Gray-level Nonuniformity Research Articles

Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung

The rapidly evolving field of radiomics has shown that radiomic features are able to capture characteristics of both tumor and normal tissue that can be used to make accurate and clinically relevant predictions. In the present study we sought to determine if radiomic features can characterize the adverse effects caused by normal tissue injury as well as identify if human embryonic stem cell (hESC) derived extracellular vesicle (EV) treatment can resolve certain adverse complications. A cohort of 72 mice (n = 12 per treatment group) were exposed to X-ray radiation to the whole lung (3 × 8 Gy) or to the apex of the right lung (3 × 12 Gy), immediately followed by retro-orbital injection of EVs. Cone-Beam Computed Tomography images were acquired before and 2 weeks after treatment. In total, 851 radiomic features were extracted from the whole lungs and < 20 features were selected to train and validate a series of random forest classification models trained to predict radiation status, EV status and treatment group. It was found that all three classification models achieved significantly high prediction accuracies on a validation subset of the dataset (AUCs of 0.91, 0.86 and 0.80 respectively). In the locally irradiated lung, a significant difference between irradiated and unirradiated groups as well as an EV sparing effect were observed in several radiomic features that were not seen in the unirradiated lung (including wavelet-LLH Kurtosis, wavelet HLL Large Area High Gray Level Emphasis, and Gray Level Non-Uniformity). Additionally, a radiation difference was not observed in a secondary comparison cohort, but there was no impact of imaging machine parameters on the radiomic signature of unirradiated mice. Our data demonstrate that radiomics has the potential to identify radiation-induced lung injury and could be applied to predict therapeutic efficacy at early timepoints.

DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery.

Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction. A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach. DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity. The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery-treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.

Urethral tissue characterization using multiparametric ultrasound imaging

A decrease in urethral closure pressure is one of the primary causes of stress urinary incontinence in women. Atrophy of the urethral muscles is a primary factor in the 15 % age-related decline in urethral closure pressure per decade. Incontinence not only affects the well-being of women but is also a leading cause of nursing home admission. The objective of this research was to develop a noninvasive test to assess urethral tissue microenvironmental changes using multiparametric ultrasound (mpUS) imaging technique. Transperineal B-scan ultrasound (US) data were captured using clinical scanners equipped with curvilinear or linear transducers. Imaging was performed on volunteers from our institution medical center (n = 15, 22 to 76 y.o.) during Valsalva maneuvers. After expert delineation of the region of interest in each frame, the central axis of the urethra was automatically defined to determine the angle between the urethra and the US beam for further analysis. By integrating angle-dependent backscatter with radiomic texture feature analysis, a mpUS technique was developed to identify biomarkers that reflect subtle microstructural changes expected within the urethral tissue. The process was repeated when the urethra and US beam were at a fixed angle. Texture selection was conducted for both angle-dependent and angle-independent results to remove redundancies. Ultimately, a distinct biomarker was derived using a random forest regression model to compute the urethra score based on features selected from both processes. Angle-dependent backscatter analysis shows that the calculated slope of US mean image intensity decreased by 0.89 (±0.31) % annually, consistent with the expected atrophic disorganization of urethral tissue structure and the associated reduction in urethral closure pressure with age. Additionally, textural analysis performed at a specific angle (i.e., 40 degrees) revealed changes in gray level nonuniformity, skewness, and correlation by 0.08 (±0.04) %, −2.16 (±1.14) %, and −0.32 (±0.35) % per year, respectively. The urethra score was ultimately determined by combining data selected from both angle-dependent and angle-independent analysis strategies using a random forest regression model with age, yielding an R2 value of 0.96 and a p-value less than 0.001. The proposed mpUS tissue characterization technique not only holds promise for guiding future urethral tissue characterization studies without the need for tissue biopsies or invasive functional testing but also aims to minimize observer-induced variability. By leveraging mpUS imaging strategies that account for angle dependence, it provides more accurate assessments. Notably, the urethra score, calculated from US images that reflect tissue microstructural changes, serves as a potential biomarker providing clinicians with deeper insight into urethral tissue function and may aid in diagnosing and managing related conditions while helping to determine the causes of incontinence.

Radiomics analysis of cerebral blood flow suggests a possible link between perfusion homogeneity and poor glioblastoma multiforme prognosis

Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies. Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes. Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively). Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.

A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer.

This rapid communication highlights the correlations between digital pathology-whole slide imaging (WSI) and radiomics-magnetic resonance imaging (MRI) features in triple-negative breast cancer (TNBC) patients. The research collected 12 patients who had both core needle biopsy and MRI performed to evaluate pathologic complete response (pCR). The results showed that higher collagenous values in pathology data were correlated with more homogeneity, whereas higher tumor expression values in pathology data correlated with less homogeneity in the appearance of tumors on MRI by size zone non-uniformity normalized (SZNN). Higher myxoid values in pathology data are correlated with less similarity of gray-level non-uniformity (GLN) in tumor regions on MRIs, while higher immune values in WSIs correlated with the more joint distribution of smaller-size zones by small area low gray-level emphasis (SALGE) in the tumor regions on MRIs. Pathologic complete response (pCR) was associated with collagen, tumor, and myxoid expression in WSI and GLN and SZNN in radiomic features. The correlations of WSI and radiomic features may further our understanding of the TNBC tumoral microenvironment (TME) and could be used in the future to better tailor the use of neoadjuvant chemotherapy (NAC). This communication will focus on the post-NAC MRI features correlated with pCR and their association with WSI features from core needle biopsies.

Radiomics Model for Predicting Pulmonary Metastasis in Sarcoma Patients

The lung is the most common site of metastasis in localized extremity soft tissue sarcomas (STS). Indeterminant lung nodules leads to challenges for both patients and providers. As such, accuracy in the diagnosis of pulmonary metastasis (PM) is important for clinical decision making. The purpose of this is study is to develop and evaluate a CT-based radiomic model to predict PM in sarcoma patients with indeterminate pulmonary nodules. Fifty-seven stage I-III sarcoma patients with indeterminate lung nodules on unenhanced CT that were approved for this study by our institution's internal review board (IRB). Twenty-two developed and categorized as PM and 35 without PM. A total number of 322 indeterminate nodules were investigated, where 165 & 157 nodules were from patients with and without PM, respectively. A total of 96 radiomic features (RF) (including first order statistics, 2d shape features, gray level co-occurrence, gray level size zone, gray level run length, neighboring gray tone difference & gray level dependence matrices) were taken from unenhanced CT images of each patient. We included 2D shape features since the size and shape of indeterminate lung nodules may have some correlation with PM. Spearman correlation were used to rule out redundant features (r2>0.9). T-test statistics was used to determine feature with significant differences that can differentiate responders with and without PM. Support Vector Machine (SVM) model was fitted to combinations of groups of 2-3 radiomic features. The AUC of the ROC curve was used to evaluate the model performance. Pointwise confidence intervals (CI) for AUC were calculated using 100 bootstrap replicates. Four features were found significant after Spearman correlation and T-test statistical testing: 1. Maximum (MAX); 2. Minor Axis Length (MAL); 3. Gray Level Non-uniformity (GLNU); and 4. Interquartile Range (IR). The best performing 2-feature radiomic SVM model had an AUC of 0.69 (95% CI 0.61,0.74) for the MAX and GLNU features. The best performing 3-feature radiomic SVM model had an AUC of 0.74 (95% CI 0.70,0.80) for the MAX, MAL and IR features. CT-based radiomics model has the potential to be used to predict PM in sarcoma patients presenting with indeterminate lung nodules. Additional research will focus on investigating derived parameters for existing RF, such as the ratio of the max RF to mean RF, to see if they improve the predictive performance of the model, as well as testing the models on external patient cohort. These radiomic models could be useful tools in guiding PM screening and providing a patient specific monitoring of sarcoma patients.

Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study

ObjectiveIdentifying MRI texture parameters able to distinguish inflammation, fibrosis, and residual cancer in patients with naso-oropharynx carcinoma after radiochemotherapy (RT-CHT).Material and methodsIn this single-centre, observational, retrospective study, texture analysis was performed on ADC maps and post-gadolinium T1 images of patients with histological diagnosis of naso-oropharyngeal carcinoma treated with RT-CHT. An initial cohort of 99 patients was selected; 57 of them were later excluded. The final cohort of 42 patients was divided into 3 groups (inflammation, fibrosis, and residual cancer) according to MRI, 18F-FDG-PET/CT performed 3–4 months after RT-CHT, and biopsy. Pre-RT-CHT lesions and the corresponding anatomic area post-RT-CHT were segmented with 3D slicer software from which 107 textural features were derived. T-Student and Wilcoxon signed-rank tests were performed, and features with p-value < 0.01 were considered statistically significant. Cut-off values—obtained by ROC curves—to discriminate post-RT-CHT non-tumoural changes from residual cancer were calculated for the parameters statistically associated to the diseased status at follow-up.ResultsTwo features—Energy and Grey Level Non-Uniformity—were statistically significant on T1 images in the comparison between ‘positive’ (residual cancer) and ‘negative’ patients (inflammation and fibrosis). Energy was also found to be statistically significant in both patients with fibrosis and residual cancer. Grey Level Non-Uniformity was significant in the differentiation between residual cancer and inflammation. Five features were statistically significant on ADC maps in the differentiation between ‘positive’ and ‘negative’ patients. The reduction in values of such features between pre- and post-RT-CHT was correlated with a good response to therapy.ConclusionsTexture analysis on post-gadolinium T1 images and ADC maps can differentiate residual cancer from fibrosis and inflammation in early follow-up of naso-oropharyngeal carcinoma treated with RT-CHT.

Radiomics analysis of bone marrow biopsy locations in [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

The combination of visual assessment of whole body [18F]FDG PET images and evaluation of bone marrow samples by Multiparameter Flow Cytometry (MFC) or Next-Generation Sequencing (NGS) is currently the most common clinical practice for the detection of Measurable Residual Disease (MRD) in Multiple Myeloma (MM) patients. In this study, radiomic features extracted from the bone marrow biopsy locations are analyzed and compared to those extracted from the whole bone marrow in order to study the representativeness of these biopsy locations in the image-based MRD assessment. Whole body [18F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A methodology for the segmentation of biopsy sites from PET images, including sternum and posterior iliac crest, and their subsequent quantification is proposed. First, starting from the bone marrow segmentation, a segmentation of the biopsy sites is performed. Then, segmentations are quantified extracting SUV metrics and radiomic features from the [18F]FDG PET images and are evaluated by Mann–Whitney U-tests as valuable features differentiating PET+/PET− and MFC+ /MFC− groups. Moreover, correlation between whole bone marrow and biopsy sites is studied by Spearman ρ rank. Classification performance of the radiomics features is evaluated applying seven machine learning algorithms. Statistical analyses reveal that some images features are significant in PET+/PET− differentiation, such as SUVmax, Gray Level Non-Uniformity or Entropy, especially with a balanced database where 16 of the features show a p value < 0.001. Correlation analyses between whole bone marrow and biopsy sites results in significant and acceptable coefficients, with 11 of the variables reaching a correlation coefficient greater than 0.7, with a maximum of 0.853. Machine learning algorithms demonstrate high performances in PET+/PET− classification reaching a maximum AUC of 0.974, but not for MFC+/MFC− classification. The results demonstrate the representativeness of sample sites as well as the effectiveness of extracted features (SUV metrics and radiomic features) from the [18F]FDG PET images in MRD assessment in MM patients.

Baseline and early digital [ 18 F]FDG PET/CT and multiparametric MRI contain promising features to predict response to neoadjuvant therapy in locally advanced rectal cancer patients: a pilot study.

In this pilot study, we investigated the feasibility of response prediction using digital [ 18 F]FDG PET/computed tomography (CT) and multiparametric MRI before, during, and after neoadjuvant chemoradiation therapy in locally advanced rectal cancer (LARC) patients and aimed to select the most promising imaging modalities and timepoints for further investigation in a larger trial. Rectal cancer patients scheduled to undergo neoadjuvant chemoradiation therapy were prospectively included in this trial, and underwent multiparametric MRI and [ 18 F]FDG PET/CT before, 2 weeks into, and 6-8 weeks after chemoradiation therapy. Two groups were created based on pathological tumor regression grade, that is, good responders (TRG1-2) and poor responders (TRG3-5). Using binary logistic regression analysis with a cutoff value of P ≤ 0.2, promising predictive features for response were selected. Nineteen patients were included. Of these, 5 were good responders, and 14 were poor responders. Patient characteristics of these groups were similar at baseline. Fifty-seven features were extracted, of which 13 were found to be promising predictors of response. Baseline [T2: volume, diffusion-weighted imaging (DWI): apparent diffusion coefficient (ADC) mean, DWI: difference entropy], early response (T2: volume change, DWI: ADC mean change) and end-of-treatment presurgical evaluation MRI (T2: gray level nonuniformity, DWI: inverse difference normalized, DWI: gray level nonuniformity normalized), as well as baseline (metabolic tumor volume, total lesion glycolysis) and early response PET/CT (Δ maximum standardized uptake value, Δ peak standardized uptake value corrected for lean body mass), were promising features. Both multiparametric MRI and [ 18 F]FDG PET/CT contain promising imaging features to predict response to neoadjuvant chemoradiotherapy in LARC patients. A future larger trial should investigate baseline, early response, and end-of-treatment presurgical evaluation MRI and baseline and early response PET/CT.

FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma

Identifying the epidermal growth factor receptor (EGFR) mutation status is important for the optimal treatment of patients with EGFR mutations. We investigated the relationship between 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) texture indices and EGFR mutation status in patients with newly diagnosed lung adenocarcinoma. We retrospectively analyzed data of patients with newly diagnosed lung adenocarcinoma who underwent pretreatment FDG PET/computed tomography and EGFR mutation testing between August 2014 and November 2020. Patients were divided into mutated EGFR and wild-type EGFR groups. The maximum standardized uptake value (SUVmax) and 31 texture indices for the primary tumor were calculated from PET images and compared between the two groups. Of the 66 patients included, 22 had mutated EGFR and 44 had wild-type EGFR. The SUVmax did not significantly differ between the two groups. Among the 31 evaluated texture indices, the following five showed a statistically significant difference between the groups: correlation (P = 0.003), gray-level nonuniformity for run (P = 0.042), run length nonuniformity (P = 0.02), coarseness (P = 0.006), and gray-level nonuniformity for zone (P = 0.04). Based on the preliminary results of this study in a small patient population, FDG PET texture indices may be potential imaging biomarkers for the EGFR mutation status in patients with newly diagnosed lung adenocarcinoma.

Texture Features of Grey Level Size Zone Matrix for Breast Cancer Detection

The texture analysis of cancer cells leads to a procedure to distinguish spatial differences within an image and extract essential information. This study used two test tumours images to determine cancer type, location, and geometric characteristics (area, size, dimensions, radius, etc.). The suggested algorithm was designed to detect and distinguish breast cancer using the segmentation-based threshold technique. The method of texture analysis Grey Level Size Zone method was used to extract 11 features: Small Zone Emphasis, Large Zone Emphasis, Low Grey Level Zone Emphasis, High Grey Level Zone Emphasis, Small Zone Low Grey Level Emphasis, Small Zone High Grey Level Emphasis, Large Zone Low Grey Level Emphasis, Large Zone High Grey Level Emphasis, Grey Level Non-Uniformity Normalized, Grey Level Non-Uniformity, and Zone Percentage. The results show that the tumuor's location is highly accurate depending on the extracting properties. The results of the suggested method give the decision to identify the type of tumours. The geometry of the tumour helps describe the tumour.

A Radiomics-Based Model to Identify Candidates for De-Intensified Treatment in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma

<h3>Purpose/Objective(s)</h3> To develop an optimal radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who can benefit from the de-intensified treatment. <h3>Materials/Methods</h3> LA-NPC patients treated between 2013 and 2018 in Shantou University Medical College Cancer Hospital receiving low dose concurrent cisplatin were divided into training and validation cohort in a 2:1 ratio. For each patient, 107 radiomics features extracted from the primary nasopharyngeal tumor in pre-treatment contrast-enhanced CT scan were analyzed. Clinical model, radiomics model, and combined model were built and validated by Cox proportional hazard analysis. <h3>Results</h3> Training and validation cohort consisted of 66 and 33 patients, respectively. Three predictive independent factors (Flatness representing the sphere-like shape, Mean calculating the average gray level, Gray Level Non-Uniformity (GLDM-GLN) measuring the heterogeneity of gray-level intensity) were remained significantly. The c-index of the clinical model, radiomics model, combined model in the validation cohort was 0.74 (0.64-0.84), 0.73 (0.55-0.91), 0.81 (0.70-0.92), respectively. Combined model, as the optimal model, has a high accuracy with area under curve (AUC) of 0.86 (p=0.01), stratifying patients into low-risk and high-risk group with the 5-year PFS rate 93.5% and 50.8%, respectively (hazard ratio [HR]: 7.37 (1.5-37.3), p=0.006), 5-year OS rate 93.5% and 54.5%, respectively (HR: 7.37 (1.5-37.3), p=0.01). <h3>Conclusion</h3> A radiomics-based combined model can select specific LA-NPC populations for de-intensified treatment without compromising efficacy.

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models.

Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-β1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-β1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.

Radiomic phenotype of epicardial adipose tissue in the prognosis of atrial fibrillation recurrence after catheter ablation in patients with lone atrial fibrillation.

BackgroundEpicardial adipose tissue (EAT) has been considered as one of the probable triggers of atrial fibrillation (AF). CT‐rediomics is a perspective noninvasive method of assessment of EAT. We evaluate the radiomic phenotype of EAT in patients with lone AF in the prognosis of AF recurrence after catheter ablation.MethodsA total of 43 patients with lone AF referred for CA and 20 out‐hospital patients without arrhythmia underwent multidetector computed tomography coronary angiography. Segmentation of EAT and extraction radiomic features were performed on calcium scoring series using by 3D‐Slicer. Clinical follow‐up was performed for 12 months period after the CA.ResultsEAT in patients with lone AF had a distinct radiomic phenotype. Thus, 45 of 93 calculated radiomic features, volume and attenuation of EAT were significantly different between patients with lone AF and persons without any arrhythmia. In addition, 17 radiomic features were significantly different in subgroups with and without AF recurrence. Multivariate regression analysis demonstrated that only gray level nonuniformity normalized (GLSZM) was an independent predictor of AF recurrence (OR 1.0027, 95%CI 1.0009–1.0044, p = 0.002). ROC analysis data showed that GLSZM >1227.4 indicates high probability of AF recurrence during 12 months (sensitivity 89.4%, specificity 70.8%, AUC: 0.809; p = 0.001).ConclusionThe radiomic parameter GLSZM is associated with late AF recurrence after CA in patients with lone AF. In current study GLSZM was a stronger predictor of lone AF recurrence in multivariate analysis comparing with other established risk factors and EAT volume and attenuation.

Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification

BackgroundWe investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT).MethodsIn this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a–d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition of representative regions of interest, 19 texture features (TFs) were calculated to analyse the voxel grey-level distribution in the included image area. ANOVA, cluster analysis, and multinomial logistic regression statistics were used. A human readout then was performed on a subset of 60 images to evaluate the reliability of the proposed feature set.ResultsOf the 19 TFs, 4 first-order features and 7 second-order features showed significant correlation with BD and were selected for further analysis. Multinomial logistic regression revealed an overall accuracy of 80% for BD assessment. The majority of TFs systematically increased or decreased with BD. Skewness (rho -0.81), as a first-order feature, and grey-level nonuniformity (GLN, -0.59), as a second-order feature, showed the strongest correlation with BD, independently of other TFs. Mean skewness and GLN decreased linearly from density a to d. Run-length nonuniformity (RLN), as a second-order feature, showed moderate correlation with BD, but resulted in redundant being correlated with GLN. All other TFs showed only weak correlation with BD (range -0.49 to 0.49, p < 0.001) and were neglected.ConclusionTA of PC-BCT images might be a useful approach to assess BD and may serve as an observer-independent tool.

Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up.

Simple SummaryNasopharyngeal carcinoma (NPC) is a frequent head and neck cancer, especially in Asian countries. Our studies investigated the value of minable data derived from standard of care PET/CT imaging in patients with NPC. The here presented evaluation found that certain specific imaging features in this patient population can be potentially used to predict overall survival and progression free survival at different time points in those patients. Purpose: We aim determine the value of PET and CT radiomic parameters on survival with serial follow-up PET/CT in patients with nasopharyngeal carcinoma (NPC) for which curative intent therapy is undertaken. Methods: Patients with NPC and available pre-treatment as well as follow up PET/CT were included from 2005 to 2006 and were followed to 2021. Baseline demographic, radiological and outcome data were collected. Univariable Cox proportional hazard models were used to evaluate features from baseline and follow-up time points, and landmark analyses were performed for each time point. Results: Sixty patients were enrolled, and two-hundred and seventy-eight (278) PET/CT were at baseline and during follow-up. Thirty-eight percent (38%) were female, and sixty-two patients were male. All patients underwent curative radiation or chemoradiation therapy. The median follow-up was 11.72 years (1.26–14.86). Five-year and ten-year overall survivals (OSs) were 80.0% and 66.2%, and progression-free survival (PFS) was 90.0% and 74.4%. Time-dependent modelling suggested that, among others, PET gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) (HR 2.74 95% CI 1.06, 7.05) was significantly associated with OS. Landmark analyses suggested that CT parameters were most predictive at 15 month, whereas PET parameters were most predictive at time points 3, 6, 9 and 15 month. Conclusions: This study with long-term follow up data on NPC suggests that mainly PET-derived radiomic features are predictive for OS but not PFS in a time-dependent evaluation. Furthermore, CT radiomic measures may predict OS and PFS best at initial and long-term follow-up time points and PET measures may be more predictive in the interval. These modalities are commonly used in NPC surveillance, and prospective validation should be considered.

Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.

Abstract P3-14-06: A transdisciplinary approach for characterizing racial differences in the biology of breast cancer by integrating imaging and -omics data

Abstract Background: Racial disparities in breast cancer incidence and clinical outcome have become increasingly recognized. In particular, African American (AA) patients have higher mortality rates than other racial groups. Research into these racial disparities has primarily focused on socioeconomic determinants, with some studies exploring molecular determinants. Biologically, few differences in genetics or transcriptomics have been found to be due to race. Understanding racial differences in breast cancer development as they relate to patient outcomes requires a comprehensive approach, integrating tumor intrinsic and extrinsic factors. We have developed a platform that combines patient-specific imaging and -omics data to elucidate how biological differences are integrated to cause racial differences in tumor behavior and disparities in patient outcome. Methods: Imaging and transcriptomic data from over 500 patients were integrated within the SimBioSys TumorScope biophysical modeling software to understand how racial differences in tumor biology are coordinated to determine tumor behavior. Specifically, we used artificial intelligence to automatically segment tumors from patient MRIs, and construct 3D models of patient tumors and the surrounding microenvironment. We then used these segmentations to identify radiological differences between AA and Caucasian patients. Simultaneously, we used patient-specific mathematical models of tumor metabolism constructed from RNA-seq data to determine racial differences in tumor metabolism. Results: We profiled structural differences in 266 tumors (181 Caucasian and 85 AA) and metabolic differences in 552 tumors (422 Caucasian and 130 AA). In our patient cohort, AA tumors were smaller (AA/Caucasian volume = 0.573, p = 0.004), more spherical (AA/Caucasian sphericity = 1.12, p = 0.0046), had lower spatial variation in tumor stiffness (AA/Caucasian Contrast GLRLM Gray Level Non-uniformity = 0.63, p = 0.0004), and had higher vascular perfusion than Caucasian tumors (AA/Caucasian vascular shell density = 1.43, p = 0.0015). In addition, AA tumors had increased levels in several features which may contribute to decreased response to therapy, including a higher spatial heterogeneity in drug delivery and secretion (AA/Caucasian WIS std = 2.2, p = 5.9x10-9, WOS std = 11.5, p &amp;lt; 10-10) and higher levels of adipose surrounding the tumor (AA/Caucasian adipose shell density = 1.13, p = 0.0024). Metabolically, we found that tumors from AA patients have increased sphingolipid metabolism (AA/Caucasian fold change (FC) = 1.4, p = 0.04) and pyruvate metabolism (FC = 1.03, p = 0.03); and decreased starch and sucrose metabolism (FC = 0.89, p = 0.03), fatty acid metabolism (average FC = 0.83, p = 0.04), glycerolipid metabolism (FC = 0.70, p = 0.025), and acylglyceride metabolism (FC = 0.69, p = 0.025). Furthermore, we found that these metabolic features integrate in such a way that tumors from AA patients had an increased specific growth rate (FC = 1.52, p = 0.014), compared to Caucasian patients (even when correcting for PAM50 subtype). Conclusion: Taken together, these results indicate that tumors of AA patients have a more aggressive phenotype (high growth rate) and potentially a lower response to chemotherapeutic drugs (high spatial heterogeneity of drug delivery and secretion and high surrounding adipose). These features likely work together to contribute to the disproportionately poor survival rates repeatedly observed in AA breast cancer patients. By characterizing racial-specific tumor intrinsic and extrinsic biological factor and their potential interactions, TumorScope simulation platform can assist in finding strategies that may improve clinical management, life-expectancy, and overall outcomes of racial minorities with breast cancer. Citation Format: Nicole Liadis, Tyler Earnest, Eduardo Bruan, Anu Antony, Michael Hallock, Daniel Cook. A transdisciplinary approach for characterizing racial differences in the biology of breast cancer by integrating imaging and -omics data [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-14-06.

Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma.

The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.

Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients

PurposeThe assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC.MethodsBy applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort.ResultsThe Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively.ConclusionRad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.