• cell differentiation
• esophageal squamous cell sarcinoma
• texture analysis
• tomography
• x-ray computed

• Humans
• Male
• Esophageal Neoplasms/diagnostic imaging
• Esophageal Neoplasms/pathology
• Female
• Middle Aged
• Tomography, X-Ray Computed/methods
• Esophageal Squamous Cell Carcinoma/diagnostic imaging
• Esophageal Squamous Cell Carcinoma/pathology
• Aged
• ROC Curve
• Diagnosis, Differential
• Adult
• Feasibility Studies
• Carcinoma, Squamous Cell/diagnostic imaging
• Carcinoma, Squamous Cell/pathology
• Retrospective Studies

• 20220585 Hebei Medical Science Research Project

• computed tomography
• computer-assisted diagnosis
• oral cancer
• radiomics
• tumor

• 23/08428-1 FAPESP (São Paulo Research Foundation)

• Colon cancer
• Computed tomography
• Machine learning
• Texture analysis

• Humans
• Retrospective Studies
• Reproducibility of Results
• Colonic Neoplasms/diagnostic imaging
• Colonic Neoplasms/surgery
• Lymph Nodes/diagnostic imaging
• Lymph Nodes/pathology
• Tomography, X-Ray Computed/methods
• Machine Learning

• FDG-PET
• diffusion-MRI
• esophageal cancer
• neoadjuvant therapy
• perfusion-CT

• Computed tomography
• Esophageal squamous cell carcinoma
• Radiomics
• Tumor stage

• Humans
• Esophageal Squamous Cell Carcinoma/diagnostic imaging
• Esophageal Neoplasms/diagnostic imaging
• Radiomics
• Tomography, X-Ray Computed
• Retrospective Studies

• computed tomography
• decision curve analysis
• esophageal squamous cell carcinoma
• lymphovascular invasion
• nomogram

• clinical biomarkers
• esophageal cancer
• metastasis
• molecular markers
• postoperative recurrence

• Humans
• Esophageal Squamous Cell Carcinoma/pathology
• Carcinoma, Squamous Cell/pathology
• Neoplasm Staging
• Neoplasm Recurrence, Local/pathology
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/surgery
• Esophageal Neoplasms/pathology
• Adaptation, Psychological

• Diagnosis
• Gastrointestinal cancer
• Hepatocellular carcinoma
• Radiomics
• Therapeutic response
• Treatment

• Humans
• Gastrointestinal Neoplasms/diagnostic imaging
• Gastrointestinal Neoplasms/therapy
• Diagnostic Imaging

• cancer of unknown primary
• cervical lymphadenopathy
• machine learning
• malignant lymphoma
• texture

To establish and verify a predictive model involving multiparameter MRI and clinical manifestations for predicting synchronous lung metastases (SLM) in osteosarcoma.

Seventy-eight consecutive patients with osteosarcoma (training dataset, n = 54; validation dataset, n = 24) were enrolled in our study. MRI features were extracted from the T1‐weighted image (T1WI), T2‐weighted image (T2WI), and contrast-enhanced T1-weighted image (CE-T1WI) of each patient. Least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression were performed to select key features and build radiomics models in conjunction with logistic regression (LR) and support vector machine (SVM) classifiers. Eight individual models based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, T1WI+T2WI+CE-T1WI, and clinical features, as well as two combined models, were built. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were employed to assess the different models.

Tumor size was the most significant univariate clinical indicator (1). The AUC values of the LR predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.686, 0.85, 0.87, 0.879, 0.736, 0.85, and 0.914, respectively (2). The AUC values of the SVM predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI +CE-T1WI, T2WI +CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.629, 0.829, 0.771, 0.879, 0.643, 0.829, and 0.929, respectively (3). The AUC values of the clinical, combined 1 (clinical and LR-radiomics) and combined 2 (clinical and SVM-radiomics) predictive models were 0.779, 0.957, and 0.943, respectively.

The combined model exhibited good performance in predicting osteosarcoma SLM and may be helpful in clinical decision-making.

• magnetic resonance imaging
• metastasis
• osteosarcoma
• predictive value of tests
• radiomics

This study retrospectively included some patients with colorectal cancer diagnosed by histopathology, to explore the feasibility of CT medical image texture analysis in predicting KRAS gene mutations in patients with colorectal cancer. Before any surgical procedure, all patients received an enhanced CT scan of the abdomen and pelvis, as well as genetic testing. To define patient groups, divide all patients into test and validation sets based on the order of patient enrollment. A radiologist took a look at the plain axial CT image of the tumor, as well as the portal vein CT image, at the corresponding level. The physician points the computer's cursor to the relevant area in the image, and TexRAD software programs together texture parameters based on various spatial scale factors, also known as total mean, total variance, statistical entropy, overall total average, mean total, positive mean, skewness value, kurtosis value, and general skewness. Using the same method again two weeks later, the observer and another physician measured the image of each patient again to see if the method was consistent between observers. With regard to clinical information, the KRAS gene mutation group and the wild group of participants in the test set and validation set each had values for the texture parameter. In a study of patients with colorectal cancer, the results demonstrated that CT texture parameters were correlated with the presence of the KRAS gene mutation. The best CT prediction model includes the values of the medium texture image's slope and the other CT fine texture image's value of entropy, the medium texture image's slope and kurtosis, and the medium texture image's mean and the other CT fine texture image's value of entropy. Regardless of the training set or the validation set, patients with and without KRAS gene mutations did not differ significantly in clinical characteristics. This method can be used to identify mutations in the KRAS gene in patients with colorectal cancer, making it practical to implement CT medical image texture analysis technology for that purpose.

• Computed tomography
• Esophageal cancer
• MicroRNAs
• N6-methyladenosine
• Radiogenomics
• Radiomics

• Esophageal Neoplasms/diagnostic imaging
• Esophageal Neoplasms/genetics
• Esophageal Neoplasms/therapy
• Humans
• MicroRNAs/genetics
• Neoplasm Staging
• Pilot Projects
• Tomography, X-Ray Computed

• Aged
• Area Under Curve
• Female
• Fluorodeoxyglucose F18/chemistry
• Humans
• Male
• Middle Aged
• Neoplasm Staging
• Palatine Tonsil/diagnostic imaging
• Positron Emission Tomography Computed Tomography
• ROC Curve
• Radiopharmaceuticals/chemistry
• Sensitivity and Specificity
• Tonsillar Neoplasms/diagnosis
• Tonsillar Neoplasms/pathology

• HCC progression
• MRI-based nomogram
• Prediction ability
• Radiomics
• TACE

• Carcinoma, Hepatocellular/diagnostic imaging
• Carcinoma, Hepatocellular/therapy
• Chemoembolization, Therapeutic
• Humans
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/therapy
• Magnetic Resonance Imaging
• Nomograms
• Retrospective Studies

Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape.

Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.

• Radiomics
• Radiomics technical principles
• oncologic imaging

Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.

• Artificial intelligence
• Radiomics
• Texture analysis
• Gastroenterology
• Esophageal disease
• Gastric diseases
• Hepatic disease

• computed tomography
• esophageal squamous cell carcinoma
• heterogeneity
• radiomics
• stage

• Aged
• Case-Control Studies
• Contrast Media
• Esophageal Neoplasms/diagnostic imaging
• Esophageal Neoplasms/pathology
• Esophageal Squamous Cell Carcinoma/diagnostic imaging
• Esophageal Squamous Cell Carcinoma/pathology
• Esophagus/diagnostic imaging
• Esophagus/pathology
• Female
• Humans
• Male
• Middle Aged
• Neoplasm Staging
• Predictive Value of Tests
• ROC Curve
• Radiometry/methods
• Radiometry/statistics & numerical data
• Reproducibility of Results
• Retrospective Studies
• Sensitivity and Specificity
• Tomography, X-Ray Computed/methods
• Tomography, X-Ray Computed/statistics & numerical data

• Computed tomography
• Machine learning
• Radiomics
• Thymic epithelial tumour
• WHO classification

Purpose: The aim of this study was to investigate the value of radiomics analysis of iodine-based material decomposition (MD) images with dual-energy computed tomography (DECT) imaging for preoperatively predicting microsatellite instability (MSI) status in colorectal cancer (CRC).

Methods: This study included 102 CRC patients proved by postoperative pathology, and their MSI status was confirmed by immunohistochemistry staining. All patients underwent preoperative DECT imaging scanned on either a Revolution CT or Discovery CT 750HD scanner, and the iodine-based MD images in the venous phase were reconstructed. The clinical, CT-reported, and radiomics features were obtained and analyzed. Data from the Revolution CT scanner were used to establish a radiomics model to predict MSI status (70% samples were randomly selected as the training set, and the remaining samples were used to validate); and data from the Discovery CT 750HD scanner were used to test the radiomics model. The stable radiomics features with both inter-user and intra-user stability were selected for the next analysis. The feature dimension reduction was performed by using Student's t-test or Mann–Whitney U-test, Spearman's rank correlation test, min–max standardization, one-hot encoding, and least absolute shrinkage and selection operator selection method. The multiparameter logistic regression model was established to predict MSI status. The model performances were evaluated: The discrimination performance was accessed by receiver operating characteristic (ROC) curve analysis; the calibration performance was tested by calibration curve accompanied by Hosmer–Lemeshow test; the clinical utility was assessed by decision curve analysis.

Results: Nine top-ranked features were finally selected to construct the radiomics model. In the training set, the area under the ROC curve (AUC) was 0.961 (accuracy: 0.875; sensitivity: 1.000; specificity: 0.812); in the validation set, the AUC was 0.918 (accuracy: 0.875; sensitivity: 0.875; specificity: 0.857). In the testing set, the diagnostic performance was slightly lower with AUC of 0.875 (accuracy: 0.788; sensitivity: 0.909; specificity: 0.727). A nomogram based on clinical factors and radiomics score was generated via the proposed logistic regression model. Good calibration and clinical utility were observed using the calibration and decision curve analyses, respectively.

Conclusion: Radiomics analysis of iodine-based MD images with DECT imaging holds great potential to predict MSI status in CRC patients.

• colorectal neoplasms
• dual-energy computed tomography
• iodine-based material decomposition image
• microsatellite instability
• radiomics

Introduction: Texture analysis is an emergent imaging technique to quantify heterogeneity in radiological images. It is still unclear whether this technique is capable to reflect tumor microstructure. The present study sought to correlate histopathology parameters with texture features derived from contrast-enhanced CT images in head and neck squamous cell carcinomas (HNSCC).

Materials and Methods: Twenty-eight patients with histopathological proven HNSCC were retrospectively analyzed. In every case EGFR, VEGF, Hif1-alpha, Ki67, p53 expression derived from immunhistochemical specimen were semiautomatically calculated. Furthermore, mean cell count was estimated. Texture analysis was performed on contrast-enhanced CT images as a whole lesion measurement. Spearman's correlation analysis was performed, adjusted with Benjamini-Hochberg correction for multiple tests.

Results: Several texture features correlated with histopathological parameters. After correction only CT texture joint entropy and CT entropy correlation with Hif1-alpha expression remained statistically significant (ρ = −0.60 and ρ = −0.50, respectively).

Conclusions: CT texture joint entropy and CT entropy were associated with Hif1-alpha expression in HNSCC and might be able to reflect hypoxic areas in this entity.

• CT
• HNSCC
• Hif1-alpha
• Ki67
• texture analysis

• Esophageal cancer
• diagnostic imaging
• tumor staging
• tumor volume