• Computed tomography
• Radiomics
• Thymic epithelial tumors

• 2022AAC03581 Natural Science Foundation of Ningxia Province

• Computed tomography
• Machine learning
• Pathological categorization
• Radiomics
• Thymoma

• CT colonography
• biomarker
• colorectal cancer
• gray level co‐occurrence matrix
• texture analysis

• Humans
• Lung Neoplasms/diagnostic imaging
• Lung Neoplasms/mortality
• Lung Neoplasms/pathology
• Lung Neoplasms/radiotherapy
• Radiosurgery/methods
• Male
• Female
• Aged
• Middle Aged
• Tomography, X-Ray Computed/methods
• Retrospective Studies
• Aged, 80 and over
• Adult
• Neoplasm Recurrence, Local/diagnostic imaging
• Neoplasm Recurrence, Local/pathology
• Kaplan-Meier Estimate
• Disease-Free Survival
• Radiomics

• hypopharynx
• neck dissection
• pharyngolaryngectomy
• recurrent
• residual
• salvage

• Humans
• Salvage Therapy/methods
• Hypopharyngeal Neoplasms/surgery
• Hypopharyngeal Neoplasms/mortality
• Hypopharyngeal Neoplasms/pathology
• Neoplasm Recurrence, Local/surgery
• Carcinoma, Squamous Cell/surgery
• Carcinoma, Squamous Cell/mortality
• Carcinoma, Squamous Cell/pathology
• Neoplasm, Residual
• Squamous Cell Carcinoma of Head and Neck/surgery
• Squamous Cell Carcinoma of Head and Neck/mortality
• Disease-Free Survival
• Postoperative Complications
• Male

• 14v station
• computed tomography
• gastric cancer
• lymph node metastasis
• radiomics

• National Key Research and Development Program of China
• National Natural Science Foundation of China

• PET-MRI
• cervical lymphadenopathy
• machine learning
• squamous cell carcinoma
• texture analysis

• Adenolymphoma (AL)
• Enhancement CT
• Pleomorphic adenoma (PA)
• Texture analysis

• Humans
• Diffusion Magnetic Resonance Imaging/methods
• Diagnosis, Differential
• Adenolymphoma
• Adenoma, Pleomorphic/diagnostic imaging
• ROC Curve
• Tomography, X-Ray Computed
• Retrospective Studies

• adenocarcinoma
• adenoma
• adrenal gland tumor
• canine
• computed tomography
• pheochromocytoma
• texture analysis

• X-ray computed
• follicular lymphoma
• positron emission tomography
• tomography

• computed tomography
• gastric cancer
• nomogram
• perineural invasion
• radiomics

• National Natural Science Foundation of China
• National Key Research and Development Program of China

• artificial intelligence
• diagnostic imaging
• head and neck tumors
• radiomics

• hepatocellular carcinoma
• immunotherapy
• magnetic resonance imaging
• radiomics
• stereotactic body radiotherapy
• trans-arterial chemoembolization

• computed tomography
• non-small cell lung cancer
• texture analysis

• Humans
• Female
• Middle Aged
• Aged
• Feasibility Studies
• Carcinoma, Non-Small-Cell Lung/diagnostic imaging
• Pilot Projects
• Retrospective Studies
• Lung Neoplasms/diagnostic imaging
• Tomography, X-Ray Computed

Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC.

Materials and Methods: Seven hundred and forty-five consecutive AGC patients (median age, 59 years; interquartile range, 52–66 years; 515 male and 230 female) were enrolled and separated into training set (n = 521) and testing set (n = 224) in this retrospective study. Radiomics features were extracted from three phases images of contrast-enhanced CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. Univariable and multivariable logistical regression analysis were used to establish predictive model with independent risk factors of HER2 overexpression. The predictive performance of radiomics model was assessed in the training and testing sets.

Results: The positive rate of HER2 was 15.9% and 13.8% in the training set and testing set, respectively. The positive rate of HER2 in intestinal-type GC was significantly higher than that in diffuse-type GC. The radiomics signature comprised eight robust features demonstrated good discrimination ability for HER2 overexpression in the training set (AUC = 0.84) and the testing set (AUC = 0.78). A radiomics-based model that incorporated radiomics signature and pathological type showed good discrimination and calibration in the training (AUC = 0.85) and testing (AUC = 0.84) sets.

Conclusion: The proposed radiomics model showed favorable accuracy for prediction of HER2 overexpression in AGC.

• National Natural Science Foundation of China

• CT radiomics
• locally advanced colorectal cancer
• neoadjuvant chemoradiotherapy
• prediction
• radiotherapy planning
• treatment response

To develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC).

This retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D’Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan–Meier curve and log-rank test for the high- and low-risk groups.

The DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit (P<0.05). The risk score, age, thyroid transcription factor-1, Ki-67, and disease stage were the independent risk factors for NSCLC.The Greenwood-Nam-D’Agostino test showed good calibration performance (P=0.39). Both high- and low-risk patients did not benefit from adjuvant chemotherapy, and chemotherapy in low-risk groups may lead to a poorer prognosis.

The DeepSurv nomogram, which is based on the risk score and independent risk factors, had good predictive performance for survival outcome. Further, it could be used to guide personalized adjuvant chemotherapy in patients with NSCLC.

• National Natural Science Foundation of China

• Biomarker
• Immunotherapy
• Metastatic melanoma
• Survival
• Texture analysis

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

Due 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).

The 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.

We 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).

Models 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.

• hepatocellular carcinoma
• microvascular invasion
• nomogram
• radiomics
• triphasic computed tomography

• Deep learning
• Diffusion magnetic resonance imaging
• Hypopharyngeal cancer
• Laryngeal cancer
• Prognosis

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.

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).

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.

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).

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.

• Colorectal cancer
• Liver metastases
• Radiomics
• Computed tomography texture analysis
• Response assessment

• Histopathology
• Neoplasm metastases
• Pancreas cancer
• Tomography
• X-ray computed

• CBCT
• head and neck cancers
• radiomics
• radiotherapy
• texture

• artificial intelligence (AI)
• artificial neural network (ANN)
• future perspectives
• machine learning (ML)
• pancreatic adenocarcinoma (PC)

• Fusion
• PET/CT
• machine learning
• non-small cell lung cancer
• radiomics

• Artificial intelligence
• Deep learning
• Head and neck cancer
• Neural network
• PET

• Artificial Intelligence
• Head and Neck Neoplasms/diagnostic imaging
• Humans
• Positron-Emission Tomography
• Radiography
• Radiology

This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). The prostate contours were also manually drawn by a physician. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). Radiomic features from 6 classes were extracted from each contour. Lin’s concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean |%Δ|RF) between auto and manual contours were calculated. The mean (± SD) DSC, MDA, ΔCM and ΔVol between the auto and manual prostate contours were 0.90 ± 0.04, 1.81 ± 0.47 mm, 2.17 ± 1.26 mm and 5.1 ± 4.1% respectively. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. 30 radiomic features had a CCC > 0.90 and 21 had a mean |%∆|RF < 5%. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. The use of auto contours for radiomic feature analysis is promising but must be done with caution.

In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient’s therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF = 2 and 1.20 for SSF = 3 were indicators of higher OS (fine texture: 192 HR = 0.56, 95% CI = 0.32–0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29–0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29–0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25–0.96, p = 0.03). In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results.

• biomarker
• image analysis
• immunotherapy
• melanoma
• precision medicine
• radiomics
• x-ray computed tomography

• Biomarkers
• Computed tomography
• Immunotherapy
• Melanoma
• Texture analysis

• Computed tomography
• abdomen
• adults
• gastrointestinal
• liver
• quantitative computed tomography

Neuroendocrine tumors (NET) are rare cancers with variable behavior. A better understanding of prognosis would aid individualized management. The aim of this hypothesis-generating pilot study was to investigate the prognostic potential of tumor heterogeneity and tracer avidity in NET using texture analysis (TA) of ⁶⁸Ga-DOTATATE positron emission tomography (PET) and non-enhanced computed tomography (CT) performed at baseline in patients treated with ¹⁷⁷Lu-DOTATATE. It aims to justify a larger-scale study to evaluate its clinical value.

The pretherapy ⁶⁸Ga-DOTATATE PET-CT scans of 44 patients with metastatic NET (carcinoid, pancreatic, thyroid, head and neck, catecholamine-secreting, and unknown primary NET) treated with ¹⁷⁷Lu-DOTATATE were analyzed retrospectively using commercially available texture analysis research software. Image filtration extracted and enhanced objects of different sizes (fine, medium, coarse), then quantified heterogeneity by statistical and histogram-based parameters (mean intensity, standard deviation, entropy, mean of positive pixels, skewness, and kurtosis). Regions of interest were manually drawn around up to five of the most ⁶⁸Ga-DOTATATE avid lesions for each patient. ⁶⁸Gallium uptake on PET was quantified as SUV_max and SUV_mean. Associations between imaging and clinical markers with progression-free (PFS) and overall survival (OS) were assessed using univariate Kaplan-Meier analysis. Independence of the significant univariate markers of survival was tested using multivariate Cox regression analysis.

Measures of heterogeneity (higher kurtosis, higher entropy, and lower skewness) on coarse-texture scale CT and unfiltered PET images predicted shorter PFS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness: p=0.03) and shorter OS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness p=0.02). Conventional PET parameters such as SUV_max and SUV_mean showed trends towards predicting outcome but were not statistically significant. Multivariate analysis identified that CT-TA (coarse kurtosis: HR=2.57, 95% CI=1.22–5.38, p=0.013) independently predicted PFS, and PET-TA (unfiltered skewness: HR=9.05, 95% CI=1.19–68.91, p=0.033) independently predicted OS.

These preliminary data generate a hypothesis that radiomic analysis of neuroendocrine cancer on ⁶⁸Ga-DOTATATE PET-CT may be of prognostic value and a valuable addition to the assessment of patients.

• 177Lu-DOTATATE molecular radiotherapy
• 68Ga-DOTATATE PET/CT
• neuroendocrine tumor (NET)
• prognostic biomarker
• radiomics
• texture analysis

• 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

• Artificial intelligence
• Autosegmentation
• Classification
• Convolutional neural network
• Deep learning
• Head and neck cancer
• Head and neck imaging
• Machine learning

Background: Although inflammatory breast cancer (IBC) has poor overall survival (OS), there is little information about using imaging features for predicting the prognosis. Computed tomography (CT)-based texture analysis, a non-invasive technique to quantify tumor heterogeneity, could be a potentially useful imaging biomarker. The aim of the article was to investigate the usefulness of chest CT-based texture analysis to predict OS in IBC patients.

Methods: Of the 3,130 patients with primary breast cancers between 2006 and 2016, 104 patients (3.3%) with IBC were identified. Among them, 98 patients who underwent pre-treatment contrast-enhanced chest CT scans, got treatment in our institution, and had a follow-up period of more than 2 years were finally included for CT-based texture analysis. Texture analysis was performed on CT images of 98 patients, using commercially available software by two breast radiologists. Histogram-based textural features, such as quantification of variation in CT attenuation (mean, standard deviation, mean of positive pixels [MPP], entropy, skewness, and kurtosis), were recorded. To dichotomize textural features for survival analysis, receiver operating characteristic curve analysis was used to determine cutoff points. Clinicopathologic variables, such as age, node stage, metastasis stage at the time of diagnosis, hormonal receptor positivity, human epidermal growth factor receptor 2 positivity, and molecular subtype, were assessed. A Cox proportional hazards model was used to determine the association of textural features and clinicopathologic variables with OS.

Results: During a mean follow-up period of 47.9 months, 41 of 98 patients (41.8%) died, with a median OS of 20.0 months. The textural features of lower mean attenuation, standard deviation, MPP, and entropy on CT images were significantly associated with worse OS, as was the M1 stage among clinicopathologic variables (all P-values < 0.05). In multivariate analysis, lower mean attenuation (hazard ratio [HR], 3.26; P = 0.003), lower MPP (HR, 3.03; P = 0.002), and lower entropy (HR, 2.70; P = 0.009) on chest CT images were significant factors independent from the M1 stage for predicting worse OS.

Conclusions: Lower mean attenuation, MPP, and entropy on chest CT images predicted worse OS in patients with IBC, suggesting that CT-based texture analysis provides additional predictors for OS.

• breast neopalsms
• computed tomgraphy
• histogram
• overall survival
• texture