Radiomic Biomarkers Research Articles

Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular interventional treatment.

This study aimed to develop a radiomic model based on non-contrast computed tomography (NCCT) after interventional treatment to predict the clinical prognosis of acute ischemic stroke (AIS) with large vessel occlusion. We retrospectively collected 141 cases of AIS from 2016 to 2020 and analyzed the patients' clinical data as well as NCCT data after interventional treatment. Then, the total dataset was divided into training and testing sets according to the subject serial number. The cerebral hemispheres on the infarct side were segmented for radiomics signature extraction. After radiomics signatures were standardized and dimensionality reduced, the training set was used to construct a radiomics model using machine learning. The testing set was then used to validate the prediction model, which was evaluated based on discrimination, calibration, and clinical utility. Finally, a joint model was constructed by incorporating the radiomics signatures and clinical data. The AUCs of the joint model, radiomics signature, NIHSS score, and hypertension were 0.900, 0.863, 0.727, and 0.591, respectively, in the training set. In the testing set, the AUCs of the joint model, radiomics signature, NIHSS score, and hypertension were 0.885, 0.840, 0.721, and 0.590, respectively. Our results provided evidence that using post-interventional NCCT for a radiomic model could be a valuable tool in predicting the clinical prognosis of AIS with large vessel occlusion.

Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma

This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma (OSCC) survival prediction by identifying Computed Tomography (CT)-based biomarkers to improve prognosis prediction. A retrospective analysis was conducted on data from 149 OSCC patients, including CT radiomics and clinical information. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as stage and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > − 0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE ≤ − 0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for patients people with OSCC to improve their survival.

Analysis of PET-CT Derived Radiomic Biomarkers with Efficacy, Safety, and Expansion of Axicabtagene Ciloleucel (Axi-Cel) in Patients with Relapsed or Refractory Large B-Cell Lymphoma (LBCL)

Introduction In LBCL, the presence of bulky disease is known to be an adverse prognostic factor, indicating poorer outcomes. The ZUMA-1 clinical trial (Neelapu, NEJM 2017) showed that high tumor burden, as measured by the sum of the product of diameters (SPD), was associated with lower durable response after Chimeric Antigen Receptor T-Cell Therapy (CAR T ). Radiomic biomarkers such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been recently studied to assess three-dimensional measurement of tumor activity and metabolic volume in LBCL. There is a paucity of understanding on the impact of radiomic biomarkers on outcomes and CAR T expansion kinetics in LBCL CAR T recipients. We studied the association of these radiomic biomarkers with efficacy, toxicity, and CAR T expansion in this single institution retrospective study of patients with LBCL who received axi-cel. Methods 189 patients with relapsed or refractory LBCL who were treated with axi-cel from 12/2017 to 5/2022 were studied. The closest PET-CT scan to CAR T leukapheresis date was identified for each patient. After excluding cases with unavailable pre-apheresis PET-CT scans, 172 patients were included in the statistical analysis. Two reviewers independently used a semi-automated contouring software (MIM, v. 7.1.2) to measure maximum standardized uptake value (SUV Max), mean SUV, total MTV and TLG values of malignant lesions. CD19 CAR T expansion was measured by real-time flow cytometry with anti-idiotype-FMC63 conjugated to Dylight 65013. Areas under the curve (AUCs) were calculated for CAR T expansion in the 28 days following infusion using the linear trapezoidal method on raw values, and the results were transformed using a base-10 logarithm. Patients were categorized into high and low biomarker groups based on whether their values exceeded or were below the respective median. For univariate analyses, continuous variables were compared using a Mann-Whitney U-test, categorical using a Chi-square test, and time-to-event using a log-rank test. Within a multivariable framework, regressions (linear, logistic, Cox) were fit to control for potential confounders. Results Median duration of follow up was 33 months (IQR: 17.9, 44.7). MTV was highly correlated with TLG (r=0.87). High MTV was associated with bulky disease defined as >10cm (p<0.01), high IPI at apheresis (p<0.04), bridging treatment (p<0.01), pre-lymphodepletion lactate dehydrogenase (p<0.01), and autologous stem cell transplant (p=0.02). Complete response (CR) rate was greater for those in the low MTV group (79.3%) compared to high MTV group (52.3%), (p<0.01), as well as those with high TLG (77.9%) compared to low TLG group (46%), (p<0.01). Low MTV was significantly associated with better progression free survival (PFS) in a univariate analysis (p=0.03); median PFS was 33.5 months (IQR 3.06, 39.55) for low MTV versus 6.1 months (IQR 6.38, NA) for high MTV. Low MTV was also significantly associated with better overall survival (OS) in a univariate analysis (p<0.01); median OS was not reached for the low MTV group but was 31.8 months for the high MTV group (IQR 16.5, NA) (Figure 1). After multivariable regression analysis, PFS was no longer different by MTV biomarker level ( p= 0.07), but OS was still significantly different ( p= 0.01). Neither PFS nor OS were significantly associated with the binary TLG variable. The proportion of cytokine release syndrome (CRS) or immune cell associated neurologic toxicity syndrome (ICANS) of any grade or severe grade ICANS were significantly higher for the high MTV and TLG groups as compared to their respective low groups. Length of stay was significantly longer for those in the high MTV and TLG groups (Table 1). A higher day 14 CAR T cell expansion was seen in the high TLG as compared to the low group (0.89 vs 0.63 cells/μL, p=0.002). However, there was no statistically significant difference in CAR T expansion as measured by AUC between the high and low MTV or TLG cohort. Conclusion Our study demonstrates poor outcomes for efficacy, safety, and healthcare utilization endpoints in patients with high tumor bulk as measured by volumetric tumor assessment on PET-CT. Furthermore, we show a differential peripheral day 14 CAR T expansion with higher expansion seen in patients with higher tumor bulk. Strategies like tumor debulking prior to CAR T therapy and earlier referral in patients with high tumor bulk may improve clinical outcomes.

Artificial Intelligence Derived Changes between Baseline and Interim FDG-PET/CT Radiomics Features Are Associated with Survival Outcomes in Diffuse Large B-Cell Lymphoma (DLBCL)

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).

Discovering pericoronary adipose tissue differences between normal and atherosclerotic coronary segments with radiomics analysis

Abstract Introduction Pericoronary adipose tissue (PCAT) extracted from coronary CT angiography (CCTA) is a novel marker for coronary inflammation. Establishing correlation between PCAT changes and the vascular lesion itself is essential for using PCAT to monitor clinical and subclinical disease status. Past studies focused on patient-level and vessel-level PCAT differences. However, lesion-level PCAT differences beyond mean attenuation values are not well understood. Purpose To investigate the differences between PCAT adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments using radiomics analysis. Material and Methods This study enrolled 100 consecutive patients who underwent CCTA. A total of 446 plaques were successfully annotated in 89 patients. The PCAT of these 446 plaques and also their normal segment counterparts were extracted. Following prior literature, adipose tissue was defined as voxels with attenuation ranging -190 to -30 HU. PCAT was extracted within 3 mm distance from the vessel wall, along the middle 10 mm of each plaque. The PCAT of normal coronary artery segments were extracted both proximal and distal with distance D mm to the plaque with 10 mm length. We used 3 different distances to define the normal segments (D={10, 20, 30}). PyRadiomics was used to generate radiomics features and the random forest classifier from Scikit-Learn was used to distinguish PCAT extracted adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments. To minimize bias due to vessel diameter or shape and focus on the texture of PCAT, we included only the First Order, GLCM, GLDM, GLRLM, GLSZM and NGTDM features (total 93 features) from PyRadiomics. We used 5-fold cross validation splitted at patient-level to develop the classifier. Result The radiomics model achieved acceptable discrimination for D=30 mm (mean AUROC = 0.70), borderline discrimination for D=20 mm (mean AUROC=0.68) and D=10 mm (mean AUROC = 0.60). These results suggest a trend toward greater differences between PCAT further away from the lesion compared with PCAT adjacent to the lesion. The feature "Gray Level Size Zone Matrix (GLSZM): Low Gray Level Zone Emphasis" had the highest importance in 4 out of 5 folds in our D = 30 mm experiment. The mean value of this feature is significantly different for the PCAT adjacent to the lesions compared to PCAT extracted from the normal segments (0.152 vs 0.166, p=0.001). Conclusion We investigated the difference between PCAT adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments using radiomics analysis. We found greater discrimination when the normal segments were extracted further away from the lesion, suggesting PCAT changes due to atherosclerosis may exist beyond the length of the plaque. Moreover, "Gray Level Size Zone Matrix (GLSZM): Low Gray Level Zone Emphasis" could serve as a novel radiomic biomarker to correlate PCAT changes to atherosclerosis.

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Cardiac MRI Radiomics: review

A study of foreign and domestic literature devoted to the application of texture analysis of magnetic resonance images of the heart was performed. The analysis included publications selected by key words and their combinations: cardiac magnetic resonance imaging (MRI), myocarditis, myocardial infarction, cardiomyopathy, radiomics, and texture analysis. Radiomics and texture analysis, as a new and rapidly developing direction of in-depth analysis of digital medical images, is developing, the number of publications on this topic is growing every year and the topic is not losing its relevance. Radiomics is a promising method of image analysis that aims to improve the diagnosis and prognosis of diseases by extracting a large number of quantitative features that can be missed by the human eye in the visual analysis of images. Radiomics biomarkers derived by extracting data from magnetic resonance images of the heart could be a valuable tool for assessing myocardial viability, myocardial lesions in myocarditis and cardiomyopathies.

Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study

BackgroundThe predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage...

Novel Fractal-Based Sub-RPE Compartment OCT Radiomics Biomarkers Are Associated With Subfoveal Geographic Atrophy in Dry AMD.

The purpose of this study was to quantitatively characterize the shape of the sub-retinal pigment epithelium (sub-RPE, i.e., space bounded by RPE and Bruch's membrane) compartment on SD-OCT using fractal dimension (FD) features and evaluate their impact on risk of subfoveal geographic atrophy (sfGA) progression. This was an IRB-approved retrospective study of 137 subjects with dry age-related macular degeneration (AMD) with subfoveal GA. Based on sfGA status at year five, eyes were categorized as "Progressors" and "Non-progressors". FD analysis allows quantification of the degree of shape complexity and architectural disorder associated with a structure. To characterize the structural irregularities along the sub-RPE surface between the two groups of patients, a total of 15 shape descriptors of FD were extracted from the sub-RPE compartment of baseline OCT scans. The top four features were identified using minimum Redundancy maximum Relevance (mRmR) feature selection method and evaluated with Random Forest (RF) classifier using three-fold cross validation from the training set (N = 90). Classifier performance was subsequently validated on the independent test set (N = 47). Using the top four FD features, a RF classifier yielded an AUC of 0.85 on the independent test set. Mean fractal entropy (p-value = 4.8e-05) was identified as the most significant biomarker; higher values of entropy being associated with greater shape disorder and risk for sfGA progression. FD assessment holds promise for identifying high-risk eyes for GA progression. With further validation, FD features could be potentially used for clinical trial enrichment and assessments for therapeutic response in dry AMD patients.

175P Radiomic biomarker of vessel tortuosity for monitoring treatment change: Preliminary findings in prospective evaluation of ECOG-ACRIN EA5163

175P Radiomic biomarker of vessel tortuosity for monitoring treatment change: Preliminary findings in prospective evaluation of ECOG-ACRIN EA5163

Using MRI Radiomics to Predict the Efficacy of Immunotherapy for Brain Metastasis in Patients with Non-small Cell Lung Cancer

Despite immune checkpoint inhibitors (ICIs) has been proven effective in patients with advanced NSCLC, controversial therapeutic responses are seen in brain metastatic lesions. There are no robust biomarkers that predict benefit from this regimen. We evaluated the utility of novel imaging biomarkers (radiomics) to distinguish NSCLC patients with brain metastases (BMs) who will benefit from ICIs from those likely to progress despite therapy. One-hundred and seventy-four NSCLC Patients with BMs treated with ICIs from June 2019 - June 2022 were identified. We collected patient clinical outcomes and pre-treatment MRI images. Images were split into training and test sets. Brain metastatic lesions were contoured on ITK-SNAP software and 3748 radiomic features capturing both intra- and peritumoral texture patterns were extracted. The primary endpoint of this study was intracranial progression-free survival (iPFS) and the secondary objective were progression-free survival (PFS) and overall survival (OS). We used the least absolute shrinkage and selection operator (LASSO) Cox regression model to build the radiomic signature for ORR. Based on the support vector machine (SVM) model, we construct the Clinical-radiomics nomogram (CRN). Multivariable Cox regression analysis were performed to evaluate the effect of each factor on iPFS. We performed Kaplan-Meier survival analysis and log-rank tests to assess prognostic value of the features. We identified 174 patients who fit our criteria with available MRI images. 122 patients treated in our center were divided into a training set and 52 patients treated in another center were divided into a test set. The intratumoral radiomic signatures(IRS), peritumoral radiomic signatures(PRS) and CRN showed favorable predictive effects for ORR with the area under the receiver operating curve (AUC) of (IRS: 0.845 (95% CI: 0.776-0.914); PRS: 0.799 (95% CI:0.720-0.879); CRN:0.907 (95% CI: 0.855-0.959))in the training set and (IRS: 0.809 (95% CI: 0.693-0.926); PRS: 0.749 (95% CI:0.616-0.883); CRN:0.888(95% CI:0.798-0.979)) in the test set. Kaplan-Meier analyses showed a significantly longer iPFS in the high-CRN group versus the low-CRN group (P < 0.001). The CRNs were also found significantly associated with PFS (P < 0.001), but not OS. Radiomic biomarkers from pre-treatment MRI images in NSCLC patients with BMs were predictive of iPFS to ICIs. Pre-treatment radiomics may allow early prediction of benefit and expedite more aggressive treatment for high-risk patients. Additional validation of these imaging biomarkers is warranted.

Automatic Prediction of Metastasis-Free Survival (MFS) Using Prostate-Specific Membrane Antigen (PSMA) PET for Oligometastatic Castration-Sensitive Prostate Cancer (omCSPC)

Recent data have shown the promise of metastasis-directed therapy (MDT) to improve outcomes in omCSPC patients. Response biomarkers are still needed to identify patients at an early time point who will not respond to the treatment. We have shown that PSMA PET-CT SUVmax changes over time may be a useful response biomarker that correlates with MFS, an important endpoint in these omCSPC patients. This study investigated radiomic imaging biomarkers derived from PSMA-PET acquired pre- and post-MDT for MFS prediction, which may provide better features to discriminate response in the future to improve the outcome of these patients. We accrued an international multi-institutional cohort of omCSPC patients treated with stereotactic ablative radiation therapy (SABR) MDT. The cohort includes 32 patients from Institution 1 (USA) and 38 patients from Institution 2 (Europe). Both pre- and 6-month post-treatment PSMA-PET/CT were acquired. Combat was used for data harmonization in the image domain to minimize imaging variations across institutions. We defined the GTV volume as zone 1 and a 3-5 mm expansion ring area outside the GTV as zone 2 for radiomics analysis. 874 radiomics features (214 original and 660 wavelet filtered features) were extracted from both zones using open-source software and used together for MFS prediction. Function Chi2 was used to select the most significant five features. Several machine learning models (Random Forest, Logistic regression, Support Vector Machine, Naïve Bayesian) were implemented to predict MFS. The models were tested using both a leave-one-out strategy and cross-validation across the two institutions. In the leave-one-out biomarker using 70 patients, random forest achieved the best accuracy, with MFS predicted correctly for 56 (80% of 70) patients. The five radiomic features identified based on their ability to predict MFS included Entropy, Skewness, and Compactness from zone 1, Skewness, and Mesh volume from zone 2. In the cross-institution tests, random forest predicted MFS correctly for 24 (75% of 32) patients when being trained using 38 Institution 2 patients and validated against 32 Institution 1 patients. Vice versa, the model predicted MFS correctly for 28 (74% of 38) patients when being trained using Institution 1 patients and validation using Institution 2 patients. The five features identified for prediction included Entropy and Skewness from both zones and Flatness from zone 1. Our study demonstrated the promise of using pre- and post-MDT PSMA-PET-based imaging radiomic biomarkers for MFS prediction for omCSPC patients. Imaging biomarkers predictive of MFS were identified in both GTV and the ring area outside GTV. Over 74% prediction accuracy was achieved in the cross-institution validation test. The model provides a valuable tool for prognosis prediction early following MDT, which opens up a unique opportunity for monitoring or treatment interventions for patients identified with poor prognoses to improve outcomes.

Radiomic Biomarkers as a Non-Invasive Tool for Assessing Biological Age.

Radiomic Biomarkers as a Non-Invasive Tool for Assessing Biological Age.

Radiological Study of Atlas Arch Defects with Meta-Analysis and a Proposed New Classification.

This study consists of a retrospective cohort study, a systematic review, and a meta-analysis which were separately conducted. This study aimed to investigate the prevalence of atlas arch defects, generate an evidence-based synthesis, and propose a common classification system for the anterior and combined atlas arch defects. Atlas arch defects are well-corticated gaps in the anterior or posterior arch of the atlas. When both arches are involved, it is known as a combined arch defect. Awareness of these defects is essential for avoiding complications during surgical procedures on the upper spine. The prevalence of arch defects was investigated in an open-access OPC-Radiomics (Radiomic Biomarkers in Oropharyngeal Carcinoma) dataset comprising 606 head and neck computed tomography scans from oropharyngeal cancer patients. A systematic review and meta-analysis were performed to generate prevalence estimates of atlas arch defects and propose a classification system for the anterior and combined atlas arch defects. The posterior arch defect was found in 20 patients (3.3%) out of the 606 patients investigated. The anterior arch defect was not observed in any patient, while a combined arch defect was observed in one patient (0.2%). A meta-analysis of 13,539 participants from 14 studies, including the present study, yielded a pooled-posterior arch defect prevalence of 2.07% (95% confidence interval [CI], 1.22%-2.92%). The prevalences of anterior and combined arch defects were 0.00% (95% CI, 0.00%-0.10%) and 0.14% (95% CI, 0.04%-0.25%), respectively. The anterior and combined arch defects were classified into five subtypes based on their morphology and frequency. The present study showed that atlas arch defects were present in approximately 2% of the general population. For future studies, larger sample sizes should be used for studying arch defects to avoid the small-study effect and to predict the prevalence accurately.

Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study

In this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.

How to Evaluate Fibrosis in IBD?

In this review, we will describe the importance of fibrosis in inflammatory bowel disease (IBD) by discussing its distinct impact on Crohn's disease (CD) and ulcerative colitis (UC) through their translation to histopathology. We will address the existing knowledge on the correlation between inflammation and fibrosis and the still not fully explained inflammation-independent fibrogenesis. Finally, we will compile and discuss the recent advances in the noninvasive assessment of intestinal fibrosis, including imaging and biomarkers. Based on the available data, none of the available cross-sectional imaging (CSI) techniques has proved to be capable of measuring CD fibrosis accurately, with MRE showing the most promising performance along with elastography. Very recent research with radiomics showed encouraging results, but further validation with reliable radiomic biomarkers is warranted. Despite the interesting results with micro-RNAs, further advances on the topic of fibrosis biomarkers depend on the development of robust clinical trials based on solid and validated endpoints. We conclude that it seems very likely that radiomics and AI will participate in the future non-invasive fibrosis assessment by CSI techniques in IBD. However, as of today, surgical pathology remains the gold standard for the diagnosis and quantification of intestinal fibrosis in IBD.

Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases.

The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors.

Pretreatment radiomic biomarker for immunotherapy responder prediction in IB-IV stage NSCLC: A multi-center retrospective study.

e21060 Background: The current biomarker of immune checkpoint inhibitors (ICIs) efficacy is not sufficient. This study investigated the correlation between radiomic biomarker and immunotherapy responder in patients with IB-IV stage non-small-cell lung cancer (NSCLC), including its biological explanation for ICIs treatment response prediction. Methods: CT images from 319 pretreatment NSCLC patients receiving immunotherapy between January, 2015 and November, 2021 were retrospectively collected and were distributed into a discovery (n = 214), independent validation (n = 54), and external test cohort (n = 51). 851 features were extracted from both tumoral and each peri-tumoral VOIs. The reference standard is the durable clinical benefit (DCB) and the crucial exclusion criteria are missing pretreatment CT scans. The predictive value of combined radiomic signature (CRS) was assessed in a cohort of 98 resectable NSCLC patients receiving ICIs. The association of radiomic features and tumor immune landscape on the online dataset (n = 60) was also evaluated. A clinical model was constructed and incorporated with radiomic signature to improve performance further. Results: CRS discriminated between DCB and non-DCB patients in the training cohort (area under the curve (AUC): 0.82, 95% CI: 0.75 to 0.88)) and independent cohort (AUC: 0.75, 95%CI: 0.62 to 0.88). In this study, the predictive value of CRS was better than PD-L1 expression (AUC of PD-L1 subset: 0.59, 95%CI: 0.50 to 0.69) or clinical model (AUC of validation cohort: 0.66, 95%CI: 0.51 to 0.81). When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95%CI: 0.67 to 0.81). Moreover, CRS showed a promising stratified ability on overall survival (OS; HR: 0.48, 95%CI: 0.27 to 0.89; p = 0.020) and progression-free survival (PFS; HR: 0.43, 95%CI: 0.26 to 0.74; p = 0.002). Biological pathway analysis on online dataset revealed that dysregulation of Hedgehog and Hippo signaling pathways got involved in the prediction of radiomic features. Conclusions: By analyzing both tumoral and peri-tumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker distinguishing responder from ICIs therapy, which might inform clinical decisions on the use of ICIs in advanced as well as resectable NSCLC patients.

Durvalumab with chemotherapy as first line treatment in advanced pleural mesothelioma: A phase 3, randomised trial (DREAM3R).

TPS8599 Background: Two phase 2 trials of durvalumab plus chemotherapy in advanced pleural mesothelioma exceeded pre-specified efficacy criteria. The recent CM 743 trial showed overall survival (OS) was longer in those assigned ipilimumab and nivolumab (ipi nivo) than standard chemotherapy, however the benefits were primarily in the subgroup with non-epithelioid histology. DREAM3R will determine the effectiveness of durvalumab plus chemotherapy as first line treatment for advanced pleural mesothelioma. The DREAM3R protocol was recently amended to allow treatment with ipi nivo as per CM 743 in the control group, and to confine further accrual to epithelioid subtype. Methods: Treatment-naïve patients with advanced, epitheloid pleural mesothelioma will be randomized (1:1) to either experimental group treatment: durvalumab 1500 mg every 3 weeks plus chemotherapy (pemetrexed 500 mg/m2 plus either cisplatin 75 mg/m2 or carboplatin AUC 5) every 3 weeks for 4-6 cycles, followed by durvalumab 1500 mg every 4 weeks until disease progression, unacceptable toxicity or patient withdrawal, or control group treatment: physician’s choice of either chemotherapy or ipi nivo (up to 2 years). The target sample size of 480 recruited over 33 months, with follow up for another 18 months provides over 85% power if the true hazard ratio for OS is 0.70, with 2-sided alpha of 0.05, assuming a median OS of 18 months in the control group. Key eligibility criteria: Epithelioid pleural mesothelioma; measurable disease per RECIST 1.1 modified for mesothelioma; ECOG PS 0-1; and adequate hematologic, renal, and liver function. Exclusions: Prior systemic anticancer treatment for pleural mesothelioma, diagnosis based solely on cytology or fine needle aspiration biopsy, contraindication to immunotherapy or conditions requiring immunosuppressive agents or corticosteroids. Participants are stratified at randomization for: age (18-70 years vs. &gt; 70), sex, planned control regimen (chemotherapy or ipi nivo), platinum (cisplatin vs. carboplatin) and geographic region (USA vs. ANZ). The primary endpoint is OS. Secondary endpoints include progression-free survival; objective tumor response; adverse events; health-related quality of life; and use of healthcare resources in ANZ. Tertiary objectives are to identify potential prognostic and/or predictive biomarkers (including those identified in prior phase 2 studies, PD-L1 expression, tumor mutation burden, genomic characteristics, and HLA subtypes), validation of radiological measures of response, and studies of possible radiomic biomarkers in mesothelioma. The study is active and enrolling in both ANZ and in the US. Clinical trial information: NCT04334759 and ACTRN12620001199909 .

Development of CT-based radiomic model to predict 5-year progression-free survival (PFS) in locally advanced head and neck squamous cell carcinoma (LAHNSCC) treated with definitive chemoradiation.

6076 Background: Definitive chemoradiation is the current standard treatment in LAHNSCC. Prognostic biomarkers that enable to reliably predict LAHNSCC disease progression are needed to stratify the risk of progression to tailor treatment intensity in future clinical trials. Imaging biomarkers have emerged as promising alternatives to non-invasively predict patient outcomes. Aim: to identify an imaging signature to predict progression-free survival (PFS) in LAHNSCC. Methods: A single-center, retrospective, observational study was designed. Clinical data and pre-treatment CT images from LAHNSCC patients treated with definitive chemoradiation from 2014 to 2022 were collected. Manual tumor segmentation was performed slice by slice by a qualified image technician and supervised by a radiologist with 20+ years in HNSCC using the DICOM viewer of Quibim platform. A total of 108 radiomic features (shape and textures) were extracted from each region of interest. Feature reduction techniques were applied to select the characteristics used in the classification model. The primary endpoint of this study was 5-year PFS. Random Forest, Support Vector Classifier (SVC), Logistic Regression, Gradient Boosting (GB) and Extreme Gradient Boosting classification models were used. A 5-fold cross validation strategy was followed to evaluate the performance of the models in terms of AUROC, sensitivity, specificity, and accuracy. The importance of each feature in the model was measured by applying the Shapley Additive exPlanations (SHAP) method. Results: Baseline CT exams from 102 LAHNSCC patients were included; 50% and 23% were stage IVA and IVB, respectively. The remaining 27% (stage II [3%] and stage III [24%]) were treated in an organ-preservation intention. 67% of patients presented locoregional or distant progression at 5 years. All radiomic features (108) and 6 clinical variables were used in the predictive model, for which a cross-validation was performed. GB as the feature selection technique and SVC as the classification model provided the best results. The final model was able to predict 5-year PFS with an average AUC of 0.82 (95% CI: [0.73, 0.91]), a sensitivity of 0.69 (95% CI: [0.57, 0.81]), a specificity of 0.82 (95% CI: [0.7, 0.94]) and an accuracy of 0.73 (95% CI: [0.62, 0.84]). From most to least important, features contributing to the predictive model were: TNM stage (SHAP: 0.038), glszm Small Area Emphasis (SHAP: 0.026), glcm Idmn (SHAP: 0.023), glszm Large Area High Gray Level Emphasis (SHAP: 0.018) and glrlm Run Entropy (SHAP: 0.015). Conclusions: Radiomic biomarkers from pre-treatment CT images in combination with TNM stage were predictive of 5-year PFS to chemoradiation in LAHNSCC patients and might be helpful for patient risk stratification. Further validation of these imaging biomarkers is ongoing.