Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas

Radiomics Features on Enhanced Computed Tomography Predict FOXP3 Expression and Clinical Prognosis in Patients with Head and Neck Squamous Cell Carcinoma

Forkhead box P3 (FOXP3) has been identified as a novel molecular marker in various types of cancer. The present study assessed the expression of FOXP3 in patients with head and neck squamous cell carcinoma (HNSCC) and its potential as a clinical prognostic indicator, and developed a radiomics model based on enhanced computed tomography (CT) imaging. Data from 483 patients with HNSCC were downloaded from the Cancer Genome Atlas for FOXP3 prognostic analysis and enhanced CT images from 139 patients included in the Cancer Imaging Archives, which were subjected to the maximum relevance and minimum redundancy and recursive feature elimination algorithms for radiomics feature extraction and processing. Logistic regression was used to build a model for predicting FOXP3 expression. A prognostic scoring system for radiomics score (RS), FOXP3, and patient clinicopathological factors was established to predict patient survival. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration curve and decision curve analysis (DCA) were used to evaluate model performance. Furthermore, the relationship between FOXP3 and the immune microenvironment, as well as the association between RS and immune checkpoint-related genes, was analyzed. Results of analysis revealed that patients with HNSCC and high FOXP3 mRNA expression exhibited better overall survival. Immune infiltration analysis revealed that FOXP3 had a positive correlation with CD4 + and CD8 + T cells and other immune cells. The 8 best radiomics features were selected to construct the radiomics model. In the FOXP3 expression prediction model, the AUC values were 0.707 and 0.702 for the training and validation sets, respectively. Additionally, the calibration curve and DCA demonstrated the positive diagnostic utility of the model. RS was correlated with immune checkpoint-related genes such as ICOS, CTLA4, and PDCD1. A predictive nomogram was established, the AUCs were 0.87, 0.787, and 0.801 at 12, 24, and 36 months, respectively, and DCA demonstrated the high clinical applicability of the nomogram. The enhanced CT radiomics model can predict expression of FOXP3 and prognosis in patients with HNSCC. As such, FOXP3 may be used as a novel prognostic marker to improve individualized clinical diagnosis and treatment decisions.

Qualitative and quantitative characteristics of CEUS for renal cell carcinoma and angiomyolipoma: a narrative review

Incidental findings of renal masses are increasing. However, a substantial portion of surgically treated renal masses turn out to be benign on histopathological examination. Thus, there is a clear need for improved pre-surgical assessment to minimize unnecessary invasive procedures. The challenge intensifies when distinguishing between renal cell carcinoma (RCC) and angiomyolipoma (AML) in renal lesions smaller than 4 cm with minimal adipose tissue. In such cases, contrast-enhanced ultrasound (CEUS) has emerged as a valuable diagnostic tool, by utilizing both qualitative and quantitative parameters. Quantitative measures offer objectivity, reliability, and reproducibility compared to qualitative parameters, enabling the characterization of RCC subtypes and differentiation from AML. Qualitative features as enhancement pattern, degree, and peak were less helpful in distinguishing triphasic minimal fat AML (TAML) from epithelioid AML (EAML), with the pseudocapsule sign potentially being the only distinguishing qualitative feature. The pseudocapsule sign was more frequently observed in ccRCCs (38.0%) than in AMLs (15.6%). Moreover, it was detected in 40.0% of EAMLs and 34.5% of ccRCCs but not in TAMLs due to similar growth patterns between EAMLs and low-grade ccRCCs. Quantitative measures such as the time-to-peak (TTP) ratio can further enhance diagnostic accuracy and also TOC ratio should be considered, as it was higher in clear cell RCCs (ccRCCs) and in EAMLs compared to TAMLs, indicating behavior similar to ccRCCs. However, CEUS remains an operator-dependent exam.

The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures

PURPOSE

To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs).

MATERIALS AND METHODS

128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model.

RESULTS

For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846.

CONCLUSION

Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.

A review of radiomics and genomics applications in cancers: the way towards precision medicine

The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field.

Associations between tumor grade, contrast-enhanced ultrasound features, and microvascular density in patients with clear cell renal cell carcinoma: a retrospective study

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises 70% of all renal cell carcinomas (RCCs). Currently, the most important prognostic factor for this type of carcinoma is the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade. However, nonsurgical methods are rarely used to determine a tumor's WHO/ISUP grade, thus limiting the development of nonsurgical therapies. Due to variations in microvascular density (MVD) at different stages of tumor growth, contrast-enhanced ultrasound (CEUS) features may provide a noninvasive method for evaluating the WHO/ISUP grade of ccRCC.

METHODS

In this study, we analyzed confirmed cases of ccRCC using CEUS features. We also used CD34 and CD31 antibodies to determine MVD. The heterogeneity of CD34 and CD31 expressions were used to determine different degrees of angiogenesis.

RESULTS

When compared to WHO/ISUP grade I/II (G1/G2) tumors, grade III/IV (G3/G4) tumors had reduced peak intensity (PI) (P=0.006), time to peak (TTP) (P<0.001), and relative enhancement percentage index (∆PI%) (P<0.001). However, the frequency of incomplete pseudocapsule (P=0.049) and slow wash-in (P=0.001) was significantly higher in G3/G4 tumors. A cut-off value of ∆PI% <33.15% (P<0.001) allowed identification of G3/G4 tumors with an area under the curve (AUC) of 0.80 [95% confidence interval (CI): 0.70 to 0.91) and a sensitivity of 80%. The mean CD34+ MVD (P<0.001) and CD31+ MVD (P<0.001) were significantly lower in G3/G4 tumors. A positive correlation was revealed between ∆PI% and MVD. There was a statistically significant difference in the density of undifferentiated vessels between the slow wash-in and fast wash-in cases (P<0.001).

CONCLUSIONS

The features of CEUS are effective for differentiating G3/G4 tumors from G1/G2. There was a positive correlation detected between ∆PI% and MVD, and the density of undifferentiated vessels showed a significant difference between slow wash-in and fast wash-in cases. These findings indicate that CEUS can enable the sonographic visualization of tumor angiogenesis and thus be considered an acceptable method for the nonsurgical assessment of tumor microvascular distribution and grade.

Development and Validation of Noninvasive MRI-Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging.

PURPOSE

To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER.

STUDY TYPE

Retrospective.

POPULATION

One hundred eighty-four patients (median age, 61.0 years; interquartile range: 53.0-66.8 years) including 115 men and 69 women.

FIELD STRENGTH/SEQUENCE

A 1.5 T; volumetric interpolated breath-hold examination (VIBE) sequence.

ASSESSMENT

The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast-enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Model^radiomic ). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Model^clinic ). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic-radiomics nomogram (Model^combine ).

STATISTICAL TESTS

Chi-squared (χ² ) test or Fisher's exact test, Mann-Whitney U-test or t-test, Delong test. Two tailed P < 0.05 was considered statistically significant.

RESULTS

Based on the comparison of area under the curves (AUC) using Delong test, Model^clinic and Model^combine had significantly better performance than Model^radiomic and tumor-node-metastasis (TNM) system in training set. In the testing set, both Model^clinic and Model^combine had significantly better performance than TNM system, whereas only Model^combine was significantly superior to Model^radiomic . However, the AUC values were not significantly different between Model^clinic and Model^combine (P = 0.156 for training set and P = 0.439 for testing set).

DATA CONCLUSION

A noninvasive model combining the MRI-based radiomics signature and clinical variables is potential to preoperatively predict ER for pCCA.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 4.

Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule

Objective

To establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability.

Materials and Methods

A total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit.

Results

We extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors-maximum diameter, lobulation, and pleural retraction-were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero (p < 0.05). The Delong test showed a significant difference (p < 0.0001) between the AUCs from the clinical model and the nomogram.

Conclusions

The radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.