

Deep Learning and Radiomics in Triple-Negative Breast Cancer: Predicting Long-Term Prognosis and Clinical Outcomes.

Triple-negative breast cancer (TNBC) is a unique breast cancer subtype characterized by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression in tumor cells. TNBC represents about 15% to 20% of all breast cancers and is aggressive and highly malignant. Currently, TNBC diagnosis primarily depends on pathological examination, while treatment efficacy is assessed through imaging, biomarker detection, pathological evaluation, and clinical symptom improvement. Among these, biomarker detection and pathological assessments are invasive, time-intensive procedures that may be difficult for patients with severe comorbidities and high complication risks. Thus, there is an urgent need for new, supportive tools in TNBC diagnosis and treatment. Deep learning and radiomics techniques represent advanced machine learning methodologies and are also emerging outcomes in the medical-engineering field in recent years. They are extensions of conventional imaging diagnostic methods and have demonstrated tremendous potential in image segmentation, reconstruction, recognition, and classification. These techniques hold certain application prospects for the diagnosis of TNBC, assessment of treatment response, and long-term prognosis prediction. This article reviews recent progress in the application of deep learning, ultrasound, MRI, and radiomics for TNBC diagnosis and treatment, based on research from both domestic and international scholars.

Developmental trends and knowledge frameworks in the application of radiomics in prostate cancer: a bibliometric analysis from 2000 to 2024

BackgroundThis research utilized the bibliometrics method to analyze the published literature related to prostate cancer (PCa) imaging. Furthermore, current knowledge and research hotspots of radiomics in PCa diagnosis and treatment were comprehensively reviewed, as well as progress and emerging trends in field were explored.MethodsIn this investigation, the relevant literature on radiomics, and PCa was retrieved from Web of Science Core Collection (WoSCC) databases from 2000 and 2024. Furthermore, a comprehensive bibliometric analysis was carried out using advanced tools like CiteSpace6.2, VOS viewer, and the 'bibliometrix' package of R software to visualize the annual distribution of publications across various aspects such as authors, countries, journals, institutions, and keywords.ResultsThis analysis included 593 from 58 countries including China and the United States. Chinese Academy of Sciences and Frontiers in Oncology were the institutions and journals that publish the most relevant articles, -while Radiology journal had the greatest number of co-cited publications. Furthermore, 3,621 authors published on this topic, of which Madabhushi Anant and Stoyanova Radka had the highest contributions. Moreover, Lambin, P. had the most co-citations. In addition, the diagnostic characteristics of radiomics in PCa imaging and treatment strategies are the current research focal points. The establishment of multi-functional imaging techniques and independent factor models warrants future investigation.ConclusionsIn summary, this analysis revealed that the research on PCa imaging is developing vigorously, focusing on the diagnostic methods and intervention measures of imaging in PCa diagnosis and treatment. In the future, there is an urgent need for improved collaboration and communication among countries and institutions.

Prospects for the application of radiomics to brain tumors

Radiomics is a new branch in diagnostics based on a quantitative approach to medical imaging able to ensure more efficient use of medical equipment, optimize imaging time per patient, and increase the accuracy of differential diagnostics in various areas of medicine. Radiogenomics is a branch of radionics intended to establish a connection between the patient’s genotype and phenotypic presentation obtained from medical imaging. The review dwells on general issues for radiomics and radiogenomics in oncology with the recent study findings, focusing on the role of these methods in neurooncology and solving problems in diagnosing brain tumors. One of the current topics in neurooncology is disease prognosis in patients with unverified midline gliomas because morphological confirmation of the diagnosis and molecular genetic testing of tissues is impossible. Besides, the high spatial and temporal heterogeneity of malignant neoplasms prevents a complete assessment of the biological properties of the tumor and even using stereotactic biopsy methods. Radiomics methods can help doctors differentiate the tumor grade, acting as a “virtual biopsy” while avoiding invasive procedures. The study findings on radiomics and radiogenomics in neurooncology indicate the undeniable promise of these methods; however, as in other areas of medicine and biology, errors cannot be completely excluded, so the expert team must aim to minimize them.

Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.

Extensive research on the application of artificial intelligence (AI) and radiomics in lung cancer has been published in recent years; however, it is necessary to identify the current status, hotspots, and trends in the field. Thus, this study conducted a bibliometric analysis of relevant studies to investigate the application of AI and radiomics in lung cancer. Related publications were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace generated the associated co-occurrence network maps in terms of institutions, authors, and keywords. Bibliometrix was used to perform a bibliometric analysis of relevant countries/regions and journals. In addition, the collected information was used to generate figures using R. A total of 2,989 publications were included in this study, of which 2,804 (93.8%) were articles and 185 (6.2%) were reviews. In 2016, there was a rapid increase in the number of publications in this field. Most of the research originated from China (n=1,365, 45.7%). While Fudan University (n=109, 3.6%) attracted the greatest attention among all institutions. In terms of the authors, Gillies (28 publications, 0.9%) published the greatest number of articles. In terms of the journals, Frontiers in Oncology (n=177, 5.9%) and Radiology (5,152 citations) had the greatest number of publications and influence, respectively. The main keywords identified were "lung cancer", "deep learning", "classification", "computed tomography", and "features". Burst detection suggested that "texture", "image classification", and "false positive reduction" have recently appeared at the frontier of research. This study used bibliometric methods to analyze the relevant literature to discuss the current research hotspots and future trends in the application of AI and radiomics in lung cancer. This information may help relevant researchers to shape the direction of future studies, such as innovations in AI techniques standardized feature extraction, and extend understandings of epidermal growth factor receptor mutations.

Application of CT radiomics in investigating the anatomical basis of chronic dacryocystitis

Objective:To explore the relevant anatomical factors in the pathogenesis of chronic dacryocystitis based on CT radiomics. Methods:A retrospective analysis was conducted on the general data and sinus CT materials of 85 patients with chronic dacryocystitis（case group） admitted to our department from December 2020 to December 2023, and 85 individuals undergoing physical examination（control group） during the same period. The differences in anatomical parameters between the two groups were compared to study the morphological characteristics of the nasolacrimal duct in patients with chronic dacryocystitis. Univariate and multivariate logistic regression analyses were used to explore the relevant anatomical factors in the pathogenesis of chronic dacryocystitis. Results:There were statistically significant differences（P<0.05） in the proportion of combined nasal septal deviation, the distance between the anterior and posterior ridges of the lacrimal fossa, the angle between the long axis of the nasolacrimal duct and the projection on the midsagittal plane, the maximum transverse diameter of the bony nasolacrimal duct, the maximum cross-sectional area of the bony nasolacrimal duct, and the thickness of the frontal process of the maxilla between the two groups. There were no statistically significant differences（P>0.05） in whether there was a combined high-position nasal septal deviation, whether there was a combined non-high-position nasal septal deviation, and whether there was a combined pneumatized middle turbinate. Multivariate analysis showed that nasal septal deviation, the distance between the anterior and posterior ridges of the lacrimal fossa, the angle between the long axis of the nasolacrimal duct and the projection on the midsagittal plane, and the maximum cross-sectional area of the bony nasolacrimal duct are independent anatomical factors affecting the pathogenesis of chronic dacryocystitis. Conclusion:Nasal septal deviation, a large distance between the anterior and posterior ridges of the lacrimal fossa, a large angle between the long axis of the nasolacrimal duct and the projection on the midsagittal plane, and a small maximum transverse diameter of the bony nasolacrimal duct are important anatomical bases for the pathogenesis of chronic dacryocystitis, providing a basis for an in-depth understanding of the disease occurrence.

Machine learning applications in placenta accreta spectrum disorders.

This review examines the emerging applications of machine learning (ML) and radiomics in the diagnosis and prediction of placenta accreta spectrum (PAS) disorders, addressing a significant challenge in obstetric care. It highlights recent advancements in ML algorithms and radiomic techniques that utilize medical imaging modalities like magnetic resonance imaging (MRI) and ultrasound for effective classification and risk stratification of PAS. The review discusses the efficacy of various deep learning models, such as nnU-Net and DenseNet-PAS, which have demonstrated superior performance over traditional diagnostic methods through high AUC scores. Furthermore, it underscores the importance of integrating quantitative imaging features with clinical data to enhance diagnostic accuracy and optimize surgical planning. The potential of ML to predict surgical morbidity by analyzing demographic and obstetric factors is also explored. Emphasizing the need for standardized methodologies to ensure consistent feature extraction and model performance, this review advocates for the integration of radiomics and ML into clinical workflows, aiming to improve patient outcomes and foster a multidisciplinary approach in high-risk pregnancies. Future research should focus on larger datasets and validation of biomarkers to refine predictive models in obstetric care.

Clinical Application of Radiomics in Oncology: Where Do We Stand?

Clinical Application of Radiomics in Oncology: Where Do We Stand?

Challenges in clinical translation of cardiac magnetic resonance imaging radiomics in non-ischemic cardiomyopathy: a narrative review.

Radiomics is an emerging technology that facilitates the quantitative analysis of multi-modal cardiac magnetic resonance imaging (MRI). This study aims to introduce a standardized workflow for applying radiomics to non-ischemic cardiomyopathies, enabling clinicians to comprehensively understand and implement this technology in clinical practice. A computerized literature search (up to August 1, 2024) was conducted using PubMed to identify relevant studies on the roles and workflows of radiomics in non-ischemic cardiomyopathy. Expert discussions were also held to ensure the accuracy and relevance of the findings. Only English-language publications were reviewed. The paper details the essential processes of radiomics, including feature extraction, feature engineering, model construction, and data analysis. It emphasizes the role of MRI in assessing cardiac structure and function and demonstrates how MRI-based radiomics can aid in diagnosing and differentiating non-ischemic cardiomyopathies such as hypertrophic cardiomyopathy, dilated cardiomyopathy, and myocarditis. The study also investigates various cardiac MRI sequences to enhance the clinical application of radiomics. The standardized radiomics workflow presented in this study aims to assist clinicians in effectively utilizing MRI-based radiomics for the diagnosis and management of non-ischemic cardiomyopathies, thereby improving clinical decision-making.

Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma

To investigate the value of prediction of the differentiation level in lung adenocarcinoma based on CT radiomics model. Data from 507 patients with postoperative pathological confirmed lung adenocarcinoma and clearly defined differentiation level of lung adenocarcinoma were retrospective analyzed. The enrolled cases were divided into poorly differentiation group and moderate-to-high differentiation group based on the grading criteria. CT image features were extracted, and seven machine learning algorithms were used to construct prediction models to obtain the AUC, accuracy, specificity, and sensitivity. The poorly differentiation group consisted of 175 cases, while the moderate-to-high differentiation group had 332 cases. The XGBoost model demonstrated the best performance, with the AUC, accuracy, specificity, and sensitivity of this model on the validation set being 0.878, 0.829, 0.667, and 0.727, respectively. CT radiomics model can effectively predict the differentiation level of poorly differentiation and moderate-to-high differentiation in lung adenocarcinoma.

Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review.

Radiomics is a quantitative tool for digital image analysis. This systematic review aims to investigate the scientific articles to evaluate the potential implications of Radiomics analysis in Dentomaxillofacial Radiology (DMFR). Studies regarding Radiomics applications in DMFR and human samples, in vivo study, a case reports/series if ≧5 samples were included, while case reports/series if < 5 samples, articles other than in English, abstracts without full text, and studies published before 2015 were excluded. Fifty-one articles were selected from 3789 literatures. The QUADAS-2 tool was used for risk of bias assessment. The accuracy of predicting dentomaxillofacial pathologies was considered as the primary outcome, and the modeling type of Radiomics was considered as the secondary outcome. A meta-analysis could not be performed due to the lack of information and standardization among the reported accuracies. The reported accuracies were found between 0.66 and 99.65%. Logistic regression (n = 6) was found to be the most common Radiomics modeling type, followed by Support Vector Machine and Decision Tree (n = 5). Second-order statistics (n = 38) was the most common type of Radiomics application, followed by first-order (n = 26), higher-order (n = 20), and shape-based (n = 15) statistics. Further work is needed to increase standardization in the Radiomics workflow. Quantitative image analysis is an alternative tool for conventional visual radiographic evaluation. Radiomics systems depend on elements such as imaging modality, feature type, data mining, or statistical method. Radiomics applications do not justify digital transformation on their own, but the potential of its integration into the digital workflow is considerable.

Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses

Abstract Objectives Radiomics involves the extraction and analysis of numerous quantitative features of medical imaging which can add more information from radiological images often beyond initial comprehension of a clinician. Unlike deep learning, radiomics allows some understanding of identified quantitative features for clinical prediction. We sought to explore the current state of radiomics applications in the skull base literature. Methods A systematic review of studies evaluating radiomics in skull base was performed, including those with and without machine-learning approaches. Studies were summarized into thematic elements as well as specific pathologies. Results A total of 102 studies with 26,280 radiographic images were included. The earliest radiomic study was published in 2017 with exponential growth in research since then. Most studies focused on tumor diagnosis (40.8%), followed by tumor prognosis (31.1%), automated segmentation (16.5%), other applications (7.8%), and lastly prediction of intraoperative features (3.9%). Pituitary adenomas (41.7%) and vestibular schwannomas (18.4%) represented the most commonly evaluated pathologies; however, radiomics could be applied to a heterogeneous collection of skull base pathologies. The average study included 258 ± 677 cases (range 4; 6,755). Conclusion Radiomics offers many functions in treating skull base pathology and will likely be an essential component of future clinical care. Larger sample sizes, validation of predictive models, and clinical application are needed. Further investigation into the strengths and weaknesses of radiomic applications in skull base treatments is warranted.

Applications of Radiomics and Radiogenomics in Lung Cancer Treatment

Applications of Radiomics and Radiogenomics in Lung Cancer Treatment

Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013-2023).

The incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC. We conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis. Our search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications. Radiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.

Advances in radiomics applications for intrahepatic cholangiocarcinoma: a comprehensive review and future directions

Radiomics was first introduced by Lambin et al. in 2012, and since then, research in this field has grown rapidly. Researchers have shown great interest in developing efficient methods for automatically extracting a large number of quantitative features from medical images, aiming to enhance diagnostic accuracy and predictive capability. Although there has been a rise in Radiomics studies focusing on intrahepatic cholangiocarcinoma (ICC) in recent years, comprehensive reviews are still relatively scarce. This study explores how Radiomics technology can be utilized in modeling analyses to predict lymph node metastasis, microvascular invasion, and early recurrence of ICC, as well as the application of deep learning in these analyses. This paper provides a brief overview of the current state of Radiomics research and offers references for future studies.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.

Applications of Radiomics for Next-Generation Oncologic Management – Current Trend, Challenges and Future Prospects

Applications of Radiomics for Next-Generation Oncologic Management – Current Trend, Challenges and Future Prospects