Predictive models for contrast-enhanced mammography often perform better at detecting and classifying enhancing masses than (non-enhancing) microcalcification clusters. We aim to investigate whether incorporating synthetic data with simulated microcalcification clusters during training can enhance model performance.

Microcalcification clusters were simulated in low-energy images of lesion-free breasts from 782 patients, considering local texture features. Enhancement was simulated in the corresponding recombined images. A deep learning (DL) model for lesion detection and classification was trained with varying ratios of synthetic and real (850 patients) data. In addition, a handcrafted radiomics classifier was trained using delineations and class labels from real data, and predictions from both models were ensembled. Validation was performed on internal (212 patients) and external (279 patients) real datasets.

The DL model trained exclusively with synthetic data detected over 60% of malignant lesions. Adding synthetic data to smaller real training sets improved detection sensitivity for malignant lesions but decreased precision. Performance plateaued at a detection sensitivity of 0.80. The ensembled DL and radiomics models performed worse than the standalone DL model, decreasing the area under this receiver operating characteristic curve from 0.75 to 0.60 on the external validation set, likely due to falsely detected suspicious regions of interest.

Synthetic data can enhance DL model performance, provided model setup and data distribution are optimized. The possibility to detect malignant lesions without real data present in the training set confirms the utility of synthetic data. It can serve as a helpful tool, especially when real data are scarce, and it is most effective when complementing real data.

Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.

This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 RCC patients who underwent contrast-enhanced CT scans were included in the analysis. Radiomic features were extracted, and a two-step feature selection methodology was applied to identify the most relevant features for classification. The XGBoost model demonstrated high performance in both training (AUC = 0.87) and testing (AUC = 0.92) sets, with no significant difference between the two (p = 0.521). The model also exhibited high sensitivity, specificity, positive predictive value, and negative predictive value. The selected radiomic features captured both the distribution of intensity values and spatial relationships, which may provide valuable insights for personalized treatment decision-making. Our findings suggest that the XGBoost model has the potential to be integrated into clinical workflows to facilitate personalized adjuvant immunotherapy decision-making, ultimately improving patient outcomes. Further research is needed to validate the model in larger, multicentre cohorts and explore the potential of combining radiomic features with other clinical and molecular data.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.

To develop and validate a comprehensive model for predicting postoperative early recurrence of hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA)-enhanced MRI.

239 patients with HCC who underwent curative surgical resection were recruited from two centers between April 2017 and December 2022. Radiomics features were extracted from the region of interest (ROI) on preoperative Gd-EOB-DTPA-enhanced MR images, and consistency analysis was performed to select stable radiomics features. Significant variables in the univariate and multivariate logistic regression analysis were included in clinical-radiologic model. Nomograms were constructed by combining the best performing radiologic and clinical-radiologic characteristics. Recurrence-free survival (RFS) comparisons were conducted using the log-rank test based on high versus low model-derived scores.

The radiomics model based on multiple phases MR outperformed all other radiomics models and had the best discrimination for early recurrence, with AUC of 0.799 and 0.743 in the training and validation sets, respectively. In the entire cohort, high-risk patients exhibited significantly lower RFS compared to low-risk patients.

The nomogram integrating Gd-EOB-DTPA enhanced MRI radiomics features and clinical-radiologic characteristics demonstrate superior predictive performance with postoperative early recurrence in patients with HCC. The model can identify patients at high risk and provide support for individualized treatment planning.

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.

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).

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).

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.

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.

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.

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.

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

Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-counting computed tomography (PCCT) in combination with radiomics and machine learning to develop a method for automated and detailed characterization of kidney stones. This approach aims to enhance the accuracy and detail of stone classification beyond what is achievable with conventional computed tomography (CT) and dual-energy CT (DECT).

In this ex vivo study, 135 kidney stones were first classified using infrared spectroscopy. All stones were then scanned in a PCCT embedded in a phantom. Various monoenergetic reconstructions were generated, and radiomics features were extracted. Statistical analysis was performed using Random Forest (RF) classifiers for both individual reconstructions and a combined model.

The combined model, using radiomics features from all monoenergetic reconstructions, significantly outperformed individual reconstructions and SPP parameters, with an AUC of 0.95 and test accuracy of 0.81 for differentiating all six stone types. Feature importance analysis identified key parameters, including NGTDM_Strength and wavelet-LLH_firstorder_Variance.

This ex vivo study demonstrates that radiomics-driven PCCT analysis can improve differentiation between kidney stone subtypes. The combined model outperformed individual monoenergetic levels, highlighting the potential of spectral profiling in PCCT to optimize treatment through image-based strategies.

Question How can photon-counting computed tomography (PCCT) combined with radiomics improve the differentiation of kidney stone types beyond conventional CT and dual-energy CT, enhancing personalized therapy? Findings Our ex vivo study demonstrates that a combined spectral-driven radiomics model achieved 95% AUC and 81% test accuracy in differentiating six kidney stone types. Clinical relevance Implementing PCCT-based spectral-driven radiomics allows for precise non-invasive differentiation of kidney stone types, leading to improved diagnostic accuracy and more personalized, effective treatment strategies, potentially reducing the need for invasive procedures and recurrence.

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical practice. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

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.

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).

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.

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).

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.

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

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.

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).

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.

Using fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), a multiparametric analysis will be performed in the differential diagnosis of patients with single solitary pulmonary lesion and extrapulmonary malignant tumor to discriminate between a second primary lung cancer (SPLC) and pulmonary metastasis (PM).

This study retrospectively studied 84 patients with preoperative exams utilizing 18F-FDG PET/CT. Using complementing PET/CT parameters, a composite model was developed. A receiver operating characteristic (ROC) analysis assessed the combined model and each independent parameter's differential diagnostic efficacies. Furthermore, this study investigated the improvement in diagnostic efficacy using other metrics, such as integrated discriminatory improvement (IDI) and net reclassification improvement (NRI).

The highest discriminative diagnostic value was obtained by the independent parameters energy (1,039,358.1 [95126.2-1,965,032.2] vs. 92,011.0 [45916.3-365,322.9], P = 0.001). In comparison to peak standardized uptake value (SUVpeak), total lesion glycolysis (TLG), energy, lobulation, and spiculation alone, the combined model (addition of these factors) significantly improved the differential diagnostic efficacy of SPLCs and PMs (sensitivity = 76.2%, specificity = 83.8%, area under the curve [AUC] = 0.826) and permitted reclassification using IDI = 0.176 (P < 0.001), 0.169 (P < 0.001), 0.127 (P < 0.001), and categorical NRI = 0.678 (P < 0.001), 0.637 (P < 0.001), and 0.592 (P < 0.001) compared to SUVpeak, TLG and energy separately. DeLong's test revealed a statistically significant enhancement in ROC when compared to SUVpeak (Z = 2.372, P = 0.018), TLG (Z = 2.095, P = 0.036), and energy (Z = 2.318, P = 0.020).

Combining multiple parameters using 18F-FDG PET/CT may further improve distinguishing between SPLCs and PMs in patients with single solitary pulmonary lesion and extrapulmonary malignant tumor.

The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and specificity, often leading to surgery for diagnostic purposes. The aim of this paper is (1) to develop a machine learning algorithm based on MR images characteristics to automatically classify parotid gland tumours and (2) compare its results with the diagnoses of junior and senior radiologists in order to evaluate its utility in routine practice. While automatic algorithms applied to parotid tumours classification have been developed in the past, we believe that our study is one of the first to leverage four different MRI sequences and propose a comparison with clinicians. In this study, we leverage data coming from a cohort of 134 patients treated for benign or malignant parotid tumours. Using radiomics extracted from the MR images of the gland, we train a random forest and a logistic regression to predict the corresponding histopathological subtypes. On the test set, the best results are given by the random forest: we obtain a 0.720 accuracy, a 0.860 specificity, and a 0.720 sensitivity over all histopathological subtypes, with an average AUC of 0.838. When considering the discrimination between benign and malignant tumours, the algorithm results in a 0.760 accuracy and a 0.769 AUC, both on test set. Moreover, the clinical experiment shows that our model helps to improve diagnostic abilities of junior radiologists as their sensitivity and accuracy raised by 6 % when using our proposed method. This algorithm may be useful for training of physicians. Radiomics with a machine learning algorithm may help improve discrimination between benign and malignant parotid tumours, decreasing the need for diagnostic surgery. Further studies are warranted to validate our algorithm for routine use.

To develop and validate a clinical combined radiomics model for predicting the treatment response and long-term survival prognosis of small cell lung cancer (SCLC) patients receiving platinum-based chemotherapy, as well as survival outcomes following chemoradiotherapy.

A total of 98 SCLC patients treated with platinum-based first-line chemotherapy were included in this study. Five prediction models for assessing the short-term efficacy of platinum-based first-line chemotherapy were developed using a logistic regression algorithm. The performance of the models was assessed by calculating the area under the curve of the receiver operating characteristic curves. For predicting progression-free survival (PFS) and overall survival in the platinum-based chemotherapy group and the chemoradiotherapy group, the optimal cutoff value was determined using X-tile software. Kaplan-Meier survival curves were plotted, and the log-rank test was used to compare survival outcomes.

Among the five models for predicting short-term efficacy, the clinical combined positron emission tomography/computed tomography (PET/CT) radiomics model performed the best, achieving areas under the curve of 0.832 and 0.833 for the training and test sets, respectively. The Kaplan-Meier survival analysis indicated that both the high-scoring Combine group and high-scoring PET/CT group were significantly associated with worse PFS and worse overall survival in the platinum-only chemotherapy group. Additionally, the high-scoring CT group was significantly associated with worse PFS in the chemoradiotherapy group.

The clinical combined PET/CT radiomics model can noninvasively and accurately predict the response to platinum-based treatments in SCLC as well as long-term survival prognosis, which can contribute to personalized treatment strategies and guide precision therapy for SCLC patients.

Advancements in magnetic resonance imaging (MRI) analysis over the past decades have significantly reshaped the field of neuroradiology. The ability to extract multiple quantitative measures from each MRI scan, alongside the development of extensive data repositories, has been fundamental to the emergence of advanced methodologies such as radiomics and artificial intelligence (AI). This educational review aims to delineate the importance of image analysis, highlight key paradigm shifts, examine their implications, and identify existing constraints that must be addressed to facilitate integration into clinical practice. Particular attention is given to aiding junior neuroradiologists in navigating this complex and evolving landscape.

A comprehensive review of the available analysis toolboxes was conducted, focusing on major technological advancements in MRI analysis, the evolution of data repositories, and the rise of AI and radiomics in neuroradiology. Stakeholders within the field were identified and their roles examined. Additionally, current challenges and barriers to clinical implementation were analyzed.

The analysis revealed several pivotal shifts, including the transition from qualitative to quantitative imaging, the central role of large datasets in developing AI tools, and the growing importance of interdisciplinary collaboration. Key stakeholders-including academic institutions, industry partners, regulatory bodies, and clinical practitioners-were identified, each playing a distinct role in advancing the field. However, significant barriers remain, particularly regarding standardization, data sharing, regulatory approval, and integration into clinical workflows.

While advancements in MRI analysis offer tremendous potential to enhance neuroradiology practice, realizing this potential requires overcoming technical, regulatory, and practical barriers. Education and structured support for junior neuroradiologists are essential to ensure they are well-equipped to participate in and drive future developments. A coordinated effort among stakeholders is crucial to facilitate the seamless translation of these technological innovations into everyday clinical practice.

Esophageal cancer ranks among the most lethal malignancies globally, with China accounting for more than half of worldwide esophageal squamous cell carcinoma (ESCC) cases. Late-stage diagnosis frequently precludes surgical intervention, contributing to poor outcomes. While precise clinical assessment is essential for treatment planning, therapeutic responses and prognosis exhibit substantial inter-patient heterogeneity, underscoring the urgent need for reliable biomarkers to enhance prognostic accuracy and guide personalized therapeutic strategies. Radiomics, an emerging field that extracts high-dimensional features from medical images, provides non-invasive approaches to improve diagnostic accuracy, predict survival, monitor adverse events, detect recurrence, and optimize treatment strategies. Radiomics has shown promising potential in the modern management of ESCC. Here, we review the critical contributions of radiomics to ESCC research and clinical practice, examining its workflow, applications, strengths, and limitations. Radiomics represents a compelling frontier with substantial potential to advance precision medicine for ESCC patients.

Non-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy characterized by altered glucose metabolism. Integration of PET/CT radiomics with glucose metabolism-related genomic signatures could provide a more comprehensive approach for prognosis and treatment guidance.

Radiomics features were extracted from PET/CT images of 156 NSCLC patients from The Cancer Imaging Archive (TCIA) database, and glucose metabolism-related gene signatures were obtained from TCGA and GEO databases. We developed a multimodal radiogenomics prognostic model (RGC-score) using least absolute shrinkage and selection operator (LASSO) regression, combining PET/CT radiomics, glucose metabolism-related genes (GMR-genes). Functional enrichment analysis, immune infiltration assessment, and drug sensitivity analysis were performed to investigate the biological significance of glucose metabolism-related genes (GMR-genes).

The RGC-score model effectively stratified NSCLC patients into distinct high- and low-risk groups with significant differences in survival outcomes (P < 0.001), demonstrating excellent predictive performance (1-year AUC = 0.907, 5-year AUC = 0.968).GMR-genes are mainly involved in the process of metabolic remodeling of tumors, which is closely related to the immune microenvironment (especially CD8+ T cell infiltration) and immune checkpoint molecule expression. Additionally, significant differences in drug sensitivity were identified between glucose metabolism subtypes.

The RGC-score robustly predicts NSCLC prognosis and informs metabolic-immune interactions for personalized therapy. Limitations include the retrospective design and modest validation cohort size, necessitating prospective multicenter trials.

This study developed a machine learning (ML) model combining clinical and radiomic features to predict surgical difficulty in pheochromocytomas and paragangliomas (PPGLs), aiming to optimize preoperative planning and reduce perioperative complications. Retrospective clinical and imaging data from PPGLs patients were analyzed to construct two sets of models: clinical parameter models and clinical-radiomic models. Seven ML algorithms were tested, with the SVM-based clinical-radiomic model achieving the highest performance (training area under the curve [AUC]: 0.96, validation AUC: 0.85), significantly surpassing the clinical parameter model. SHAP analysis highlighted radiomic signature (Rad-score) as the strongest predictor, followed by body mass index, age, tumor size, and preoperative heart rate. The model enables objective stratification of surgical difficulty, aiding tailored preoperative strategies. Future directions include integrating multi-omics data, refining surgical difficulty criteria through multicenter studies, developing real-time intraoperative predictive tools, and automating radiomic workflows via deep learning. This research advances personalized surgical management for PPGLs, demonstrating significant clinical translation potential.

Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can increase the degree of clinical benefit. The present study aimed to investigate the diagnostic performance of magnetic resonance imaging (MRI)-derived artificial intelligence for accurate preoperative assessment of the invasive risk.

The PubMed, Embase, Cochrane Library and Web of Science databases were searched, and pertinent English-language papers were collected. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and positive and negative likelihood ratios (PLR and NLR, respectively) of all the papers were calculated using Stata software. The results were plotted on a summary receiver operating characteristic (SROC) curve, publication bias and threshold effects were evaluated, and meta-regression and subgroup analyses were conducted to explore the possible causes of intratumoral heterogeneity.

MRI-based radiomics revealed pooled sensitivity (SEN) and specificity (SPE) values of 0.85 and 0.82 for the prediction of high-grade EC; 0.80 and 0.85 for deep myometrial invasion (DMI); 0.85 and 0.73 for lymphovascular space invasion (LVSI); 0.79 and 0.85 for microsatellite instability (MSI); and 0.90 and 0.72 for lymph node metastasis (LNM), respectively. For LVSI prediction and high-grade histological analysis, meta-regression revealed that the image segmentation and MRI-based radiomics modeling contributed to heterogeneity (p = 0.003 and 0.04).

Through a systematic review and meta-analysis of the reported literature, preoperative MRI-derived ML could help clinicians accurately evaluate EC risk factors, potentially guiding individual treatment thereafter.

Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches-such as CNNs and deep learning-have improved the detection and classification of brain tumors through advanced imaging techniques and genomic analysis. Explainable AI methods mitigate the "black box" problem, promoting model transparency and clinical trust. Mechanistic models complement AI by integrating biological principles, enabling precise tumor growth predictions and treatment response assessments. AI applications also include the creation of digital twins for personalized therapy optimization, virtual clinical trials, and predictive modeling for estimation of tumor resection and pattern of recurrence. However, challenges such as data bias, ethical concerns, and regulatory compliance persist. The European Artificial Intelligence Act and the Health Data Space Regulation impose strict data protection and transparency requirements. This review explores AI's methodological foundations, clinical applications, and ethical challenges in neuro-oncology, emphasizing the need for interdisciplinary collaboration and regulatory adaptation.

This study aimed to develop and validate a CT-based radiomics nomogram for predicting the progression-free survival (PFS) of epithelial ovarian cancer (EOC).

A total of 144 EOC patients were retrospectively enrolled from two hospitals and The Cancer Genome Atlas and The Cancer Imaging Archive, divided into a training set (n = 101) and a test set (n = 43) using a 7:3 ratio. Radiomic features were extracted from contrast enhanced CT images. The radiomics score (rad-score) was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression. Clinical semantic features with P < 0.05 in multivariate Cox regression were combined with rad-score to develop radiomics nomogram. The predictive performance of the nomogram was assessed using the concordance index (C-index) and calibration curves.

Multivariate Cox regression analysis revealed that the International Federation of Obstetrics and Gynecology stage and residual tumor are significant predictors of PFS. Twelve radiomic features were selected by LASSO Cox regression. The combined model demonstrated superior predictive performance, with a C-index of 0.78 (95% CI: 0.689-0.889) in the training set, and 0.73 (95% CI: 0.572-0.886) in the test set. The combined model outperformed the clinical and radiomics models in predicting 1-, 3-, and 5-year PFS, with area under curves of 0.850 (95% CI: 0.722-0.943), 0.828 (95% CI: 0.722-0.901), and 0.845 (95% CI: 0.722-0.943), respectively. Calibration curves of the radiomic nomogram for prediction of 1-year, 3-year, 5-year PFS showed excellent calibrations in both training and test sets.

The combined model integrating rad-score and clinical semantic features effectively evaluates PFS in EOC patients. The radiomics nomogram provides a non-invasive, simple, and feasible method to predict PFS in EOC patients, which may facilitate clinical decision-making.

To evaluate the diagnostic value of magnetic resonance imaging (MRI) radiomics in distinguishing high-grade meningiomas (HGM) from low-grade meningiomas (LGM). A systematic search was conducted in PubMed, EMbase, Web of Science, and The Cochrane Library databases up to December 31, 2023. Two researchers independently screened studies, extracted data, and assessed risk of bias and quality of included studies as well. Meta-analysis was performed using Stata 14 software to calculate pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). A total of 21 studies with 2253 patients were included (607 HGM, 1646 LGM). Meta-analysis showed an overall SEN of 0.82 (95% CI 0.74-0.88) and SPE of 0.85 (95% CI 0.81-0.89). The PLR and NLR were 5.64 (95% CI 4.17-7.64) and 0.21 (95% CI 0.14-0.31), respectively, with a pooled DOR of 26.66 (95% CI 14.42-49.27) and an AUC of 0.91 (95% CI 0.88-0.93), indicating high diagnostic accuracy. Although additional research is required to validate suitable techniques, MRI radiomics shows strong potential as an accurate tool for meningioma grading. Standardizing radiomics application could enhance diagnostic precision and clinical decision-making for meningioma grading in the future.Trial Registration: CRD42024500086.

Oral cavity carcinomas (OCCs) represent roughly 50% of all head and neck cancers. The risk of occult neck metastases for early-stage OCCs ranges from 15% to 35%, hence the need to develop tools that can support the diagnosis of detecting these neck metastases. Machine learning and radiomic features are emerging as effective tools in this field. Thus, the aim of this study is to demonstrate the effectiveness of radiomic features to predict the risk of occult neck metastases in early-stage (T1-T2/N0) OCCs.

Retrospective study.

A single-institution analysis (Maxillo-facial Surgery Unit, University of Naples Federico II).

A retrospective analysis was conducted on 75 patients surgically treated for early-stage OCC. For all patients, data regarding TNM, in particular pN status after the histopathological examination, have been obtained and the analysis of radiomic features from MRI has been extrapolated.

56 patients confirmed N0 status after surgery, while 19 resulted in pN+. The radiomic features, extracted by a machine-learning algorithm, exhibited the ability to preoperatively discriminate occult neck metastases with a sensitivity of 78%, specificity of 83%, an AUC of 86%, accuracy of 80%, and a positive predictive value (PPV) of 63%.

Our results seem to confirm that radiomic features, extracted by machine learning methods, are effective tools in detecting occult neck metastases in early-stage OCCs. The clinical relevance of this study is that radiomics could be used routinely as a preoperative tool to support diagnosis and to help surgeons in the surgical decision-making process, particularly regarding surgical indications for neck lymph node treatment.

The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions.

A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions.

The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway.

The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.

Neoadjuvant immunotherapy has shown impressive outcomes in treating non-small cell lung cancer (NSCLC) recently due to advancements in immune checkpoint inhibitors (ICIs) research. Neoadjuvant immunotherapy can lower the tumor load, raise the complete surgical (R0) resection rate, and improve clinical outcomes by alleviating the immune system repression caused by tumor cells. This review provides a comprehensive evaluation of neoadjuvant immunotherapy in NSCLC, focusing on: (1) its safety and efficacy profiles, (2) the most recent clinical trial evidence, and (3) critical unresolved challenges including predictive biomarker development, management of driver mutation-positive patients, chronic obstructive pulmonary disease (COPD) comorbidity considerations, and its application in stage III-IVA (oligometastatic) disease. Furthermore, we explore future research directions to optimize neoadjuvant immunotherapy approaches for resectable NSCLC, aiming to guide clinical practice and investigation.

To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data, dosimetric parameters, and clinical data for predicting radiation-induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) following intensity-modulated radiation therapy (IMRT).

A retrospective analysis of 150 HCC patients was performed, with a 7:3 ratio used to divide the data into training and validation cohorts. Radiomic features from the original MRI sequences and Delta-radiomic features were extracted. Seven ML models based on radiomics were developed: logistic regression (LR), random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), decision tree (DT), and artificial neural network (ANN). The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis and calibration curves. Shapley additive explanations (SHAP) were employed to interpret the contribution of each variable and its risk threshold.

Original radiomic features and Delta-radiomic features were extracted from DCE-MRI images and filtered to generate Radiomics-scores and Delta-Radiomics-scores. These were then combined with independent risk factors (Body Mass Index (BMI), V5, and pre-Child-Pugh score(pre-CP)) identified through univariate and multivariate logistic regression and Spearman correlation analysis to construct the ML models. In the training cohort, the AUC values were 0.8651 for LR, 0.7004 for RF, 0.6349 for SVM, 0.6706 for XGBoost, 0.7341 for AdaBoost, 0.6806 for Decision Tree, and 0.6786 for ANN. The corresponding accuracies were 84.4%, 65.6%, 75.0%, 65.6%, 71.9%, 68.8%, and 71.9%, respectively. The validation cohort further confirmed the superiority of the LR model, which was selected as the optimal model. SHAP analysis revealed that Delta-radiomics made a substantial positive contribution to the model.

The interpretable ML model based on radiomics provides a non-invasive tool for predicting RIHT in patients with HCC, demonstrating satisfactory discriminative performance.

Young-onset colorectal cancer (YOCRC) refers to colorectal cancer diagnosed in individuals under the age of 50. Whilst the overall incidence of colorectal cancer is decreasing, YOCRC cases are increasing and now accounts for up to 10% of all colorectal cancers. YOCRC more frequently presents with acute symptoms, where radiologists play an important role in identifying malignancy and distinguishing it from benign colonic pathologies. Risk factors associated with YOCRC, such as inflammatory bowel disease and hereditary syndromes, may exhibit specific imaging manifestations. In addition, YOCRC is frequently associated with a mucinous histopathological subtype which may be identifiable based on the presence of specific imaging features. Given their younger age, these patients are more likely to undergo aggressive treatment and complex surgical interventions. Specific considerations such as fertility preserving surgical techniques must be factored in when managing these patients. As the incidence of YOCRC increases, guidance for colonoscopy screening protocols may need revision. This includes evaluating the role of ionising imaging techniques in both diagnosing and follow-up to balance early detection and minimising radiation exposure in this younger patient population.

High-grade clear cell renal cell carcinoma (ccRCC) is linked to lower survival rates and more aggressive disease progression. This study aims to assess the diagnostic performance of MRI-derived radiomics as a non-invasive approach for pre-operative differentiation of high-grade from low-grade ccRCC.

A systematic search was conducted across PubMed, Scopus, and Embase. 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 estimated using a bivariate model. Separate meta-analyses were conducted for radiomics models and combined models, where the latter integrated clinical and radiological features with radiomics. Subgroup analysis was performed to identify potential sources of heterogeneity. Sensitivity analysis was conducted to identify potential outliers.

A total of 15 studies comprising 2,265 patients were included, with seven and six studies contributing to the meta-analysis of radiomics and combined models, respectively. The pooled estimates of the radiomics model were as follows: sensitivity, 0.78; specificity, 0.84; PLR, 4.17; NLR, 0.28; DOR, 17.34; and AUC, 0.84. For the combined model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.87, 0.81, 3.78, 0.21, 28.57, and 0.90, respectively. Radiomics models trained on smaller cohorts exhibited a significantly higher pooled specificity and PLR than those trained on larger cohorts. Also, radiomics models based on single-user segmentation demonstrated a significantly higher pooled specificity compared to multi-user segmentation.

Radiomics has demonstrated potential as a non-invasive tool for grading ccRCC, with combined models achieving superior performance.

To develop and validate a predictor for early treatment response in hepatocellular carcinoma (HCC) patients accompanied by portal vein tumor thrombus (PVTT) undergoing transarterial chemoembolization (TACE), lenvatinib and a programmed cell death protein 1 (PD-1) inhibitor (TLP) therapy.

In this retrospective study, patients with HCC and PVTT from two institutions receiving triple TLP therapy were enrolled. Radiomics features derived from pretreatment contrast-enhanced MRI were curated using intraclass correlation coefficient (ICC), Student's t-test, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) to ensure robust selection. Various machine learning (ML) algorithms were then used to construct the models. The meaningful clinical indicators were obtained via logistic regression analysis and ultimately integrated with radiomics features to develop a combined model. In addition, we used Shapley Additive exPlanation (SHAP) to clarify the model's operational dynamics.

Our study ultimately included 115 patients (7:3 randomization, 80 and 35 in the training and test cohorts, respectively) in total. No patients achieved complete remission, 47 achieved partial remission, 29 achieved stable disease, and 39 experienced disease progression. Among objective response rates (ORRs) and disease control rates (DCRs), 40.9% and 66.1% were reported. One of the four ML classifiers with optimal performance, namely random forest, was adopted as the radiomics model after testing. Regarding the performance assessment, the radiomics model's area under the curve (AUC) values reached 0.92 (95% CI: 0.86-0.97) and 0.79 (95% CI: 0.61-0.95), inferior to the combined model's AUCs of 0.95 (95% CI: 0.68-0.98) and 0.84 (95% CI: 0.91-0.99). Moreover, the SHAP plots illustrate the importance of global variables and the prediction process for individual samples.

The model based on machine learning and radiomics showed favorable performance, and the operating mode was visualized through SHAP.

BackgroundOsteosarcoma (OS) is the most common primary malignant bone tumor. Exploring quantitative parameters that reflect the outcome of neoadjuvant chemotherapy (NACT) in patients with OS can help advance the treatment of patients.PurposeTo explore the role of T2-weighted (T2W) magnetic resonance imaging (MRI) radiogenomic features in characterizing changes in patients with OS and on NACT.Material and MethodsA total of 21 patients with OS were examined retrospectively and divided into a poor-response group (n = 13) and a good-response group (n = 8). A total of 98 radiomic features and 31 gene expression profiles were analyzed for each patient. Age, sex, alkaline phosphatase, pathologic type, tumor size, and tumor location were also analyzed. Comparisons between the good- and poor-response groups were made using the t-test, Mann-Whitney U test, or Fisher's exact test. The relationships between radiomic features and gene expression profiles were conducted using Spearman's correlative analyses.ResultsStatistical differences in 19 radiomics features and glutathione-s-transferase 1 were found between the good- and poor-response groups (P < 0.05). The receiver operating characteristic curve showed that four NGTDM busyness features had the best performance in predicting the NACT of patients with OS, with an area under the curve of 0.788, sensitivity of 0.750, and specificity of 0.923. Correlation analysis showed that the HLA_I, CD274, GSTP1, and CCND3 were significantly correlated with one or more radiomics features (P < 0.05).ConclusionThe T2W MRI radiogenomic features can be used as biomarkers for the early response evaluation of NACT in OS. This is the first study to analyze the association of T2 radiogenomic features with NACT in patients with OS to assist in the assessment of NACT.

To assess the publications' bibliographic features and look into how the advancement of artificial intelligence (AI) and its subfields in radiomics has affected the growth of oncology.

The researchers conducted a search in the Web of Science (WoS) for scientific publications in cancer pertaining to AI and radiomics, published in English from 1 January 2015 to 31 December 2024.The research included a scientometric methodology and comprehensive data analysis utilising scientific visualization tools, including the Bibliometrix R software package, VOSviewer, and CiteSpace. Bibliometric techniques utilised were co-authorship, co-citation, co-occurrence, citation burst, and performance Analysis.

The final study encompassed 4,127 publications authored by 5,026 individuals and published across 597 journals. China (2087;50.57%) and USA (850;20.6%) were the two most productive countries. The authors with the highest publication counts were Tian Jie (60) and Cuocolo Renato (30). Fudan University (169;4.09%) and Sun Yat-sen University (162;3.93%) were the most active institutions. The foremost journals were Frontiers in Oncology and Cancer. The predominant author keywords were radiomics, artificial intelligence, and oncology research.

Investigations into the integration of AI with radiomics in oncology remain nascent, with numerous studies concentrating on biology, diagnosis, treatment, and cancer risk evaluation.

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.

The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas from another hospital composed the test set. The enhanced T1 WI images were used for analysis. The clinical, radiomics and DTL features were selected to construct the model. Radiomics and DTL scores were calculated. The deep transfer learning radiomics (DTLR) nomogram was developed on the basis of selected clinical features, radiomics scores and DTL scores. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were drawn. The clinical features of sex, shape, indistinct margin and peritumoral edema were selected and used to construct the clinical model. Thirty-two radiomics features and 28 DTL features were selected for model construction. The clinical model had an AUC of 0.788. (95% CI: 0.6996-0.8756), with an accuracy of 0.745, a sensitivity of 0.941, and a specificity of 0.549 in the test set. The DTLR nomogram had the highest AUC of 0.866 (95% CI: 0.7984-0.9340), with an accuracy of 0.804, a sensitivity of 0.745, and a specificity of 0.863 in the test set. Compared with the other models, the DTLR nomogram had the greatest net benefit according to the DCA. There was a significant difference between the DTLR nomogram and the clinical model, no significant difference between the rest models in DeLong test.The DTLR nomogram has superior predictive value in DCA and could be a valuable method in clinical decision-making. Given the results of DeLong test, only the radiomics model is sufficient and there is no need to add DTL features. As a new attempt, the DTLR nomogram needs to be improved in the future study.

Differentiating between parotid adenolymphoma and malignant tumors remains challenging.

This study aims to improve preoperative diagnosis accuracy by evaluating the role of multimodal functional magnetic resonance imaging (MRI) and advanced radiomics analysis.

We retrospectively analyzed 124 patients with adenolymphoma and malignant parotid tumors, divided into primary (n = 84) and test (n = 40) cohorts. Tumor regions were manually labeled on susceptibility-weighted imaging (SWI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI). Seven radiomics models were constructed using logistic regression. We also incorporated intratumoral susceptibility signal (ITSS) grading and performed histogram analysis of apparent diffusion coefficient (ADC) maps.

The united radiomics model combining SWI, DWI, and CE-T1WI showed the highest diagnostic performance (area under the curve (AUC) = 0.95, accuracy = 0.93, specificity = 0.93) in the primary cohort, outperforming single-sequence and double-sequence models. The test set validated the model's good diagnostic performance (AUC = 0.9). ITSS grading significantly differed between adenolymphomas and malignant tumors (p < 0.001). ADC histogram analysis revealed significant differences in mean, 10th percentile, and kurtosis values between the two groups.

The multisequence radiomics model combining DWI, SWI, and CE-T1WI provides a comprehensive and accurate noninvasive approach for differentiating parotid adenolymphoma from malignant tumors. This method helps avoid the risks associated with invasive procedures, such as tumor cell implantation and metastasis, while guiding personalized surgical decision-making. By offering a novel diagnostic tool, this study enhances the precision of preoperative tumor characterization and supports more effective treatment planning and prognosis assessment for patients with parotid gland tumors.

According to the updated classification system, human epidermal growth factor receptor 2 (HER2) expression statuses are divided into the following three groups: HER2-over-expression, HER2-low-expression, and HER2-zero-expression. HER2-negative expression was reclassified into HER2-low-expression and HER2-zero-expression. This study aimed to identify three different HER2 expression statuses for breast cancer (BC) patients using PET/CT radiomics and clinicopathological characteristics.

A total of 315 BC patients who met the inclusion and exclusion criteria from two institutions were retrospectively included. The patients in institution 1 were divided into the training set and the independent validation set according to the ratio of 7:3, and institution 2 was used as the external validation set. According to the results of pathological examination, all BC patients were divided into HER2-over-expression, HER2-low-expression, and HER2-zero-expression. First, PET/CT radiomic features and clinicopathological features based on each patient were extracted and collected. Second, multiple methods were used to perform feature screening and feature selection. Then, four machine learning classifiers, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), were constructed to identify HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others. The receiver operator characteristic (ROC) curve was plotted to measure the model's predictive power.

According to the feature screening process, 8, 10, and 2 radiomics features and 2 clinicopathological features were finally selected to construct three prediction models (HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others). For HER2-over-expression vs. others, the RF model outperformed other models with an AUC value of 0.843 (95%CI: 0.774-0.897), 0.785 (95%CI: 0.665-0.877), and 0.788 (95%CI: 0.708-0.868) in the training set, independent validation set, and external validation set. Concerning HER2-low-expression vs. others, the outperformance of the LR model over other models was identified with an AUC value of 0.783 (95%CI: 0.708-0.846), 0.756 (95%CI: 0.634-0.854), and 0.779 (95%CI: 0.698-0.860) in the training set, independent validation set, and external validation set. Whereas, the KNN model was confirmed as the optimal model to distinguish HER2-zero-expression from others, with an AUC value of 0.929 (95%CI: 0.890-0.958), 0.847 (95%CI: 0.764-0.910), and 0.835 (95%CI: 0.762-0.908) in the training set, independent validation set, and external validation set.

Combined PET/CT radiomic models integrating with clinicopathological characteristics are non-invasively predictive of different HER2 statuses of BC patients.

The surgeries in drug-resistant ulcerative colitis are determined by complex factors. This study evaluated the predictive performance of radiomics analysis on the basis of whether patients with ulcerative colitis in hospital were in the surgical or medical treatment group by discharge from hospital.

This single-center retrospective cohort study used CT at admission of patients with US admitted from 2015 to 2022. The target of prediction was whether the patient would undergo surgery by the time of discharge. Radiomics features were extracted using the rectal wall at the level of the tailbone tip of the CT as the region of interest. CT data were randomly classified into a training cohort and a validation cohort, and LASSO regression was performed using the training cohort to create a formula for calculating the radiomics score.

A total of 147 patients were selected, and data from 184 CT scans were collected. Data from 157 CT scans matched the selection criteria and were included. Five features were used for the radiomics score. Univariate logistic regression analysis of clinical information detected a significant influence of severity (p < 0.001), number of drugs used until surgery (p < 0.001), Lichtiger score (p = 0.024), and hemoglobin (p = 0.010). Using a nomogram combining these items, we found that the discriminatory power in the surgery and medical treatment groups was AUC 0.822 (95% confidence interval (CI) 0.841-0.951) for the training cohort and AUC 0.868 (95% CI 0.729-1.000) for the validation cohort, indicating a good ability to discriminate the outcomes.

Radiomics analysis of CT images of patients with US at the time of admission, combined with clinical data, showed high predictive ability regarding a treatment strategy of surgery or medical treatment.

To explore the predictive potential of radiomics features extracted from preoperative multiparametric magnetic resonance imaging (MRI) for identifying pathological subtypes of primary central nervous system diffuse large B-cell lymphomas (PCNS-DLBCL).

This study recruited 186 patients with PCNS-DLBCL, including 55 with germinal center B-cell-like (GCB) subtype and 131 with non-GCB subtype. The largest abnormal signal regions of the tumor were automatically segmented in T1-weighted images (T1WI), T2-weighted images, T2 fluid-attenuated inversion recovery, contrast-enhanced T1-weighted (CE-T1WI), and apparent diffusion coefficient (ADC) maps, respectively. Radiomics features were extracted from preprocessed multiparameter preoperative MRI images. To identify GCB and non-GCB subtypes, radiomics models were constructed based on each MRI sequence and combinations of sequences. Clinical models and models combining radiomics and clinical features were also constructed to compare performance.

Radiomics models combining multiple sequences generally outperformed single-sequence radiomics models. The ADC+CE-T1WI model exhibited superior discriminative power, with an area under the curve of 0.867 (95% CI, 0.745-0.988). Models incorporating more sequences (3-5 sequences) did not demonstrate better performance. The performance of the model combining radiomics features with clinical features showed no improvement.

Radiomics based on multiparametric MRI have independent value in predicting the pathological subtypes of PCNS-DLBCL patients.

Background: PSMA PET is essential tool in the management of prostate cancer (PCa) patients in various clinical settings of disease. The tremendous growth of the implementation of radiomics and artificial intelligence (AI) in medical imaging techniques has led to an increasing interest in their application in prostate-specific membrane antigen (PSMA) PET. The aim of this article is to systemically review the current literature that explores radiomics and AI analyses of staging PSMA PET towards its potential application in clinical practice. Methods: A systematic research of the literature on three international databases (PubMed, Scopus, and Web of Science) identified a total of 166 studies. An initial screening excluded 68 duplicates and 72 articles relevant to other topics. Finally, 21 studies met the inclusion criteria. Conclusions: The literature suggests that radiomic analysis could improve the characterization of tumor aggressiveness, the prediction of extra-capsular extension, and seminal vesicles involvement. Moreover, AI models could contribute to predicting BCR after radical treatment. Limitations regarding heterogeneous objectives of investigation, and methodological standardization of radiomics analysis still represent the main obstacle to overcome in order to see these technology break through into daily clinical practice.