Radiomics: extracting more information from medical images using advanced feature analysis

A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair

OBJECTIVE

This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to predict the prognosis of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) patients.

METHODS

In this retrospective study, 164 AAA patients underwent EVAR and were categorized into shrinkage (good prognosis) or stable (poor prognosis) groups based on post-EVAR sac regression. From preoperative AAA and perivascular adipose tissue (PVAT) image, radiomics features (RFs) were extracted for model creation. Patients were split into 80 % training and 20 % test sets. A support vector machine model was constructed for prediction. Accuracy is evaluated via the area under the receiver operating characteristic curve (AUC).

RESULTS

Demographics and comorbidities showed no significant differences between shrinkage and stable groups. The model containing 5 AAA RFs (which are original_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glrlm_GrayLevelNonUniformityNormalized, log-sigma-3-0-mm-3D_glrlm_RunPercentage, log-sigma-4-0-mm-3D_glrlm_ShortRunLowGrayLevelEmphasis, wavelet-LLH_glcm_SumEntropy) had AUCs of 0.86 (training) and 0.77 (test). The model containing 7 PVAT RFs (which are log-sigma-3-0-mm-3D_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glcm_Correlation, wavelet-LHL_firstorder_Energy, wavelet-LHL_firstorder_TotalEnergy, wavelet-LHH_firstorder_Mean, wavelet-LHH_glcm_Idmn, wavelet-LHH_glszm_GrayLevelNonUniformityNormalized) had AUCs of 0.76 (training) and 0.78 (test). Combining AAA and PVAT RFs yielded the highest accuracy: AUCs of 0.93 (training) and 0.87 (test).

CONCLUSIONS

Radiomics-based CTA model predicts aneurysm sac regression post-EVAR in AAA patients. PVAT RFs from preoperative CTA images were closely related to AAA prognosis after EVAR, enhancing accuracy when combined with AAA RFs. This preliminary study explores a predictive model designed to assist clinicians in optimizing therapeutic strategies during clinical decision-making processes.

Application of radiomics in acute and severe non-neoplastic diseases: A literature review

Radiomics involves the integration of computer technology, big data analysis, and clinical medicine. Currently, there have been initial advancements in the fields of acute cerebrovascular disease and cardiovascular disease. The objective of radiomics is to extract quantitative features from medical images for analysis to predict the risk or treatment outcome, help in differential diagnosis, and guide clinical decisions and management. Radiomics applied research has reached a more advanced stage yet encounters several obstacles, including the need for standardization of radiomics features and alignment with treatment requirements for acute and severe illnesses. Future research should aim to seamlessly incorporate radiomics with various disciplines, leverage big data and artificial intelligence advancements, cater to the requirements of acute and critical medicine, and enhance the effectiveness of technological innovation and application in diagnosing and treating acute and critical illnesses.

MRI and CT radiomics for the diagnosis of acute pancreatitis

Purpose

To evaluate the single and combined diagnostic performances of CT and MRI radiomics for diagnosis of acute pancreatitis (AP).

Materials and methods

We prospectively enrolled 78 patients (mean age 55.7 ± 17 years, 48.7 % male) diagnosed with AP between 2020 and 2022. Patients underwent contrast-enhanced CT (CECT) within 48-72 h of symptoms and MRI ≤ 24 h after CECT. The entire pancreas was manually segmented tridimensionally by two operators on portal venous phase (PVP) CECT images, T2-weighted imaging (WI) MR sequence and non-enhanced and PVP T1-WI MR sequences. A matched control group (n = 77) with normal pancreas was used. Dataset was randomly split into training and test, and various machine learning algorithms were compared. Receiver operating curve analysis was performed.

Results

The T2WI model exhibited significantly better diagnostic performance than CECT and non-enhanced and venous T1WI, with sensitivity, specificity and AUC of 73.3 % (95 % CI: 71.5-74.7), 80.1 % (78.2-83.2), and 0.834 (0.819-0.844) for T2WI (p = 0.001), 74.4 % (71.5-76.4), 58.7 % (56.3-61.1), and 0.654 (0.630-0.677) for non-enhanced T1WI, 62.1 % (60.1-64.2), 78.7 % (77.1-81), and 0.787 (0.771-0.810) for venous T1WI, and 66.4 % (64.8-50.9), 48.4 % (46-50.9), and 0.610 (0.586-0.626) for CECT, respectively.The combination of T2WI with CECT enhanced diagnostic performance compared to T2WI, achieving sensitivity, specificity and AUC of 81.4 % (80-80.3), 78.1 % (75.9-80.2), and 0.911 (0.902-0.920) (p = 0.001).

Conclusion

The MRI radiomics outperformed the CT radiomics model to detect diagnosis of AP and the combination of MRI with CECT showed better performance than single models. The translation of radiomics into clinical practice may improve detection of AP, particularly MRI radiomics.

Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with 68Ga-PSMA-617 PET/CT

OBJECTIVE

Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers associated with poor pathology, ultimately developing a multi-omics marker model for better clinical risk stratification.

METHODS

In this retrospective study, 191 patients with PCa or benign prostatic hyperplasia confirmed via 68Ga-PSMA-617 PET/CT scans were analyzed. Radiomic features were extracted from scan contours, and six machine learning algorithms were used to predict malignancy and adverse pathological features like Gleason score, ISUP group, tumor stage, lymph node infiltration, and perineural invasion. Feature selection and dimensionality reduction were performed using minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. Proteomics analysis on 39 patients identified protein biomarkers, followed by correlation analysis between radiomic features and identified proteins.

RESULTS

The radiomics model showed an AUC of 0.938 for predicting malignant prostate lesions and 0.916 for adverse pathological features in the test set, with validation set AUCs of 0.918 and 0.855, respectively. Three quantitative radiomic features and ten protein molecules associated with adverse pathology were identified, with significant correlations observed between radiomic features and protein biomarkers. Radioproteomic analysis revealed that molecular changes in protein molecules could influence imaging biomarkers.

CONCLUSIONS

The machine learning models based on 68 Ga-PSMA-617 PET/CT radiomic features performed well in stratifying patients, supporting clinical risk stratification and highlighting connections between radiomic characteristics and protein biomarkers.

CT-based radiomics model for predicting perineural invasion status in gastric cancer

PURPOSE

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients.

METHODS

This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models.

RESULTS

A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility.

CONCLUSION

We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

Association between radiomics of diffusion-weighted imaging and histopathology in hepatocellular carcinoma. A preliminary investigation

OBJECTIVE

Diffusion-weighted imaging and the quantified apparent diffusion coefficient (ADC) correlate with cell density and histopathological features in tumors. Radiomics analysis may provide more insight into the underlying microstructure and may better correlate with histopathology. The present study used cross-sectional guided biopsy specimens to exploit the precise spatial localization of the performed biopsy to correlate radiomics features of the ADC map with immunohistochemical features in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

A total of 51 patients (11 female patients, 21.6 %) were included in the present study. The mean age was 71.9 ± 9.9 years, ranging from 42 to 91 years. Prebioptic liver MRI with diffusion-weighted imaging was used to correlate the radiomics features of the ADC maps with the immunohistochemical features quantified in liver biopsy. Proliferation potential Ki 67, leukocyte count and tumor-stroma ratio were evaluated as histopathological parameters.

RESULTS

The following ADC texture features were correlated with the Ki 67 index _MinNorm (r = -0.307, p = 0.03), Vertl_RLNonUni (r = - 0.309, p = 0.03), 135dr_RLNonUni (r = -0.346, p = 0.01). The texture feature _MinNorm achieved the best diagnostic accuracy with an area under the curve of 0.76 (95 % CI 0.60-0.91, p < 0.01) to discriminate between low and high proliferative HCC. Multiple statistically significant correlations were found between ADC texture features and tumor-stroma-ratio, the highest for S(0,1)Contrast (r = 0.460, p = 0.001). No statistically significant correlations were found between the ADC texture features with the CD45+ leukocyte count and grading.

CONCLUSION

Radiomics features of the ADC maps can reflect the underlying histopathology in HCC patients including the proliferation potential and tumor-stroma ratio but not CD45 positive cells and tumor grading. The complex interactions between quantitative imaging and histopathology need to be further investigated in a validation cohort.

Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research

This review explores the current understanding and recent advancements in neuroblastoma, one of the most common extracranial solid pediatric cancers, accounting for ~ 15% of childhood cancer-related mortality. The hallmarks of NBL, including angiogenesis, metastasis, apoptosis resistance, cell cycle dysregulation, drug resistance, and responses to hypoxia and ROS, underscore its complex biology. The tumor microenvironment's significance in disease progression is acknowledged in this study, along with the pivotal role of cancer stem cells in sustaining tumor growth and heterogeneity. A number of molecular signatures are being studied in order to better understand the disease, with many of them serving as targets for the development of new therapeutics. This includes inhibitor therapies for NBL patients, which notably concentrate on ALK signaling, MDM2, PI3K/Akt/mTOR, Wnt, and RAS-MAPK pathways, along with regulators of epigenetic mechanisms. Additionally, this study offers an extensive understanding of the molecular therapies used, such as monoclonal antibodies and CAR-T therapy, focused on both preclinical and clinical studies. Radiation therapy's evolving role and the promise of stem cell transplantation-mediated interventions underscore the dynamic landscape of NBL treatment. This study has also emphasized the recent progress in the field of diagnosis, encompassing the adoption of artificial intelligence and liquid biopsy as a non-intrusive approach for early detection and ongoing monitoring of NBL. Furthermore, the integration of innovative treatment approaches such as CRISPR-Cas9, and cancer stem cell therapy has also been emphasized in this review.

Radiomics and Artificial Intelligence Landscape for [18F]FDG PET/CT in Multiple Myeloma

[18F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treatment response in this disease. On the other hand, due to the heterogeneous and sometimes complex patterns of bone marrow infiltration in MM, the interpretation of PET/CT can be particularly challenging, hampering interobserver reproducibility and limiting the diagnostic and prognostic ability of the modality. Although many approaches have been developed to address the issue of standardization, none can yet be considered a standard method for interpretation or objective quantification of PET/CT. Therefore, advanced diagnostic quantification approaches are needed to support and potentially guide the management of MM. In recent years, radiomics has emerged as an innovative method for high-throughput mining of image-derived features for clinical decision making, which may be particularly helpful in oncology. In addition, machine learning and deep learning, both subfields of artificial intelligence (AI) closely related to the radiomics process, have been increasingly applied to automated image analysis, offering new possibilities for a standardized evaluation of imaging modalities such as CT, PET/CT and MRI in oncology. In line with this, the initial but steadily growing literature on the application of radiomics and AI-based methods in the field of [18F]FDG PET/CT in MM has already yielded encouraging results, offering a potentially reliable tool towards optimization and standardization of interpretation in this disease. The main results of these studies are presented in this review.

Dynamic changes of radiological and radiomics patterns based on MRI in viable hepatocellular carcinoma after transarterial chemoembolization

OBJECTIVES

This study aims to analyze the magnetic resonance imaging (MRI) change patterns of viable hepatocellular carcinomas (HCCs) following the initial transarterial chemoembolization (TACE).

METHODS

A retrospective analysis of HCC patients' initial TACE from February 2015 to October 2022 across three centers and a clinical trial (NCT03113955) was conducted. The viability of residual HCCs at one and six months after TACE was evaluated using the LI-RADS Treatment Response Algorithm (LR-TRA) v2024. The radiological and radiomics features of post-TACE viable tumors between baseline and one-month, and between one- and six- months were compared using Wilcoxon signed-rank test and McNemar's test.

RESULTS

A total of 160 viable tumors were included in the study. Viable tumors at one month after TACE exhibited higher T1WI intensity (P =.024), lower T2WI intensity (P =.005), fewer washout features (P <.001), smaller size (P <.001), and higher ADC values (P <.001) compared to baseline HCC imaging.A significant reduction in DWI intensity (P =.002) and ADC values (P <.001) were observed in viable tumors at one month compared to those at six months. There were 82 (45.1%) radiomics features that changed significantly between the baseline and one-month. Only three radiomics features showed statistically significant difference of viable tumors between one- and six-month.

CONCLUSIONS

Compared to the baseline, viable HCCs after TACE demonstrated significant changes of imaging characteristics in a series of radiological and radiomics features at one- and six-month follow-ups.

CLINICAL RELEVANCE STATEMENT

Clinically diagnosing of viable HCCs using radiological methods is challenging. A comprehensive analysis of these imaging characteristics can facilitate the accurate identification of viable tumors.

Understandings 18 FDG PET radiomics and its application to lymphoma

The early identification of lymphoma patients who fail front-line treatment is crucial for optimizing disease management. Positron emission tomography, a well-established tool for staging and response evaluation in lymphoma, is typically assessed visually or semiquantitatively, leaving much of its latent information unexploited. Radiomic analysis, which employs mathematical descriptors, can enable the extraction of quantitative features from baseline images that correlate with the disease's biological characteristics. Emerging radiomic features such as metabolic tumour volume, total lesion glycolysis and markers of disease dissemination and metabolic heterogeneity are proving to be powerful prognostic biomarkers in lymphoma. Texture analysis, the most advanced area of radiomics, offers highly complex features that require further standardization and validation before being adopted as reliable biomarkers. Combining radiomic features with clinical risk factors and genomic data holds promising potential for improving clinical risk prediction. This review explores the current state of radiomic analysis, progress towards its standardization and its incorporation into clinical practice and trial designs. The integration of radiomic markers with circulating tumour DNA may provide a comprehensive approach to developing baseline and dynamic risk scores, facilitating the testing of novel treatments and advancing personalized treatment of aggressive lymphomas.

MRI based radiomics nomogram for predict recurrence of non functioning pituitary macroadenomas post surgery

OBJECTIVE

To establish and validate a comprehensive predictive model combining clinical data and radiomics features to improve the accuracy of predicting recurrence within five years after surgery in patients with non-functioning pituitary macroadenomas (NFMA).

METHODS

This retrospective study included 292 NFMA patients who underwent surgery between January 2012 and January 2018, with an additional 123 patients as an external test set. Clinical, pathological, and conventional imaging features were collected and analyzed using univariate and multivariate logistic regression to identify independent risk factors for postoperative recurrence. Radiomic features were extracted from preoperative T1-weighted (T1WI), T2-weighted (T2WI), and T1-enhanced images using 3D Slicer software. A radiomics prediction model was developed, and a combined model integrating clinical and radiomics features was established. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The clinical model (Cli-score), radiomics model (Rad-score) and combined model were developed. The diagnostic performance of the clinical model in the external test set, showed an AUC of 0.757 (95%CI: 0.671-0.830), with SEN, SPE, and ACC of 82.5%, 59.04%, and 71.54%, respectively. The diagnostic performance of the radiomics model in the external test set showed an AUC of 0.835 (95% CI: 0.757-0.896), with 80%, 79.52% and 63.41% for SEN, SPE and ACC%, respectively. The diagnostic performance of the combined model in the external test set showed an AUC of 0.863 (95% CI: 0.790-0.919), with SEN, SPE, and ACC of 80%, 81.93%, and 68.30%, respectively. The calibration curve indicated good predictive performance, and DCA confirmed the high clinical utility of the combined model.

CONCLUSION

The combined model provides a more accurate prediction of NFMA recurrence. This model can guide postoperative follow-up strategies and aid in early initiation of adjuvant therapy for high-risk patients.

Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization

BACKGROUND

This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE).

METHODS

In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve.

RESULTS

Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1-2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1-1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805-0.861), internal validation cohort (0.823; 95% CI 0.771-0.876) and external validation cohort (0.846; 95% CI 0.768-0.924). The calibration curve exhibited a satisfactory correspondence.

CONCLUSION

A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis.

The pitfalls of fixed-ratio data splitting in radiomics model performance evaluation

Over the past decade, radiomics has seen exponential growth, with over ten thousand publications in PubMed and a steady increase in related studies in journals like Abdominal Radiology. Despite the potential of radiomics, a major challenge lies in validating radiomics models, as most studies rely on single-center datasets with fixed-ratio splits, which can lead to variability in performance due to randomness in data splitting. Therefore, researchers should adopt more robust cross-validation methods rather than relying solely on the fixed-ratio holdout method to ensure robust and reliable radiomics model performance evaluation.

Incorporating frequency domain features into radiomics for improved prognosis of esophageal cancer

Esophageal cancer is a highly aggressive gastrointestinal malignancy with a poor prognosis, making accurate prognostic assessment essential for patient care. The performance of the esophageal cancer prognosis model based on conventional radiomics is limited, as it mainly characterizes the spatial features such as texture of the tumor area, and cannot fully describe the complexity of esophageal cancer tumors. Therefore, we incorporate the frequency domain features into radiomics to improve the prognostic ability of esophageal cancer. Three hundred fifteen esophageal cancer patients participated in the death risk prediction experiment, with 80% being the training set and 20% being the testing set. We use fivefold cross validation for training, and fuse the 5 trained models through voting to obtain the final prognostic model for testing. The CatBoost achieved the best performance compared to machine learning methods such as random forests and decision tree. The experimental results showed that the combination of frequency domain and radiomics features achieved the highest performance in death predicting esophageal cancer (accuracy: 0.7423, precision: 0.7470, recall: 0.7375, specification: 0.8030, AUC: 0.8487), which was significantly better than the performance of frequency domain or radiomics features alone. The results of Kaplan-Meier survival analysis validated the performance of our method in death predicting esophageal cancer. The proposed method provides technical support for accurate prognosis of esophageal cancer.

A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease

OBJECTIVES

To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD).

MATERIALS AND METHODS

The data of 264 CTD-ILD patients were retrospectively collected. GAP Stage I, II, III patients are 195, 56, 13 cases respectively. The latter two stages were combined into one group. The patients were randomized into a training set and a validation set. Single-input models were separately constructed using the selected radiomics and DL features, while DLR model was constructed from both sets of features. For all models, the support vector machine (SVM) and logistic regression (LR) algorithms were used for construction. The nomogram models were generated by integrating age, gender, and DLR features.

RESULTS

The DLR model outperformed the radiomics and DL models in both the training set and the validation set. The predictive performance of the DLR model based on the LR algorithm was the best among all the feature-based models (AUC = 0.923). The comprehensive models had even greater performance in predicting the GAP stage of CTD-ILD patients. The comprehensive model using the SVM algorithm had the best performance of the two models (AUC = 0.951).

CONCLUSION

The DLR model extracted from CT images can assist in the clinical prediction of the GAP stage of CTD-ILD patients. A nomogram showed even greater performance in predicting the GAP stage of CTD-ILD patients.

A fully automatic radiomics pipeline for postoperative facial nerve function prediction of vestibular schwannoma

Vestibular schwannoma (VS) is the most prevalent intracranial schwannoma. Surgery is one of the options for the treatment of VS, with the preservation of facial nerve (FN) function being the primary objective. Therefore, postoperative FN function prediction is essential. However, achieving automation for such a method remains a challenge. In this study, we proposed a fully automatic deep learning approach based on multi-sequence magnetic resonance imaging (MRI) to predict FN function after surgery in VS patients. We first developed a segmentation network 2.5D Trans-UNet, which combined Transformer and U-Net to optimize contour segmentation for radiomic feature extraction. Next, we built a deep learning network based on the integration of 1DConvolutional Neural Network (1DCNN) and Gated Recurrent Unit (GRU) to predict postoperative FN function using the extracted features. We trained and tested the 2.5D Trans-UNet segmentation network on public and private datasets, achieving accuracies of 89.51% and 90.66%, respectively, confirming the model's strong performance. Then Feature extraction and selection were performed on the private dataset's segmentation results using 2.5D Trans-UNet. The selected features were used to train the 1DCNN-GRU network for classification. The results showed that our proposed fully automatic radiomics pipeline outperformed the traditional radiomics pipeline on the test set, achieving an accuracy of 88.64%, demonstrating its effectiveness in predicting the postoperative FN function in VS patients. Our proposed automatic method has the potential to become a valuable decision-making tool in neurosurgery, assisting neurosurgeons in making more informed decisions regarding surgical interventions and improving the treatment of VS patients.

Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis

PURPOSE

Microsatellite instability (MSI), caused by defects in mismatch repair (MMR) genes, serves as a critical molecular biomarker with therapeutic implications for endometrial cancer (EC). This study aims to assess the diagnostic performance of radiomics as a non-invasive approach for predicting MSI status in EC.

METHODS

A systematic search across PubMed, Scopus, Embase, Web of Science, Cochrane library and Clinical Trials was conducted. Quality assessment was performed using QUADAS-2 and METRICS. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were computed using a bivariate model. Separate meta-analyses for radiomics and combined models were conducted. Subgroup analysis and sensitivity analysis were conducted to find potential sources of heterogeneity. Likelihood ratio scattergram was used to evaluate the clinical applicability.

RESULTS

A total of 9 studies (1650 patients) were included in the systematic review, with seven studies contributing to the meta-analysis of radiomics model and five for combined model. The pooled diagnostic performance of the radiomics model was as follows: sensitivity, 0.66; specificity, 0.89; PLR, 5.48; NLR, 0.43; DOR, 18.56; and AUC, 0.87. For combined model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.58, 0.94, 7.37, 0.50, 16.43, and 0.85, respectively. Subgroup analysis of radiomics models revealed that studies employing non-linear classifiers achieved superior performance compared to those utilizing linear classifiers.

CONCLUSION

Radiomics showed promise as non-invasive tool for MSI prediction in EC, with potential clinical utility in guiding personalized treatments. However, further studies are required to validate these findings.

Evaluating the Predictive Capability of Radiomics Features of Perirenal Fat in Enhanced CT Images for Staging and Grading of UTUC Tumours Using Machine Learning

Background: Upper tract urothelial carcinoma (UTUC) often presents with aggressive behaviour, demanding accurate preoperative assessment to guide management. Radiomics-based approaches have shown promise in extracting quantitative features from imaging, yet few studies have explored whether perirenal fat (PRF) radiomics can augment tumour-only models. Methods: A retrospective cohort of 103 UTUC patients undergoing radical nephroureterectomy was analysed. Tumour regions of interest (ROI) and concentric PRF expansions (10-30 mm) were segmented from computed tomography (CT) scans. Radiomic features were extracted using PyRadiomics, filtered by correlation and intraclass correlation coefficients, and integrated with clinical variables (e.g., age, BMI, multifocality). Multiple machine learning models, including MLPClassifier and CatBoost, were evaluated via repeated cross-validation. Performance was assessed using the area under the ROC curve (AUC), sensitivity, specificity, F1-score, and DeLong tests. Results: The best tumour grade model (AUC = 0.961) merged tumour-derived features with a 10 mm PRF margin, exceeding PRF-only (AUC = 0.900) and tumour-only (AUC = 0.934) approaches. However, the improvement over tumour-only was not always statistically significant. For stage prediction, combining tumour and 15 mm PRF features yielded the top AUC of 0.852, surpassing the tumour-alone model (AUC = 0.802) and outperforming PRF-only (AUC ≤ 0.778). PRF features provided an additional predictive value for both grade and stage models. Conclusions: Integrating PRF radiomics with tumour-based analyses enhances predictive accuracy for UTUC grade and stage, suggesting that the tumour microenvironment contains complementary imaging cues. These findings, pending external validation, support the potential for radiomics-driven risk stratification and personalised treatment planning in UTUC.

Enhancing radiomics features via a large language model for classifying benign and malignant breast tumors in mammography

BACKGROUND AND OBJECTIVES

Radiomics is widely used to assist in clinical decision-making, disease diagnosis, and treatment planning for various target organs, including the breast. Recent advances in large language models (LLMs) have helped enhance radiomics analysis.

MATERIALS AND METHODS

Herein, we sought to improve radiomics analysis by incorporating LLM-learned clinical knowledge, to classify benign and malignant tumors in breast mammography. We extracted radiomics features from the mammograms based on the region of interest and retained the features related to the target task. Using prompt engineering, we devised an input sequence that reflected the selected features and the target task. The input sequence was fed to the chosen LLM (LLaMA variant), which was fine-tuned using low-rank adaptation to enhance radiomics features. This was then evaluated on two mammogram datasets (VinDr-Mammo and INbreast) against conventional baselines.

RESULTS

The enhanced radiomics-based method performed better than baselines using conventional radiomics features tested on two mammogram datasets, achieving accuracies of 0.671 for the VinDr-Mammo dataset and 0.839 for the INbreast dataset. Conventional radiomics models require retraining from scratch for an unseen dataset using a new set of features. In contrast, the model developed in this study effectively reused the common features between the training and unseen datasets by explicitly linking feature names with feature values, leading to extensible learning across datasets. Our method performed better than the baseline method in this retraining setting using an unseen dataset.

CONCLUSIONS

Our method, one of the first to incorporate LLM into radiomics, has the potential to improve radiomics analysis.

Development of Multiparametric Prognostic Models for Stereotactic Magnetic Resonance Guided Radiation Therapy of Pancreatic Cancers

PURPOSE

Stereotactic magnetic resonance guided adaptive radiation therapy (SMART) is a new option for local treatment of unresectable pancreatic ductal adenocarcinoma, showing interesting survival and local control (LC) results. Despite this, some patients will experience early local and/or metastatic recurrence leading to death. We aimed to develop multiparametric prognostic models for these patients.

METHODS AND MATERIALS

All patients treated in our institution with SMART for an unresectable pancreatic ductal adenocarcinoma between October 21, 2019, and August 5, 2022 were included. Several initial clinical characteristics as well as dosimetric data of SMART were recorded. Radiomics data from 0.35-T simulation magnetic resonance imaging were extracted. All these data were combined to build prognostic models of overall survival (OS) and LC using machine learning algorithms.

RESULTS

Eighty-three patients with a median age of 64.9 years were included. A majority of patients had a locally advanced pancreatic cancer (77%). The median OS was 21 months after SMART completion and 27 months after chemotherapy initiation. The 6- and 12-month post-SMART OS was 87.8% (IC95%, 78.2%-93.2%) and 70.9% (IC95%, 58.8%-80.0%), respectively. The best model for OS was the Cox proportional hazard survival analysis using clinical data, with a concordance index inverse probability of censoring weighted of 0.87. Tested on its 12-month OS prediction capacity, this model had good performance (sensitivity 67%, specificity 71%, and area under the curve 0.90). The median LC was not reached. The 6- and 12-month post-SMART LC was 92.4% [IC95%, 83.7%-96.6%] and 76.3% [IC95%, 62.6%-85.5%], respectively. The best model for LC was the component-wise gradient boosting survival analysis using clinical and radiomics data, with a concordance index inverse probability of censoring weighted of 0.80. Tested on its 9-month LC prediction capacity, this model had good performance (sensitivity 50%, specificity 97%, and area under the curve 0.78).

CONCLUSIONS

Combining clinical and radiomics data in multiparametric prognostic models using machine learning algorithms showed good performance for the prediction of OS and LC. External validation of these models will be needed.

Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy

PURPOSE

To establish prediction models to predict 2-year overall survival (OS) and stratify patients with different risks based on radiomics features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

Patients with locally advanced ESCC were recruited. We extracted 547 radiomics features from MRI and CT images. The least absolute shrinkage and selection operator (LASSO) for COX algorithm was used to obtain features highly correlated with survival outcomes in the training cohort. Based on MRI, CT, and the hybrid image data, three prediction models were built. The predictive performance of the radiomics models was evaluated in the training cohort and verified in the validation cohort using AUC values.

RESULTS

A total of 192 patients were included and randomized into the training and validation cohorts. In predicting 2-year OS, the AUCs of the CT-based model were 0.733 and 0.654 for the training and validation sets. The MRI radiomics-based model was observed with similar AUCs of 0.750 and 0.686 in the training and validation sets. The AUC values of hybrid model combining MRI and CT radiomics features in predicting 2-year OS were 0.792 and 0.715 in the training and validation cohorts. It showed significant differences in 2-year OS in the high-risk and low-risk groups divided by the best cutoff value in the hybrid radiomics-based model.

CONCLUSIONS

The hybrid radiomics-based model demontrated the best performance of predicting 2-year OS and can differentiate the high-risk and low-risk patients.

Colon cancer biology and treatment in the era of precision oncology: A primer for Radiologists

In the era of precision oncology, systemic therapies for colon cancer are becoming increasingly biomarker-led, with implications for patients in the neoadjuvant, adjuvant and metastatic settings. As the landscape for colon cancer treatment evolves and becomes more complex, it is important that all members of the multidisciplinary team keep abreast of developments to ensure the most effective care is delivered to patients. As core members of the colorectal multidisciplinary team, Radiologists play a central role throughout the patient journey. This review serves as an educational summary of current and emerging treatment pathways in colon cancer, standards for biomarker testing, mechanisms of action for key drugs, important treatment-related complications, relevant tumour biology that underpins patterns of disease and treatment response, and the specific implications systemic therapies have for cancer imaging and Radiologists. We also highlight the increasing role for radiology in patient stratification and the importance of imaging biomarkers. It is crucial that Radiologists understand the current landscape of colon cancer treatment and emerging strategies on the horizon in clinical trials. Only through engagement across the wider multidisciplinary team will we deliver true personalised medicine for patients with colon cancer.

Beyond the status quo: when disease volume and metastatic timing are not enough to personalize treatment in mHSPC

This review explores the complexities of treatment intensification in metastatic hormone-sensitive prostate cancer (mHSPC), emphasizing the limitations of using disease volume and metastatic timing as sole prognostic factors. Current algorithms focus on clinical factors like ECOG, comorbidities, and patient preferences, yet lack biomarkers for more individualized therapy. By examining prognostic indicators - clinical, analytical, pathological, molecular, and imaging - this article highlights the importance of a personalized approach. Multimodal strategies and predictive biomarkers are proposed to optimize therapy selection between doublet and triplet regimens, ultimately improving patient outcomes. Future trials incorporating emerging biomarkers may provide the basis for precision treatment in mHSPC, shifting management beyond conventional classifications.

Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model

Background

Colorectal cancer is a prevalent malignancy with a significant risk of metastasis, including to bones, which severely impacts patient outcomes. Accurate prediction of bone metastasis risk is crucial for optimizing treatment strategies and improving prognosis.

Purpose

This study aims to develop a predictive model combining radiomics and Vision Transformer (ViT) deep learning techniques to assess the risk of bone metastasis in colorectal cancer patients using both plain and contrast-enhanced CT images.

Materials and methods

We conducted a retrospective analysis of 155 colorectal cancer patients, including 81 with bone metastasis and 74 without. Radiomic features were extracted from segmented tumors on both plain and contrast-enhanced CT images. LASSO regression was applied to select key features, which were then used to build traditional machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, LightGBM, and XGBoost. Additionally, a dual-modality ViT model was trained on the same CT images, with a late fusion strategy employed to combine outputs from the different modalities. Model performance was evaluated using AUC-ROC, accuracy, sensitivity, and specificity, and differences were statistically assessed using DeLong's test.

Results

The ViT model demonstrated superior predictive performance, achieving an AUC of 0.918 on the test set, significantly outperforming all traditional radiomics-based models. The SVM model, while the best among traditional models, still underperformed compared to the ViT model. The ViT model's strength lies in its ability to capture complex spatial relationships and long-range dependencies within the imaging data, which are often missed by traditional models. DeLong's test confirmed the statistical significance of the ViT model's enhanced performance, highlighting its potential as a powerful tool for predicting bone metastasis risk in colorectal cancer patients.

Conclusion

The integration of radiomics with ViT-based deep learning offers a robust and accurate method for predicting bone metastasis risk in colorectal cancer patients. The ViT model's ability to analyze dual-modality CT imaging data provides greater precision in risk assessment, which can improve clinical decision-making and personalized treatment strategies. These findings underscore the promise of advanced deep learning models in enhancing the accuracy of metastasis prediction. Further validation in larger, multicenter studies is recommended to confirm the generalizability of these results.

Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features

PURPOSE

Identification of the molecular subtypes in breast cancer allows to optimize treatment strategies, but usually requires invasive needle biopsy. Recently, non-invasive imaging has emerged as promising means to classify them. Magnetic resonance imaging is often used for this purpose because it is three-dimensional and highly informative. Instead, only a few reports have documented the use of mammograms. Given that mammography is the first choice for breast cancer screening, using it to classify molecular subtypes would allow for early intervention on a much wider scale. Here, we aimed to evaluate the effectiveness of combining global and local mammographic features by using Vision Transformer (ViT) and Convolutional Neural Network (CNN) to classify molecular subtypes in breast cancer.

METHODS

The feature values for binary classification were calculated using the ViT and EfficientnetV2 feature extractors, followed by dimensional compression via principal component analysis. LightGBM was used to perform binary classification of each molecular subtype: triple-negative, HER2-enriched, luminal A, and luminal B.

RESULTS

The combination of ViT and CNN achieved higher accuracy than ViT or CNN alone. The sensitivity for triple-negative subtypes was very high (0.900, with F-value = 0.818); whereas F-value and sensitivity were 0.720 and 0.750 for HER2-enriched, 0.765 and 0.867 for luminal A, and 0.614 and 0.711 for luminal B subtypes, respectively.

CONCLUSION

Features obtained from mammograms by combining ViT and CNN allow the classification of molecular subtypes with high accuracy. This approach could streamline early treatment workflows and triage, especially for poor prognosis subtypes such as triple-negative breast cancer.

Assessment of Age-Related Differences in Lower Leg Muscles Quality Using Radiomic Features of Magnetic Resonance Images

Sarcopenia, characterised by a decline in muscle mass and strength, affects the health of the elderly, leading to increased falls, hospitalisation, and mortality rates. Muscle quality, reflecting microscopic and macroscopic muscle changes, is a critical determinant of physical function. To utilise radiomic features extracted from magnetic resonance (MR) images to assess age-related changes in muscle quality, a dataset of 24 adults, divided into older (male/female: 6/6, 66-79 years) and younger (male/female: 6/6, 21-31 years) groups, was used to investigate the radiomics features of the dorsiflexor and plantar flexor muscles of the lower leg that are critical for mobility. MR images were processed using MaZda software for feature extraction. Dimensionality reduction was performed using principal component analysis and recursive feature elimination, followed by classification using machine learning models, such as support vector machine, extreme gradient boosting, and naïve Bayes. A leave-one-out validation test was used to train and test the classifiers, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance. The analysis revealed that significant differences in radiomic feature distributions were found between age groups, with older adults showing higher complexity and variability in muscle texture. The plantar flexors showed similar or higher AUC than the dorsiflexors in all models. When the dorsiflexor muscles were combined with the plantar flexor muscles, they tended to have a higher AUC than when they were used alone. Radiomic features in lower-leg MR images reflect ageing, especially in the plantar flexor muscles. Radiomic analysis can offer a deeper understanding of age-related muscle quality than traditional muscle mass assessments.

Decision Fusion Model for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Multi-MR Habitat Imaging and Machine-Learning Classifiers

RATIONALE AND OBJECTIVES

Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-learning radiomics, feature fusion, and decision fusion models based on multi-region MR habitat imaging using six machine-learning classifiers.

MATERIALS AND METHODS

We retrospectively included 300 HCC patients. The intratumoral and peritumoral regions were segmented into distinct habitats, from which radiomics and deep-learning features were extracted using arterial phase MR images. To reduce feature dimensionality, we applied intra-class correlation coefficient (ICC) analysis, Pearson correlation coefficient (PCC) filtering, and recursive feature elimination (RFE). Based on the selected optimal features, prediction models were constructed using decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost (XGB) classifiers. Additionally, fusion models were developed utilizing both feature fusion and decision fusion strategies. The performance of these models was validated using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis.

RESULTS

The decision fusion model (VOI-Peri10-1) using LR and integrating clinicoradiologic, radiomics, and deep-learning features achieved the highest AUC of 0.808 (95% confidence interval [CI]: 0.807-0.912) in the test cohort, with good calibration (Hosmer-Lemeshow test, P > 0.050) and clinical net benefit.

CONCLUSION

The LR-based decision fusion model integrating clinicoradiologic, radiomics, and deep-learning features shows promise for preoperative prediction of MVI in HCC, aiding in patient outcome predictions and personalized treatment planning.

New insights into imaging of pulmonary metastases from extra-thoracic neoplasms

The lung is one of the most common sites of metastases from extra-thoracic neoplasms. Lung metastases can show heterogeneous imaging appearance, thus mimicking a wide range of lung diseases, from benign lesions to primary lung cancer. The proper interpretation of pulmonary findings is crucial for prognostic assessment and treatment planning, even to avoid unnecessary procedures and patient anxiety. For this purpose, computed tomography (CT) is one of the most used imaging modalities. In the last decades, cancer patients' population has steadily increased and, due to the widespread application of CT for staging and surveillance, the detection of pulmonary nodules has raised, making their characterization and management an urgent and mostly unsolved problem for both radiologists and clinicians. This review will highlight the pathways of dissemination of extra-thoracic tumours to the lungs and the heterogeneous CT imaging appearance of pulmonary metastases, providing useful clues to properly address the diagnosis. Furthermore, we will deal with the promising applications of radiomics in this field. Finally, a focus on the hot-topic of pulmonary nodule management in patients with extra-thoracic neoplasms (ETNs) will be discussed.

CT-based radiomics: A potential indicator of KRAS mutation in pulmonary adenocarcinoma

PURPOSE

This study aimed to validate a CT-based radiomics signature for predicting Kirsten rat sarcoma (KRAS) mutation status in lung adenocarcinoma (LADC).

MATERIALS AND METHODS

A total of 815 LADC patients were included. Radiomics features were extracted from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images using Pyradiomics. CT-based radiomics were combined with clinical features to distinguish KRAS mutation status. Four feature selection methods and four deep learning classifiers were employed. Data was split into 70% training and 30% test sets, with SMOTE addressing imbalance in the training set. Model performance was evaluated using AUC, accuracy, precision, F1 score, and recall.

RESULTS

The analysis revealed that 10.4% of patients showed KRAS mutations. The study extracted 1061 radiomics features and combined them with 17 clinical features. After feature selection, two signatures were constructed using top 10, 20, and 50 features. The best performance was achieved using Multilayer Perceptron with 20 features. CECT, it showed 66% precision, 76% recall, 69% F1-score, 84% accuracy, and AUC of 93.3% and 87.4% for train and test sets, respectively. For NECT, accuracy was 85% and 82%, with AUC of 90.7% and 87.6% for train and test sets, respectively.

CONCLUSIONS

CT-based radiomics signature is a noninvasive method that can predict KRAS mutation status of LADC when mutational profiling is unavailable.

Current and Future Applications of PET Radiomics in Radiation Oncology

This review delves into the principles of PET imaging and radiomics, emphasizing their importance in detecting, staging, and monitoring various cancers. It highlights the clinical applications of PET radiomics in oncology, showcasing its impact on personalized cancer care. Additionally, the review addresses challenges such as standardizing PET radiomics, integrating multiomics data, and ethical concerns in clinical decision-making. Future directions are also discussed, including broader applications of PET radiomics in clinical trials, artificial intelligence integration for automated analysis, and incorporating multiomics data for a comprehensive understanding of tumor biology.

Quality of radiomics research: comprehensive analysis of 1574 unique publications from 89 reviews

PURPOSE

This study aims to comprehensively evaluate the quality of radiomics research by examining unique papers from reviews using the radiomics quality score (RQS).

METHODS

A literature search was conducted in PubMed (last search date: April 14, 2024). Systematic or non-systematic reviews using the RQS to evaluate radiomic studies were potentially included. Exclusion was applied at two levels: first, at the review level, and second, at the study level (i.e., for the individual articles previously evaluated within the reviews). Score-wise and item-wise analyses were performed, along with trend, multivariable, and subgroup analyses based on baseline study characteristics and validation methods.

RESULTS

A total of 1574 unique papers (published online between 1999 and 2023) from 89 reviews were included in the final analysis. The median RQS percentage was 31% with an IQR of 25% (25th-75th percentiles, 14-39%). A positive correlation between median RQS percentage and publication year (2014-2023) was found, with Kendall's tau coefficient of 0.908 (p < 0.001), suggesting an improvement in quality over time. The quality of radiomics publications significantly varied according to different subfields of radiology (p < 0.001). Around one-third of the publications (32%) lacked a separate validation set. Papers with internal validation (54%) dominated those with external validation (14%). Higher-quality validation practices were significantly associated with better RQS percentage scores, independent of the validation's effect on the final score. Item-wise analysis revealed significant shortcomings in several areas.

CONCLUSION

Radiomics research quality is low but improving according to RQS. Significant variation exists across radiology subfields. Critical areas were identified for targeted improvement.

CLINICAL RELEVANCE STATEMENT

Our study shows that the quality of radiomics research is generally low but improving over time, with item-wise analysis highlighting critical areas needing improvement. It also reveals that the quality of radiomics research differs across subfields and validation methods.

KEY POINTS

Overall quality of radiomics research remains low and highly variable, although a significant positive trend suggests an improvement in quality over time. Considerable variations exist in the quality of radiomics publications across different subfields of radiology and validation types. The item-wise analysis highlights several critical areas requiring attention, emphasizing the need for targeted improvements.

End-to-end [18F]PSMA-1007 PET/CT radiomics-based pipeline for predicting ISUP grade group in prostate cancer

OBJECTIVES

To develop an end-to-end radiomics-based pipeline for the prediction of International Society of Urological Pathology grade group (ISUP GG) in prostate cancer (PCa).

METHODS

This retrospective study includes 356 patients (241 in training set and 115 in independent test set) with histopathologically confirmed PCa who underwent [18F]PSMA-1007 PET/CT scan. Patients were classified into two groups according to their ISUP GG (1-3 vs. 4-5). Radiomics features were extracted from the whole, automatically segmented prostate on PET/CT images, 30 models were constructed by combining 6 feature selection algorithms and 5 machine learning classifiers. The clinical model incorporated age, total prostate-specific antigen (tPSA), maximum standardized uptake value (SUVmax), and prostate volume. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), balanced accuracy (bAcc), and decision curve analysis (DCA).

RESULTS

The best-performing radiomics model significantly outperformed clinical model (AUC 0.879 ± 0.041 vs. 0.799 ± 0.051, bAcc 0.745 ± 0.074 vs. 0.629 ± 0.045). On an external independent test set, best-performing radiomics model perform better than clinical model, with an AUC of 0.861 vs. 0.750, p = 0.002 (Delong), and bAcc of 0.764 vs. 0.582, p = 0.043 (McNemar). The learning curve, calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice.

CONCLUSION

The end-to-end radiomics-based pipeline is an effective non-invasive tool to predict ISUP GG in PCa.

Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment

JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.

Ultrasound-based radiomics to predict the volume reduction rate of benign thyroid nodules after microwave ablation

OBJECTIVE

To evaluate the predictive power of ultrasound-based radiomics models for benign thyroid nodules with a volume reduction rate (VRR) of < or ≥75% at 12 months after microwave ablation.

METHODS

A retrospective study was conducted on 194 individuals with benign thyroid nodules who received ultrasound-guided microwave ablation between November 2019 and June 2023. The clinical and ultrasound features, including age, gender, volume, echogenicity, duration of ablation, and so on were analysed by t-test or chi-square test. Radiomics features were extracted from longitudinal and transverse ultrasound images of the nodules. The features were selected using methods such as least absolute shrinkage and selection operator (LASSO). Radiomics models were established using longitudinal, transverse, and longitudinal + transverse ultrasound images to predict the VRR of benign thyroid nodules after ablation. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curve analysis were used to assess the models' performance.

RESULTS

At 12 months following ablation, the VRR of the nodules was 77.8 ± 19.4% (7.4-98.8%). Statistical analysis revealed that the duration of ablation and the proportion of liquid extracted were significantly correlated with the 12-month VRR (P <0.05). In the radiomics models, Logistic Regression (LR) performed the best. In the training cohorts, the area under the curve (AUC) for the longitudinal, transverse, and combined groups were 0.935, 0.800, and 0.937. The AUC values in the test cohort were 0.820, 0.844, and 0.917.

CONCLUSION

The radiomics models established based on pre-ablation ultrasound images showed good predictive efficacy for the VRR of nodules at 12 months following ablation. The predictive efficacy is best in the combined group. With the models, we can preoperatively predict patients' prognoses and thereby determine whether to proceed with ablation therapy.

Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography

Objectives

To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images.

Methods

A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve.

Results

The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model.

Conclusions

Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Deciphering 18F-DOPA uptake in SDH-related head and neck paragangliomas: a radiomics approach

PURPOSE

To investigate the influence of germline succinate dehydrogenase (SDHx) pathogenic variants on 6-[18F]-fluoro-3,4-dihydroxyphenylalanine (18F-DOPA) Positron Emission Tomography (PET) radiomic signature of head and neck paragangliomas (HNPGLs).

METHODS

Forty-seven patients (20 SDH pathogenic variants carriers) harboring 55 HNPGLs were retrospectively included. HNPGLs were delineated using Nestle adaptive threshold. 128 radiomic features were extracted and harmonized to correct for batch effects. Principal Component Analysis (PCA) was performed to remove redundancy and avoid collinearity. The most representative feature of each component was tested with multivariate stepwise logistic binary regression analysis (LBRA) to identify variables predictive of genetic status.

RESULTS

18F-DOPA Positron Emission Tomography/Computed Tomography (PET/CT) detected 28/29 carotid body HNPGLs, 23/23 jugulotympanic HNPGLs, and 4/4 vagal HNPGLs. SUVmax was significantly higher in SDH-related HNPGLs (p = 0.003). PCA allowed identification of 4 Components. The most representative variables of Component 1 and 2 (including intensity and intensity-related textural features, and not intensity-related textural features, respectively) were Intensity-based (IB)-SUVmedian and Grey Level Run Length Matrix-Long Run Low Gray Level Emphasis (GLRLM-LRLGLE). SDHx HNPGLs exhibited higher activity scores and more homogeneous texture. At patient level, SDHx cases showed significantly higher IB-SUVmedian values (p < 0.001), and lower GLRLM-LRLGLE than sporadic patients (p = 0.005). IB-SUVmedian was found to be an independent predictor of genetic status at lesion (71.0%) and patient level (77.8%).

CONCLUSION

The present study pioneers the application of 18F-DOPA PET radiomics for HNPGLs, suggesting the influence of germline SDH pathogenic variants on 18F-FDOPA uptake intensity and textural heterogeneity. Integrating radiomics with genetic data provides new insights into the correlation between PET features and underlying molecular dysregulation.

Multimodal radiomics based on fluorine-18 prostate-specific membrane antigen positron emission tomography and multiparametric magnetic resonance imaging in predicting persistent prostate-specific antigen after radical prostatectomy

Background

Persistent prostate-specific antigen (PSA) after radical prostatectomy (RP) is associated with increased metastasis and mortality. However, the value of the radiomics for predicting persistent PSA is unclear. Our study aimed to evaluate the diagnostic performance of 18F-PSMA-1007 positron emission tomography (PET) and multiparametric magnetic resonance imaging (mpMRI) radiomics for the prediction of persistent PSA after RP.

Methods

Retrospective analysis was performed on 141 patients with prostate cancer (PCa) who had undergone 18F-prostate-specific membrane antigen (PSMA)-1007 PET and mpMRI scans before RP. Patients were placed into two groups according to PSA levels examined within 4-8 weeks after surgery: a nonpersistent PSA group and a persistent PSA group. PET-derived and mpMRI-derived radiomics features were used to develop radiomics models. Age and initial PSA were incorporated into the clinical model. Individual models and their various combinations were developed and their performance evaluated.

Results

All radiomics models consistently outperformed the clinical model [C model: area under curve (AUC) =0.744]. The best-performing radiomics model was the PET- and mpMRI-derived model (PM model) created by combining the radiomics features of PET and mpMRI, which yielded an AUC of 0.849 in the validation cohort, and was superior to the other radiomics models, including the PET-derived model (P model: AUC =0.794) and the mpMRI-derived model (M model: AUC =0.815). The combined model, integrating the clinical variables and the best-performing radiomics model, demonstrated the highest performance (AUC =0.903) and significantly outperformed the C model (P<0.05). Decision curve analysis indicated that the combined model provided greater net benefits than did the C model and PM model.

Conclusions

The combined radiomics-clinical model was the best-performing model and outperformed both clinical and radiomics models in predicting persistent PSA, indicating that clinical variables can complement PSMA-PET and mpMRI radiomics for early risk stratification following RP.

Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer

OBJECTIVES

This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis.

METHODS

Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability.

RESULTS

Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes.

CONCLUSIONS

Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary.

CLINICAL RELEVANCE STATEMENT

Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies.

RESEARCH REGISTRATION UNIQUE IDENTIFYING NUMBER (UIN)

International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.

DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT

BACKGROUND

This study aimed to assess the predictive value of radiomic analysis derived from primary lesions and ipsilateral axillary suspicious lymph nodes (SLN) on dynamic contrast-enhanced MRI (DCE-MRI) for evaluating the response to neoadjuvant therapy (NAT) in early high-risk and advanced breast cancer (BC) patients.

METHODS

A retrospective analysis was conducted on 222 BC patients (192 from Center I and 30 from Center II) who underwent NAT. Radiomic features were extracted from the primary lesion (intra- and peritumoral regions) and ipsilateral axillary SLN to develop radiomic signatures (RS-primary, RS-SLN). An integrated signature (RS-Com) combined features from both regions. Feature selection was performed using correlation analysis, the Mann-Whitney U test, and least absolute shrinkage and selection operator (LASSO) regression. A diagnostic nomogram was constructed by integrating RS-Com with key clinical factors. Model performance was evaluated using receiver operating characteristic (ROC) and decision curve analysis (DCA).

RESULTS

RS-Com demonstrated superior predictive performance compared to RS-primary and RS-SLN alone. The DeLong test confirmed that axillary SLNs provide supplementary information to the primary lesion. Among clinical factors, N staging and HER2 status were significant contributors. The nomogram, integrating RS-Com, N staging, and HER2 status, achieved the highest performance in the training (AUC: 0.926), validation (AUC: 0.868), and test (AUC: 0.839) cohorts, outperforming both the clinical models and RS-Com alone.

CONCLUSION

Radiomic features from axillary SLNs offer valuable supplementary information for predicting NAT response in BC patients. The proposed nomogram, incorporating radiomics and clinical factors, provides a robust tool for individualized treatment planning.

Predicting the clinical prognosis of non-small cell lung cancer patients by predicting ALOX5 expression: a radiomics model

Background

Arachidonic acid 5-lipoxygenase (ALOX5) may play an important role in non-small cell lung cancer (NSCLC) progression and treatment and may be a potential prognostic biomarker for NSCLC. This study aimed to predict the clinical prognosis of NSCLC patients by predicting ALOX5 expression using a radiomics model.

Methods

Clinical and transcriptomic data of NSCLC patients were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and used for survival analysis (Kaplan-Meier survival curves: univariate and multivariate factors, Cox regression analysis, subgroup analysis and interaction test), correlation analysis of tumor clinical characteristics and immune cell abundance, and differential analysis of ferroptosis-related genes to evaluate the prognostic value of ALOX5. Contrast-enhanced computed tomography (CECT) scans of NSCLC patients from The Cancer Imaging Archive (TCIA) database were used to extract radiomics features to establish two radiomics models [logistic regression (LR) and Support Vector Machine (SVM) models]. Receiver operating characteristic (ROC), calibration, and decision curves were used to evaluate the two models, and the radiomics score (RS) of the model with the best prediction performance was selected to establish the Cox model for predicting NSCLC prognosis. A nomogram was used to visualize the prediction model, and its efficacy was evaluated and verified.

Results

The prognostic value analysis of ALOX5 showed that high ALOX5 expression was a protective factor for overall survival (OS) of NSCLC patients, and it negatively correlated with histology (P<0.001). Overall, 107 features were obtained from CECT images of NSCLC patients, and 8 optimal features were selected. The LR [area under the curve (AUC) =0.783] and SVM (AUC =0.763) models with good performance and clinical benefit were established using the LR and SVM algorithms, respectively. The RS output by the LR model strongly correlated with ALOX5 expression (P<0.05).

Conclusions

The findings suggest that evaluating ALOX5 expression using a radiomics model to predict the clinical prognosis of NSCLC patients could have potential clinical applications.

Development and validation of multi-sequence magnetic resonance imaging radiomics models for predicting tumor response to radiotherapy in locally advanced non-small cell lung cancer

Background

The significant heterogeneity of locally advanced non-small cell lung cancer (LA-NSCLC) poses challenges for clinical decision-making. Although various predictive methods currently exist, there remains a lack of an accurate and comprehensive approach effectively assessing the response of LA-NSCLC patients to radiotherapy. Therefore, the objective of this study was to develop a model based on multisequence magnetic resonance imaging (MRI) radiomics features to predict tumor response following radiotherapy in patients with LA-NSCLC and to evaluate its clinical utility.

Methods

Data was retrospectively collected from stage III non-small cell lung cancer (NSCLC) who underwent radiotherapy and had MRI scans performed prior to treatment from November 2018 to April 2024. The patients were randomly divided into a training set and a testing set in a ratio of 7:3. Tumor response, assessed using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, classified complete or partial responses as regression sensitive (RS) and stable or progressive disease as regression resistant (RR). A total of 3,045 radiomic features were extracted and integrated from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Feature selection was conducted using the maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO). Four machine learning algorithms, including logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and random forest, were utilized to construct radiomic models for individual and combined sequences, with the optimal model selected. The models were evaluated with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results

A total of 86 patients were included in this study, with 60 patients (RS: n=32, RR: n=28) in the training set and 26 patients (RS: n=14, RR: n=12) in the testing set. Among them, there were 45 males in the training set and 16 males in the testing set. Four, eight, six, and nine features were selected from the T1, T2, DWI sequences, and combined sequences, respectively. Among the four machine learning algorithms, the LR model demonstrated high accuracy and stability. The combined sequence model exhibited the highest predictive performance. The area under the curve (AUC) of the training set was 0.888 [95% confidence interval (CI): 0.803-0.974], and the accuracy, sensitivity, and specificity were 83.3%, 92.9% and 75%. The AUC of the testing set was 0.815 (95% CI: 0.648-0.983), and the accuracy, sensitivity, and specificity were 73.1%, 91.7% and 57.1%. Calibration curve and DCA demonstrated that the combined sequence model possessed good predictive performance and clinical utility.

Conclusions

The radiomics model developed using multisequence MRI demonstrates strong potential in predicting the radiotherapy response in patients with LA-NSCLC. Notably, the combined sequence model outperforming individual sequence models in both predictive accuracy and clinical applicability.

Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer

Background

There are individualized differences in the prognosis of radiochemotherapy for non-small cell lung cancer (NSCLC), and accurate prediction of prognosis is essential for individualized treatment. This study proposes to explore the potential of multiregional two-dimensional (2D) dosiomics combined with radiomics as a new imaging marker for prognostic risk stratification of NSCLC patients receiving radiochemotherapy.

Methods

In this study, 365 patients with histologically confirmed NSCLC, who had computed tomography (CT) scans before treatment, received standard radiochemotherapy, and had Karnofsky Performance Scale (KPS) scores ≥70 were included in three medical institutions, and 145 cases were excluded due to surgery, data accuracy, poor image quality, and the presence of other tumors. Finally, 220 patients were included in the study. Efficacy evaluation criteria for solid tumors are used to evaluate efficacy. Complete and partial remission indicate the radiochemotherapy-sensitive group, and disease stability and progression indicate the radiochemotherapy-resistant group. We combined all the data and then randomised them into a training cohort (154 cases) and a validation cohort (66 cases) in a 7:3 ratio. Radiomics and dosiomics features were extracted for gross tumor volume (GTV), GTV-heat, and 50 Gy-heat and screened. 2D dosiomics model (DMGTV and DM50Gy), radiomics model (RMGTV), 2D radiomics-dosiomics model (RDM), and combined models were constructed, and the predictive performances for radiochemotherapy resistance were compared. Subsequently, the predictive performance of various models for radiochemotherapy resistance was compared by receiver operating characteristic (ROC) curves and calculating accuracy, sensitivity and specificity. The multi-omics and clinical models were integrated for patient risk stratification.

Results

DM50Gy had better predictive performance than RMGTV and DMGTV, with the area under the curve (AUC) of the ROC in the training and validation cohorts for DM50Gy were 0.764 [95% confidence interval (CI): 0.687-0.841] and 0.729 (95% CI: 0.568-0.889). And the RDM performed significantly better than the single radiomics and dosiomics models, with AUC of 0.836 (95% CI: 0.773-0.899) and 0.748 (95% CI: 0.617-0.879), respectively. Hemoglobin level and T stage were independent predictors in the clinical model. The combined model containing independent predictors further improved the predictive performance in both the training and validation cohorts, with AUC of 0.844 (95% CI: 0.781-0.907) and 0.753 (95% CI: 0.618-0.887). Grouping of patients according to the critical value of the combined model revealed significant differences in progression-free survival (PFS) and overall survival (OS) between the high-risk and low-risk groups (P<0.05).

Conclusions

Compared to the traditional radiomics model, the 2D dosiomics model demonstrates superior predictive performance. The combined model based on clinical data, radiomics, and dosiomics has improved the prediction of radiochemotherapy resistance in NSCLC and effectively performed survival stratification. Through precise risk assessment, doctors can better understand which patients may develop resistance to treatment and optimize treatment plans accordingly.

Association between diffusion-weighted imaging and tumor matrix in liver cancer: a cross-sectional study

Background

Imaging modalities can reflect the underlying histopathology of tumors. However, the precise interactions between histopathological microstructure and the resulting imaging phenotype remain elusive. Predicting histopathological features, including the extracellular matrix, in a non-invasive manner could improve clinical care of liver tumors. The present study used cross-sectional guided biopsy specimens to utilize accurate spatial biopsy localization to correlate magnetic resonance imaging (MRI) derived the apparent diffusion coefficient (ADC) values with collagen IV expression in various liver cancers.

Methods

A total of 127 patients (n=68 female; 45.6%) with a mean age of 65.3±12.3 years were included in the analysis. Inclusion criteria were an available cross-sectional biopsy, available biopsy specimens and a pre-interventional MRI with diffusion-weighed imaging (DWI) sequence. The tumors included 45 patients (35.4%) with hepatocellular carcinoma (HCC), 26 patients (20.5%) with cholangiocellular carcinoma and 56 patients (44.1%) with liver metastases of various primary tumors. Prebioptic liver MRI with diffusion-weighted imaging was used to correlate ADC values with collagen IV expression obtained from liver biopsy. The ADC values were measured in a co-registered way with cross-sectional biopsy imaging to ensure the spatial concordance between imaging and histopathology. The stained area and signal intensity of the immunohistochemical staining were examined.

Results

The mean average stained area of collagen IV was 32.6%±27.4% and the mean staining intensity was 2.03±1.01. HCC showed statistically less stained area compared to the other tumor types (analysis of variance P<0.0001). In the overall patient sample, there was no correlation between ADCmean and average stained area (r=0.05, P=0.55) and staining intensity (r=-0.04, P=0.60). In a subgroup analysis of HCC patients, there was a significant correlation between ADCmin and the staining intensity (r=-0.33, P=0.02).

Conclusions

ADC values are not associated with collagen IV expression in liver tumors. The complex extracellular matrix is not reflected by the DWI signal, which can be discussed as mainly be influenced by the cellularity of the tumors. Further research is needed to investigate the complex interactions between histopathology and the resulting imaging phenotype of MRI for clinical care.

Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging

Background

Breast cancer is one of the most commonly diagnosed malignancies in women worldwide, and the disease burden continues to aggravate. The tumor-node-metastasis (TNM) staging information is crucial for oncology physicians to develop appropriate clinical strategies. This study aimed to investigate the value of a radiomics-clinical model for predicting TNM stage in breast cancer patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

DCE-MRI images from 166 patients with pathologically confirmed breast cancer were retrospectively collected, including early stage (TNM0-TNM2) and locally advanced or advanced stage (TNM3-TNM4). Included patients were divided into a training cohort (n=116) and a test cohort (n=50). The radiomics, clinical and integrated models were constructed and a nomogram was established to distinguish the TNM0-TNM2 stage from the TNM3-TNM4 stage. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were employed to assess the predictability of the models.

Results

Eighty-five patients were at the early stages, while 81 patients were at the other stages. In the training and test cohorts, the area under the curve (AUC) values for distinguishing early and advanced breast cancer were 0.870 and 0.818 for the nomogram, respectively. The nomogram calibration curves showed good agreement between the predicted and observed TNM stages in the training and test cohorts. The Hosmer-Lemeshow test showed that the nomogram fit perfectly in the two cohorts. DCA indicated that the nomogram displayed clear superiority in forecasting TNM staging over clinical and radiomic signatures.

Conclusions

Compared to traditional imaging methods, the clinical-radiomics nomogram acquired by DCE-MRI could potentially be utilized to preoperatively evaluate the TNM stage of breast cancer with relatively high accuracy. It can be an effective method to guide clinical decisions.

18F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study

PURPOSE

To explore the development and validation of automated machine learning (AutoML) models for 18F-FDG PET imaging-based radiomics signatures to predict treatment response in elderly patients with diffuse large B-cell lymphoma (DLBCL).

METHODS

A retrospective analysis was conducted on 175 elderly (≥ 60 years) DLBCL patients diagnosed between March 2015 and March 2023 at two medical centers, with a total of 1010 lesions. The baseline PET imaging-based radiomics features of the training cohort were processed using AutoML model AutoGluon to generate a radiomics score (radscore) and predict treatment response at the lesion and patient levels. Furthermore, a multivariable logistic analysis was used to design and evaluate a multivariable model in the training and validation cohorts.

RESULTS

ROC curve analysis showed that the radscore generated by AutoML exhibited higher accuracy in predicting treatment response at the lesion level compared to metabolic parameters (SUVmax, MTV, and TLG) in both the training group (AUC: 0.791, 0.542, 0.667, 0.651, respectively) and the validation group (AUC: 0.712, 0.616, 0.639, 0.657, respectively). Multivariable logistic analysis indicated that NCCN-IPI (OR = 5.427, 95% CI: 1.163-25.317), BCL-2 (OR = 3.714, 95% CI: 1.406-9.816), TMTV (OR = 4.324, 95% CI: 1.095-17.067), and avg-radscore (OR = 3.176, 95% CI: 1.313-7. 686) were independent predictors of treatment response. The multivariable model comprising NCCN-IPI, BCL-2, TMTV, and avg-radscore outperformed conventional models and clinical-pathological models in predicting treatment response. (P<0.05).

CONCLUSION

The radscore generated by AutoML can predict the treatment response of elderly DLBCL patients, potentially aiding in clinical decision-making.

Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review

Introduction

Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies predictive markers, offering a non-invasive alternative to biopsies. Machine learning (ML) based radiomics models enhances diagnostic and prognostic accuracy of tumors. Comprehensive review on ML-based radiomics models for predicting meningioma recurrence and survival are lacking. Hence, the aim of the study is to summarize the performance measures of ML based radiomics models in the prediction of outcomes such as progression/recurrence (P/R) and overall survival analysis of meningioma.

Methods

Data bases such as Scopus, Web of Science, PubMed, and Embase were used to conduct a literature search in order to find pertinent original articles that concentrated on meningioma outcome prediction. PRISMA (Preferred reporting items for systematic reviews and meta-analysis) recommendations were used to extract data from selected studies.

Results

Eight articles were included in the study. MRI Radiomics-based models combined with clinical and pathological data showed strong predictive performance for meningioma recurrence. A decision tree model achieved 90% accuracy, outperforming an apparent diffusion coefficient (ADC) based model (83%). A support vector machine (SVM) model reached an area under curve (AUC) of 0.80 with radiomic features, improving to 0.88 with ADC integration. A combined clinico-pathological radiomics model (CPRM) achieved an AUC of 0.88 in testing. Key predictors of recurrence include ADC values, radiomic scores, ki-67 index, and Simpson grading. For predicting overall survival analysis of meningioma, the combined clinicopathological and radiomic features achieved an AUC of 0.78.

Conclusion

Integrating radiomics with clinical and pathological data through ML models greatly improved the outcome prediction for meningioma. These ML models surpass conventional MRI in predicting meningioma recurrence and aggressiveness, providing crucial insights for personalized treatment and surgical planning.

Radiomics analysis of placental MRI for prenatal prediction of placenta accreta spectrum in pregnant women in the third trimester: A retrospective study of 594 patients

OBJECTIVE

To develop and validate a model based on placental MRI for the prenatal prediction of placenta accreta spectrum (PAS) in pregnant women in the third trimester.

MATERIALS AND METHODS

A total of 594 pregnant women who were suspected of having PAS and underwent placental MRI antenatally were included and were allocated into the training cohort and testing cohort at a 2:1 ratio. MRI diagnosis was determined by three experienced radiologists. Radiomic features were extracted from images of T2 weighted imaging for each patient. After a feature selection strategy, a radiomics signature and a clinical-radiomics nomogram combining radiomics score and clinical risk factors were constructed to predict PAS. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and clinical utility.

RESULTS

MRI diagnosis yielded AUCs of 0.77 and 0.79 for predicting PAS in the training and testing cohorts, respectively. The AUCs of the radiomics signature used to predict PAS in both cohorts were 0.80 and 0.83, respectively. The nomogram accurately predicted PAS in both cohorts (AUC = 0.84 and 0.89), with better results than those of MRI diagnosis and radiomics signature in the training (p = 0.009 and 0.003, respectively) and testing cohorts (p = 0.010 and 0.008, respectively), decision curve analysis confirmed its best clinical utility compared to the other models.

CONCLUSION

Radiomics analysis based on placental MRI may serve as an effective tool to predict PAS in patients with possible PAS in the third trimester.

A bibliometric analysis of nasopharyngeal carcinoma radiomics: trends and insights

Background

Nasopharyngeal carcinoma (NPC) is a malignant tumor characterized by distinct geographic and pathological features. Enhancing diagnostic accuracy and timeliness in NPC is crucial for clinical implications. Radiomics has demonstrated significant potential in the clinical management of NPC. Nonetheless, a paucity of bibliometric studies has systematically examined the existing literature in th is domain. The objective of this study was to assess the current landscape and project future trends in NPC research.

Methods

This study conducted a search on English-language literature concerning the application of radiomics within the field of nasopharyngeal carcinoma (NPC) research from January 2015 to July 1, 2024, utilizing the Web of Science Core Collection (WoSCC) database. Bibliometric and visual analyses were performed using VOSviewer and CiteSpace software on publications related to countries/regions, authors, journals, references, and keywords.

Results

A total of 311 documents were retrieved, yielding 229 eligible documents after screening, comprising 209 articles and 20 reviews. Annual publications showed an upward trend, while citations revealed a generally declining trend. Notably, China contributed the most publications (n=175). Tian Jie and Dong Di each published 13 papers, and Zhang B was the most frequently co-cited author. Frontiers in Oncology published the most articles (n=25), and the International Journal of Radiation Oncology Biology Physics had the highest citation count (n=331). Sun Yat-sen University led institutional publications (n=39). The radiomics research in NPC focuses on survival prediction, texture analysis, and distant metastasis, and may guide future research directions.

Conclusion

The application of radiomics in NRC is growing annually, as indicated by bibliometric analysis. Radiomics has enhanced the precision of preoperative diagnosis, prediction, and prognosis in NRC. Bibliometric findings offer insights into radiomics research trends. However, creating extensive NPC datasets and bridging the research-to-clinical gap pose significant challenges. Future research should focus on these areas to advance the development.

Predictive power of artificial intelligence for malignant cerebral edema in stroke patients: a CT-based systematic review and meta-analysis of prevalence and diagnostic performance

Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is critical for initiating timely interventions such as decompressive hemicraniectomy. Artificial intelligence (AI) and radiomics have emerged as promising tools for predicting MCE, offering the potential to transform reactive stroke management into proactive care. However, variability in methodologies and inconsistent reporting limits the widespread adoption of these technologies. A comprehensive search of PubMed, Embase, Web of Science, and Scopus identified studies reporting on the sensitivity, specificity, and area under the curve (AUC) of AI models in MCE prediction. Data were synthesized using random-effects meta-analyses. Subgroup analyses explored the impact of study design, machine learning input type, and other key factors on diagnostic accuracy. Publication bias was assessed using Egger's test and funnel plot analyses. Data from ten studies encompassing 1,594 unique stroke patients were included in the analysis. The pooled sensitivity and specificity of AI models for predicting MCE were 81.1% (95% CI: 73.0-87.2%) and 92.6% (95% CI: 91.2-93.9%), respectively, with an AUC of 0.939. The diagnostic odds ratio was 43.73 (95% CI: 24.78-77.15), demonstrating excellent discriminative ability. Subgroup analyses revealed higher sensitivity and specificity in prospective studies (92.0% and 93.3%) compared to retrospective studies (76.1% and 91.4%). Radiomics-based models showed slightly higher sensitivity (84.2%) compared to non-radiomics models (80.4%), though both input types achieved comparable specificity. Interestingly, patients undergoing revascularization had a higher prevalence of MCE, likely due to their more severe initial presentations. Minimal heterogeneity was observed in specificity across studies, while publication bias was noted for sensitivity estimates. AI models show excellent diagnostic performance for predicting malignant cerebral edema (MCE), offering high sensitivity and specificity. Prospective studies, radiomics integration, and multi-center collaborations enhance their accuracy. However, external validation and standardized methodologies are needed to ensure broader clinical adoption and improve outcomes for stroke patients at risk of MCE. Clinical trial number Not applicable.