Radiomics Analysis Research Articles

NIMG-75. SIMILARITY DISTANCES-BASED DELTA RADIOMICS (SDDR) ENABLES LONGITUDINAL MRI COMPARISONS FOR SURVIVAL PREDICTION IN GLIOMAS: A MULTI-INSTITUTIONAL VALIDATION

Abstract Radiomics (computerized feature analysis) on treatment-naive MRI scans has demonstrated great value in outcome prediction for Glioblastoma (GBM). However, delta radiomics (analysis of radiomic feature variation between different events, e.g., before and after treatment) has not been explored on account of challenges with precise spatial correspondences between pre- and post-operative scans. This work presents a survival prediction model for GBM, Similarity Distances-based delta radiomics (SDDR), based on the hypothesis that similarity distance-measures between radiomic features on pre-, and post-treatment images can accurately capture temporal changes within-and-around the tumor, and provide improved prediction of overall survival, compared to models based on a single timepoint. 150 patients from 2 publicly-available Glioma repositories (University of California San Francisco “UCSF” (n=48), LUMIERE (n=67)) and 2 two proprietary datasets (from xCures (n=13), and Cleveland Clinic (CCF) (n=74)) were obtained. UCSF and LUMIERE cohorts were used as separate hold-out sets, in each of which the remaining collections were used as discovery set. Following pre-processing, tumor segmentation into Edema (ED) and Enhancing tumor (ET) were performed. SDDR was then computed, which is the distance between probability densities functions from 156 gray co-occurrence (GLCM) and 52 gradient-based co-occurrences statistics (COLLAGE) features extracted from pre and-post-treatment MRI. Six statistical distances (e.g., Earth mover distance) normalized by the scanning interval (in weeks) yielded n=2496 SDDR measurements per tumor region (1248/region, 936 delta-GLCM, and 312 delta-COLLAGE). The same set of features were computed from pretreatment MRI (Pre-R) for comparison. Features were fed to LASSO-Cox regression for survival analysis. SDDR for GLCM features extracted from ET outperformed Pre-R, and demonstrated significant differences between high and low risk groups for both hold out tests: UCSF (p=0.0069, HR =2.8, C-index=0.69) and LUMIERE (p=0.0061, HR = 1.9, C-index 0.65). SDDR-based multi-institutional survival analysis demonstrated promise in reliable longitudinal risk-stratification in GBM.

Unveiling Tim-3 immune checkpoint expression in hepatocellular carcinoma through abdominal contrast-enhanced CT habitat radiomics

IntroductionImmune checkpoint inhibitors (ICIs) are important systemic therapeutic agents for hepatocellular carcinoma (HCC), among which T-cell immunoglobulin and mucin-domain containing protein 3 (Tim-3) is considered an emerging target for ICI therapy. This study aims to evaluate the prognostic value of Tim-3 expression and develop a predictive model for Tim-3 infiltration in HCC.MethodsWe collected data from 424 HCC patients in The Cancer Genome Atlas (TCGA) and data from 102 pathologically confirmed HCC patients from our center for prognostic analysis. Multivariate Cox regression analyses were performed on both datasets to determine the prognostic significance of Tim-3 expression. In radiomics analysis, we used the K-means algorithm to cluster regions of interest in arterial phase enhancement and venous phase enhancement images from patients at our center. Radiomic features were extracted from three subregions as well as the entire tumor using pyradiomics. Five machine learning methods were employed to construct Habitat models based on habitat features and Rad models based on traditional radiomic features. The predictive performance of the models was compared using ROC curves, DCA curves, and calibration curves.ResultsMultivariate Cox analyses from both our center and the TCGA database indicated that high Tim-3 expression is an independent risk factor for poor prognosis in HCC patients. Higher levels of Tim-3 expression were significantly associated with worse prognosis. Among the ten models evaluated, the Habitat model constructed using the LightGBM algorithm showed the best performance in predicting Tim-3 expression status (training set vs. test set AUC 0.866 vs. 0.824).DiscussionThis study confirmed the importance of Tim-3 as a prognostic marker in HCC. The habitat radiomics model we developed effectively predicted intratumoral Tim-3 infiltration, providing valuable insights for the evaluation of ICI therapy in HCC patients.

Enhancing Survival Outcome Predictions in Metastatic Non-Small Cell Lung Cancer Through PET Radiomics Analysis

(1) Background: Advanced-stage lung cancer poses significant management challenges. The goal of this study was to identify crucial clinical and PET radiomics features that enable prognostic stratification for predicting outcomes. (2) Methods: PET radiomics features of the primary lung lesions were extracted from 99 patients with stage IVB NSCLC, and the robustness of these PET radiomics features was evaluated against uncertainties stemming from extraction parameters and contour variation. We trained three survival risk models (clinical, radiomics, and a composite) through a penalized Cox model framework. We also created a Balanced Random Forest classification predictive model, using the selected features, to predict 1-year survival. (3) Results: We identified 367 common PET radiomics features that exhibited robustness to perturbations introduced by contour variation and extraction parameters. Our findings indicated that both the radiomics and the composite model outperformed the clinical model in stratifying the risk for survival with statistical significance. In predicting 1-year survival, the radiomics model and the composite model also achieved better predicting accuracies compared to the clinical model. (4) Conclusions: Robust PET radiomics analysis successfully facilitated the stratification of patient risk for survival outcomes and predicted 1-year survival in stage IVB NSCLC.

Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging

This study was performed to investigate the diagnostic value of radiomics models constructed by fat suppressed T2-weighted imaging (T2WI-FS) and contrast-enhanced T1-weighted imaging (CET1) based on magnetic resonance imaging (MRI) for differentiation of osteosarcoma (OS) and chondrosarcoma (CS). In this retrospective cohort study, we included all inpatients with pathologically confirmed OS or CS from Second Xiangya Hospital of Central South University (Hunan, China) as of October 2020. Demographic and imaging variables were extracted from electronic medical records and compared between OS and CS group. Totals of 530 radiomics features were extracted from CET1 and T2WI-FS sequences based on MRI. The least absolute shrinkage and selection operator (LASSO) method was used for screening and dimensionality reduction of the radiomics model. Multivariate logistic regression analysis was performed to construct the radiomics model, and receiver operating characteristic curve (ROC) was generated to evaluate the diagnostic accuracy of the radiomics model. The training cohort and validation cohort included 87 and 29 patients, respectively. 8 CET1 features and 15 T2WI-FS features were screened based on the radiomics features. In the training group, the area under the receiver-operator characteristic curve (AUC) value for CET1 and T2WI-FS sequences in the radiomics model was 0.894 (95% CI 0.817–0.970) and 0.970 (95% CI 0.940–0.999), respectively. In the validation group, the AUC value for CET1 and T2WI-FS sequences in the radiomics model was 0.821 (95% CI 0.642–1.000) and 0.899 (95% CI 0.785–1.000), respectively. In this study, we developed a radiomics model based on T2WI-FS and CET1 sequences to differentiate between OS and CS. This model exhibits good performance and can help clinicians make decisions and optimize the use of healthcare resources.

Role of tumor-specific and whole-body imaging biomarkers for prediction of recurrence in patients with stage III colorectal cancer.

Imaging biomarkers are emerging as non-invasive predictors of cancer prognosis and clinical outcome. We assessed tumor-specific ("radiomics") and body composition imaging features ("morphomics") extracted from baseline pre-treatment CT for prediction of recurrence in patients with stage III colorectal cancer (CRC). Patients with newly diagnosed stage III CRC were enrolled in this prospective observational study. Patients with available preoperative scans were included (N = 101). The tumor, if visible, was manually segmented and first-order radiomics features were extracted with a commercially available software. The morphomics features (reflecting muscle, fat, and bone characteristics) were extracted in a standardized fashion using a proprietary software and the values were adjusted and normalized based on a reference standard. Time to recurrence was the final outcome. Correlation between demographics, clinical features, radiomics, and morphomics features and outcome were assessed using univariate and multivariate tests as well as Kaplan-Meier and log-rank tests. Morphomic analysis was performed in all 101 patients. 60 patients had discrete tumors suitable for radiomics analysis. These patients had lower ECOG score (p < 0.05), more muscle mass (p > 0.05), and lower fat density (p > 0.05) compared to the patients in whom radiomics analysis could not be performed. Pathological stage (HR: 2.69; p = 0.03), CEA level after surgery (HR: 1.11 for 1ng/mL; p < 0.005), bone mineral density (HR: 1.01 for 1 Hounsfield Unit; p < 0.01), and tumor skewness (HR: 0.33 for 1 unit; p < 0.05) had association with recurrence based on both univariate and multivariate analyses. A model using Cox's regression analyses was able to divide the patients into low-, medium-, and high-risk for recurrence. Both radiomics and morphomics features were independently associated with the risk of CRC recurrence and, when combined, each contributed valuable information to explain risk of recurrence. Clinical trial.gov NCT02842203. Patient recruitment occurred between 22/07/2016 and 18/03/2020.

Radiomics for Predicting Prognostic Factors in Breast Cancer: Insights from Contrast-Enhanced Mammography (CEM)

Objectives: To evaluate the correlation between radiomic features extracted from contrast-enhanced mammography (CEM) tumor lesions and peritumoral background with prognostic factors in breast cancer (BC). Methods: In this retrospective, single-center study, 134 women with histologically confirmed breast cancer underwent CEM examination. Radiomic features were extracted from manually segmented lesions and lesion contours were automatically delineated using PyRadiomics. The extracted features were categorized into seven classes: First-order Features, Shape Features (2D), Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM). Histological examination assessed tumor type, grade, receptor structure (ER, PgR, HER2), Ki67 index, and lymph node involvement. Pearson correlation and multivariate regression were applied to evaluate associations between radiomic features and prognostic factors. Results: Significant correlations were found between First-order Features and prognostic factors such as ER, PgR, and Ki67 (p &lt; 0.05). GLCM-based texture features showed strong associations with Ki67 and HER2 (p &lt; 0.01). Radiomic features from peritumoral regions, especially shape and GLSZM metrics, were significantly correlated with Ki67 and lymph node involvement. Conclusions: Radiomic analysis of both tumor and peritumoral regions offers significant insights into BC prognosis. These findings support the integration of radiomics into personalized diagnostic and therapeutic strategies, potentially improving clinical decision making in BC management.

Machine learning‐based radiomics in neurodegenerative and cerebrovascular disease

AbstractCognitive impairments, which can be caused by neurodegenerative and cerebrovascular disease, represent a growing global health crisis with far‐reaching implications for individuals, families, healthcare systems, and economies worldwide. Notably, neurodegenerative‐induced cognitive impairment often presents a different pattern and severity compared to cerebrovascular‐induced cognitive impairment. With the development of computational technology, machine learning techniques have developed rapidly, which offers a powerful tool in radiomic analysis, allowing a more comprehensive model that can handle high‐dimensional, multivariate data compared to the traditional approach. Such models allow the prediction of the disease development, as well as accurately classify disease from overlapping symptoms, therefore facilitating clinical decision making. This review will focus on the application of machine learning‐based radiomics on cognitive impairment caused by neurogenerative and cerebrovascular disease. Within the neurodegenerative category, this review primarily focuses on Alzheimer's disease, while also covering other conditions such as Parkinson's disease, Lewy body dementia, and Huntington's disease. In the cerebrovascular category, we concentrate on poststroke cognitive impairment, including ischemic and hemorrhagic stroke, with additional attention given to small vessel disease and moyamoya disease. We also review the specific challenges and limitations when applying machine learning radiomics, and provide our suggestion to overcome those limitations towards the end, and discuss what could be done for future clinical use.

Automated deep learning approach for identifying etiologies of hypertrophic phenocopies by echocardiography

Abstract Background Left ventricular hypertrophy (LVH) is an indicator of poor prognosis across various cardiovascular diseases and is developed by various etiologies. Transthoracic echocardiography (TTE) is the most available cardiac imaging tool to detect LVH. However, it is still challenging to differentiate three non-ischemic cardiomyopathies which frequently presenting LVH: Fabry cardiomyopathy (FC), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA) from morphology or cardiac function evaluated by TTE. Purpose We aimed to develop stepwise deep learning models to differentiate FC, HCM, and CA using radiomics extracted from TTE. Methods We conducted a retrospective cohort study between 2011 and 2022 in Taiwan. Patients with left ventricular (LV) mass index≥95g/m² in women or ≥115g/m² in men, or the maximal LV wall thickness≥13mm on echocardiography were enrolled. The TTE automatic processing models included four steps: image quality assessment, view classification, auto-segmentation of interventricular septum (IVS), and dynamic radiomics analysis. One echocardiographic study was scanned per patient, and each study contains multiple cardiac cycles. One sample was extracted from each cycle and all samples were evaluated by image quality assessment and view classification model that based on VGG16. Samples with apical four (A4C) and apical five chamber (A5C) views were retained for following steps. Manual contouring of the septal region served as the ground truth for auto-segmentation based on U-Net. The last step was the extraction of dynamic radiomic features from IVS and the dynamic radiomic features were analyzed using an LSTM model to differentiate three hypertrophic cardiomyopathies. Results There were 103 patients with FC, 96 patients with HCM, and 49 patients with CA in this study. Patients with CA were older and had lower LV ejection fraction (EF) than those with FC or HCM (age: 65.8±9.7 vs. 59.3±11.1 vs. 58.6±14.2 years, P=0.002; LVEF: 56.6±10.8 vs. 61.9±8.8 vs. 64.5±8.3%, P&amp;lt;0.001). In addition, patient with FC had larger LV mass index than those with HCM or CA. (Fig. 2A) The details of the performances of four stepwise models were provided in Figure 2 (B-F). The image quality assessment and view classification models achieved accuracies of 91.7% and 95.2%, respectively. The auto-segmentation had the median Dice score as 0.88 for contouring IVS. The results indicated that deep learning algorithms had great ability to recognize different views with proper image quality, and could contour the IVS adequately. The accuracy of dynamic radiomics-based LSTM model to differentiate three hypertrophic phenocopies was 74.5%. Conclusion We developed an automatic process with four stepwise models for analyzing the radiomics of IVS on TTE to differentiate FC, HCM and CA with the modest performance by LSTM model. The developed automatic models may provide potential implications to identify various etiologies LVH in the future.Figure 1-Workflow of this studyFigure 2-Performances of stepwise models

ESR Essentials: radiomics-practice recommendations by the European Society of Medical Imaging Informatics.

Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline-such as image, application of image filters, and selection of feature extraction parameters-can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics. KEY POINTS: Radiomics' inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making. Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor. Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.

Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics

BackgroundSentinel lymph node (SLN) biopsy (SLNB) is considered the gold standard for detecting SLN metastases in patients with invasive ductal breast cancer (IDC). However, SLNB is invasive and associated with several complications. Thus, this study aimed to evaluate the diagnostic performance of a non-invasive radiomics analysis utilizing 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography (18F-FDG-PET/CT) for assessing SLN metastasis in IDC patients.MethodsThis retrospective study included 132 patients with biopsy-confirmed IDC, who underwent 18F-FDG PET/CT scans prior to mastectomy or breast-conserving surgery with SLNB. Tumor resection or SLNB was conducted within one-week post-scan. Clinical data and metabolic parameters were analyzed to identify independent SLN metastasis predictors. Radiomic features were extracted from each PET volume of interest (VOI) and CT-VOI. Feature selection involved univariate and multivariate logistic regression analysis, and the least absolute shrinkage and selection operator (LASSO) method. Three models were developed to predict SLN status using the random forest (RF), decision tree (DT), and k-Nearest Neighbors (KNN) classifiers. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe study included 91 cases (32 SLN-positive and 59 SLN-negative patients) in the training cohort and 41 cases (29 SLN-positive and 12 SLN-negative patients) in the validation cohort. Multivariate logistic regression analysis identified Ki 67 and TLG as independent predictors of SLN status. Five PET-derived features, three CT-derived features, and two clinical variables were selected for model development. The AUC values of the RF, KNN, and DT models for the training cohort were 0.887, 0.849, and 0.824, respectively, and for the validation cohort were 0.856, 0.830, and 0.819, respectively. The RF model demonstrated the highest accuracy for the preoperative prediction of SLN metastasis in IDC patients.ConclusionThe PET-CT radiomics approach may offer robust and non-invasive predictors for SLN status in IDC patients, potentially aiding in the planning of personalized treatment strategies for IDC patients.

Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study

This study investigates Intraobserver Features Variability (IFV) in radiomics studies and assesses the effectiveness of the ComBat harmonization method in mitigating these effects. Methods: This study utilizes data from the NSCLC-Radiomics-Interobserver1 dataset, comprising CT scans of 22 Non-Small Cell Lung Cancer (NSCLC) patients, with multiple Gross Tumor Volume (GTV) delineations performed by five radiation oncologists. Segmentation was completed manually (“vis”) or by autosegmentation with manual editing (“auto”). A total of 1229 radiomic features were extracted from each GTV, segmentation method, and oncologist. Features extracted included first order, shape, GLCM, GLRLM, GLSZM, and GLDM from original, wavelet-filtered, and LoG-filtered images. Results: Before implementing ComBat harmonization, 83% of features exhibited p-values below 0.05 in the “vis” approach; this percentage decreased to 34% post-harmonization. Similarly, for the “auto” approach, 75% of features demonstrated statistical significance prior to ComBat, but this figure declined to 33% after its application. Among a subset of three expert radiation oncologists, percentages changed from 77% to 25% for “vis” contouring and from 64% to 23% for “auto” contouring. This study demonstrates that ComBat harmonization could effectively reduce IFV, enhancing the feasibility of multicenter radiomics studies. It also highlights the significant impact of physician experience on radiomics analysis outcomes.

Differentiating MYCN-amplified RB1 wild-type retinoblastoma from biallelic RB1 mutant retinoblastoma using MR-based radiomics: a retrospective multicenter case–control study

MYCN-amplified RB1 wild-type (MYCNampRB1+/+) retinoblastoma is a rare and aggressive subtype, often resistant to standard therapies. Identifying unique MRI features is crucial for diagnosing this subtype, as biopsy is not recommended. This study aimed to differentiate MYCNampRB1+/+ from the most prevalent RB1-/- retinoblastoma using pretreatment MRI and radiomics. Ninety-eight unilateral retinoblastoma patients (19 MYCN cases and 79 matched controls) were included. Tumors on T2-weighted MR images were manually delineated and validated by experienced radiologists. Radiomics analysis extracted 120 features per tumor. Several combinations of feature selection methods, oversampling techniques and machine learning (ML) classifiers were evaluated in a repeated fivefold cross-validation machine learning pipeline to yield the best-performing prediction model for MYCN. The best model used univariate feature selection, data oversampling (duplicating MYCN cases), and logistic regression classifier, achieving a mean AUC of 0.78 (SD 0.12). SHAP analysis highlighted lower sphericity, higher flatness, and greater gray-level heterogeneity as predictive for MYCNampRB1+/+ status, yielding an AUC of 0.81 (SD 0.11). This study shows the potential of MRI-based radiomics to distinguish MYCNampRB1+/+ and RB1-/- retinoblastoma subtypes.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.

Evaluation of an Image-based Classification Model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset.

Glioma is acomplex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and mutations. Atotal of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using amachine learning framework consisting of ReliefF feature ranking and logistic model tree classification algorithms. The results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 ± 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 ± 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 ± 32.72 selected features. Radiomics and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

The RECONCILE study protocol: Exploiting image-based risk stratification in early prostate cancer to discriminate progressors from non-progressors (RECONCILE).

RECONCILE (ClinicalTrials.gov:NCT04340245) will identify molecular and radiomic markers associated with clinical progression and radiological progression events in a cohort of localised, newly diagnosed Gleason 3 + 4 tumours. Molecular markers will be correlated against standard of care MRI-targeted histology and oncological outcomes. RECONCILE is an ethics approved (20/LO/0366) single centre, prospective, longitudinal, observational cohort study of recently diagnosed (within 12 months), organ-confined Gleason 3 + 4 cancers (MCCL ≤10mm) currently under active surveillance. 60 treatment-naïve participants with a concordant MRI lesion (Likert score 4 or 5) and PSA ≤ 15 ng/ml will be recruited. Blood, urine and targeted prostate tissue cores will be subject to next generation sequencing at baseline and one year in all participants. Semen will be collected from a specified sub-population. Baseline and interval MR images will be extracted from standard of care prostate MRI ahead of radiomic analysis. Data extracted from radiological and biological samples will be used to derive the association of molecular change and radiological progression, the primary outcome of the study. To compensate for spatial intratumoral heterogeneity and inherent sampling bias, a molecular index will be derived for each participant using the molecular profile of tumour tissue at both baseline (MolBL) and one year (MolFU). We will extract a ΔMolBL:MolFU score for each participant. Molecular progression will be defined as a MolBL:MolFU score >95% CI of the combined ΔMolBL scores. Radiological progression is defined as a PRECISE score of 4 or 5. The study is powered to detect an association with a statistical power of 80%. Recruitment began in July 2020 (n = 62). To date, 37 participants have donated tissue for analysis. We have designed and implemented a prospective, longitudinal study to evaluate the underlying molecular landscape of intermediate risk, MR-visible prostate tumours. Recruitment is ongoing.

Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?

BackgroundTo compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC).MethodsTwo hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors’ 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA).ResultsThere were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model.ConclusionsBoth 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.

Platform for the radiomics analysis of brain regions: The case of Alzheimer's disease and metabolic imaging

ObjectiveThis study introduces a PET-based platform for brain radiomics analysis. We automatically identify key brain regions and features associated with Alzheimer's disease (AD), enabling more accurate diagnosis and staging compared to using predefined regions. MethodsTo create an integrated platform that covers all the phases of radiomics, we obtained FDG-PET images of 549 individuals from the ADNI database. We used FastSurfer to segment the brain into 95 regions. We then obtained 120 features for each of the 95 ROIs. We employed eight feature selection methods to select and analyze the features. We finally utilized nine different classifiers on the 20 most significant features extracted. ResultsFor all three predictions AD vs. cognitively normal (CN), AD vs. mild cognitive impairments (MCI), and CN vs. MCI the Random Forest (RF) classifier with LASSO demonstrated the highest accuracy with an AUC of 0.976 for AD vs CN, AUC=0.917 for AD vs MCI, and AUC=0.877 for MCI vs CN. This is the highest performance that we encountered compared to the studies in the literature. Three subregions hippocampus, entorhinal, and amygdala could then be identified as critical. ConclusionA brain radiomics platform can enable an efficient, standardized, and optimally accurate AD and MCI diagnosis from FDG PET images by using an automated pipeline. The three regions identified as having the highest discriminating power confirm the findings of previous clinical research results on AD. While the focus was AD in this study, the platform can potentially be used to address other brain conditions.

Draw on advantages and avoid disadvantages: CT-derived individualized radiomic signature for predicting chemo-radiotherapy sensitivity in unresectable advanced non-small cell lung cancer

BackgroundPresently, the options of concurrent chemo-radiotherapy (CCR) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) are controversial and there is no reliable prediction tool to stratify poor- and good-responders. Although radiomic analysis has provided new opportunities for personalized medicine in oncological practice, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This study aimed to develop a qualitative radiomic signature based on the within-sample rank of radiomics features, and to use this novel method to predict CCR sensitivity in LA-NSCLC, avoiding the variability of quantitative signatures to multicenter effect.MethodsWe retrospectively analyzed 125 patients with stage III NSCLC who received treatment from our hospital. Radiomic features were extracted from pretreatment plain CT scans and constructed as feature pairs based on their within-sample rank. Fisher and univariate Cox analyses were performed to select feature pairs significantly associated with patients’ overall survival (OS). NSCLC-Radiomic (R422) cohort including 104 NSCLC patients was used as an independent testing cohort. NSCLC-Radiogenomic (RG211) cohort with matched RNA sequencing profiles, was used for functional enrichment analysis to reveal the underlying biological mechanism reflected by the signature.ResultsA qualitative signature, consisting of 15 radiomic feature pairs (termed as 15-RiFPS), was developed based on the Genetic Algorithm, which could optimally distinguish responder from non-responder with significantly improved OS if they received CCR treatment (log-rank P = 0.0009, HR = 13.79, 95% CIs 1.83–104.1). The performance of 15-RiFPS was validated in an independent public cohort (log-rank P = 0.0037, HR = 2.40, 95% CIs 1.30–4.40). Furthermore, the transcriptomic analyses provided biological pathways (‘glutathione metabolic process’, ‘cellular oxidant detoxification’) underlying the signature.ConclusionsWe developed a CT-derived 15-RiFPS, which could potentially help predict individualized therapeutic benefit of CCR in patients with LA-NSCLC. Additionally, we investigated the underlying intra-tumoral biological characteristics behind 15-RiFPS which would accelerate its clinical application. This approach could be applied to a wider range of treatments and cancer types.

Creation of a Prediction Model of Local Tumor Recurrence After a Successful Conventional Transcatheter Arterial Chemoembolization Using Cone-Beam Computed Tomography Based-Radiomics.

To create and evaluate prediction models of local tumor recurrence after successful conventional transcatheter arterial chemoembolization (c-TACE) via radiomics analysis of lipiodol deposition using cone-beam computed tomography (CBCT) images obtained at the completion of TACE. A total of 103 hepatocellular carcinoma nodules in 71 patients, who achieved a complete response (CR) based on the modified Response Evaluation Criteria in Solid Tumors 1month after TACE, were categorized into two groups: prolonged CR and recurrence groups. Three types of areas were segmented on CBCT: whole segment (WS), tumor segment (TS), and peritumor segment (PS). From each segment, 105 radiomic features were extracted. The nodules were randomly divided into training and test datasets at a ratio of 7:3. Following feature reduction for each segment, three models (clinical, radiomics, and clinical-radiomics models) were developed to predict recurrence based on logistic regression. The clinical-radiomics model of WS showed the best performance, with the area under the curve values of 0.853 (95% confidence interval: 0.765-0.941) in training and 0.752 (0.580-0.924) in test dataset. In the analysis of radiomic feature importance of all models, among all radiomic features, glcm_MaximumProbability, shape_MeshVolume and shape_MajorAxisLength had negative coefficients. In contrast, shape_SurfaceVolumeRatio, shape_Elongation, glszm_SizeZoneNonUniformityNormalized, and gldm_GrayLevelNonUniformity had positive coefficients. In this study, a machine-learning model based on cone-beam CT images obtained at the completion of c-TACE was able to predict local tumor recurrence after successful c-TACE. Nonuniform lipiodol deposition and irregular shapes may increase the likelihood of recurrence.

Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics

Objectives: This study aims to investigate MRI features predicting the grade of STS malignancy using conventional image reading and radiomics. Methods: Pretherapeutic imaging data regarding size, tissue heterogeneity, peritumoral changes, necrosis, hemorrhage, and cystic degeneration were evaluated in conventional image reading. Furthermore, the tumors’ apparent diffusion coefficient (ADC) values and radiomics features were extracted and analyzed. A random forest machine learning algorithm was trained and evaluated based on the extracted features. Results: A total of 139 STS cases were included in this study. The mean tumor ADC and the ratio between tumor ADC to healthy muscle ADC were significantly lower in high-grade tumors (p = 0.001 and 0.005, respectively). Peritumoral edema (p &lt; 0.001) and peritumoral contrast enhancement (p &lt; 0.001) were significantly more extensive in high-grade tumors. Tumor heterogeneity was significantly increased in high-grade sarcomas, particularly in T2w- and contrast-enhanced sequences using conventional image reading (p &lt; 0.001) as well as in the radiomics analysis (p &lt; 0.001). Our trained random forest machine learning model predicted high-grade status with an area under the curve (AUC) of 0.97 and an F1 score of 0.93. Biopsy-underestimated tumors exhibited differences in tumor heterogeneity and peritumoral changes. Conclusions: Tumor heterogeneity is a key characteristic of high-grade STSs, which is discernible through conventional imaging reading and radiomics analysis. Higher STS grades are also associated with low ADC values, peritumoral edema, and peritumoral contrast enhancement.