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OBJECTIVES: To assess radiologists' current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template. MATERIALS AND METHODS: An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients. RESULTS: A total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists' experience (p < 0.001), and experience also showed low but significant correlations with communication with clinicians (r = - 0.27, p = 0.003), report quality (r = 0.19, p = 0.043), and error rate (r = - 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality (p = 0.036). CONCLUSION: Radiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians. CLINICAL RELEVANCE STATEMENT: Radiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement. KEY POINTS: ⢠The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice. ⢠The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%). ⢠Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans.
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Atitude do Pessoal de Saúde , Neoplasias , Radiologistas , Sociedades Médicas , Humanos , Europa (Continente) , Inquéritos e Questionários , Neoplasias/diagnóstico por imagem , Radiologistas/estatística & dados numéricos , Sistemas de Informação em Radiologia/estatística & dados numéricosRESUMO
Cholangiocarcinoma (CCA) is a rare malignancy originating from the biliary tree and is anatomically categorized as intrahepatic (iCCA), perihilar, and extrahepatic or distal. iCCA, the second most prevalent hepatobiliary cancer following hepatocellular carcinoma (HCC), constitutes 5-20 % of all liver malignancies, with an increasing incidence. The challenging nature of iCCA, combined with nonspecific symptoms, often leads to late diagnoses, resulting in unfavorable outcomes. The advanced phase of this neoplasm is difficult to treat with dismal results. Early diagnosis could significantly reduce mortality attributed to iCCA but remains an elusive goal. The identification of biomarkers specific to iCCA and their translation into clinical practice could facilitate diagnosis, monitor therapy response, and potentially reveal novel interventions and personalized medicine. In this review, we present the current landscape of biomarkers in each of these contexts. In addition to CA19.9, a widely recognized biomarker for iCCA, others such as A1BG, CYFRA 21-1, FAM19A5, MMP-7, RBAK, SSP411, TuM2-PK, WFA, etc., as well as circulating tumor DNA, RNA, cells, and exosomes, are under investigation. Advancing our knowledge and monitoring of biomarkers may enable us to improve diagnosis, prognostication, and apply treatments dynamically and in a more personalized manner.
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Antígenos de Neoplasias , Neoplasias dos Ductos Biliares , Carcinoma Hepatocelular , Colangiocarcinoma , Queratina-19 , Neoplasias Hepáticas , Humanos , Carcinoma Hepatocelular/complicações , Carcinoma Hepatocelular/patologia , Neoplasias Hepáticas/diagnóstico , Neoplasias Hepáticas/tratamento farmacológico , Neoplasias Hepáticas/genética , Neoplasias dos Ductos Biliares/diagnóstico , Neoplasias dos Ductos Biliares/tratamento farmacológico , Neoplasias dos Ductos Biliares/patologia , Detecção Precoce de Câncer , Colangiocarcinoma/diagnóstico , Colangiocarcinoma/tratamento farmacológico , Colangiocarcinoma/genética , Biomarcadores , Ductos Biliares Intra-Hepáticos/patologiaRESUMO
PURPOSE: To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard. METHODS: Patients with degenerative pathologies of lumbar spine, evaluated with MRI study, were enrolled in a retrospective study approved by local ethical committee. A comprehensive software solution (CoLumbo; SmartSoft Ltd., Varna, Bulgaria) designed to label the segments of the lumbar spine and to detect a broad spectrum of degenerative pathologies based on a convolutional neural network (CNN) was employed, utilizing an automatic segmentation. The AI tool efficacy was compared to data obtained by a senior neuroradiologist that employed a semiquantitative score. Chi-square test was used to assess the differences among groups, and Spearman's rank correlation coefficient was calculated between the grading assigned by radiologist and the grading obtained by software. Moreover, agreement was assessed between the value assigned by radiologist and software. RESULTS: Ninety patients (58 men; 32 women) affected with degenerative pathologies of lumbar spine and aged from 60 to 81 years (mean 66 years) were analyzed. Significant correlations were observed between grading assigned by radiologist and the grading obtained by software for each localization. However, only when the localization was L2-L3, there was a good correlation with a coefficient value of 0.72. The best agreements were obtained in case of L1-L2 and L2-L3 localizations and were, respectively, of 81.1% and 72.2%. The lowest agreement of 51.1% was detected in case of L4-L5 locations. With regard canal stenosis and compression, the highest agreement was obtained for identification of in L5-S1 localization. CONCLUSIONS: AI solution represents an efficacy and useful toll degenerative pathologies of lumbar spine to improve radiologist workflow.
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Inteligência Artificial , Vértebras Lombares , Masculino , Humanos , Feminino , Vértebras Lombares/diagnóstico por imagem , Estudos Retrospectivos , Dados Preliminares , Imageamento por Ressonância Magnética/métodosRESUMO
PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.
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Imageamento por Ressonância Magnética , Radiômica , Humanos , Reprodutibilidade dos Testes , Estudos Retrospectivos , Aprendizado de MáquinaRESUMO
PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.
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Neoplasias Colorretais , Neoplasias Hepáticas , Aprendizado de Máquina , Mutação , Tomografia Computadorizada por Raios X , Humanos , Neoplasias Colorretais/patologia , Neoplasias Colorretais/genética , Neoplasias Colorretais/diagnóstico por imagem , Neoplasias Hepáticas/secundário , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/genética , Estudos Retrospectivos , Masculino , Feminino , Pessoa de Meia-Idade , Tomografia Computadorizada por Raios X/métodos , Idoso , Valor Preditivo dos Testes , Adulto , Idoso de 80 Anos ou mais , Sensibilidade e Especificidade , Reprodutibilidade dos Testes , RadiômicaRESUMO
Acknowledging the increasing use of whole-body magnetic resonance imaging (WB-MRI) in the oncological setting, we conducted a narrative review focusing on practical aspects of the examination and providing a synthesis of various acquisition protocols described in the literature. Firstly, we addressed the topic of patient preparation, emphasizing methods to enhance examination acceptance. This included strategies for reducing anxiety and patient distress, improving staff-patient interactions, and increasing overall patient comfort. Secondly, we analysed WB-MRI acquisition protocols recommended in existing imaging guidelines, such as MET-RADS-P, MY-RADS, and ONCO-RADS, and provided an overview of acquisition protocols reported in the literature regarding other expanding applications of WB-MRI in oncology, in patients with breast cancer, ovarian cancer, melanoma, colorectal and lung cancer, lymphoma, and cancers of unknown primary. Finally, we suggested possible acquisition parameters for whole-body images across MR systems from three different vendors.
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OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.
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Inteligência Artificial , Neoplasias da Mama , Meios de Contraste , Mamografia , Humanos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Pessoa de Meia-Idade , Mamografia/métodos , Idoso , Itália , Adulto , Gradação de Tumores , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Receptor ErbB-2 , Sensibilidade e Especificidade , RadiômicaRESUMO
In biomedical image processing, Deep Learning (DL) is increasingly exploited in various forms and for diverse purposes. Despite unprecedented results, the huge number of parameters to learn, which necessitates a substantial number of annotated samples, remains a significant challenge. In medical domains, obtaining high-quality labelled datasets is still a challenging task. In recent years, several works have leveraged data augmentation to face this issue, mostly thanks to the introduction of generative models able to produce artificial samples having the same characteristics as the acquired ones. However, we claim that biological principles must be considered in this process, as all medical imaging techniques exploit one or more physical laws or properties directly associated with the physiological characteristics of the tissues under analysis. A notable example is the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the contrast agent (CA) highlights both morphological and physiological aspects. In this paper, we introduce a novel generative approach explicitly relying on Physiologically Based Pharmacokinetic (PBPK) modelling and on an Intrinsic Deforming Autoencoder (DAE) to implement a physiologically-aware data augmentation strategy. As a case of study, we consider breast DCE-MRI. In particular, we tested our proposal on two private and one public datasets with different acquisition protocols, demonstrating that the proposed method significantly improves the performance of several DL-based lesion classifiers.
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The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Neoplasias da Mama , Neoplasias Hepáticas , Humanos , Feminino , Radiômica , Estudos Prospectivos , Bases de Dados FactuaisRESUMO
Background: In patient with a complete or near-complete clinical response after neoadjuvant treatment for locally advanced rectal cancer, the organ-sparing approach [watch & wait (W&W) or local excision (LE)] is a possible alternative to major rectal resection. Although, in case of local recurrence or regrowth, after these treatments, a total mesorectal excision (TME) can be operated. Method: In this retrospective study, we selected 120 patients with locally advanced rectal cancer (LARC) who had a complete or near-complete clinical response after neoadjuvant treatment, from June 2011 to June 2021. Among them, 41 patients were managed by W&W approach, whereas 79 patients were managed by LE. Twenty-three patients underwent salvage TME for an unfavorable histology after LE (11 patients) or a local recurrence/regrowth (seven patients in LE group - five patients in W&W group), with a median follow-up of 42 months. Results: Following salvage TME, no patients died within 30 days; serious adverse events occurred in four patients; 8 (34.8%) patients had a definitive stoma; 8 (34.8%) patients undergone to major surgery for unfavorable histology after LE - a complete response was confirmed. Conclusion: Notably active surveillance after rectal sparing allows prompt identifying signs of regrowth or relapse leading to a radical TME. Rectal sparing is a possible strategy for LARC patients although an active surveillance is necessary.
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PURPOSE: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. METHODS: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. RESULTS: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. CONCLUSIONS: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.
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Amyloidosis is a rare infiltrative condition resulting from the extracellular accumulation of amyloid fibrils at the cardiac level. It can be an acquired condition or due to genetic mutations. With the progression of imaging technologies, a non-invasive diagnosis was proposed. In this study, we discuss the role of CMR in cardiac amyloidosis, focusing on the two most common subtypes (AL and ATTR), waiting for evidence-based guidelines to be published.
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Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.
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Traumatic vascular injuries consist of direct or indirect damage to arteries and/or veins and account for 3% of all traumatic injuries. Typical consequences are hemorrhage and ischemia. Vascular injuries of the extremities can occur isolated or in association with major trauma and other organ injuries. They account for 1-2% of patients admitted to emergency departments and for approximately 50% of all arterial injuries. Lower extremities are more frequently injured than upper ones in the adult population. The outcome of vascular injuries is strictly correlated to the environment and the time background. Treatment can be challenging, notably in polytrauma because of the dilemma of which injury should be prioritized, and treatment delay can cause disability or even death, especially for limb vascular injury. Our purposes are to discuss the role of computed tomography angiography (CTA) in the diagnosis of vascular trauma and its optimized protocol to achieve a definitive diagnosis and to assess the radiological signs of vascular injuries and the possible pitfalls.
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Pulmonary sequestration is a rare congenital pulmonary anomaly where a portion of the lung parenchyma is supplied by an anomalous systemic artery, usually originating from the thoracic or abdominal aorta. Traditionally surgical resection and ligation of the aberrant feeding vessel are the gold standard treatments of this disease. Hybrid operations consisting in endovascular arterial embolization and surgical resection is a promising treatment option. We report a case of a 69-years-old man with symptomatic intralobular sequestration successfully treated by hybrid approach.
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This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (DCs), Odontogenic Keratocysts (OKCs), and Unicystic Ameloblastomas (UAs). This retrospective study involves 103 patients who underwent jaw lesion surgery in the Maxillofacial Surgery Unit of Federico II University Of Naples between January 2018 and January 2023. Nonparametric Wilcoxon-Mann-Whitney and Kruskal Wallis tests were used for continuous variables. Linear and non-logistic regression models (LRM and NLRM) were employed, along with machine learning techniques such as decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), to predict the outcomes. When individual inflammatory biomarkers were considered alone, their ability to differentiate between OKCs, UAs, and DCs was below 50 % accuracy. However, a linear regression model combining four inflammatory biomarkers achieved an accuracy of 95 % and an AUC of 0.96. The accuracy of single radiomics predictors was lower than that of inflammatory biomarkers, with an AUC of 0.83. The Fine Tree model, utilizing NLR, SII, and one radiomic feature, achieved an accuracy of 94.3 % (AUC = 0.95) on the training and testing sets, and a validation set accuracy of 100 %. The Fine Tree model demonstrated the capability to discriminate between OKCs, UAs, and DCs. However, the LRM utilizing four inflammatory biomarkers proved to be the most effective algorithm for distinguishing between OKCs, UAs, and DCs.
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Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.
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The use of Vascular Endothelial Growth Factor inhibitors (VEGFi) has become prevalent in the field of medicine, given the high incidence of various pathological conditions necessitating VEGF inhibition within the general population. These conditions encompass a range of advanced neoplasms, such as colorectal cancer, non-small cell lung cancer, renal cancer, ovarian cancer, and others, along with ocular diseases. The utilization of VEGFi is not without potential risks and adverse effects, requiring healthcare providers to be well-prepared for identification and management. VEGFi can be broadly categorized into two groups: antibodies or chimeric proteins that specifically target VEGF (bevacizumab, ramucirumab, aflibercept, ranibizumab, and brolucizumab) and non-selective and selective small molecules (sunitinib, sorafenib, cabozantinib, lenvatinib, regorafenib, etc.) designed to impede intracellular signaling of the VEGF receptor (RTKi, receptor tyrosine kinase inhibitors). The presentation and mechanisms of adverse effects resulting from VEGFi depend primarily on this distinction and the route of drug administration (systemic or intra-vitreal). This review provides a thorough examination of the causes, recognition, management, and preventive strategies for VEGFi toxicities with the goal of offering support to oncologists in both clinical practice and the design of clinical trials.