Volume Of Interest Research Articles

Habitat Imaging With Tumoral and Peritumoral Radiomics for Prediction of Lung Adenocarcinoma Invasiveness on Preoperative Chest CT: A Multicenter Study.

Background: Tumors' growth processes result in spatial heterogeneity, with the development of tumor subregions (i.e., habitats) having unique biologic characteristics. Objective: To develop and validate a habitat model combining tumor and peritumoral radiomics features on chest CT for predicting invasiveness of lung adenocarcinoma. Methods: This retrospective study included 1156 patients (mean age, 57.5 years; 464 male, 692 female) from three centers and a public dataset, who underwent chest CT before lung adenocarcinoma resection (variable date ranges across datasets). Patients from one center formed training (n=500) and validation (n=215) sets; patients from the other sources formed three external test sets (n=249, 113, 79). For each patient, a single nodule was manually segmented on chest CT. The nodule segmentation was combined with an automatically generated 4-mm peritumoral region into a whole-volume volume-of-interest (VOI). A Gaussian mixture model (GMM) identified voxel clusters with similar first-order energy across patients. GMM results were used to divide each patient's whole-volume VOI into multiple habitats, defined consistently across patients. Radiomic features were extracted from each habitat. After feature selection, a habitat model was developed for predicting invasiveness, using pathologic assessment as a reference. An integrated model was constructed, combining features extracted from habitats and whole-volume VOIs. Model performance was evaluated, including in subgroups based on nodule density (pure ground-glass, part-solid, solid). Results: Invasive cancer was diagnosed in 625/1156 patients. GMM identified four as the optimal number of voxel clusters and thus of per-patient tumor habitats. The habitat model had AUC of 0.932 in the validation set, and 0.881, 0.880, and 0.764 in the three external test sets. The integrated model had AUC of 0.947 in the validation set and 0.936, 0.908, and 0.800 in the three external test sets. In the three external test sets combined, across nodule densities, AUCs for the habitat model were 0.836-0.969 and for the integrated model were 0.846-0.917. Conclusions: Habitat imaging combining tumoral and peritumoral radiomic features could help predict lung adenocarcinoma invasiveness. Prediction is improved when combining information on tumor subregions and the tumor overall. Clinical Impact: The findings may aid personalized preoperative assessments to guide clinical decision-making in lung adenocarcinoma.

Peri- and intra-nodular radiomic features based on 18F-FDG PET/CT to distinguish lung adenocarcinomas from pulmonary granulomas.

To compare the effectiveness of radiomic features based on 18F-FDG PET/CT images within (intranodular) and around (perinodular) lung nodules/masses in distinguishing between lung adenocarcinoma and pulmonary granulomas. For this retrospective study, 18F-FDG PET/CT images were collected for 228 patients. Patients diagnosed with lung adenocarcinoma (n = 156) or granulomas (n = 72) were randomly assigned to a training (n = 159) and validation (n = 69) groups. The volume of interest (VOI) of intranodular, perinodular (1-5 voxels, termed Lesion_margin1 to Lesion_margin5) and total area (intra- plus perinodular region, termed Lesion_total1 to Lesion_total5) on PET/CT images were delineated using PETtumor and Marge tool of segmentation editor. A total of 1,037 radiomic features were extracted separately from PET and CT images, and the optimal features were selected to develop radiomic models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Good and acceptable performance was, respectively, observed in the training (AUC = 0.868, p < 0.001) and validation (AUC = 0.715, p = 0.004) sets for the intranodular radiomic model. Among the perinodular models, the Lesion_margin2 model demonstrated the highest AUC in both sets (0.883 and 0.616, p < 0.001 and p = 0.122). Similarly, in terms of total models, Lesion_total2 model was found to outperform others in the training (AUC = 0.879, p < 0.001) and validation (AUC = 0.742, p = 0.001) sets, slightly surpassing the intranodular model. When intra- and perinodular radiomic features extracted from the immediate vicinity of the nodule/mass up to 2 voxels distance on 18F-FDG PET/CT imaging are combined, improved differential diagnostic performance in distinguishing between lung adenocarcinomas and granulomas is achieved compared to the intra- and perinodular radiomic features alone.

Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature.

To investigate the prognostic value of clinical, volumetric, and radiomics-based textural parameters in baseline [18F]FDG-PET/CT scans of diffuse large B-cell lymphoma (DLBCL) patients. We retrospectively investigated baseline PET/CT scans and collected clinical data of fifty DLBCL patients. PET images were segmented semiautomatically to determine metabolic tumor volume (MTV), then the largest segmented lymphoma volume of interest (VOI) was used to extract first-, second-, and high-order textural features. A novel value, MTVrate was introduced as the quotient of the largest lesion's volume and total body MTV. Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression. The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. A combined survival analysis including MTV and MTVrate identified a subgroup with particularly low PFS at 38%. In the machine learning-based model had an AUC of 0.83 and the highest relative importance was attributed to three textural features and both MTV and MTVrate as important predictors of PFS. Individual evaluation of different biomarkers yielded only limited prognostic data, whereas a machine learning-based combined analysis had higher effectivity. MTVrate had the highest prognostic ability on individual analysis and, combined with MTV, it identified a patient group with particularly poor prognosis.

Pharmacokinetic Comparison of Selective Prostatic Arterial and Intravenous PSMA PET/CT Radioligand Infusions in Primary Prostatic Adenocarcinoma.

Background Intravenous prostate-specific membrane antigen (PSMA)-targeted radioligand therapy improves survival in men with metastatic castration-resistant prostate cancer. Yet, the impact of selective prostatic arterial administration on primary tumor uptake is unclear. Purpose To compare gallium 68 (68Ga)-PSMA-11 uptake using dynamic PET/CT in prostatic tumoral volumes of interest (VOIs) during intravenous and selective prostatic arterial infusions for individuals with untreated, high-risk prostate cancer. Materials and Methods In this prospective, intraindividual comparative study conducted at an academic medical center, five men aged 58, 61, 64, 66, and 68 years with treatment-naive prostate cancer were enrolled between January 2022 and February 2023 and underwent two dynamic 68Ga-PSMA-11 PET/CT examinations 1 week apart. During the first examination, the radiotracer was administered intravenously. During the second administration, the radiotracer was delivered into either the right or left prostatic artery through an angiographically placed microcatheter. The primary outcome was maximum standardized uptake value (SUVmax) in prostatic tumoral VOIs. The secondary outcomes included mean SUV (SUVmean) in prostatic tumoral VOIs and area under the SUVmean curves (AUC). Longitudinal mixed-effects models were used to compare dynamic SUVmax and SUVmean time-activity curves (TACs), and paired t tests were used for the remaining data. Results The mean SUVmax within tumoral VOIs was 14 (range, 3-43) for venous sessions and 938 (range, 460-1436) for arterial sessions (P = .008). The SUVmean within VOIs was greater during arterial sessions (P < .001) overall and 46-fold and 19-fold greater at peak uptake and final time points, respectively. The mean AUC was greater on arterial TACs than on venous TACs at 14600 SUV × min (range, 8353-20025 SUV × min) and 240 SUV × min (range, 69-622 SUV × min), respectively (P = .002). Conclusion Selective prostatic arterial infusion resulted in greater 68Ga-PSMA-11 tumoral SUV than intravenous infusion. Further study of local-regional, intra-arterial delivery of a PSMA-targeted theranostic agent is warranted in high-risk prostate cancer. ClinicalTrials.gov identifier: NCT04976257 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Civelek in this issue.

Differentiating low-risk thymomas from high-risk thymomas: preoperative radiomics nomogram based on contrast enhanced CT to minimize unnecessary invasive thoracotomy

BackgroundThis study was designed to develop a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas based on contrast-enhanced computed tomography (CE-CT) images.MaterialsThe clinical and CT data of 178 patients with thymoma (100 patients with low-risk thymomas and 78 patients with high-risk thymomas) collected in our hospital from March 2018 to July 2023 were retrospectively analyzed. The patients were randomly divided into a training set (n = 125) and a validation set (n = 53) in a 7:3 ratio. Qualitative radiological features were recorded, including (a) tumor diameter, (b) location, (c) shape, (d) capsule integrity, (e) calcification, (f) necrosis, (g) fatty infiltration, (h) lymphadenopathy, and (i) enhanced CT value. Radiomics features were extracted from each CE-CT volume of interest (VOI), and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select the optimal discriminative ones. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The differentiating efficacy was determined using receiver operating characteristic (ROC) analysis.ResultsOnly one clinical factor (incomplete capsule) and seven radiomics features were found to be independent predictors and were used to establish the radiomics nomogram. In differentiating low-risk thymomas (types A, AB, and B1) from high-risk ones (types B2 and B3), the nomogram demonstrated better diagnostic efficacy than any single model, with the respective area under the curve (AUC), accuracy, sensitivity, and specificity of 0.974, 0.921, 0.962 and 0.900 in the training cohort, 0.960, 0.892, 0923 and 0.897 in the validation cohort, respectively. The calibration curve showed good agreement between the prediction probability and actual clinical findings.ConclusionsThe nomogram incorporating clinical factors and radiomics features provides additional value in differentiating the risk categorization of thymomas, which could potentially be useful in clinical practice for planning personalized treatment strategies.

Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.

This study utilizes [99mTc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine learning algorithms can identify microscopic early bone metastases. Additionally, we also determine the optimal machine learning approach. We retrospectively studied 63 patients with early bone metastasis from July 2020 to March 2023. The ITK-SNAP software was used to delineate early bone metastases and normal bone tissue in SPECT images of each patient, which were then registered onto CT images to outline the volume of interest (VOI). The VOI includes 63 early bone metastasis volumes and 63 normal bone tissue volumes. 126 VOIs were randomly distributed in a 7:3 ratio between the training and testing groups, and 944 radiomics features were extracted from every VOI. We established 20 machine learning models using 5 feature selection algorithms and 4 classification methods. Evaluate the performance of the model using the area under the receiver operating characteristic curve (AUC). Most machine learning models demonstrated outstanding discriminative capacity, with AUCs higher than 0.70. Notably, the K-Nearest Neighbors (KNN) classifier exhibited significant performance improvement compared to the other four classifiers. Specifically, the model constructed utilizing eXtreme Gradient Boosting (XGBoost) feature selection method integrated with KNN classifier achieved the maximum AUC, which is 0.989 in the training set and 0.975 in the testing set. Radiomics features integrated with machine learning methods can identify early bone metastases that are not visible on CT images. In our analysis, KNN is considered the optimal classification method.

Probing diffusion of water and metabolites to assess white matter microstructure in Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a progressive X-linked neuromuscular disorder caused by the absence of functional dystrophin protein. In addition to muscle, dystrophin is expressed in the brain in both neurons and glial cells. Previous studies have shown altered white matter microstructure in patients with DMD using diffusion tensor imaging (DTI). However, DTI measures the diffusion properties of water, a ubiquitous molecule, making it difficult to unravel the underlying pathology. Diffusion-weighted spectroscopy (DWS) is a complementary technique which measures diffusion properties of cell-specific intracellular metabolites. Here we performed both DWS and DTI measurements to disentangle intra- and extracellular contributions to white matter changes in patients with DMD. Scans were conducted in patients with DMD (15.5 ± 4.6 y/o) and age- and sex-matched healthy controls (16.3 ± 3.3 y/o). DWS measurements were obtained in a volume of interest (VOI) positioned in the left parietal white matter. Apparent diffusion coefficients (ADCs) were calculated for total N-acetylaspartate (tNAA), choline compounds (tCho), and total creatine (tCr). The tNAA/tCr and tCho/tCr ratios were calculated from the non-diffusion-weighted spectrum. Mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and fractional anisotropy of water within the VOI were extracted from DTI measurements. DWS and DTI data from patients with DMD (respectively n= 20 and n= 18) and n= 10 healthy controls were included. No differences in metabolite ADC or in concentration ratios were found between patients with DMD and controls. In contrast, water diffusion (MD, t= -2.727, p= 0.011; RD, t= -2.720, p= 0.011; AD, t= -2.715, p= 0.012) within the VOI was significantly higher in patients compared with healthy controls. Taken together, our study illustrates the potential of combining DTI and DWS to gain a better understanding of microstructural changes and their association with disease mechanisms in a clinical setting.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study

ObjectivesTo develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset.Materials and methodsRetrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions.ResultsThe Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort.ConclusionThe multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model.Clinical relevance statementPredicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study

PurposeDevelopment and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment. MethodsThe study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity. ResultsA total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT1–2N0M0 diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort. ConclusionThe radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.

The value of PET/CT radiomic texture analysis of primary mass and mediastinal lymph node on survival in patients with non-small cell lung cancer

ObjectiveThis study was designed to determine the potential prognostic value of radiomic texture analysis and metabolic-volumetric parameters obtained from positron emission tomography (PET) in primary mass and metastatic hilar/mediastinal lymph nodes in stage 2–3 non-small cell lung cancer (NSCLC). MethodsImages of patients diagnosed with stage 2–3 NSCLC who underwent 18F-FDG PET/CT imaging for staging up to 4 weeks before the start of treatment were evaluated using LIFEx software. Volume of interest (VOI) was generated from the primary tumor and metastatic lymph node separately, and volumetric and textural features were obtained from these VOIs. The relationship between the parameters obtained from PET of primary mass and the metastatic hilar/mediastinal lymph nodes with overall survival (OS) and progression-free survival (PFS) was analyzed. ResultsWhen radiomic features, gender and stage obtained from lymph nodes were evaluated by Cox regression analysis; GLCM_correlation (p: 0.033, HR: 4,559, 1.660–12.521, 95% CI), gender and stage were determined as prognostic factors predicting OS. In predicting PFS; stage, smoking and lymph node MTV (p: 0.033, HR: 1.008, 1.001–1.016, 95% CI) were determined as prognostic factors. However, the radiomic feature of the primary tumor could not show a significant relationship with either OS or PFS. ConclusionsIn a retrospective cohort of NSCLC patients with Stage 2 and 3 disease, volumetric and radiomic texture characteristics obtained from metastatic lymph nodes were associated with PFS and OS. Tumor heterogeneity, defined by radiomic texture features of 18 F-FDG PET/CT images, may provide complementary prognostic value in NSCLC.

Quantification of volumetric thigh and paravertebral muscle fat content: comparison of quantitative Dixon (Q-Dixon) magnetic resonance imaging (MRI) with high-speed T2-corrected multiecho MR spectroscopy.

Muscle fat infiltration (MFI) is increasingly recognized as a critical factor influencing muscle function and metabolic health. Accurate quantification of MFI is essential for diagnosing and monitoring various muscular and metabolic disorders. Quantitative Dixon (Q-Dixon) magnetic resonance imaging (MRI) and high-speed T2-corrected multi-echo (HISTO) magnetic resonance spectroscopy (MRS) are both advanced imaging techniques that offer potential for detailed assessment of MFI. However, the validity and reliability of these methods in measuring volumetric changes in muscle composition, particularly in both thigh and paravertebral muscles, have not been thoroughly compared. This study aims to validate volumetric measurements using Q-Dixon MRI against HISTO MRS in thigh and paravertebral muscles, taking into account the heterogeneity of MFI. A retrospective study was conducted with 54 subjects [mean age, 60 years; 38 male (M)/16 female (F)] for thigh muscle and 56 subjects (mean age, 50 years; 22 M/34 F) for paravertebral muscle assessment using a 3-Tesla MRI. The proton density fat fraction (PDFF) was measured with Q-Dixon MRI and HISTO MRS within the upper-middle part of quadriceps femoris and paravertebral muscles at L4/5 level in volumes-of-interest (VOIs). The corresponding volumetric Q-Dixon freehand VOI PDFF was measured. Scatterplots, Bland-Altman plots, Spearman correlation coefficients, and Wilcoxon signed rank test with Bonferroni correction were employed. The Kruskal-Wallis H tests followed by Bonferroni-corrected post hoc tests were analyzed to compare parameter differences with visual MFI grades. Q-Dixon cubic VOI PDFF correlated positively with HISTO MRS PDFF in thigh (r=0.96, P<0.001) and paravertebral groups (r=0.98, P<0.001), with insignificant differences (P=0.29, 0.82, respectively). Both PDFF values from cubic VOIs in Q-Dixon and HISTO MRS differed from the freehand Q-Dixon PDFF (all P<0.001). Only for <5% HISTO MRS PDFF, there was a difference between HISTO MRS PDFF and Q-Dixon cubic VOI PDFF (P=0.002). Volumetric Q-Dixon cubic VOI PDFF exhibited good correlation and consistency with HISTO MRS PDFF for quantitative fat assessment in thigh and paravertebral muscles except for muscles with fat fraction <5%, and the Q-Dixon freehand VOI PDFF offers a more comprehensive assessment of the actual MFI compared to cubic VOI.

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.

Optimizing SUV Analysis: A Multicenter Study on Preclinical FDG-PET/CT Highlights the Impact of Standardization

PurposePreclinical imaging, with translational potential, lacks a standardized method for defining volumes of interest (VOIs), impacting data reproducibility. The aim of this study was to determine the interobserver variability of VOI sizes and standard uptake values (SUVmean and SUVmax) of different organs using the same [18F]FDG-PET and PET/CT datasets analyzed by multiple observers. In addition, the effect of a standardized analysis approach was evaluated.ProceduresIn total, 12 observers (4 beginners and 8 experts) analyzed identical preclinical [18F]FDG-PET-only and PET/CT datasets according to their local default image analysis protocols for multiple organs. Furthermore, a standardized protocol was defined, including detailed information on the respective VOI size and position for multiple organs, and all observers reanalyzed the PET/CT datasets following this protocol.ResultsWithout standardization, significant differences in the SUVmean and SUVmax were found among the observers. Coregistering CT images with PET images improved the comparability to a limited extent. The introduction of a standardized protocol that details the VOI size and position for multiple organs reduced interobserver variability and enhanced comparability.ConclusionsThe protocol offered clear guidelines and was particularly beneficial for beginners, resulting in improved comparability of SUVmean and SUVmax values for various organs. The study suggested that incorporating an additional VOI template could further enhance the comparability of the findings in preclinical imaging analyses.

Radiomics Analysis of Preprocedural CT Imaging for Outcome Prediction after Transjugular Intrahepatic Portosystemic Shunt Creation.

To evaluate the role of radiomics in preoperative outcome prediction in cirrhotic patients who underwent transjugular intrahepatic portosystemic shunt (TIPS) using "controlled expansion covered stents". This retrospective institutional review board-approved study included cirrhotic patients undergoing TIPS with controlled expansion covered stent placement. From preoperative CT images, the whole liver was segmented into Volumes of Interest (VOIs) at the unenhanced and portal venous phase. Radiomics features were extracted, collected, and analyzed. Subsequently, receiver operating characteristic (ROC) curves were drawn to assess which features could predict patients' outcomes. The endpoints studied were 6-month overall survival (OS), development of hepatic encephalopathy (HE), grade II or higher HE according to West Haven Criteria, and clinical response, defined as the absence of rebleeding or ascites. A radiomic model for outcome prediction was then designed. A total of 76 consecutive cirrhotic patients undergoing TIPS creation were enrolled. The highest performances in terms of the area under the receiver operating characteristic curve (AUROC) were observed for the "clinical response" and "survival at 6 months" outcome with 0.755 and 0.767, at the unenhanced and portal venous phase, respectively. Specifically, on basal scans, accuracy, specificity, and sensitivity were 66.42%, 63.93%, and 73.75%, respectively. At the portal venous phase, an accuracy of 65.34%, a specificity of 62.38%, and a sensitivity of 74.00% were demonstrated. A pre-interventional machine learning-based CT radiomics algorithm could be useful in predicting survival and clinical response after TIPS creation in cirrhotic patients.

Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored

ObjectivesTo compare the diagnostic performance of intratumoral and peritumoral features from different contrast phases of breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building radiomics models for differentiating molecular subtypes of breast cancer.MethodsThis retrospective study included 377 patients with pathologically confirmed breast cancer. Patients were divided into training set (n = 202), validation set (n = 87) and test set (n = 88). The intratumoral volume of interest (VOI) and peritumoral VOI were delineated on primary breast cancers at three different DCE-MRI contrast phases: early, peak, and delayed. Radiomics features were extracted from each phase. After feature standardization, the training set was filtered by variance analysis, correlation analysis, and least absolute shrinkage and selection (LASSO). Using the extracted features, a logistic regression model based on each tumor subtype (Luminal A, Luminal B, HER2-enriched, triple-negative) was established. Ten models based on intratumoral or/plus peritumoral features from three different phases were developed for each differentiation.ResultsRadiomics features extracted from delayed phase DCE-MRI demonstrated dominant diagnostic performance over features from other phases. However, the differences were not statistically significant. In the full fusion model for differentiating different molecular subtypes, the most frequently screened features were those from the delayed phase. According to the Shapley additive explanation (SHAP) method, the most important features were also identified from the delayed phase.ConclusionsThe intratumoral and peritumoral radiomics features from the delayed phase of DCE-MRI can provide additional information for preoperative molecular typing. The delayed phase of DCE-MRI cannot be ignored.Critical relevance statementRadiomics features extracted and radiomics models constructed from the delayed phase of DCE-MRI played a crucial role in molecular subtype classification, although no significant difference was observed in the test cohort.Key PointsThe molecular subtype of breast cancer provides a basis for setting treatment strategy and prognosis.The delayed-phase radiomics model outperformed that of early-/peak-phases, but no differently than other phases or combinations.Both intra- and peritumoral radiomics features offer valuable insights for molecular typing.Graphical

Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer

To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593–0.9996) in the training cohort and 0.909 (95% CI 0.7921–1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.

Prognostic value of visual IMPeTUs criteria and metabolic tumor burden at baseline [18F]FDG PET/CT in patients with newly diagnosed multiple myeloma

Background2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography combined with low-dose computed tomography (PET/CT) can be used at diagnosis to identify myeloma-defining events and also provides prognostic factors. The aim of this study was to assess the prognostic significance of baseline [18F]FDG PET/CT visual IMPeTUs (Italian myeloma criteria for PET Use)-based parameters and/or total metabolic tumor volume (TMTV) in a single-center population of patients with newly diagnosed multiple myeloma (NDMM) eligible for transplantation.MethodsPatients with MM who underwent a baseline [18F]FDG PET/CT were retrospectively selected from a large internal database of the University Hospital of Liege (Liege, Belgium). Initially, all PET/CT images were visually analyzed using IMPeTUs criteria, followed by delineation of TMTV using a semi-automatic lesion delineation workflow, including [18F]FDG-positive MM focal lesions (FL) with an absolute SUV threshold set at 4.0. In a first step, to ensure PET/CT scans accurate reporting, the agreement between two nuclear medicine physicians with distinct experience was assessed. In the second step, univariable and multivariable analyses were conducted to determine the prognostic significance of [18F]FDG PET/CT parameters on progression free survival (PFS) and overall survival (OS), respectively.ResultsA total of 40 patients with NDMM were included in the study. The observers agreement in the analysis [18F]FDG PET/CT images was substantial for the presence of spine FL, extra spine FL, at least one fracture and paramedullary disease (Cohen’s kappa 0.79, 0.87, 0.75 and 0.64, respectively). For the presence of skull FL and extramedullary disease the agreement was moderate (Cohen’s kappa 0.56 and 0.53, respectively). Among [18F]FDG PET/CT parameters, a high number of delineated volumes of interest (VOI) using the SUV4.0 threshold was the only independent prognostic factor associated with PFS [HR (95% CI): 1.03 (1.004–1.05), P = 0.019] while a high number of FL (n > 10; F group 4) was the only independent prognostic factor associated with OS [HR (95% CI): 19.10 (1.90–191.95), P = 0.01].ConclusionOur work confirms the reproducibility IMPeTUs criteria. Furthermore, it demonstrates that a high number of FL (n > 10; IMPeTUs F group 4), reflecting a high [18F]FDG-avid tumor burden, is an independent prognostic factor for OS. The prognostic value of the TMTV delineated using a SUV4.0 threshold was not significant. Nevertheless, the count of delineated [18F]FDG-avid lesions VOI using a SUV4.0 threshold was an independent prognostic factor for PFS.

Preoperative prediction of HER-2 expression status in breast cancer based on MRI radiomics model

Objective: This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer. Methods: A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness. Results: The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model (P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model (P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort (P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort (P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions: T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Challenges in Pharmacokinetic Modelling of [18F]fluoro-PEG-folate PET/CT Imaging in Epithelial Ovarian Cancer Patients

PurposeTo describe the pharmacokinetic properties of the [18F]fluoro-polyethylene glycol(PEG)-folate radiotracer in PET/CT imaging of patients with advanced stage epithelial ovarian cancer (EOC).ProceduresIn five patients with advanced EOC (FIGO stage IIIB/IIIC, Fédération Internationale de Gynécologie et d’Obstétrique), a 90-min dynamic PET acquisition of the pelvis was performed directly after i.v. administration of 185 MBq [18F]fluoro-PEG6-folate. Arterial blood samples collected at nineteen timepoints were used to determine the plasma input function. A static volume of interest (VOI) for included tumor lesions was drawn manually on the PET images. Modelling was performed using PMOD software. Three different models (a 1-tissue compartment model (1T2k) and two 2-tissue compartment models, irreversible (2T3k) and reversible (2T4k)) were compared in goodness of fit with the time activity curves by means of the Akaike information criterion.ResultsThe pharmacokinetic analysis in the pelvic area has proven to be much more challenging than expected. Only four out of 22 tumor lesions in five patients were considered suitable to perform modelling on. The remaining tumor lesions were inapt due to either low tracer uptake, small size, proximity to other [18F]fluoro-PEG6-folate -avid structures and/or displacement by abdominal organ motion in the dynamic scan. Data from the four analyzed tumor lesions suggest that the irreversible 2T3k may best describe the pharmacokinetics. All 22 lesions were immunohistochemically stained positive for the folate receptor alpha (FRα) after resection.ConclusionPerforming pharmacokinetic analysis in the abdominal pelvic region is very challenging. This brief article describes the challenges and pitfalls in pharmacokinetic analysis of a tracer with high physiological accumulation in the intestines, in case of lesions of limited size in the abdominal pelvic area.

A comparison of methods for in vivo activity and absorbed dose quantification with PET/CT following yttrium-90 radioembolization.

Yttrium-90 (90Y) positron emission tomography (PET)/computed tomography (CT) imaging is increasingly being used to perform tumor (T) and normal liver (NL) voxel dosimetry after 90Y-radioembolization (90Y-RE). Yet, the accuracy of in vivo 90Y-PET/CT imaging, subject to motion blur and co-registration inaccuracies, and 90Y-PET/CT dose quantification, subject to availability of different voxel dosimetry algorithms, are not well understood. The purpose of this study was to investigate the accuracy of 90Y-PET/CT-based activity estimates following 90Y-RE and characterize differences between 90Y-PET/CT-based voxel dosimetry algorithms. Thirty-five patients underwent 90Y-PET/CT imaging after 90Y-RE with TheraSphere. The net administered 90Y activity (Aadmin) was determined using a dose calibrator and pre- and post-procedure exposure rate measurements. The summation of image-based activity (Aimage) was extracted from perfused volume (PV) and 3D-isotropically 2-cm expanded PV contour (PV+2cm). Absorbed doses were calculated using voxel S-value (VSV), local deposition method (LDM), and LDM with known activity (LDMKA) dosimetry algorithms. Linear regression and Bland-Altman analysis quantified the relationship between Aimage and Aadmin and between mean dose estimates (DLDM, DVSV, DLDM-KA) for PV, T, and perfused NL volumes. While Aadmin and Aimage in PV were highly correlated (R2>0.95), the mean bias±standard error (SE) and (95% limits of agreement, LOA) was significantly non-zero with -22.7±4.7% (±28.4%). In PV+2cm, the mean bias±SE (±LOA) decreased to 1.3±3.4% (±18.0%) consistent with zero mean error. DLDM and DVSV were highly correlated (R2>0.99) for all volumes of interest (VOIs) and the meanbias±SE(±LOA) was 2.2±0.2% (±1.0%), 0.7±0.4% (±2.8%), and 3.2±0.5% (±2.8%) for PV, T, and NL, respectively. DLDM-KA and DVSV were correlated with R2=0.86, 0.80, and 0.86 for PV, T, and NL, respectively. The meanbias±SE(±LOA) between DLDM-KA and DVSV was significantly non-zero with -19.6±5.1% (±31.0%), -20.8±4.4% (±29.0%), and -18.1±5.3% (±31.1%) for PV, T, and NL, respectively. The summation of Aimage in PV was underestimated relative to Aadmin. Only by accounting for respiratory motion, limited spatial resolution, and PET/CT co-registration errors through VOI expansion was Aimage, on average, equal to Aadmin. The differences between DLDM and DVSV were not clinically relevant, though DLDM-KA was approximately 20% greater than DVSV. Given the high quantitative accuracy of dose calibrators and challenges associated with accurate 90Y-PET/CT quantification, LDMKA is the preferred algorithm for accurate 90Y-PET/CT-based dosimetry following 90Y-RE.