CT Texture Analysis Research Articles

Predicting Survival for Hepatic Arterial Infusion Chemotherapy of Unresectable Colorectal Liver Metastases: Radiomics Analysis of Pretreatment Computed Tomography.

ObjectiveHepatic arterial infusion chemotherapy (HAIC) is an effective treatment for advanced unresectable colorectal cancer liver metastases (CRLM). This study was conducted to predict the efficacy of HAIC in patients with unresectable CRLM by radiomics methods based on pretreatment computed tomography (CT) examinations and clinical data.Materials and MethodsA total of 63 patients were included in this study (41 in the training group and 22 in the validation group). All these patients underwent CT examination before HAIC. During the follow-up period, CT scans and laboratory examinations were performed regularly. Eighty-five radiological features were extracted from the regions of interest (ROIs) of CT images using the PyRadiomics program. The t-test and correlation were applied to select features. These features were analyzed using LASSO-Cox regression, and a linear model was developed to predict overall survival (OS).ResultsAfter reducing features by t-test and correlation test, seven features remained. After LASSO-Cox cross-validation, four features remained at λ = 0.232. They were gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM), and the location of the primary tumor. The C-index was 0.758 in the training group and 0.743 in the test group. Nomograms predicting 1-, 2-, and 3-year survival were established.ConclusionOur study demonstrates that a radiomics approach based on pretreatment CT texture analysis has the ability to predict early the outcome of HAIC in patients with advanced unresectable colorectal cancer with a high degree of accuracy and feasibility.

Computed Tomography Texture Analysis for Predicting Clinical Outcomes in Patients With Metastatic Renal Cell Carcinoma Treated With Immune Checkpoint Inhibitors.

The treatment responses of immune checkpoint inhibitors in metastatic renal cell carcinoma (mRCC) vary, requiring reliable prognostic biomarkers. We assessed the prognostic ability of computed tomography (CT) texture analysis in patients with mRCC treated with programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors. Sixty-eight patients with mRCC treated with PD-1/PD-L1 inhibitors between 2012 and 2019 were revaluated. Using baseline and first follow-up CT, baseline and follow-up texture models were developed to predict overall survival (OS) and progression-free survival (PFS) using least absolute shrinkage and selection operator Cox-proportional hazards analysis. Patients were divided into high-risk or low-risk group, and the survival difference was assessed using Kaplan-Meier and log-rank test. Multivariable Cox models were constructed by including only the clinical variables (clinical models) and by combining the clinical variables and the texture models (combined clinical-texture models), and their predictive performance was evaluated using Harrell's C-index. The baseline texture models distinguished longer- and shorter-term survivors for both OS (median, 60.1 vs. 17.0 months; P = .048) and PFS (5.2 vs. 2.8 months; P = .003). The follow-up texture models distinguished longer- and shorter-term overall survivors (40.3 vs. 15.2 months; P = .008) but not for PFS (5.0 vs. 3.6 months; P = .25). The combined clinical-texture model outperformed the clinical model in both predicting the OS (C-index, 0.70 vs. 0.63; P = .03) and PFS (C-index, 0.63 vs. 0.55; P = .04). CT texture analysis performed at baseline and early after starting PD-1/PD-L1 inhibitors is associated with clinical outcomes of patients with mRCC.

Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

BackgroundDue to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsThe preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI.ResultsWe found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67–0.88), 0.75 (95% CI: 0.64–0.87), 0.79 (95% CI: 0.69–0.89), 0.73 (95% CI: 0.61–0.85), and 0.80 (95% CI: 0.70–0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74–0.93).ConclusionsModels derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.

Risk assessment of external apical root resorption associated with orthodontic treatment using computed tomography texture analysis.

This study aimed to quantitatively assess maxillary central incisor roots using pre-orthodontics computed tomography (CT) texture analysis as part of a radiomics quantitative analysis. This retrospective case-control study included 16 patients with external apical root resorption (EARR) and 16 age- and sex-matched patients without EARR, after orthodontic treatment who underwent pre-orthodontics CT for jaw deformities. All patients were treated with a fixed orthodontic appliance before and after surgical orthodontic treatment. EARR was defined as root resorption ≥ 2mm of the left and right maxillary central incisors on CT images more than 2years after the start of orthodontic treatment. Texture features of the maxillary central incisor with and without EARR after orthodontic treatment were analyzed using the open-access software, MaZda Ver. 3.3. Ten texture features were selected using the Fisher method in MaZda from 279 original parameters, which were calculated for each of the maxillary central incisors with and without EARR. The results were tested using the Student's t test, Welch's t test, or Mann-Whitney U test. Four gray-level run length matrix features and six gray-level co-occurrence matrix features displayed significant differences between both the groups (p < 0.01). CT texture analysis was able to quantitatively assess maxillary central incisor roots and distinguish between maxillary central incisor roots with and without EARR. CT texture analysis may be a useful method for predicting EARR after orthodontic treatment.

Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence

PurposeTo investigate the role of CT-texture analysis of liver ablation area to predict local recurrence after HCC ablation. MethodsPatients treated with liver ablation were retrospectively enrolled. CT-texture analysis was performed on the core and borders of ablation area 1–2 months after procedure. Tumors were grouped according to the onset of local recurrence at follow-up (persistence, recurrence-free, short- or long-term recurrence). Differences in texture parameters and which parameters were predictive of recurrence risk were assessed using a Cox regression model. Results151 HCCs were treated in 98 patients (72 ± 9 years, 83 men). 68 HCCs reported no disease recurrence, 32 persistent disease, 19 short-term and 32 long-term recurrence. Median follow-up was 280 [IQR: 156–569] days. Venous phase (Ven)Skewness (HR 6.07, 1.29–28.6, p =.02) and VenKurtosis (HR 2.27, 1.23–4.21, p =.01) of the ablation core were predictive of short-term recurrence. VenHUmean (HR 0.30, 0.11–0.81, p =.02) and VenGLRLM_HGRE (HR 1.06, 1.01–1.11, p =.02) of the core were independent predictors of tumor recurrence (C-index 0.64, CI 0.52–0.76, p =.03). Arterial phase (Art)Entropy of ablation border predicted the recurrence risk (HR 3.15, 1.05–9.42, p =.04) and values higher than 3.71 reported an increased recurrence incidence (p =.05). ArtHUstd (HR 1.14, 1.04–1.24, p =.01), LateHUmean (HR 8.69, 1.11–68.23, p =.04), LateGLRLM_HGRE (HR 0.9, 0.82–0.99, p =.03), LateGLZLM_HGZE (HR 1.01, 1.00–1.02, p <.01) and LateGLZLM_SZHGE (HR 0.99, 0.99–1.00, p =.02) of ablation border were independent predictors of local recurrence risk (C-index 0.73, CI 0.61–0.86, p <.01). ConclusionsCT texture analysis of ablation area performed at 1–2 months follow-up could estimate the risk of local recurrence of hepatocellular carcinoma treated by radiofrequency ablation.

High resolution computed tomography texture analysis identifies patients at risk of pulmonary fibrosis after COVID-19 pneumonia.

High resolution computed tomography texture analysis identifies patients at risk of pulmonary fibrosis after COVID-19 pneumonia.

Comparison of Foaming Properties Between the Shirasu Porous Glass Membrane Device and Tessari’s Three-way Stopcock Technique for Polidocanol and Ethanolamine Oleate Foam Production: A Benchtop Study

PurposeTo compare the characteristics of polidocanol (POL) and ethanolamine oleate (EO) sclerosing foams produced by a Shirasu porous glass membrane (SPGM) device with those made using a 3-way stopcock (3WSC). Materials and MethodsFoam half-life times were measured in an ex-vivo benchtop study. Computed tomography (CT) images of each foam were obtained over the time course, and a CT texture analysis was conducted. The bubble size in each foam was measured by an optical microscope. ResultsMedian foam half-life times were longer in the SPGM group than in the 3WSC group (POL: 198 vs 166 s, P = .02; EO: 640 vs 391 s, P < .01). In the CT texture analysis, median standard deviation (SD) and entropy (randomness) were lower, and median energy (uniformity) and gray-level cooccurrence matrix (GLCM) homogeneity were higher in the SPGM group than in the 3WSC group (POL SD: at 30 s and 50–300 s; POL entropy: at 0–60 s; EO SD: at 0–600 s; EO entropy: at 0–460 s; POL energy: at 0–40 s; POL GLCM homogeneity: at 0–250 s; EO energy: at 0–360 s; EO GLCM homogeneity: at 0–480 s; all P < .05). Median bubble diameters in the SPGM group and in the 3WSC group were 69 and 83 μm (P < .01), respectively, in the POL foam; and 36 and 36 μm (P = .45), respectively, in the EO foam. ConclusionsPOL and EO foams had greater uniformity and longer foam half-life time when prepared with an SPGM device than with a 3WSC.

Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma.

The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.

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

BACKGROUNDArtificial 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.AIMTo 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).METHODSPatients 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.RESULTSThere 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). CONCLUSIONEight 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.

Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT.

PurposeTo identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework.MethodsWe performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30–90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.2 years) to determine long-term reproducibility. TFs with a long-term reproducibility error Δrel<5% were further analyzed for an association with age and vertebral level in a cohort of 376 patients (129 females, 247 males, age 62.5 ± 9.2 years). Automated analysis comprised labeling and segmentation of vertebrae into subregions using a convolutional neural network, calculation of volumetric bone mineral density (vBMD) with asynchronous calibration and TF extraction. Varianceglobal measures the spread of the gray-level distribution in an image while Entropy reflects the uniformity of gray-levels. Short-run emphasis (SRE), Long-run emphasis (LRE), Run-length non-uniformity (RLN) and Run percentage (RP) contain information on consecutive voxels of a particular grey-level, or grey-level range, in a particular direction. Long runs (LRE) represent coarse texture while short runs (SRE) represent fine texture. RLN reflects similarities in the length of runs while RP reflects distribution and homogeneity of runs with a specific direction.ResultsSix of the 24 extracted TFs had Δrel<5% (Varianceglobal, Entropy, SRE, LRE, RLN, RP), and were analyzed further in 4716 thoracolumbar vertebrae. Five TFs (Varianceglobal,SRE,LRE, RLN,RP) showed a significant difference between genders (p<0.001), potentially being caused by a finer and more directional vertebral trabecular microstructure in females compared to males. Varianceglobal and Entropy showed a significant increase from the thoracic to the lumbar spine (p<0.001), indicating a higher degree and earlier initiation of trabecular microstructure deterioration at lower spinal levels. The four higher-order TFs showed significant variations between spine regions without a clear directional gradient (p ≤ 0.001-0.012). No TF showed a clear age dependence. vBMD differed significantly between genders, age groups and spine regions (p ≤ 0.001–0.002).ConclusionLong-term reproducible CT-based TFs of the thoracolumbar spine were established and characterized in a predominantly older adult study population. The gender-, age- and vertebral-level-specific values may serve as foundation for osteoporosis diagnostics and facilitate future studies investigating vertebral microstructure.

Short-term outcomes of robotic liver resection: An initial single-institution experience.

BACKGROUNDLiver surgery has traditionally been characterized by the complexity of its procedures and potentially high rates of morbidity and mortality in inexperienced hands. The robotic approach has gradually been introduced in liver surgery and has increased notably in recent years. However, few centers currently perform robotic liver surgery and experiences in robot-assisted surgical procedures continue to be limited compared to the laparoscopic approach.AIMTo analyze the outcomes and feasibility of an initial robotic liver program implemented in an experienced laparoscopic hepatobiliary center.METHODSA total of forty consecutive patients underwent robotic liver resection (da Vinci Xi, intuitive.com, United States) between June 2019 and January 2021. Patients were prospectively followed and retrospectively reviewed. Clinicopathological characteristics and perioperative and short-term outcomes were analyzed. Data are expressed as mean and standard deviation. The study was approved by the Institutional Review Board.RESULTSThe mean age of patients was 59.55 years, of which 18 (45%) were female. The mean body mass index was 29.41 kg/m². Nine patients (22.5%) were cirrhotic. Patients were divided by type of resection as follows: Ten segmentectomies, three wedge resections, ten left lateral sectionectomies, six bisegmentectomies (two V-VI bisegmentectomies and four IVb-V bisegmentectomies), two right anterior sectionectomies, five left hepatectomies and two right hepatectomies. Malignant lesions occurred in twenty-nine (72.5%) of the patients. The mean operative time was 258.11 min and two patients were transfused intraoperatively (5%). Inflow occlusion was used in thirty cases (75%) and the mean total clamping time was 32.62 min. There was a single conversion due to uncontrollable hemorrhage. Major postoperative complications (Clavien–Dindo > IIIb) occurred in three patients (7.5%) and mortality in one (2.5%). No patient required readmission to the hospital. The mean hospital stay was 5.6 d. CONCLUSIONAlthough robotic hepatectomy is a safe and feasible procedure with favorable short-term outcomes, it involves a demanding learning curve that requires a high level of training, skill and dexterity.

Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer.

Aim of the study was to perform CT texture analysis in patients with gastric cancer (GC) to investigate potential role of radiomics for predicting the occurrence of peritoneal metastases (PM). In this single-centre retrospective study, patients with gastric adenocarcinoma and surgically confirmed presence or absence of PM were, respectively, enrolled in group PM and group non-PM. Patients with T1-staging, previous treatment or presence of imaging artifacts were excluded from the study. Pre-operative CT examinations were evaluated. Acquisition protocol consisted of gastric distension with water, pre-contrast and arterial phases on upper abdomen and portal phase on thorax and whole abdomen. Texture analysis was performed on portal phase images: the region of interest was manually drawn along the margins of the primitive lesion on each slice and the volume of interest of the whole tumour was obtained. A total of 38 texture parameters were extracted and analysed. ROC curves were performed on significant texture features (p < 0.05). Multiple logistic regression was conducted on features with the best AUC to identify differentiating variables for both groups. A total of 90 patients were evaluated (group PM, n = 45; group non-PM, n = 45). T2/T3 tumours were prevalent in group non-PM, T4 was significantly associated with group PM. Significant differences between the two groups were observed for 22/38 texture parameters. Volume and GLRLM_LRHGE showed the greatest AUC in ROC curve analysis (0.737 and 0.734, respectively) and were found to be independent differentiating variables of group PM in the multiple regression analysis (OR 8.44, [95% CI, 1.52-46.8] and OR 18.99 [95% CI, 84-195.31], respectively). Our preliminary results suggest the potential value of CT texture analysis for predicting the risk of PM from GC, which may be helpful to stratify patients and address them to the most appropriate treatment.

Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review.

Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions.

Imaging Tool for Predicting Renal Clear Cell Carcinoma Fuhrman Grade: Comparing R.E.N.A.L. Nephrometry Score and CT Texture Analysis

Background Clear cell renal cell carcinoma (ccRCC) is the most common renal malignant tumor. Preoperative imaging boasts advantages in diagnosing and choosing treatment methods for ccRCC. Purpose This study is aimed at building models based on R.E.N.A.L. nephrometry score (RNS) and CT texture analysis (CTTA) to estimate the Fuhrman grade of ccRCC and comparing the advantages and disadvantages of the two models. Materials and Methods 143 patients with pathologically confirmed ccRCC were enrolled. All patients were stratified into Fuhrman low-grade and high-grade groups with complete CT data and R.E.N.A.L. nephrometry scores. CTTA features were extracted from the ROI delineated at the largest tumor level, and RNS and CTTA features were included in the logistic regression model, respectively. Results RNS model constructed based on multivariate logistic regression analysis showed that 3 pts for R-scores, 2 pts for E-scores, and 3 pts for L-scores were significant indicators to predict high-grade ccRCC, the AUC of RNS model was 0.911, and the sensitivity and specificity were 71.11% and 83.67%, respectively. The CTTA-model confirmed energy, kurtosis, and entropy as independent predictive factors, and the AUC of CTTA model was 0.941, with an optimal sensitivity and specificity of 84.44% and 93.88%. Conclusions R.E.N.A.L. nephrometry score has a certain provocative effect on the Fuhrman pathological grading of ccRCC. As a potential emerging technology, CTTA is expected to replace R.E.N.A.L. nephrometry score in evaluating patients' Fuhrman classification, and this approach might become an available method for assisting clinicians in choosing appropriate operation.

Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis.

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis

ObjectivesThis study aimed to assess the accuracy of CT texture analysis (CTTA) for differentiating low-grade and high-grade renal cell carcinoma (RCC).DesignSystematic review and meta-analysis.Data sourcesPubMed, Cochrane Library, Embase, Web of...

CT Texture analysis and CT scores for characterization of fluid collections

BackgroundTexture analysis derived from Computed tomography (CT) might be able to better characterize fluid collections undergoing CT-guided percutaneous drainage treatment. The present study tested, whether texture analysis can reflect microbiology results in fluid collections suspicious for septic focus.MethodsOverall, 320 patients with 402 fluid collections were included into this retrospective study. All fluid collections underwent CT-guided drainage treatment and were microbiologically evaluated. Clinically, serologically parameters and conventional imaging findings as well as textures features were included into the analysis. A new CT score was calculated based upon imaging features alone. Established CT scores were used as a reference standard.ResultsThe present score achieved a sensitivity of 0.78, a specificity of 0.69, area under curve (AUC 0.82). The present score and the score by Gnannt et al. (AUC 0.81) were both statistically better than the score by Radosa et al. (AUC 0.75). Several texture features were statistically significant between infected fluid collections and sterile fluid collections, but these features were not significantly better compared with conventional imaging findings.ConclusionsTexture analysis is not superior to conventional imaging findings for characterizing fluid collections. A novel score was calculated based upon imaging parameters alone with similar diagnostic accuracy compared to established scores using imaging and clinical features.

Computed Tomography Texture Analysis of Carotid Plaque as Predictor of Unfavorable Outcome after Carotid Artery Stenting: A Preliminary Study

Novel biomarkers are advocated to manage carotid plaques. Therefore, we aimed to test the association between textural features of carotid plaque at computed tomography angiography (CTA) and unfavorable outcome after carotid artery stenting (CAS). Between January 2010 and January 2021, were selected 172 patients (median age, 77 years; 112/172, 65% men) who underwent CAS with CTA of the supra-aortic vessels performed within prior 6 months. Standard descriptors of the density histogram were derived by open-source software automated analysis obtained by CTA plaque segmentation. Multiple logistic regression analysis, receiver operating characteristic (ROC) curve analysis and the area under the ROC (AUC) were used to identify potential prognostic variables and to assess the model performance for predicting unfavorable outcome (periprocedural death or myocardial infarction and any ipsilateral acute neurological event). Unfavorable outcome occurred in 17/172 (10%) patients (median age, 79 years; 12/17, 70% men). Kurtosis was an independent predictor of unfavorable outcome (odds ratio, 0.79; confidence interval, 0.65–0.97; p = 0.029). The predictive model for unfavorable outcome including CTA textural features outperformed the model without textural features (AUC 0.789 vs. 0.695, p = 0.004). In patients with stenotic carotid plaque, kurtosis derived by CTA density histogram analysis is an independent predictor of unfavorable outcome after CAS.

CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation.

Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, “S(1,1)SumEntrp” was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed “S(4,4)InvDfMom” (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. “Sumentrp” might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.

A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.

We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma. Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated. A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 [Formula: see text] 0.1, 0.80 [Formula: see text] 0.1, and 0.77 [Formula: see text] 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R2 = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β = - 0.75), and molecular subtype (β = - 0.30). The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis. • The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.