Differentiation of pyogenic hepatic abscesses from malignant mimickers using multislice-based texture acquired from contrast-enhanced computed tomography

Ultrasonic-Based Radiomics Signature With Machine Learning for Differentiating Prognostic Subsets of Pediatric Peripheral Neuroblastic Tumors: A Retrospective Study

OBJECTIVE

To construct and select a better model based on ultrasonic-based radiomics features and clinical characteristics for prognostic subsets of pediatric neuroblastic tumors.

METHODS

Data from 73 children with neuroblastic tumors were included and divided into a training group and a validation group. Data 1 contained the subjects' radiomics features and clinical characteristics, while data 2 contained radiomics features. With the help of machine learning, five models were constructed for data 1 and data 2, respectively. The model with the highest accuracy and area under the curve was selected as the combined model and radiomics model for data 1 and data 2, respectively. A superior model was then chosen from the models after further comparison.

RESULTS

The extreme gradient-boosting model for data 1 was chosen as the combined model and the extreme gradient-boosting model for data 2 was chosen as the radiomics model. The area under the curve of the combined and radiomics models in the validation group was 0.941 and 0.918 (p = 0.6906). The balanced accuracy, kappa value and F1 score of the radiomics model (0.9045, 0.8091 and 0.9091, respectively) were higher than those of the combined model (0.8545, 0.7123 and 0.8696, respectively). The top eight features of the radiomics model included five first-order statistical features and three textural features, all of which were high-dimensional features.

CONCLUSION

Our study proved that the radiomics model outperformed the combined model at differentiating prognostic subsets of pediatric neuroblastic tumors. Additionally, we found that high-dimensional ultrasonic-based radiomics features surpassed other features and clinical characteristics.

Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the third leading cause of cancer-related deaths. Imaging plays a crucial role in the screening, diagnosis, and monitoring of HCC; however, the potential mechanism regarding phenotypes or molecular subtyping remains underexplored. Radiomics significantly expands the selection of features available by extracting quantitative features from imaging data. Radiogenomics bridges the gap between imaging and genetic/transcriptomic information by associating imaging features with critical genes and pathways, thereby providing biological annotations to these features. Despite challenges in interpreting these connections, assessing their universality, and considering the diversity in HCC etiology and genetic information across different populations, radiomics and radiogenomics offer new perspectives for precision treatment in HCC. This article provides an up-to-date summary of the advancements in radiomics and radiogenomics throughout the HCC care continuum, focusing on the clinical applications, advantages, and limitations of current techniques and offering prospects. Future research should aim to overcome these challenges to improve the prognosis of HCC patients and leverage imaging information for patient benefit.

The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study

BACKGROUND

Extrathyroidal extension was related with worse survival for patients with papillary thyroid carcinoma. For its preoperative evaluation, we measured and compared the predicting value of sonographic method and ultrasonic radiomics method in nodules of papillary thyroid carcinoma.

PATIENTS AND METHODS

Data from 337 nodules were included and divided into training group and validation group. For ultrasonic radiomics method, a best model was constructed based on clinical characteristics and ultrasonic radiomic features. The predicting value was calculated then. For sonographic method, the results were calculated using all samples.

RESULTS

For ultrasonic radiomics method, we constructed 9 models and selected the extreme gradient boosting model for its highest accuracy (0.77) and area under curve (0.813) in validation group. The accuracy and area under curve of sonographic method was 0.70 and 0.569. Meanwhile. We found that the top-6 important features of xgboost model included no clinical characteristics, all of whom were high-dimensional radiomic features.

CONCLUSIONS

The study showed the superior value of ultrasonic radiomics method to sonographic method for preoperative detection of extrathyroidal extension in papillary thyroid carcinoma. Furthermore, high-dimensional radiomic features were more important than clinical characteristics.

Focal Lesions of the Liver and Radiomics: What Do We Know?

Despite differences in pathological analysis, focal liver lesions are not always distinguishable in contrast-enhanced magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CT), and positron emission tomography (PET). This issue can cause problems of differential diagnosis, treatment, and follow-up, especially in patients affected by HBV/HCV chronic liver disease or fatty liver disease. Radiomics is an innovative imaging approach that extracts and analyzes non-visible quantitative imaging features, supporting the radiologist in the most challenging differential diagnosis when the best-known methods are not conclusive. The purpose of this review is to evaluate the most significant CT and MRI texture features, which can discriminate between the main benign and malignant focal liver lesions and can be helpful to predict the response to pharmacological or surgical therapy and the patient's prognosis.

Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study

Background

Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules.

Methods

Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured.

Results

Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features.

Conclusions

RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.

Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma

PURPOSE

Conventional magnetic resonance imaging (MRI) technics are insufficient in the differentiation of tumor progression from post-treatment changes in patients with treated glioblastoma. Previous studies have suggested that histogram analysis is a useful tool in the assessment of treatment response in different cancer types. The aim of the study was to to evaluate the effectiveness of MRI histogram analysis in the differentiation of tumor progression from pseudoprogression in patients with treated glioblastoma.

METHODS

Forty-six patients with glioblastoma who newly developed enhancing lesions following chemoradiation treatment were included in this retrospective study. Histogram analysis was performed from new enhancing lesions on T1-weighted contrast-enhanced MRI. Histogram analysis findings of patients with progression (23) and pseudoprogression (23) were compared.

RESULTS

Mean, minimum, median, maximum, standard deviation, variance, entropy, skewness, uniformity values were found to be significantly higher in progressive disease (p < 0.05). A receiver-operating characteristic (ROC) curve analysis was performed for mean value, and area under the curve (AUC) was found as 0.975. When the threshold value was selected as 528.86, two groups could be differentiated with 95.7% sensitivity and 87.0% specificity.

CONCLUSION

MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.

Differentiating infected focal liver lesions from malignant mimickers: value of ultrasound-based radiomics

AIM

To establish an ultrasound-based radiomics model through machine learning methods and then to assess the ability of the model to differentiate infected focal liver lesions from malignant mimickers.

MATERIALS AND METHODS

A total of 104 patients with infected focal liver lesions and 485 patients with malignant hepatic tumours were included, consisting of hepatocellular carcinoma (HCC), cholangiocarcinoma (CC), combined hepatocellular-cholangiocarcinoma (cHCC-CC), and liver metastasis. Radiomics features were extracted from grey-scale ultrasound images. Feature selection and predictive modelling were carried out by dimensionality reduction methods and classifiers. The diagnostic effect of the prediction mode was assessed by receiver operating characteristic (ROC) curve analysis.

RESULTS

In total, 5,234 radiomics features were extracted from grey-scale ultrasound image of every focal liver lesion. The ultrasound-based radiomics model had a favourable predictive value for differentiating infected focal liver lesions from malignant hepatic tumours, with an area under the curve (AUC) of 0.887 and 0.836 (HCC group), 0.896 and 0.766 (CC group), 0.944 and 0.754 (cHCC-CC group), 0.918 and 0.808 (liver metastasis group), and 0.949 and 0.745 (malignant hepatic tumour group) for the training set and validation set, respectively.

CONCLUSIONS

Ultrasound-based radiomics is helpful in differentiating infected focal liver lesions from malignant mimickers and has the potential for use as a supplement to conventional grey-scale ultrasound and contrast-enhanced ultrasound (CEUS).

Texture analysis of dorsal striatum in functional neurological (conversion) disorder

In this study, it was aimed to evaluate the dorsal striatum nuclei of patients diagnosed with Functional Neurological Disorder by texture analysis method from magnetic resonance imaging images and to compare them with healthy controls. Study groups consisted of 20 female patients and 20 healthy women. The brains of patients and controls were scanned for high-resolution images with a 1.5T scanner using the sagittal plane and 3D spiral fast spin echo sequence. Using the texture analysis method, mean, standard deviation, minimum, maximum, median, variance, entropy, size %L, size %U, size %M, kurtosis, skewness and homogeneity values of the dorsal striatum nuclei were calculated from the images. The data were compared with comparison tests according to Kolmogorov-Smirnov test results. There was no statistically significant difference between paired regions in terms of texture analysis findings in the cross-sectional images of the participants. In patients, mean, standard deviation, minimum, maximum, median, variance and entropy values for the putamen nucleus, and mean, standard deviation, minimum, maximum, median, variance, entropy and kurtosis values for the caudate nucleus were found significantly higher than controls. Additional receiver operating characteristic curve and logistic regression analyzes were performed. The implications of the results of the study are that there are significant microstructural changes in the dorsal striatum nuclei of patients and their reflection on brain images. Texture analysis is a useful technique to show tissue changes in the dorsal striatum of patients using images. It is highly recommended to use texture analysis to identify and evaluate potentially affected areas of the brain in new studies.

CT-Based Radiomics Nomogram: A Potential Tool for Differentiating Hepatocellular Adenoma From Hepatocellular Carcinoma in the Noncirrhotic Liver

RATIONALE AND OBJECTIVES

To evaluate the value of a radiomics nomogram for preoperative differentiating hepatocellular adenoma (HCA) from hepatocellular carcinoma (HCC) in the noncirrhotic liver.

MATERIALS AND METHODS

One hundred and thirty-one patients with HCA (n = 46) and HCC (n = 85) were divided into a training set (n = 93) and a test set (n = 38). Clinical data and CT findings were analyzed. Radiomics features were extracted from the triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm and a radiomics score was calculated. Combined with the radiomics score and independent clinical factors, a radiomics nomogram was developed by multivariate logistic regression analysis. The performance of the radiomics nomogram was assessed by calibration, discrimination and clinical usefulness.

RESULTS

Gender, age, and enhancement pattern were the independent clinical factors. Three thousand seven hundred and sixty-eight features were extracted and reduced to 7 features as the optimal discriminators to build the radiomics signature. The radiomics nomogram (area under the curve [AUC], 0.96; 95% confidence interval [CI], 0.93-0.99) and the clinical factors model (AUC, 0.93; 95%CI, 0.88-0.99) showed better discrimination capability (p = 0.001 and 0.047) than the radiomics signature (AUC, 0.83; 95%CI, 0.74-0.92) in the training set. In the test set, the radiomics nomogram (AUC, 0.94; 95%CI, 0.87-1.00) performed better (p = 0.013) than the radiomics signature (AUC, 0.75; 95%CI, 0.59-0.91). Decision curve analysis showed the radiomics nomogram outperformed the clinical factors model and the radiomics signature in terms of clinical usefulness.

CONCLUSION

The CT-based radiomics nomogram has the potential to accurately differentiate HCA from HCC in the noncirrhotic liver.

CT-based radiomics model to distinguish necrotic hepatocellular carcinoma from pyogenic liver abscess

AIM

To investigate the feasibility of a computed tomography (CT)-based radiomics model in distinguishing necrotic hepatocellular carcinoma (nHCC) from pyogenic liver abscess (PLA).

MATERIAL AND METHODS

One hundred-four enrolled patients with nHCC (n=56) and PLA (n=48) were divided randomly into a training cohort (n=62) and validation cohort (n=42). ROI (region of interest) of the wall (ROI-wall) and ROI of the necrotic cavity (ROI-necrotic cavity) of the lesion were delineated from each arterial phase (AP) and portal venous phase (PP) image. The least absolute shrinkage and the selection operator logistic regression method was used to select radiomics features, and radiomics scores (R-scores) were calculated. Four radiomics models, including R-score (ROI-wall) in the AP, R-score (ROI-necrotic cavity) in the AP, R-score (ROI-wall) in the PP and R-score (ROI-necrotic cavity) in the PP, were constructed and evaluated by area under the curve (AUC) of receiver operating characteristic curve.

RESULTS

The AUCs of R-score (ROI-wall) in the AP, R-score (ROI-necrotic cavity) in the AP, R-score (ROI-wall) in the PP, and R-score (ROI-necrotic cavity) in the PP were 0.935 and 0.917, 0.906 and 0.824, 0.985 and 0.928, 0.899 and 0.850, in the training and validation cohorts, respectively. In the training cohort, the AUC of R-score (ROI-wall) in the PP was higher than that of R-score (ROI-wall) in the AP (p=0.024) or R-score (ROI-necrotic cavity) in the AP (p=0.046) or R-score (ROI-necrotic cavity) in the PP (p=0.044).

CONCLUSION

CT-based radiomics models can be used to distinguish nHCC from PLA.

Differentiation of Multiple Myeloma and Lytic Bone Metastases: Histogram Analysis

OBJECTIVE

Metastasis and multiple myeloma are common malignant bone marrow lesions. It may be difficult to distinguish because of similar imaging findings. The purpose of this study was to determine the value of histogram analysis on the computed tomography imaging.

METHODS

Twenty-three patients with primary tumor and 23 patients with multiple myeloma were included in the study. All patients had lytic bone lesions on the thorax and abdominal computed tomography scan with contrast. Multiple bone lesions of patients with primary tumor were accepted as metastasis. Multiple bone lesions of patients with multiple myeloma were accepted as multiple myeloma involvement. Histogram analysis was performed from lytic bone metastases and bone involvement of multiple myeloma. Results of both groups were compared.

RESULTS

In histogram analysis, minimum, median, and maximum gray level parameters were found to be significantly higher in lytic bone metastases (P < 0.001).

CONCLUSION

Computed tomography histogram analysis can be considered as a method to be used in the differentiation of multiple myeloma and lytic bone metastases.

A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver

Background

The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver.

Methods

A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Rad-score) was calculated. Clinical data and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness.

Results

Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934–0.995) and the validation set (AUC, 0.865; 95% CI, 0.725–1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959–0.998) and the validation set (AUC, 0.917; 95% CI, 0.800–1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719–0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model in terms of clinical usefulness.

Conclusions

The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating FNH from HCC in the non-cirrhotic liver, which might facilitate clinical decision-making process.

An evaluation of magnetic resonance imaging with histogram analysis in patients with idiopathic subjective tinnitus

OBJECTIVE

Tinnitus is defined as the perception of sound in the ears without any acoustic stimulus. It has still not been determined whether tinnitus originates peripherally, centrally, or from several locations. The aim of the study was to determine any possible cochlear pathology in patients with normal hearing and tinnitus of unspecified etiology.

METHODS

The study included 22 patients who presented at the clinic with the complaint of idiopathic subjective tinnitus that had been ongoing for at least 1 year. A total of 44 ears were included; 28 with tinnitus and 16 healthy ears. Unilateral tinnitus was determined in 16 patients and bilateral in six patients. The temporal magnetic resonance imaging (MRI) and audiogram results of the patients were retrieved from the hospital records. The MRI histogram measurement was made automatically and calculated for each ear separately.

RESULTS

Entropy was found to be significantly higher in the control group and distribution was lower (p>0.01). In the tinnitus group, distribution was found to be broader, and kurtosis was statistically significantly greater (p<0.001).

CONCLUSION

MRI histogram analysis can be considered as a method to be used in the clinical evaluation of the determination of possible cochlear pathology in ears with tinnitus.