Application Of Radiomics Research Articles

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)

BackgroundThe 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.MethodsWe 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.ResultsOur 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.ConclusionsRadiomics 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.

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

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; 6755). 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 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

A Mesenteric Fat-Derived Radiomic Model to Identify Colonic Fibrosis and Predict Treatment Response to Biologics in Chronic Ulcerative Colitis.

Evidence suggested the lesion of ulcerative colitis stretches beyond mucosa. The application of radiomics on ulcerative colitis fibrosis is unclear. We aimed to characterize the colonic fibrosis and treatment response to biologics in chronic ulcerative colitis using radiomic features extracted from bowel wall and mesenteric adipose tissue. Retrospective analysis of prospective database. This study was conducted in a single tertiary center. A total of 72 patients who underwent proctocolectomy and 47 patients who received biologics induction were included. Computed Tomography images were collected and radiomic features were extracted to develop radiomic models using logistic regression. Main outcome was colonic fibrosis, which was classified into mild and severe based on histological scoring. The area under curve of the bowel wall model to predict severe fibrosis was 0.931 (p < 0.001) and 0.869 (p < 0.001) in the training and test cohort, respectively. For mesenteric adipose tissue model, area under curve was 0.947 (p < 0.001) and 0.837 (p < 0.001), respectively. The mesenteric adipose tissue model was superior to bowel wall model (area under curve, 0.809, p < 0.001 and 0.722, p = 0.006) in predicting response to biologics in chronic ulcerative colitis. Retrospective single center study. Two radiomic models derived from bowel wall and mesenteric adipose tissue features readily predicted colonic fibrosis and treatment response of biologics in chronic ulcerative colitis. The mesentery harbors critical information and was essentially involved in fibrogenesis. See Video Abstract.

Ultrasound based radiomics model for assessment of placental function in pregnancies with preeclampsia

The goal of our research is to elucidate and better assess placental function in rats with preeclampsia through an innovative application of ultrasound-based radiomics. Using a rat model induced with L-NAME, we carefully investigated placental dysfunction via microstructural analysis and immunoprotein level assessment. Employing the Boruta feature selection method on ultrasound images facilitated the identification of crucial features, consequently enabling the development of a robust model for classifying placental dysfunction. Our study included 12 pregnant rats, and thorough placental evaluations were conducted on 160 fetal rats. Distinct alterations in placental microstructure and angiogenic factor expression were evident in rats with preeclampsia. Leveraging high-throughput mining of quantitative image features, we extracted 558 radiomic features, which were subsequently used to construct an impressive evaluation model with an area under the receiver operating curve (AUC) of 0.95. This model also exhibited a remarkable sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 88.7%, 91.5%, 90.2%, 90.4%, and 90.0%, respectively. Our findings highlight the ability of ultrasound-based radiomics to detect abnormal placental features, demonstrating its potential for evaluating both normative and impaired placental function with high precision and reliability.

Research on the application of radiomics in breast cancer: A bibliometrics and visualization analysis.

Breast cancer is the most prevalent form of cancer worldwide. Therefore, improved disease detection has emerged as a focal point in clinical studies. At the forefront of innovation, radiomics has the capability to extract comprehensive insights from medical images, ultimately enhancing the accuracy of diagnostic procedures. There has been rapid growth in the field of radiomics research on breast cancer in the past few years. We explored pertinent research articles in the Web of Science Core Collection database to gain a thorough understanding of breast cancer radiomics. We used CiteSpace to conduct a bibliometric analysis of the annual distribution of different nations, institutions, journals, authors, keywords, and references in the field of breast cancer radiomics. GraphPad Prism software was used to examine and graph yearly and country-specific trends and the proportions of publications. The tools utilized for the visualization of science mapping included CiteSpace and VOSviewer. Of the 891 publications, most were original articles (731, 91.09%) and a few were reviews (160, 8.91%). Most academic research has been published in China and the United States. The study centers predominantly consisted of major academic institutions, such as Fudan University and the Chinese Academy of Sciences, with some of their members being prominent figures in the field. Pinker, Katja has published the largest number of research papers. The majority of these studies have been published in medical journals focusing on radiology and oncology in recent years. In the realm of cutting-edge medical research, the top two keywords, magnetic resonance imaging and machine learning stand at the forefront as current areas of intense focus. Breast cancer radiomics is advancing rapidly, presenting numerous opportunities and obstacles. Our study of the literature in this academic area aimed to pinpoint the primary themes addressed in the studies and anticipate prospective avenues for research.

Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences

The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, including 65 low-risk patients and 66 high-risk patients, were retrospectively recruited. Among them, 78 patients composed the training cohort, while the remaining 53 patients formed the validation cohort. We extracted 1702 features each from the patients’ arterial-, venous-, and plain-phase images. Pairwise subtraction of these features yielded 1702 arterial-venous, arterial-plain, and venous-plain difference features each. The Mann‒Whitney U test and least absolute shrinkage and selection operator (LASSO) and SelectKBest methods were employed to select the best features from the training set. Six models were built with a stacked learning algorithm. By applying stacked ensemble learning, three machine learning algorithms (XGBoost, multilayer perceptron (MLP), and random forest) were combined by XGBoost to produce the the six basic imaging models. Then, the XGBoost algorithm was applied to the six basic imaging models to construct a combined radiomic model. Finally, the radiomic model was combined with clinical information to create a nomogram that could easily be used in clinical practice to predict the thymoma risk category. The areas under the curve (AUCs) of the combined radiomic model in the training and validation cohorts were 0.999 (95% CI 0.988–1.000) and 0.967 (95% CI 0.916–1.000), respectively, while those of the nomogram were 0.999 (95% CI 0.996–1.000) and 0.983 (95% CI 0.990–1.000). This study describes the application of CT-based radiomics in thymoma patients and proposes a nomogram for predicting the risk category for this disease, which could be advantageous for clinical decision-making for affected patients.

Can dynamic contrast-enhanced MR imaging based on radiomics improve the diagnostic efficiency of clinically significant prostate cancer?

Aims/Background Prostate cancer stands out as one of the most prevalent malignant tumours among males. The non-invasive identification of clinically significant prostate cancer via magnetic resonance imaging plays a critical role in circumventing unnecessary biopsies and determining suitable treatment strategies for patients. Our study aimed to evaluate the potential improvement in predictive accuracy for clinically significant prostate cancer by incorporating perfusion data obtained from dynamic contrast-enhanced magnetic resonance imaging acquisition protocols into multiparametric magnetic resonance imaging parameters. Methods Radiomics extracted from perfusion parameters (Ktrans, Kep, Ve) of dynamic contrast-enhanced magnetic resonance imaging were analysed in patients suspected of prostate cancer who underwent 3T multiparametric magnetic resonance imaging between January 2017 and June 2023 in this retrospective study. The pathological findings obtained from biopsy or therapy were categorised into groups based on the Gleason sum score as either clinically significant prostate cancer (Gleason sum score > 7) or non-clinically significant prostate cancer (Gleason sum score ≤ 6). Diagnostic models were constructed using logistic regression analysis, incorporating prostate imaging reporting and data system V2.1 scores and clinical data, with or without radiomics extracted from dynamic contrast-enhanced. The area under curve (AUC) values were compared using the DeLong test. Results Overall, 214 men (clinically significant prostate cancer [n=78] and non-clinically significant prostate cancer [n=136]) were included. The clinical-prostate imaging reporting and data system model demonstrated an AUC of 0.89 (95% confidence interval: 0.84-0.95) in the training cohort and 0.91 (95% confidence interval: 0.84-0.98) in the test cohort. For the clinical-prostate imaging reporting and data system-radscore model, the AUC values were 0.97 (95% confidence interval: 0.95-0.99) for Ktrans, 0.98 (95% confidence interval: 0.96-1.00) for Ve, and 0.96 (95% confidence interval: 0.93-0.98) for Kep in the training cohort, and 0.97 (95% confidence interval: 0.94-1.00) for Ktrans, 0.95 (95% confidence interval: 0.91-1.00) for Ve, and 0.97 (95% confidence interval: 0.94-1.00) for Kep in the test cohort. Radiomics based on perfusion parameters exhibited good diagnostic performance in predicting clinically significant prostate cancer. The clinical-prostate imaging reporting and data system-radscore model demonstrated superior diagnostic capability compared to perfusion-based radiomics or clinical-prostate imaging reporting and data system models alone. Conclusion The application of radiomics, which involves extracting perfusion parameters from dynamic contrast-enhanced imaging, has the potential to enhance diagnostic accuracy for clinically significant prostate cancer.

Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience.

Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers. The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms "radiomics", "texture analysis", "oesophageal cancer", "gastroesophageal junction cancer", "oesophagogastric junction cancer", "gastric cancer", "stomach cancer", "staging", and "treatment response" until May 2024. Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature. Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models.

A study predicting long-term survival capacity in postoperative advanced gastric cancer patients based on MAOA and subcutaneous muscle fat characteristics

BackgroundThe prognosis of advanced gastric cancer (AGC) is relatively poor, and long-term survival depends on timely intervention. Currently, predicting survival rates remains a hot topic. The application of radiomics and immunohistochemistry-related techniques in cancer research is increasingly widespread. However, their integration for predicting long-term survival in AGC patients has not been fully explored.MethodsWe Collected 150 patients diagnosed with AGC at the Affiliated Zhongshan Hospital of Dalian University who underwent radical surgery between 2015 and 2019. Following strict inclusion and exclusion criteria, 90 patients were included in the analysis. We Collected postoperative pathological specimens from enrolled patients, analyzed the expression levels of MAOA using immunohistochemical techniques, and quantified these levels as the MAOAHScore. Obtained plain abdominal CT images from patients, delineated the region of interest at the L3 vertebral body level, and extracted radiomics features. Lasso Cox regression was used to select significant features to establish a radionics risk score, convert it into a categorical variable named risk, and use Cox regression to identify independent predictive factors for constructing a clinical prediction model. ROC, DCA, and calibration curves validated the model’s performance.ResultsThe enrolled patients had an average age of 65.71 years, including 70 males and 20 females. Multivariate Cox regression analysis revealed that risk (P = 0.001, HR = 3.303), MAOAHScore (P = 0.043, HR = 2.055), and TNM stage (P = 0.047, HR = 2.273) emerged as independent prognostic risk factors for 3-year overall survival (OS) and The Similar results were found in the analysis of 3-year disease-specific survival (DSS). The nomogram developed could predict 3-year OS and DSS rates, with areas under the ROC curve (AUCs) of 0.81 and 0.797, respectively. Joint calibration and decision curve analyses (DCA) confirmed the nomogram’s good predictive performance and clinical utility.ConclusionIntegrating immunohistochemistry and muscle fat features provides a more accurate prediction of long-term survival in gastric cancer patients. This study offers new perspectives and methods for a deeper understanding of survival prediction in AGC.

Prediction power of radiomics in early recurrence of hepatocellular carcinoma: A systematic review and meta-analysis.

Raiomics is an emerging auxiliary diagnostic tool, but there are still differences in whether it can be applied to predict early recurrence of hepatocellular carcinoma (HCC). The purpose of this meta-analysis was to systematically evaluate the predictive power of radiomics in the early recurrence (ER) of HCC. Comprehensive studies on the application of radiomics to predict ER in HCC patients after hepatectomy or curative ablation were systematically screened in Embase, PubMed, and Web of Science. Ten studies which is involving a total of 1929 patients were reviewed. The overall estimates of radiomic models for sensitivity and specificity in predicting the ER of HCC were 0.79 (95% confidence interval [CI]: 0.68-0.87) and 0.83 (95% CI: 0.73-0.90), respectively. The area under the summary receiver operating characteristic curve (SROC) was 0.88 (95% CI: 0.85-0.91). The imaging method is a reliable method for diagnosing HCC. Radiomics, which is based on medical imaging, has excellent power in predicting the ER of HCC. With the help of radiomics, we can predict the recurrence of HCC after surgery more effectively and provide a useful reference for clinical practice.

Comparison of conventional MRI analysis versus MRI-based radiomics to predict the circumferential margin resection involvement of rectal cancer

BackgroundTo compare the application of conventional MRI analysis and MRI-based radiomics to identify the circumferential resection margin (CRM) status of rectal cancer (RC).MethodsA cohort of 301 RC patients with 66 CRM invloved status and 235 CRM non-involved status were enrolled in this retrospective study between September 2017 and August 2022. Conventional MRI characteristics included gender, age, diameter, distance to anus, MRI-based T/N phase, CEA, and CA 19 − 9, then the relevant logistic model (Logistic-cMRI) was built. MRI-based radiomics of rectal cancer and mesorectal fascia were calculated after volume of interest segmentation, and the logistic model of rectal cancer radiomics (Logistic-rcRadio) and mesorectal fascia radiomics (Logistic-mfRadio) were constructed. And the combined nomogram (nomo-cMRI/rcRadio/mfRadio) containing conventional MRI characteristics, radiomics of rectal cancer and mesorectal fascia was developed. The receiver operator characteristic curve (ROC) was delineated and the area under curve (AUC) was calculated the efficiency of models.ResultsThe AUC of Logistic-cMRI was 0.864 (95%CI, 0.820 to 0.901). The AUC of Logistic-rcRadio was 0.883 (95%CI, 0.832 to 0.928) in the training set and 0.725 (95%CI, 0.616 to 0.826) in the testing set. The AUCs of Logistic-mfRadio was 0.891 (95%CI, 0.838 to 0.936) in the training set and 0.820 (95%CI, 0.725 to 0.905) in the testing set. The AUCs of nomo-cMRI/rcRadio/mfRadio were the highest in both the training set of 0.942 (95%CI, 0.901 to 0.969) and the testing set of 0.909 (95%CI, 0.830 to 0.959).ConclusionMRI-based radiomics of rectal cancer and mesorectal fascia showed similar efficacy in predicting the CRM status of RC. The combined nomogram performed better in assessment.

Radiomics in esophagogastric junction cancer: A scoping review of current status and advances

PurposeThis scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer. MethodsWe conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively. ResultsA total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%). ConclusionAlthough there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.

Resource requirements to accelerate clinical applications of next-generation sequencing and radiomics: workshop commentary and review.

The National Institutes of Health-US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of Health-Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the "ground truth" gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.

An automatic radiomic-based approach for disease localization: A pilot study on COVID-19

Radiomics is an innovative field in Personalized Medicine to help medical specialists in diagnosis and prognosis. Mainly, the application of Radiomics to medical images requires the definition and delimitation of the Region Of Interest (ROI) on the medical image to extract radiomic features. The aim of this preliminary study is to define an approach that automatically detects the specific areas indicative of a particular disease and examines them to minimize diagnostic errors associated with false positives and false negatives. This approach aims to create a nxn grid on the DICOM image sequence and each cell in the matrix is associated with a region from which radiomic features can be extracted.The proposed procedure uses the Model Checking technique and produces as output the medical diagnosis of the patient, i.e., whether the patient under analysis is affected or not by a specific disease. Furthermore, the matrix-based method also localizes where appears the disease marks. To evaluate the performance of the proposed methodology, a case study on COVID-19 disease is used. Both results on disease identification and localization seem very promising. Furthermore, this proposed approach yields better results compared to methods based on the extraction of features using the whole image as a single ROI, as evidenced by improvements in Accuracy and especially Recall. Our approach supports the advancement of knowledge, interoperability and trust in the software tool, fostering collaboration among doctors, staff and Radiomics.

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease

BackgroundThe prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension.Methods132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity.ResultsThe feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively.ConclusionsRadiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis.

This study aimed to assess the diagnostic accuracy of radiomics for predicting osteoporosis and the quality of radiomic studies. The study protocol was prospectively registered on PROSPERO (CRD42023425058). We searched PubMed, EMBASE, Web of Science, and Cochrane Library databases from inception to June 1, 2023, for eligible articles that applied radiomic techniques to diagnosing osteoporosis or abnormal bone mass. Quality and risk of bias of the included studies were evaluated with radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tools. The data analysis utilized the R program with mada, metafor, and meta packages. Ten retrospective studies with 5926 participants were included in the systematic review and meta-analysis. The overall risk of bias and applicability concerns for each domain of the studies were rated as low, except for one study which was considered to have a high risk of flow and time bias. The mean METRICS score was 70.1% (range 49.6-83.2%). There was moderate heterogeneity across studies and meta-regression identified sources of heterogeneity in the data, including imaging modality, feature selection method, and classifier. The pooled diagnostic odds ratio (DOR) under the bivariate random effects model across the studies was 57.22 (95% CI 27.62-118.52). The pooled sensitivity and specificity were 87% (95% CI 81-92%) and 87% (95% CI 77-93%), respectively. The area under the summary receiver operating characteristic curve (AUC) of the radiomic models was 0.94 (range 0.8 to 0.98). The results supported that the radiomic techniques had good accuracy in diagnosing osteoporosis or abnormal bone mass. The application of radiomics in osteoporosis diagnosis needs to be further confirmed by more prospective studies with rigorous adherence to existing guidelines and multicenter validation.