Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-762. [PMID: 28975929 DOI: 10.1038/nrclinonc.2017.141]

Barriers and facilitators of the application of precision medicine to the genitourinary cancer care pathway: Perspective from a low- and middle- income country in sub-Saharan Africa

The benefit of the delivery of the right form of cancer care, tailored to the right patient, at the right time is increasingly being recognized in the global oncology community. Information on the role and feasible potential of precision oncology during the management of genitourinary cancer in Nigeria, the most populous country in Africa, is limited. This article, therefore, describes the present application of personalized medicine in Nigeria and its barriers and facilitators. It provided granular details on manpower distribution and epidemiological disparities. It also explored the use of clinical and biological markers for screening and early diagnosis, the application of team science to support genomic profiling, cost-effective approaches for image-based phenotypic precision oncology, the emerging role of molecular imaging, access to clinical trials; and their potential to support data driven diagnosis, treatment decision and care availability in order to address gaps in genitourinary cancer management in the country.

Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation learning method

BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive subtype of HCC and is associated with poor survival.

PURPOSE

To investigate the performance of a representation learning-based feature fusion strategy that employs a multiphase contrast-enhanced CT (mpCECT)-based latent feature fusion (MCLFF) model for MTM-HCC identification.

METHODS

A total of 206 patients (54 MTM HCC, 152 non-MTM HCC) who underwent preoperative mpCECT with surgically confirmed HCC between July 2017 and December 2022 were retrospectively included from two medical centers. Multiphasic radiomics features were extracted from manually delineated volume of interest (VOI) of all lesions on each mpCECT phase. Representation learning based MCLFF model was built to fuse multiphasic features for MTM HCC prediction, and compared with competing models using other fusion methods. Conventional imaging features and clinical factors were also evaluated and analyzed. Prediction performance was validated by ROC analysis and statistical comparisons on an internal validation and an external testing dataset.

RESULTS

Fusion of radiomics features from the arterial phase (AP) and portal venous phase (PAP) using MCLFF demonstrated superior performance in MTM HCC prediction, with a higher AUC of 0.857 compared with all competing models in the internal validation set. Integration of multiple radiological or clinical features further improved the overall performance, with the highest AUCs of 0.857 and 0.836 respectively achieved in the internal validation and external testing set.

CONCLUSIONS

Multiphasic radiomics features of AP and PVP fused by the MCLFF have demonstrated substantial potential in the accurate prediction of MTM HCC. Clinical factors and Radiological features in mpCECT contribute incremental values to the developed MCLFF strategy.

Application of imaging technology for the diagnosis of malignancy in the pancreaticobiliary duodenal junction (Review)

The pancreaticobiliary duodenal junction (PBDJ) is the connecting area of the pancreatic duct, bile duct and duodenum. In a broad sense, it refers to a region formed by the head of the pancreas, the pancreatic segment of the common bile duct and the intraduodenal segment, the descending and the horizontal part of the duodenum, and the soft tissue around the pancreatic head. In a narrow sense, it refers to the anatomical Vater ampulla. Due to its complex and variable anatomical features, and the diversity of pathological changes, it is challenging to make an early diagnosis of malignancy at the PBDJ and define the histological type. The unique anatomical structure of this area may be the basis for the occurrence of malignant tumors. Therefore, understanding and subclassifying the anatomical configuration of the PBDJ is of great significance for the prevention and treatment of malignant tumors at their source. The present review comprehensively discusses commonly used imaging techniques and other new technologies for diagnosing malignancy at the PBDJ, offering evidence for physicians and patients to select appropriate examination methods.

Predictive [18F]-FDG PET/CT-Based Radiogenomics Modelling of Driver Gene Mutations in Non-small Cell Lung Cancer

RATIONALE AND OBJECTIVES

To investigate whether [18F]-FDG PET/CT-derived radiomics may correlate with driver gene mutations in non-small cell lung cancer (NSCLC) patients.

MATERIALS AND METHODS

In this IRB-approved retrospective study, 203 patients with surgically treated NSCLC who underwent subsequent genomic analysis of the primary tumour at our institution between December 2004 and January 2014 were identified. Of those, 128 patients (mean age 62.4 ± 10.8 years; range: 35-84) received preoperative [18F]-FDG PET/CT as part of their initial staging and thus were included in the study. PET and CT image segmentation and feature extraction were performed semi-automatically with an open-source software platform (LIFEx, Version 6.30, lifexsoft.org). Molecular profiles using different next-generation sequencing (NGS) panels were collected from a web-based resource (cBioPortal.ca for Cancer genomics). Two statistical models were then built to evaluate the predictive ability of [18F]-FDG PET/CT-derived radiomics features for driver gene mutations in NSCLC.

RESULTS

More than half (68/128, 53%) of all tumour samples harboured three or more gene mutations. Overall, 55% of tumour samples demonstrated a mutation in TP53, 26% of samples had alterations in KRAS and 17% in EGFR. Extensive statistical analysis resulted in moderate to good predictive ability. The highest Youden Index for TP53 was achieved using combined PET/CT features (0.70), for KRAS using PET features only (0.57) and for EGFR using CT features only (0.60).

CONCLUSION

Our study demonstrated a moderate to good correlation between radiomics features and driver gene mutations in NSCLC, indicating increased predictive ability of genomic profiles using combined [18F]-FDG PET/CT-derived radiomics features.

AI Applications to Breast MRI: Today and Tomorrow

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study

As the hallmark of cancer, genetic and phenotypic heterogeneity leads to biomarkers that are typically tailored to specific cancer type or subtype. This specificity introduces complexities in facilitating streamlined evaluations across diverse cancer types and optimizing therapeutic outcomes. In this study, we comprehensively characterized the radiological patterns underlying liver cancer (LC) by integrating radiomics profiles from computed tomography (CT) images of hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and colorectal cancer liver metastases (CRLM) through unsupervised clustering analysis. We identified three distinct radiomics clusters, displaying heterogeneity in prognosis. Subsequently, we formulated a shared prognosticator, the liver cancer radiomics signature (LCRS), by discovering and manifesting connectivity among radiomics phenotypes using GGI strategy. We validated that the LCRS is independent prognostic factor after adjusting for clinic-pathologic variables (all P < 0.05), with the LCRS-High group consistently associated with worse survival outcomes across HCC, ICC, and CRLM. However, the LCRS-High group showed clinical benefit from adjuvant chemotherapy, leading to reduced disease recurrence risk and improved survival. By contrast, the LCRS-Low group, including a subset of gastric cancer liver metastases (GCLM), exhibited more favorable response to immune checkpoint inhibitors (ICIs)-based combinational therapy (P = 0.02, hazard ratio (HR): 0.34 [95 % confidence interval (CI): 0.13-0.88]). Further analysis revealed that Notch signaling pathway was enriched in LCRS-High tumors, while LCRS-Low tumors exhibited higher infiltration of natural killer cell. These findings highlight the promise of this universal scoring model to personalize management strategies for patients with LC.

Development and validation of an individualized nomogram for predicting distant metastases in gastric cancer using a CT radiomics-clinical model

Purpose

This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC).

Methods

A total of 301 patients (training cohort, n = 210; testing cohort, n = 91) with GC were retrospectively collected. Relevant clinical predictors were determined through the application of univariate and multivariate logistic regression analyses. Then the clinical model was established. Venous phase computed tomography (VPCT) images were utilized to extract radiomic features, and relevant features were selected using univariate analysis, Spearman correlation coefficient, and the least absolute shrinkage and selection operator (Lasso) regression. Subsequently, radiomics scores were calculated based on the selected features. Radiomics models were constructed using five machine learning algorithms according to the screened features. Furthermore, separate joint models incorporating radiomic features and clinically independent predictors were established using traditional logistic regression algorithms and machine learning algorithms, respectively. All models were comprehensively assessed through discrimination, calibration, reclassification, and clinical benefit analysis.

Results

The multivariate logistic regression analysis revealed that age, histological grade, and N stage were independent predictors of distant metastases. The radiomics score was derived from 15 selected features out of a total of 944 radiomic features. The predictive performance of the joint model 1 [AUC (95% CI) 0.880 (0.811-0.949)] constructed using logistic regression is superior to that of the joint model 2 [AUC (95% CI) 0.834 (0.736-0.931)] constructed using SVM algorithm. The joint model 1 [AUC(95% CI) 0.880(0.811-0.949)], demonstrated superior performance compared to the clinical model [AUC(95% CI) 0.781(0.689-0.873)] and radiomics model [AUC(95% CI) 0.740(0.626-0.855), using LR algorithm]. The NRI and IDI values for the joint model 1 and clinical model were 0.115 (95% CI 0.014 -0.216) and 0.132 (95% CI 0.093-0.171), respectively; whereas for the joint model 1 and LR model, they were found to be 0.130 (95% CI 0.018-0.243) and 0.116 (95% CI 0.072-0.160), respectively. Decision curve analysis indicated that the joint model 1 exhibited a higher clinical net benefit than other models.

Conclusions

The nomogram of the joint model, integrating radiomic features and clinically independent predictors, exhibits robust predictive capability for early identification of high-risk patients with a propensity for distant metastases of GC.

Progression-Free Survival Prediction for Locally Advanced Cervical Cancer After Chemoradiotherapy With MRI-based Radiomics

AIMS

A significant proportion of locally advanced cervical cancer (LACC) patients experience disease progression post chemoradiotherapy (CRT). Currently existing clinical variables are suboptimal predictors of treatment response. This study reported a radiomics-based model leveraging information extracted from magnetic resonance (MR) T2-weighted image (T2WI) to predict the progression-free survival (PFS) for LACC following CRT.

MATERIALS AND METHODS

Radiomics features were extracted from pre-treatment MR T2WI in 105 LACC patients. Following pre-feature selection and a step forward feature selection method, an optimal feature set was determined with a Cox proportional hazard (CPH) model. The PFS predictions were generated through a radiomics-clinical combined model utilized five repeated nested 5-fold cross-validation (5-fold CV). Disease progression risk was stratified into high- and low-risk groups based on the predicted PFS and assessed by Kaplan-Meier analysis.

RESULTS

The radiomics texture feature extracted from MR T2WI significantly predict PFS in LACC after CRT. In comparison with the model using clinical variables alone, the radiomics-clinical combined model achieves significantly improved performance in testing patient cohort, achieving higher C-index (0.748 vs 0.655) and area under the curve (0.798 vs 0.660 for 2-year PFS). Meanwhile, the proposed method significantly differentiated the high- and low-risk patients groups for disease progression (P < 0.001).

CONCLUSION

An MR T2WI-based radiomics and clinical combined model provided improved prognostic capabilities in predicting the PFS for LACC patients treated with CRT, outperforming a model using clinical variables alone. The incorporation of MR T2WI-based radiomics is promising in assisting in personalized management in LACC, indicating the potential of MR T2WI radiomics as imaging biomarker.

Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability

OBJECTIVE

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple gray-level discretization levels for classifying Parkinson's disease (PD) subtypes, and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance.

METHODS

T1-weighted MRI scans from 140 PD patients (70 tremor-dominant, 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's progression markers initiative (PPMI) database. Radiomic features were extracted from 16 brain regions using 6 discretization levels (8, 16, 32, 64, 128, and 256 bins). ComBat harmonization was applied using a combined batch variable incorporating both scanner models and discretization levels. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Support vector machine classifiers were used for PD subtype classification.

RESULTS

ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased substantially after harmonization. The proportion of features significantly affected by discretization levels was reduced following harmonization. Classification accuracy improved dramatically, from a range of 0.42-0.49 before harmonization to 0.86-0.96 after harmonization across most discretization levels. AUC values similarly increased from 0.60-0.67 to 0.93-0.99 after harmonization.

CONCLUSIONS

ComBat harmonization significantly enhanced the reproducibility of radiomic features across discretization levels and improved PD subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

Prediction of acute pancreatitis severity based on early CT radiomics

BACKGROUND

This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity.

METHODS

A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis.

RESULTS

A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793-0.949) in the training cohort and 0.859 (95% CI, 0.751-0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756-0.910) and 0.810 (95% CI, 0.692-0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837-0.973) in the training cohort and 0.908 (95% CI, 0.824-0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone.

CONCLUSION

The clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making.

Periprostatic fat magnetic resonance imaging based radiomics nomogram for predicting biochemical recurrence-free survival in patients with non-metastatic prostate cancer after radical prostatectomy

OBJECTIVE

To build and validate a periprostatic fat magnetic resonance imaging (MRI) based radiomics nomogram for prediction of biochemical recurrence-free survival (bRFS) of patients with non-metastatic prostate cancer (PCa) receiving radical prostatectomy (RP).

METHODS

A retrospective study was conducted on 356 patients with non-metastatic PCa who underwent preoperative mpMRI followed by RP treatment at our institution. Radiomic features were extracted from both intratumoral region and the periprostatic fat region, which were segmented on images obtained through T2-weighted imaging (T2WI) and apparent-diffusion coefficient (ADC) imaging. Three radiomics models were developed by applying the Least absolute shrinkage and selection operator (LASSO) Cox regression, followed by Cox risk regression to construct a combined radiomics-clinical model by integrating the optimal radiomics score and clinicopathological risk factors to draw a nomogram. The predictive performance was evaluated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis and calibration curves.

RESULTS

One hundred and twenty-one patients (33.98%) experienced biochemical recurrence. ROC analyses showed that the Area Under the Curve (AUC) of the periprostatic fat-intratumoral radiomics model demonstrated the highest AUC at 0.921 (95%CI, 0.857-0.981), 0.875 (95%CI, 0.763-0.950), 0.854 (95%CI, 0.706-0.923) for 1-year, 3-years and 5-years bRFS. Multivariate Cox regression analysis revealed that Pathological T stage, ISUP grading group and Positive surgical margin were independent prognostic factors for predicting bRFS. A radiomics-clinical nomogram based on these clinical predictors and periprostatic fat-intratumoral radiomics score was constructed. Kaplan-Meier analyses showed that radiomics-clinical nomogram was significantly related with survival of PCa (P < 0.001); and calibration curves revealed the predicted and observed survival probability of 1-year, 3-year and 5-year bRFS had high degree of consistency in the training and validation group. The radiomics-clinical nomogram showed a significant improvement than the clinical model for 1-year (AUC, 0.944; 95%CI, 0.912-0.990 vs. AUC, 0.839; 95%CI, 0.661-0.928; P = 0.009), 3-year (AUC, 0.864; 95%CI, 0.772-0.969 vs. AUC, 0.776; 95%CI, 0.602-0.872; P = 0.008), and 5-year bRFS (AUC, 0.907; 95%CI, 0.836-0.982 vs. AUC, 0.819; 95%CI, 0.687-0.915; P = 0.027).

CONCLUSIONS

This study developed and validated the radiomics-clinical nomogram for the prediction of bRFS in non-metastatic PCa patients underwent RP.

Development of a flexible feature selection framework in radiomics-based prediction modeling: Assessment with four real-world datasets

There are several important challenges in radiomics research; one of them is feature selection. Since many quantitative features are non-informative, feature selection becomes essential. Feature selection methods have been mixed with filter, wrapper, and embedded methods without a rule of thumb. This study aims to develop a framework for optimal feature selection in radiomics research. We developed the framework that the optimal features were selected to quickly through controlling relevance and redundancy among features. A 'FeatureMap' was generated containing information for each step and used as a platform. Through this framework, we can explore the optimal combination of radiomics features and evaluate the predictive performance using only selected features. We assessed the framework using four real datasets. The FeatureMap generated 6 combinations, with the number of features selected varying for each combination. The predictive models obtained high performances; the highest test area under the curves (AUCs) were 0.792, 0.820, 0.846 and 0.738 in the cross-validation method, respectively. We developed a flexible framework for feature selection methods in radiomics research and assessed its usefulness using various real-world data. Our framework can assist clinicians in efficiently developing predictive models based on radiomics.

The Value of Whole-Volume Radiomics Machine Learning Model Based on Multiparametric MRI in Predicting Triple-Negative Breast Cancer

OBJECTIVE

This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps.

METHODS

This retrospective study included 326 patients with pathologically proven breast cancer (TNBC: 129, non-TNBC: 197). The lesions were segmented using the ITK-SNAP software, and whole-volume radiomics features were extracted using a radiomics platform. Radiomics features were obtained from DCE-MRI and ADC maps. The least absolute shrinkage and selection operator regression method was employed for feature selection. Three prediction models were constructed using a support vector machine classifier: Model A (based on the selected features of the ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). Receiver operating characteristic curves were used to evaluate the diagnostic performance of the conventional MR image model and the 3 radiomics models in predicting TNBC.

RESULTS

In the training dataset, the AUCs for the conventional MR image model and the 3 radiomics models were 0.749, 0.801, 0.847, and 0.896. The AUCs for the conventional MR image model and 3 radiomics models in the validation dataset were 0.693, 0.742, 0.793, and 0.876, respectively.

CONCLUSIONS

Radiomics based on the combination of whole volume DCE-MRI and ADC maps is a promising tool for distinguishing between TNBC and non-TNBC.

Intratumoral and peritumoral MRI-based radiomics for predicting extrapelvic peritoneal metastasis in epithelial ovarian cancer

OBJECTIVES

To investigate the potential of intratumoral and peritumoral radiomics derived from T2-weighted MRI to preoperatively predict extrapelvic peritoneal metastasis (EPM) in patients with epithelial ovarian cancer (EOC).

METHODS

In this retrospective study, 488 patients from four centers were enrolled and divided into training (n = 245), internal test (n = 105), and external test (n = 138) sets. Intratumoral and peritumoral models were constructed based on radiomics features extracted from the corresponding regions. A combined intratumoral and peritumoral model was developed via a feature-level fusion. An ensemble model was created by integrating this combined model with specific independent clinical predictors. The robustness and generalizability of these models were assessed using tenfold cross-validation and both internal and external testing. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanation method was employed for model interpretation.

RESULTS

The ensemble model showed superior performance across the tenfold cross-validation, with the highest mean AUC of 0.844 ± 0.063. On the internal test set, the peritumoral and ensemble models significantly outperformed the intratumoral model (AUC = 0.786 and 0.832 vs. 0.652, p = 0.007 and p < 0.001, respectively). On the external test set, the AUC of the ensemble model significantly exceeded those of the intratumoral and peritumoral models (0.843 vs. 0.750 and 0.789, p = 0.008 and 0.047, respectively).

CONCLUSION

Peritumoral radiomics provide more informative insights about EPM than intratumoral radiomics. The ensemble model based on MRI has the potential to preoperatively predict EPM in EOC patients.

CRITICAL RELEVANCE STATEMENT

Integrating both intratumoral and peritumoral radiomics information based on MRI with clinical characteristics is a promising noninvasive method to predict EPM to guide preoperative clinical decision-making for EOC patients.

KEY POINTS

Peritumoral radiomics can provide valuable information about extrapelvic peritoneal metastasis in epithelial ovarian cancer. The ensemble model demonstrated satisfactory performance in predicting extrapelvic peritoneal metastasis. Combining intratumoral and peritumoral MRI radiomics contributes to clinical decision-making in epithelial ovarian cancer.

A CT-Based Clinical-Radiomics Nomogram for Predicting the Overall Survival to TACE Combined with Camrelizumab and Apatinib in Patients with Advanced Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

To construct a computed tomography (CT)-based clinical-radiomics nomogram for estimating overall survival (OS) in advanced hepatocellular carcinoma (HCC) patients receiving transcatheter arterial chemoembolization (TACE) in combination with camrelizumab and apatinib.

METHODS

A retrospective recruitment of 150 patients with clinically or pathologically confirmed HCC was conducted, followed by their division into training cohort (n = 105) and test cohort (n = 45). To generate the radiomics score (Rad-score), a series of analyses were performed, including Pearson correlation analysis, univariate Cox analysis, and least absolute shrinkage and selection operator Cox regression analysis. Subsequently, a clinical-radiomics nomogram was constructed using the Rad-score combined with independent clinical prognostic factors, followed by assessments of its calibration, discrimination, reclassification, and clinical utility.

RESULTS

Five CT radiomics features were selected. The Rad-score showed a significant correlation with OS (P < 0.001). The clinical-radiomics nomogram demonstrated superior performance in estimating OS, with a concordance index (C-index) of 0.840, compared to the radiomics nomogram (C-index: 0.817) and the clinical nomogram (C-index: 0.661). It also exhibited high 1-year and 2-year area under the curves of 0.936 and 0.946, respectively. Additionally, the clinical-radiomics nomogram markedly enhanced classification accuracy for OS outcomes, as evidenced by net reclassification improvement and integrated discrimination improvement. Decision curve analysis confirmed its clinical utility.

CONCLUSION

A CT-based clinical-radiomics nomogram exhibits strong potential for predicting OS in advanced HCC patients undergoing TACE combined with camrelizumab and apatinib.

Prediction of methylphenidate treatment response for ADHD using conventional and radiomics T1 and DTI features: Secondary analysis of a randomized clinical trial

BACKGROUND

Attention-Deficit/Hyperactivity Disorder (ADHD) is commonly treated with methylphenidate (MPH). Although highly effective, MPH treatment still has a relatively high non-response rate of around 30%, highlighting the need for a better understanding of treatment response. Radiomics of T1-weighted images and Diffusion Tensor Imaging (DTI) combined with machine learning approaches could offer a novel method for assessing MPH treatment response.

PURPOSE

To evaluate the accuracy of both conventional and radiomics approaches in predicting treatment response based on baseline T1 and DTI data in stimulant-naive ADHD participants.

METHODS

We performed a secondary analysis of a randomized clinical trial (ePOD-MPH), involving 47 stimulant-naive ADHD participants (23 boys aged 11.4 ± 0.4 years, 24 men aged 28.1 ± 4.3 years) who underwent 16 weeks of treatment with MPH. Baseline T1-weighted and DTI MRI scans were acquired. Treatment response was assessed at 8 weeks (during treatment) and one week after cessation of 16-week treatment (post-treatment) using the Clinical Global Impressions - Improvement scale as our primary outcome. We compared prediction accuracy using a conventional model and a radiomics model. The conventional approach included the volume of bilateral caudate, putamen, pallidum, accumbens, and hippocampus, and for DTI the mean fractional anisotropy (FA) of the entire brain white matter, bilateral Anterior Thalamic Radiation (ATR), and the splenium of the corpus callosum, totaling 14 regional features. For the radiomics approach, 380 features (shape-based and first-order statistics) were extracted from these 14 regions. XGBoost models with nested cross-validation were used and constructed for the total cohort (n = 47), as well as children (n = 23) and adults (n = 24) separately. Exact binomial tests were employed to compare model performance.

RESULTS

For the conventional model, balanced accuracy (bAcc) in predicting treatment response during treatment was 63 % for the total cohort, 32 % for children, and 36 % for adults (Area Under the Receiver Operating Characteristic Curve (AUC-ROC): 0.69, 0.33, 0.41 respectively). Radiomics models demonstrated bAcc's of 68 %, 64 %, and 64 % during treatment (AUC-ROCs of 0.73, 0.62, 0.69 respectively). These predictions were better than chance for both conventional and radiomics models in the total cohort (p = 0.04, p = 0.003 respectively). The radiomics models outperformed the conventional models during treatment in children only (p = 0.02). At post-treatment, performance was markedly reduced.

CONCLUSION

While conventional and radiomics models performed equally well in predicting clinical improvement across children and adults during treatment, radiomics features offered enhanced structural information beyond conventional region-based volume and FA averages in children. Prediction of symptom improvement one week after treatment cessation was poor, potentially due to the transient effects of stimulant treatment on symptom improvement.

The novel developed and validated multiparametric MRI-based fusion radiomic and clinicoradiomic models predict the postoperative progression of primary skull base chordoma

Local progression of primary skull base chordoma (PSBC) is a sign of treatment failure. Predicting the postoperative progression of PSBC can aid in the development of individualized treatment plans to improve patients' progression-free survival (PFS) after surgery. This study aimed to develop a multiparametric MRI-based fusion radiomic model (FRM) and clinicoradiomic model (CRM) via radiomic and clinical analysis and to explore their validity in predicting postoperative progression in PSBC patients before surgery. In this retrospective study, a total of 129 patients with PSBC from our institution, including 57 patients with progression, were enrolled and randomized to the training set (TS) or the validation set (VS) at a 2:1 ratio. Radiomic features were extracted and dimensionally reduced from 3.0 T/axial T2-weighted imaging (T2WI), T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences for each patient, and the features were used for radiomic analysis. Univariate and multivariate Cox regression analyses were used to screen for key clinical factors. We constructed models on the basis of multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate the performance of the clinical model (CM), FRM and CRM. Through analysis, we found that blood supply was the only significantly different clinical factor in the CM. For the FRM, the area under the receiver operating characteristic curve (AUC) of the TS was 0.925, and the calibration curves were consistent across the TS. In the CRM, the AUC of the TS was 0.929, the calibration curve analysis was consistent for both the TS and the VS, and the DCA showed that the net benefit was greater at a threshold probability of > 0% for both the TS and the VS. Our proposed FRM can help clinicians better predict PSBC progression preoperatively, and the use of the CRM can lead to the development of more appropriate protocols to improve patients' PFS after surgery.

The value of multi-parameter radiomics combined with imaging features in predicting the therapeutic efficacy of HIFU treatment for uterine fibroids

Objectives

To evaluate the effectiveness of high-intensity focused ultrasound (HIFU) therapy for treating uterine fibroids by utilizing multi-sequence magnetic resonance imaging radiomic models.

Methods

One hundred and fifty patients in our hospital were randomly divided into a training cohort (n=120) and an internal test cohort (n=30), and forty-five patients from another hospital serving as an external test cohort. Radiomics features of uterine fibroids were extracted and selected based on preoperative T2-weighted imaging fat suppression(T2WI-FS)and contrast-enhanced T1WI(CE-T1WI)images, and logistic regression was used to develop the T2WI-FS, CE-T1WI, and combined T2WI-FS + CE-T1WI models, along with the radiomics-clinical model integrating radiomics features with imaging characteristics. The performance and clinical applicability of each model were assessed through receiver operating characteristic (ROC) curve, decision curve analysis (DCA), as well as Network Readiness Index (NRI) and Integrated Discrimination Index (IDI).

Results

The AUC values of the radiomics-clinical model and the T2WI-FS + CE-T1WI model were the highest. In the training cohort, the radiomics-clinical model showed higher AUC values than the T2WI-FS + CE-T1WI model, while in the internal and external testing cohorts, the AUC values of the T2WI-FS + CE-T1WI model were higher than that of the radiomics-clinical model. DCA further demonstrated that these two models achieved the greatest net benefit. NRI and IDI analyses suggested that the T2WI-FS + CE-T1WI model had higher clinical utility.

Conclusions

Both the T2WI-FS + CE-T1WI model and the radiomics-clinical model demonstrate higher predictive value and larger net benefit compared to other models.

A Comprehensive Review of Performance Metrics for Computer-Aided Detection Systems

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers guidelines to assist in selecting appropriate metrics. Evaluation methods for CAD systems for lung nodule detection are primarily categorized into per-scan and per-nodule approaches. For per-scan analysis, a key metric is the area under the receiver operating characteristic (ROC) curve (AUROC), which evaluates the ability of the system to distinguish between scans with and without nodules. For per-nodule analysis, the nodule-level sensitivity at fixed false positives per scan is often used, supplemented by the free-response receiver operating characteristic (FROC) curve and the competition performance metric (CPM). However, the CPM does not provide normalized scores because it theoretically ranges from zero to infinity and largely varies depending on the characteristics of the data. To address the advantages and limitations of ROC and FROC curves, an alternative FROC (AFROC) was introduced to combine the strengths of both per-scan and per-nodule analyses. This paper discusses the principles of each metric and their relative strengths, providing insights into their clinical implications and practical utility.

A combined model integrating radiomics and deep learning based on multiparametric magnetic resonance imaging for classification of brain metastases

BACKGROUND

Radiomics and deep learning (DL) can individually and efficiently identify the pathological type of brain metastases (BMs).

PURPOSE

To investigate the feasibility of utilizing multi-parametric MRI-based deep transfer learning radiomics (DTLR) for the classification of lung adenocarcinoma (LUAD) and non-LUAD BMs.

MATERIAL AND METHODS

A retrospective analysis was performed on 342 patients with 1389 BMs. These instances were randomly assigned to a training set of 273 (1179 BMs) and a testing set of 69 (210 BMs) in an 8:2 ratio. Eight machine learning algorithms were employed to construct the radiomics models. A DL model was developed using four pre-trained convolutional neural networks (CNNs). The DTLR model was formulated by integrating the optimal performing radiomics model and the DL model using a classification probability averaging approach. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the models.

RESULTS

The AUC for the optimal radiomics and DL model in the testing set were 0.824 (95% confidence interval [CI]= 0.726-0.923) and 0.775 (95% CI=0.666-0.884), respectively. The DTLR model demonstrated superior discriminatory power, achieving an AUC of 0.880 (95% CI=0.803-0.957). In addition, the DTLR model exhibited good consistency between actual and predicted probabilities based on the calibration curve and DCA analysis, indicating its significant clinical value.

CONCLUSION

Our study's DTLR model demonstrated high diagnostic accuracy in distinguishing LUAD from non-LUAD BMs. This method shows potential for the non-invasive identification of the histological subtype of BMs.

Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.

Predictive value of contrast-enhanced MRI for the regrowth of residual uterine fibroids after high-intensity focused ultrasound treatment

OBJECTIVES

To investigate whether the signal intensity (SI) ratio of residual fibroid (RF) to myometrium using Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) could predict fibroid regrowth after high-intensity focused ultrasound (HIFU) treatment.

MATERIALS AND METHODS

A retrospective analysis was conducted among 164 patients with uterine fibroids who underwent HIFU. To predict the RF regrowth, the SI perfusion parameters were quantified using the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU and then compared with the fibroid-myometrium SI ratio on the T2-weighted image (T2WI) and Funaki classification 1 year later. Thirty cases from another center were used as an external validation set to evaluate the performance of RF-myometrium SI ratio.

RESULTS

The predictive performance of the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU (Area Under Curve, AUC: 0.869) was superior to that of the preoperative and postoperative fibroid-myometrium SI ratios on the T2WI (AUC: 0.724, 0.696) and Funaki classification (AUC: 0.663, 0.623). Multivariate analysis showed that the RF- myometrium SI ratio and RF thickness were independent factors. The RF-myometrium SI ratio reflects the long-term rate of re-intervention (r = 0.455, p < 0.001).

CONCLUSION

The RF-myometrium SI ratio on CE-MRI exhibits greater accuracy in predicting RF regrowth compared to the SI classification and the SI ratio on T2WI.

CRITICAL RELEVANCE STATEMENT

The ratio of residual uterine fibroid to myometrial signal intensity on contrast-enhanced (CE)-MRI can reflect residual blood supply, predict regrowth of fibroids, and thus reflect long-term re-intervention rate and recovery situation of clinical high-intensity focused ultrasound (HIFU) treatment.

KEY POINTS

Contrast-enhanced (CE)-MRI can indicate the blood supply of residual uterine fibroids after high-intensity focused ultrasound (HIFU) treatment. The predictive capability of CE-MRI ratio surpasses T2WI ratio and the Funaki. Residual fibroids can serve as a measure of the long-term efficacy of HIFU.

Image-based deep learning model to predict stoma-site incisional hernia in patients with temporary ileostomy: A retrospective study

The prophylactic implantation of biological mesh can effectively prevent the occurrence of stoma-site incisional hernia (SSIH) in patients undergoing stoma retraction. Therefore, our study prospectively established and validated a mixed model, which combined radiomics, stepwise regression, and deep learning for the prediction of SSIH in patients with temporary ileostomy. The mixed model showed good discrimination of the SSIH patients on all cohorts, which outperformed deep learning, radiomics, and clinical models alone (overall area under the curve [AUC]: 0.947 in the primary cohort, 0.876 in the external validation cohort 1, and 0.776 in the external validation cohort 2). Moreover, the sensitivity, specificity, and precision for predicting SSIH were improved in the mixed model. Thus, the mixed model can provide more information for SSIH precaution and clinical decision-making.

Visualization analysis of research hotspots and trends in MRI-based artificial intelligence in rectal cancer

Background

Rectal cancer (RC) is one of the most common types of cancer worldwide. With the development of artificial intelligence (AI), the application of AI in preoperative evaluation and follow-up treatment of RC based on magnetic resonance imaging (MRI) has been the focus of research in this field. This review was conducted to develop comprehensive insight into the current research progress, hotspots, and future trends in AI based on MRI in RC, which remains to be studied.

Methods

Literature related to AI based on MRI and RC, as of November 2023, was obtained from the Web of Science Core Collection database. Visualization and bibliometric analyses of publication quantity and content were conducted to explore temporal trends, spatial distribution, collaborative networks, influential articles, keyword co-occurrence, and research directions.

Results

A total of 177 papers (152 original articles and 25 reviews) were identified from 24 countries/regions, 351 institutions, and 81 journals. Since 2019, the number of studies on this topic has rapidly increased. China and the United States have contributed the highest number of publications and institutions, cultivating the most intimate collaborative relationship. The highest number of articles derive from Sun Yat-sen University, while Frontiers in Oncology has published the highest number of relevant articles. Research on MRI-based AI in this field has mainly focused on preoperative diagnosis and prediction of treatment efficacy and prognosis.

Conclusions

This study provides an objective and comprehensive overview of the publications on MRI-based AI in RC and identifies the present research landscape, hotspots, and prospective trends in this field, which can provide valuable guidance for scholars worldwide.

Segregation of the regional radiomics similarity network exhibited an increase from late childhood to early adolescence: A developmental investigation

Brain development is characterized by an increase in structural and functional segregation, which supports the specialization of cognitive processes within the context of network neuroscience. In this study, we investigated age-related changes in morphological segregation using individual Regional Radiomics Similarity Networks (R2SNs) constructed with a longitudinal dataset of 494 T1-weighted MR scans from 309 typically developing children aged 6.2 to 13 years at baseline. Segertation indices were defined as the relative difference in connectivity strengths within and between modules and cacluated at the global, system and local levels. Linear mixed-effect models revealed longitudinal increases in both global and system segregation indices, particularly within the limbic and dorsal attention network, and decreases within the ventral attention network. Superior performance in working memory and inhibitory control was associated with higher system-level segregation indices in default, frontoparietal, ventral attention, somatomotor and subcortical systems, and lower local segregation indices in visual network regions, regardless of age. Furthermore, gene enrichment analysis revealed correlations between age-related changes in local segregation indices and regional expression levels of genes related to developmental processes. These findings provide novel insights into typical brain developmental changes using R2SN-derived segregation indices, offering a valuable tool for understanding human brain structural and cognitive maturation.

Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma

OBJECTIVES

This study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).

MATERIALS AND METHODS

Patients with ESCC undergoing nCRT followed by surgery were retrospectively enrolled from three institutions and divided into training and testing cohorts. Both traditional and deep-learning radiomics features were extracted from pre-treatment CT, T2, and DWI. Multiple radiomics models were developed, both single modality and integrated, using machine learning algorithms. The models' performance was assessed using receiver operating characteristic curve analysis, with the area under the curve (AUC) as a primary metric, alongside sensitivity and specificity from the cut-off analysis.

RESULTS

The study involved 151 patients, among whom 63 achieved pCR. The training cohort consisted of 89 patients from Institution 1 (median age 62, 73 males) and the testing cohort included 52 patients from Institution 2 (median age 62, 41 males), and 10 in a clinical trial from Institution 3 (median age 69, 9 males). The integrated model, combining traditional and deep learning radiomics features from CT, T2, and DWI, demonstrated the best performance with an AUC of 0.868 (95% CI: 0.766-0.959), sensitivity of 88% (95% CI: 73.9-100), and specificity of 78.4% (95% CI: 63.6-90.2) in the testing cohort. This model outperformed single-modality models and the clinical model.

CONCLUSION

A multimodality deep learning radiomics model, utilizing CT, T2, and DWI images, was developed and validated for accurately predicting pCR of ESCC following nCRT.

CRITICAL RELEVANCE STATEMENT

Our research demonstrates the satisfactory predictive value of multimodality deep learning radiomics for the response of nCRT in ESCC and provides a potentially helpful tool for personalized treatment including organ preservation strategy.

KEY POINTS

After neoadjuvant chemoradiotherapy, patients with ESCC have pCR rates of about 40%. The multimodality deep learning radiomics model, could predict pCR after nCRT with high accuracy. The multimodality radiomics can be helpful in personalized treatment of esophageal cancer.

Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinoma: a two-center study

Objective

The objective of this study is to build and verify the performance of machine learning-based ultrasomics in predicting the objective response to combination therapy involving a tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody for individuals with unresectable hepatocellular carcinoma (HCC). Radiomic features can reflect the internal heterogeneity of the tumor and changes in its microenvironment. These features are closely related to pathological changes observed in histology, such as cellular necrosis and fibrosis, providing crucial non-invasive biomarkers to predict patient treatment response and prognosis.

Methods

Clinical, pathological, and pre-treatment ultrasound image data of 134 patients with recurrent unresectable or advanced HCC who treated with a combination of TKI and anti-PD-1 antibody therapy at Henan Provincial People's Hospital and the First Affiliated Hospital of Zhengzhou University between December 2019 and November 2023 were collected and retrospectively analyzed. Using stratified random sampling, patients from the two hospitals were assigned to training cohort (n = 93) and validation cohort (n = 41) at a 7:3 ratio. After preprocessing the ultrasound images, regions of interest (ROIs) were delineated. Ultrasomic features were extracted from the images for dimensionality reduction and feature selection. By utilizing the extreme gradient boosting (XGBoost) algorithm, three models were developed: a clinical model, an ultrasomic model, and a combined model. By analyzing the area under the receiver operating characteristic (ROC) curve (AUC), specificity, sensitivity, and accuracy, the predicted performance of the models was evaluated. In addition, we identified the optimal cutoff for the radiomic score using the Youden index and applied it to stratify patients. The Kaplan-Meier (KM) survival curves were used to examine differences in progression-free survival (PFS) between the two groups.

Results

Twenty ultrasomic features were selected for the construction of the ultrasomic model. The AUC of the ultrasomic model for the training cohort and validation cohort were 0.999 (95%CI: 0.997-1.000) and 0.828 (95%CI: 0.690-0.966), which compared significant favorably to those of the clinical model [AUC = 0.876 (95%CI: 0.815-0.936) for the training cohort, 0.766 (95%CI: 0.597-0.935) for the validation cohort]. Compared to the ultrasomic model, the combined model demonstrated comparable performance within the training cohort (AUC = 0.977, 95%CI: 0.957-0.998) but higher performance in the validation cohort (AUC = 0.881, 95%CI: 0.758-1.000). However, there was no statistically significant difference (p > 0.05). Furthermore, ultrasomic features were associated with PFS, which was significantly different between patients with radiomic scores (Rad-score) greater than 0.057 and those with Rad-score less than 0.057 in both the training (HR = 0.488, 95% CI: 0.299-0.796, p = 0.003) and validation cohorts (HR = 0.451, 95% CI: 0.229-0.887, p = 0.02).

Conclusion

The ultrasomic features demonstrates excellent performance in accurately predicting the objective response to TKI in combination with anti-PD-1 antibody immunotherapy among patients with unresectable or advanced HCC.

Preoperative Noninvasive Prediction of Recurrence-Free Survival in Hepatocellular Carcinoma Using CT-Based Radiomics Model

Purpose

This study aims to explore the value of radiomics combined with clinical parameters in predicting recurrence-free survival (RFS) after the resection of hepatocellular carcinoma (HCC).

Patients and Methods

In this retrospective study, a total of 322 patients with HCC who underwent contrast-enhanced computed tomography (CT) and radical surgical resection were enrolled and randomly divided into a training group (n = 223) and a validation group (n = 97). In the training group, Univariate and multivariate Cox regression analyses were employed to obtain clinical variables related to RFS for constructing the clinical model. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were employed to construct the radiomics model, and the clinical-radiomics model was further constructed. Model prediction performance was subsequently assessed by the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. Additionally, Kaplan-Meier analysis was used to evaluate the model's value in predicting RFS. Correlations between radiomics features and pathological parameters were analyzed.

Results

The clinical-radiomics model predicted RFS at 1, 2, and 3 years more accurately than the clinical or radiomics model alone (training group, AUC = 0.834, 0.765 and 0.831, respectively; validation group, AUC = 0.715, 0.710 and 0.793, respectively). The predicted high-risk subgroup based on the clinical-radiomics nomogram had shorter RFS than predicted low-risk subgroup in data sets, enabling risk stratification of various clinical subgroups. Correlation analysis revealed that the rad-score was positively related to microvascular invasion (MVI) and Edmondson-Steiner grade.

Conclusion

The clinical-radiomics model effectively predicts RFS in HCC patients and identifies high-risk individuals for recurrence.

Comprehensive analysis of transcriptomics and radiomics revealed the potential of TEDC2 as a diagnostic marker for lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a widely occurring cancer with a high death rate. Radiomics, as a high-throughput method, has a wide range of applications in different aspects of the management of multiple cancers. However, the molecular mechanism of LUAD by combining transcriptomics and radiomics in order to probe LUAD remains unclear.

Methods

The transcriptome data and radiomics features of LUAD were extracted from the public database. Subsequently, we used weighted gene co-expression network analysis (WGCNA) and a series of machine learning algorithms including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, and Support Vector Machines Recursive Feature Elimination (SVM-RFE) to proceed with the screening of diagnostic genes for LUAD. In addition, the CIBERSORT and ESTIMATE algorithms were utilized to assess the association of these genes with immune profiles. The LASSO algorithm further identified the features most relevant to the expression levels of LUAD diagnostic genes and validated the model based on receiver operating characteristic (ROC), precision-recall (PR), calibration curves and decision curve analysis (DCA) curves. Finally, RT-qPCR, transwell and cell counting kit-8 (CCK8) based assays were performed to assess the expression levels and potential functions of the screened genes in LUAD cell lines.

Results

We screened a total of 214 modular genes with the highest correlation with LUAD samples based on WGCNA, of which 192 genes were shown to be highly expressed in LUAD patients. Subsequently, three machine learning algorithms identified a total of four genes, including UBE2T, TEDC2, RCC1, and FAM136A, as diagnostic molecules for LUAD, and the ROC curves showed that these diagnostic molecules had good diagnostic performance (AUC values of 0.989, 0.989, 989, and 0.987, respectively). The expression of these diagnostic molecules was significantly higher in tumor samples than in normal para-cancerous tissue samples and also correlated significantly and negatively with stromal and immune scores. Specifically, we also constructed a model based on TEDC2 expression consisting of seven radiomic features. Among them, the ROC and PR curves showed that the model had an AUC value of up to 0.96, respectively. Knockdown of TEDC2 slowed down the proliferation, migration and invasion efficiency of LUAD cell lines.

Conclusion

In this study, we screened for diagnostic markers of LUAD and developed a non-invasive radiomics model by innovatively combining transcriptomics and radiomics data. These findings contribute to our understanding of LUAD biology and offer potential avenues for further exploration in clinical practice.

Prognostic significance of collagen content in solitary fibrous tumors of the central nervous system

Purpose

We aimed to explore the prognostic significance of collagen content in solitary fibrous tumors (SFTs) of the central nervous system (CNS) and preliminarily investigate its relationship with magnetic resonance imaging (MRI) features of SFTs.

Methods

Collagen content was identified using Masson's trichrome staining, and quantitatively assessed. Radiomic methods were applied to extract quantitative MRI features of SFTs, which were then analyzed in relation to collagen content.

Results

The collagen content in CNS SFTs was categorized into high- and low-content groups, with a cutoff value of 6%. Survival analysis indicated a positive correlation between collagen content and overall survival (OS). In multivariate Cox regression analysis, incorporating factors such as mitosis, necrosis, Ki67, and collagen content and other indicators, collagen content emerged as an independent prognostic factor. Collagen content demonstrated a negative correlation with tumor histological phenotype, Ki67, WHO grade, mitosis, necrosis, and brain invasion. Additionally, the signal intensity of SFTs on T2-weighted imaging (T2WI) decreased with increasing collagen content. Radiomics analysis identified 1,702 features from each patient's region of interest, with 12 features showing significant differences between the high and low collagen content groups. Among the quantitative parameters and radiomic models, the combined T1- and T2WI models exhibited the highest diagnostic performance.

Conclusion

These findings suggest that collagen content is an independent prognostic risk factor for OS. Furthermore, combined radiomic models based on T1-and T2WI sequences may offer a more comprehensive, objective, and accurate assessment of collagen content in CNS SFTs.

Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology

BACKGROUND

Cuprotosis has been identified as a novel way of cell death. The key regulator ferredoxin 1 (FDX1) was explored via pan-cancer analysis, and its prediction models were proposed across seven malignancies and two imaging modalities.

METHODS

The prognostic value of FDX1 was explored via 1654 cases of 33 types of cancer in the Cancer Genome Atlas database. The MRI cohort of hepatocellular carcinoma in the First Affiliated Hospital of Fujian Medical University, and CT and MRI images from the Cancer Imaging Archive, REMBRANDT and Duke databases were exploited to formulate radiomic models to predict FDX1 expression. After segmentation of volumes of interest and feature extraction, the recursive feature elimination algorithm was used to screen features, logistic regression was used to model features, immunohistochemistry staining with FDX1 antibody was performed to test the radiomic model.

RESULTS

FDX1 was found to be prognostic in various types of cancer. The area under the receiver operating characteristic curve of radiomic models to predict FDX1 expression reached 0.825 (95% CI = 0.739-0.911). Cross-tissue compatibility was confirmed in pan-cancer validation and test cohorts. Mechanistically, the radiomic score was significantly correlated with various immunosuppressive genes and gene mutations. The radiomic score was also found to be an independent prognostic factor, making it a potentially actionable biomarker in the clinical setting.

CONCLUSIONS

The expression of FDX1 could be non-invasively predicted via radiomics. The radiomic patterns with biological and clinical relevance across histology and modalities could have a broad impact on a larger population of patients.

A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023

BACKGROUND

Recent research has demonstrated that the use of artificial intelligence (AI) in radiotherapy (RT) has significantly streamlined the process for physicians to treat patients with tumors; however, bibliometric studies examining the correlation between AI and RT are not available. Providing a thorough overview of the knowledge structure and research hotspots between AI and RT was the main goal of the current study.

METHOD

A search was conducted on the Web of Science Core Collection (WoSCC) database for publications pertaining to AI and RT between 2003 and 2023. VOSviewers, CiteSpace, and the R program "bibliometrix" were used to do the bibliometric analysis.

RESULTS

The analysis comprised 615 publications from 64 countries, with USA and China leading the pack. Since 2017, there have been more and more publications about RT and AI every year. The research center that made the biggest contribution to this topic was Maastricht University. The most articles published journal in this field was Frontiers in Oncology, while Medical Physics received the greatest number of citations. Dekker Andre is the author with the greatest number of published articles, while Philippe Lambin was the most often co-cited author. In the newly identified research hotspots, "autocontouring algorithm", "deep learning", and "machine learning" stand out as the main terms.

CONCLUSION

In fact, our bibliometric analysis offers insightful information on current research directions and advancements pertaining to the use of AI in RT. For academics looking to understand the connection between AI and RT, this study is a great resource because it highlights current research frontiers and hot trends.

Impact of Harmonization on MRI Radiomics Feature Variability Across Preprocessing Methods for Parkinson's Disease Motor Subtype Classification

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple preprocessing methods for classifying Parkinson's disease (PD) motor subtypes and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant and 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's Progression Markers Initiative (PPMI) database, acquired using different scanner models. Radiomic features were extracted from 16 brain regions using various preprocessing pipelines. ComBat harmonization was applied using a combined batch variable incorporating both scanner models and preprocessing methods. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Feature selection was performed using Linear Support Vector Classifier with L1 regularization. Support vector machine classifiers were used for PD subtype classification. ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased from 40.2 to 56.3% after harmonization. First-order statistic features showed the highest robustness, with 71.11% demonstrating excellent ICC after harmonization. The proportion of features significantly affected by preprocessing methods was reduced following harmonization. Classification accuracy improved dramatically, from a range of 34-75% before harmonization to 89-96% after harmonization across all preprocessing methods. AUC values similarly increased from 0.28-0.87 to 0.95-0.99 after harmonization. ComBat harmonization significantly enhanced the reproducibility of radiomic features across preprocessing methods and improved PD motor subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

Glioma subtype prediction based on radiomics of tumor and peritumoral edema under automatic segmentation

Comprehensive and non-invasive preoperative molecular diagnosis is important for prognostic and therapy decision-making in adult-type diffuse gliomas. We employed a deep learning method for automatic segmentation of brain gliomas directly from conventional magnetic resonance imaging (MRI) scans of the tumor core and peritumoral edema regions based on available glioma MRI data provided in the BraTS2021. Three-dimensional volumes of interest were segmented from 424 cases of glioma imaging data retrospectively obtained from two medical centers using the segmentation method and radiomic features were extracted. We developed a subtype prediction model based on extracted radiomic features and analyzed significance and correlations between glioma morphological characteristics and pathological features using data from patients with adult-type diffuse glioma. The automated segmentation achieved mean Dice scores of 0.884 and 0.889 for the tumor core and whole tumor, respectively. The area under the receiver operating characteristic curve for the prediction of adult-type diffuse gliomas subtypes was 0.945. "Glioblastoma, IDH-wildtype", "Astrocytoma, IDH-mutant", and "Oligodendroglioma, IDH-mutant, 1p/19q-coded" showed AUCs of 0.96, 0.914, and 0.961, respectively, for subtype prediction. Glioma morphological characteristics, molecular and pathological levels, and clinical data showed significant differences and correlations. An automatic segmentation model for gliomas based on 3D U-Nets was developed, and the prediction model for gliomas built using the parameters obtained from the automatic segmentation model showed high overall performance.

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts and contribute to improving patient outcomes in pancreatic cancer management.

Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors

Background: Upper tract urothelial carcinoma (UTUC) presents significant challenges in prognostication due to its rarity and complex anatomy. This study introduces a novel approach integrating perirenal fat (PRF) radiomics with clinical factors to enhance prognostic accuracy in UTUC. Methods: The study retrospectively analyzed 103 UTUC patients who underwent radical nephroureterectomy. PRF radiomics features were extracted from preoperative CT scans using a semi-automated segmentation method. Three prognostic models were developed: clinical, radiomics, and combined. Model performance was assessed using concordance index (C-index), time-dependent Area Under the Curve (AUC), and integrated Brier score. Results: The combined model demonstrated superior performance (C-index: 0.784, 95% CI: 0.707-0.861) compared to the radiomics (0.759, 95% CI: 0.678-0.840) and clinical (0.653, 95% CI: 0.547-0.759) models. Time-dependent AUC analysis revealed the radiomics model's particular strength in short-term prognosis (12-month AUC: 0.9281), while the combined model excelled in long-term predictions (60-month AUC: 0.8403). Key PRF radiomics features showed stronger prognostic value than traditional clinical factors. Conclusions: Integration of PRF radiomics with clinical data significantly improves prognostic accuracy in UTUC. This approach offers a more nuanced analysis of the tumor microenvironment, potentially capturing early signs of tumor invasion not visible through conventional imaging. The semi-automated PRF segmentation method presents advantages in reproducibility and ease of use, facilitating potential clinical implementation.

A Machine Learning Model Based on Thyroid US Radiomics to Discriminate Between Benign and Malignant Nodules

Background/Objectives: Thyroid nodules are a very common finding, mostly benign but sometimes malignant, and thus require accurate diagnosis. Ultrasound and fine needle biopsy are the most widely used and reliable diagnostic methods to date, but they are sometimes limited in addressing benign from malignant nodules, mainly with regard to ultrasound, by the operator's experience. Radiomics, quantitative feature extraction from medical images and machine learning offer promising avenues to improve diagnosis. The aim of this work was to develop a machine learning model based on thyroid ultrasound images to classify nodules into benign and malignant classes. Methods: For this purpose, images of ultrasonography from 142 subjects were collected. Among these subjects, 40 patients (28.2%) belonged to the class "malignant" and 102 patients (71.8%) belonged to the class "benign", according to histological diagnosis from fine-needle aspiration. This image set was used for the training, cross-validation and internal testing of three different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic feature could capture the disease heterogeneity among the two groups. Three models consisting of four ensembles of machine learning classifiers (random forests, support vector machines and k-nearest neighbor classifiers) were developed for the binary classification task of interest. The best performing model was then externally tested on a cohort of 21 new patients. Results: The best model (ensemble of random forest) showed Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) (%) of 85 (majority vote), 83.7 ** (mean) [80.2-87.2], accuracy (%) of 83, 81.2 ** [77.1-85.2], sensitivity (%) of 70, 67.5 ** [64.3-70.7], specificity (%) of 88, 86.5 ** [82-91], positive predictive value (PPV) (%) of 70, 66.5 ** [57.9-75.1] and negative predictive value (NPV) (%) of 88, 87.1 ** [85.5-88.8] (* p < 0.05, ** p < 0.005) in the internal test cohort. It achieved an accuracy of 90.5%, a sensitivity of 100%, a specificity of 86.7%, a PPV of 75% and an NPV of 100% in the external testing cohort. Conclusions: The model constituted of four ensembles of random forest classifiers could identify all the malignant nodes and the consistent majority of benign in the external testing cohort.

Multi-task Bayesian model combining FDG-PET/CT imaging and clinical data for interpretable high-grade prostate cancer prognosis

We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network (BSN), for predicting high-grade (Gleason  ≥  8) prostate cancer (PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival (PCSS). Experiments are conducted using a dataset of 295 patients. BSN outperforms widely used nomograms on all tasks except PCSS, leveraging multi-task learning and imaging data. BSN also provides automated prostate segmentation, uncertainty quantification, personalized feature-based explanations, and introduces dynamic predictions, a novel approach that relies on short-term outcomes to refine long-term prognosis. Overall, BSN shows great promise in its ability to exploit imaging and clinicopathological data to predict poor outcome patients that need treatment intensification with loco-regional or systemic adjuvant therapy for high-risk PCa.

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.

CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm

OBJECTIVES

To develop a deep learning (DL) model based on computed tomography (CT) images to predict the success of extracorporeal shock wave lithotripsy (SWL) treatment for patients with ureteral stones larger than 1 cm.

MATERIALS AND METHODS

We enrolled 333 patients who underwent SWL treatment for ureteral stones and randomly divided them into training and test sets. A DL model was built based on CT images of ureteral stones to predict SWL outcomes. The predictive efficacy of the DL model was assessed by comparing it with traditional and radiomics models.

RESULTS

The DL model demonstrated significantly better predictive performance in both training and test sets compared to radiomics (training set, AUC: 0.993 vs. 0.923, P < 0.001; test set AUC: 0.982 vs. 0.846, P < 0.001) and traditional models (training set AUC: 0.993 vs. 0.75, P = 0.005; test set AUC: 0.982 vs. 0.677, P < 0.001). Decision curve analysis (DCA) also proved that the DL model brought more benefit in predicting the success of SWL treatment than other methods.

CONCLUSION

The DL model based on CT images showed excellent ability to predict the probability of success of SWL treatment for patients with ureteral stones larger than 1 cm, providing a new auxiliary tool for clinical treatment decision-making.

Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies

Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)-a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation.

Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences

OBJECTIVES

Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI.

METHODS

Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics.

RESULTS

In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance.

CONCLUSION

The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis.

CRITICAL RELEVANCE STATEMENT

This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts.

KEY POINTS

Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.

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.

MRI-based radiomics models for noninvasive evaluation of lymphovascular space invasion in cervical cancer: a systematic review and meta-analysis

AIM

Aimed to evaluate the diagnostic performance of preoperative MRI-based radiomic models for noninvasive prediction of lymphovascular space invasion (LVSI) in patients with cervical cancer (CC).

MATERIALS AND METHODS

A systematic search of the PubMed, Embase, Web of Science, and Cochrane databases was conducted up to December 21, 2023. The quality of the studies was assessed utilizing the Radiomics Quality Score (RQS) system and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled estimates of sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) were computed. The clinical utility was evaluated using the Fagan nomogram. Heterogeneity was investigated and subgroup analyses were conducted.

RESULTS

Eleven studies were included, with nine studies reporting independent validation sets. In the training sets, the pooled sensitivity, specificity, DOR, and AUC of SROC were 0.81 (95% CI: 0.75-0.85), 0.78 (95% CI: 0.73-0.83), 15 (95% CI: 11-20), and 0.86 (95% CI: 0.79-0.92), respectively. For the validation sets, the overall sensitivity, specificity, DOR, and AUC of SROC were 0.79 (95% CI: 0.73-0.84), 0.73 (95% CI: 0.67-0.78), 10 (95% CI: 7-15), and 0.83 (95% CI: 0.71-0.91), respectively. The Fagan nomogram indicated good clinical utility. Subgroup analysis revealed that multi-sequence MRI-based models exhibited superior diagnostic performance compared to single-sequence MRI-based models in validation sets.

CONCLUSION

This meta-analysis highlights the potential diagnostic efficacy of MRI-based radiomic models for predicting LVSI in CC. Nevertheless, large-sample, multicenter studies are still warranted, and improvements in the standardization of radiomics methodology are necessary.

Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives

Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients' history. However, the incorporation of radiomics in clinical decision-making process is still limited by low data reproducibility and study variability. Moreover, the need for prospective validations and standardizations is emerging. In this narrative review, we summarize current evidence regarding radiomic applications in high-incidence cancers (breast and lung) for screening, diagnosis, staging, treatment choice, response, and clinical outcome evaluation. We also discuss pro and cons of the radiomic approach, suggesting possible solutions to critical issues which might invalidate radiomics studies and propose future perspectives.

Sexual Dimorphism of Radiomic Features in the Brain: An Exploratory Study Using 700 μm MP2RAGE MRI at 7 T

OBJECTIVES

The aim of this study was to determine whether MRI radiomic features of key cerebral structures differ between women and men, and whether detection of such differences depends on the image resolution.

MATERIALS AND METHODS

Ultrahigh resolution (UHR) 3D MP2RAGE (magnetization-prepared 2 rapid acquisition gradient echo) T1-weighted MR images (voxel size, 0.7 × 0.7 × 0.7 mm 3 ) of the brain of 30 subjects (18 women and 12 men; mean age, 39.0 ± 14.8 years) without abnormal findings on MRI were retrospectively included. MRI was performed on a whole-body 7 T MR system. A convolutional neural network was used to segment the following structures: frontal cortex, frontal white matter, thalamus, putamen, globus pallidus, caudate nucleus, and corpus callosum. Eighty-seven radiomic features were extracted respectively: gray-level histogram (n = 18), co-occurrence matrix (n = 24), run-length matrix (n = 16), size-zone matrix (n = 16), and dependence matrix (n = 13). Feature extraction was performed at UHR and, additionally, also after resampling to 1.4 × 1.4 × 1.4 mm 3 voxel size (standard clinical resolution). Principal components (PCs) of radiomic features were calculated, and independent samples t tests with Cohen d as effect size measure were used to assess differences in PCs between women and men for the different cerebral structures.

RESULTS

At UHR, at least a single PC differed significantly between women and men in 6/7 cerebral structures: frontal cortex ( d = -0.79, P = 0.042 and d = -1.01, P = 0.010), frontal white matter ( d = -0.81, P = 0.039), thalamus ( d = 1.43, P < 0.001), globus pallidus ( d = 0.92, P = 0.020), caudate nucleus ( d = -0.83, P = 0.039), and corpus callosum ( d = -0.97, P = 0.039). At standard clinical resolution, only a single PC extracted from the corpus callosum differed between sexes ( d = 1.05, P = 0.009).

CONCLUSIONS

Nonnegligible differences in radiomic features of several key structures of the brain exist between women and men, and need to be accounted for. Very high spatial resolution may be required to uncover and further investigate the sexual dimorphism of brain structures on MRI.

Radiomics-driven personalized radiotherapy for primary and recurrent tumors: A general review with a focus on reirradiation

PURPOSE

This review systematically investigates the role of radiomics in radiotherapy, with a particular emphasis on the use of quantitative imaging biomarkers for predicting clinical outcomes, assessing toxicity, and optimizing treatment planning. While the review encompasses various applications of radiomics in radiotherapy, it particularly highlights its potential for guiding reirradiation of recurrent cancers.

METHODS

A systematic review was conducted based on a Medline search with the search engine PubMed using the keywords "radiomics or radiomic" and "radiotherapy or reirradiation". Out of 189 abstracts reviewed, 147 original articles were included in the analysis. These studies were categorized by tumor localization, imaging modality, study objectives, and performance metrics, with a particular emphasis on the inclusion of external validation and adherence to a standardized radiomics pipeline.

RESULTS

The review identified 14 tumor localizations, with the majority of studies focusing on lung (33 studies), head and neck (27 studies), and brain (15 studies) cancers. CT was the most frequently employed imaging modality (77 studies) for radiomics, followed by MRI (46 studies) and PET (13 studies). The overall AUC across all studies, primarily focused on predicting the risk of recurrence (94 studies) or toxicity (41 studies), was 0.80 (SD=0.08). However, only 24 studies (16.3%) included external validation, with a slightly lower AUC compared to those without it. For studies using CT versus MRI or PET, both had a median AUC of 0.79, with IQRs of 0.73-0.86 for CT and 0.76-0.855 for MRI/PET, showing no significant differences in performance. Five studies involving reirradiation reported a median AUC of 0.81 (IQR: 0.73-0.825).

CONCLUSION

Radiomics demonstrates considerable potential in personalizing radiotherapy by improving treatment precision through better outcome prediction and treatment planning. However, its clinical adoption is hindered by the lack of external validation and variability in study designs. Future research should focus on implementing rigorous validation methods and standardizing imaging protocols to enhance the reliability and generalizability of radiomics in clinical radiotherapy, with particular attention to its application in reirradiation.

Performance of radiomics in preoperative determination of malignant potential and Ki-67 expression levels in gastrointestinal stromal tumors: a systematic review and meta-analysis

Empirical evidence for radiomics predicting the malignant potential and Ki-67 expression in gastrointestinal stromal tumors (GISTs) is lacking. The aim of this review article was to explore the preoperative discriminative performance of radiomics in assessing the malignant potential, mitotic index, and Ki-67 expression levels of GISTs. We systematically searched PubMed, EMBASE, Web of Science, and the Cochrane Library. The search was conducted up to 30 September 2023. Quality assessment was performed using the Radiomics Quality Score (RQS). A total of 35 original studies were included in the analysis. Among them, 26 studies focused on determining malignant potential, three studies on mitotic index discrimination, and six studies on Ki-67 discrimination. In the validation set, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of radiomics in the determination of high malignant potential were 0.74 (95% CI=0.69-0.78), 0.90 (95% CI=0.83-0.94), and 0.81 (95% CI=0.14-0.99), respectively. For moderately to highly malignant potential, the sensitivity, specificity, and AUC were 0.86 (95% CI=0.83-0.88), 0.73 (95% CI=0.67-0.78), and 0.88 (95% CI=0.27-0.99), respectively. Regarding the determination of high mitotic index, the sensitivity, specificity, and AUC of radiomics were 0.86 (95% CI=0.83-0.88), 0.73 (95% CI=0.67-0.78), and 0.88 (95% CI=0.27-0.99), respectively. When determining high Ki-67 expression, the combined sensitivity, specificity, and AUC were 0.74 (95% CI=0.65-0.81), 0.81 (95% CI=0.74-0.86), and 0.84 (95% CI=0.61-0.95), respectively. Radiomics demonstrates promising discriminative performance in the preoperative assessment of malignant potential, mitotic index, and Ki-67 expression levels in GISTs.