CT-based radiomics to distinguish progressive from stable neuroendocrine liver metastases treated with somatostatin analogues: an explorative study.

BACKGROUND: Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment. PURPOSE: To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment. MATERIAL AND METHODS: A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model. RESULTS: In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1-50 months). Radiomics models could not accurately classify response (area under the curve 0.44-0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model. CONCLUSION: Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.

Development and validation of a nomogram based on CT texture analysis for discriminating minimally invasive adenocarcinoma from glandular precursor lesions in sub­centimeter pulmonary ground glass nodules.

In a recent reclassification, adenocarcinoma in situ has been redefined as a glandular precursor lesion (GPL), alongside adenomatous hyperplasia. This updated classification necessitates corresponding adaptations in clinical diagnostic and therapeutic protocols. Consequently, the present study aimed to construct and validate a nomogram utilizing computed tomography (CT) texture features to effectively discriminate between minimally invasive adenocarcinoma (MIA) and GPL within sub-centimeter pulmonary ground glass nodules (GGNs). To achieve this objective, the present study employed rigorous statistical methodologies, including the Mann-Whitney U test and binary logistic regression analysis, to identify distinguishing features and establish predictive models. Subsequently, the diagnostic performance of these models underwent evaluation through receiver operating characteristic (ROC) curves. The area under the curve (AUC) in ROC curves was compared using DeLong's test. Additionally, the nomogram was constructed using R software and its diagnostic performance was validated through calibration curves. Within both the training and validation datasets, the AUCs were observed to be 0.992 [95% confidence interval (CI): 0.980-1.000] and 0.975 (95% CI: 0.935-1.000), respectively. DeLong's test revealed significant disparities in the AUCs between the nomogram and single-parameter models (P<0.001). Furthermore, calibration curves demonstrated concordance between the training and validation datasets. In conclusion, the application of a CT texture-based nomogram model has demonstrated aptitude in differentiating between MIA and GPL within sub-centimeter GGNs. This model streamlines the identification of optimal surgical interventions and enhances the sphere of clinical decision-making and management.

Exploring the Paradox of Bone Mineral Density in Type 2 Diabetes: A Comparative Study Using Opportunistic Chest CT Texture Analysis and DXA.

BACKGROUND: This study aimed to validate the application of CT texture analysis in estimating Bone Mineral Density (BMD) in patients with Type 2 Diabetes (T2D) and comparing it with the results of dual-energy X-ray absorptiometry (DXA) in a normative cohort. METHODS: We analyzed a total of 510 cases (145 T2D patients and 365 normal patients) from a single institution. DXA-derived BMD and CT texture analysis-estimated BMD were compared for each participant. Additionally, we investigated the correlation among 45 different texture features within each group. RESULTS: The correlation between CT texture analysis-estimated BMD and DXA-derived BMD in T2D patients was consistently high (0.94 or above), whether measured at L1 BMD, L1 BMC, total hip BMD, or total hip BMC. In contrast, the normative cohort showed a modest correlation, ranging from 0.66 to 0.75. Among the 45 texture features, significant differences were found in the Contrast V 64 and Contrast V 128 features in the normal group. CONCLUSION: In essence, our study emphasizes that the clinical assessment of bone health, particularly in T2D patients, should not merely rely on traditional measures, such as DXA BMD. Rather, it may be beneficial to incorporate other diagnostic tools, such as CT texture analysis, to better comprehend the complex interplay between various factors impacting bone health.

Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?

BACKGROUND: Artificial intelligence in radiology has the potential to assist with the diagnosis, prognostication and therapeutic response prediction of various cancers. A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases, however, the results have varied. Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated. This study was designed to determine if the computed tomography (CT) texture analysis results of non-necrotic colorectal liver metastases differ from previous reports. A larger range of texture features were also evaluated to identify potential new biomarkers. AIM: To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases (CRLMs). METHODS: Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice. The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm (diagnosed on archived 1.25 mm portal venous phase CT scans) which were treated with standard first-line cytotoxic chemotherapy (FOLFOX, FOLFIRI, FOLFOXIRI, CAPE-OX, CAPE-IRI or capecitabine). The final study cohort consisted of 29 patients. The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria. By means of CT texture analysis, various first and second order texture features were extracted from a single non-necrotic target CRLM in each responding and non-responding patient. Associations between features and response to chemotherapy were assessed by logistic regression models. The prognostic accuracy of selected features was evaluated by using the area under the curve. RESULTS: There were 15 responders (partial response) and 14 non-responders (7 stable and 7 with progressive disease). The responders presented with a higher number of CRLMs (P = 0.05). In univariable analysis, eight texture features of the responding CRLMs were associated with treatment response, but due to strong correlations among some of the features, only two features, namely minimum histogram gradient intensity and long run low grey level emphasis, were included in the multiple analysis. The area under the receiver operating characteristic curve of the multiple model was 0.80 (95%CI: 0.64 to 0.96), with a sensitivity of 0.73 (95%CI: 0.48 to 0.89) and a specificity of 0.79 (95%CI: 0.52 to 0.92). CONCLUSION: Eight first and second order texture features, but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.

Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection.

PURPOSE: To evaluate the feasibility of using computed tomography texture analysis (CTTA) parameters for predicting malignant risk grade and mitosis index of gastrointestinal stromal tumors (GISTs), compared with visual inspection. METHOD AND MATERIALS: CTTA was performed on portal phase CT images of 145 surgically confirmed GISTs (mean size: 42.9 ± 37.5 mm), using TexRAD software. Mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis of CTTA parameters, on spatial scaling factor (SSF), 2-6 were compared by risk grade, mitosis rate, and the presence or absence of necrosis on visual inspection. CTTA parameters were correlated with risk grade. Diagnostic performance was evaluated with receiver operating characteristic curve analysis. Enhancement pattern, necrosis, heterogeneity, calcification, growth pattern, and mucosal ulceration were subjectively evaluated by two observers. RESULTS: Three to four parameters at different scales were significantly different according to the risk grade, mitosis rate, and the presence or absence of necrosis (p < 0.041). MPP at fine or medium scale (r = - 0.547 to - 393) and kurtosis at coarse scale (r = 0.424-0.454) correlated significantly with risk grade (p < 0.001). HG-GIST was best differentiated from LG-GIST by MPP at SSF 2 (AUC, 0.782), and kurtosis at SSF 4 (AUC, 0.779) (all p < 0.001). CT features predictive of HG-GIST were density lower than or equal to that of the erector spinae muscles on enhanced images (OR 2.1; p = 0.037; AUC, 0.59), necrosis (OR, 6.1; p < 0.001; AUC, 0.70), heterogeneity (OR, 4.3; p < 0.001; AUC, 0.67), and mucosal ulceration (OR, 3.3; p = 0.002; AUC, 0.62). CONCLUSION: Using TexRAD, MPP and kurtosis are feasible in predicting risk grade and mitosis index of GISTs. CTTA demonstrated meaningful accuracy in preoperative risk stratification of GISTs.

CT signal heterogeneity of abdominal aortic aneurysm as a possible predictive biomarker for expansion.

OBJECTIVE: There is a need for prognostic biomarkers for risk assessment of small abdominal aortic aneurysm (AAA). Since CT textural analysis of tissue is a recognized feature of adverse biology and patient outcome in other diseases, we investigated it as a possible biomarker in small AAA. METHODS: Fifty consecutive patients (46-men, 4-woman, median-age 75 y, range 56-85) with small AAA (3-5.5 cm) under surveillance undergoing serial ultrasound were prospectively recruited and assessed at baseline with CT texture analysis (CTTA) and 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG-PET). We followed forty patients (36-men, 4-woman, median-age=74 y, range 60-85, participation rate=80% for 1 year. For each axial image, CTTA using the filtration-histogram technique was carried out using a software algorithm that selectively extracts texture features of different coarseness (fine, medium and coarse) and intensity variation. Standard-deviation (SD) and kurtosis (K) at each feature-scale were measured. The maximum standardized uptake value (SUVmax) of 18F-FDG in each axial image of the AAA was also measured with corrections for blood pool 18F-FDG activity to assess AAA metabolic activity. Specificity, sensitivity, and c-statistics were calculated with 95% confidence intervals for prediction of significant AAA expansion (≥2 mm) by CTTA measures before and after adjusting for clinical variables. RESULTS: The median aneurysm expansion at 12 months was 2.0 mm, (IQR 0.0-4.0). Coarse texture SD correlated inversely with AAA SUVmax (rs=-0.456, P=0.003). Medium coarse texture K correlated significantly with future AAA expansion adjusted for baseline size (rs=0.343, P=0.030). AAA SUVmax correlated inversely with AAA expansion corrected for baseline size (rs=-0.383, P=0.015). Medium texture K was a strong predictor of significant AAA expansion (area under the Receiver-operating-characteristic (ROC) curve was 0.813) after adjusting for clinical variables. CONCLUSION: We have shown evidence that CT signal heterogeneity measurements in small aortic aneurysm may be considered as a risk stratification tool in future prospective studies to identify aneurysms at risk of significant expansion. CT textural data appears to reflect AAA metabolism measured by PET.