Neoadjuvant chemoradiotherapy (CRT) is known to increase sphincter preservation rates and decrease the risk of postoperative recurrence in patients with locally advanced rectal tumors. However, the response to CRT in patients with locally advanced rectal cancer (LARC) varies significantly. The objective of this study was to compare the performance of models based on radiomics features of the tumor alone, the mesorectum alone, and a combination of both in predicting tumor response to neoadjuvant CRT in LARC. This retrospective study included 101 patients with LARC. Patients were categorized as responders (modified Ryan score 0-1) and non-responders (modified Ryan score 2-3). Pre-CRT magnetic resonance imaging evaluations included tumor-T2 weighted imaging (T2WI), tumor-diffusion weighted imaging (DWI), tumor-apparent diffusion coefficient (ADC) maps, and mesorectum-T2WI. The first radiologist segmented the tumor and mesorectum from T2-weighted images, and the second radiologist performed tumor segmentation using DWI and ADC maps. Feature reproducibility was assessed by calculating the intraclass correlation coefficient (ICC) using a two-way mixed-effects model with absolute agreement for single measurements [ICC(3,1)]. Radiomic features with ICC values <0.60 were excluded from further analysis. Subsequently, the least absolute shrinkage and selection operator method was applied to select the most relevant radiomic features. The top five features with the highest coefficients were selected for model training. To address class imbalance between groups, the synthetic minority over-sampling technique was applied exclusively to the training folds during cross-validation. Thereafter, classification learner models were developed using 10-fold cross-validation to achieve the highest performance. The performance metrics of the final models, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC), were calculated to evaluate the classification performance. Among the 101 patients, 36 were classified as responders and 65 as non-responders. A total of 25 radiomic features from the tumor and 20 from the mesorectum were found to be statistically significant (<i>P</i> < 0.05). The AUC values for predicting treatment response were 0.781 for the tumor-only model (random forest), 0.726 for the mesorectum-only model (logistic regression), and 0.837 for the combined model (logistic regression). Radiomic features derived from both the tumor and mesorectum demonstrated complementary prognostic value in predicting treatment response. The inclusion of mesorectal features substantially improved model performance, with the combined model achieving the highest AUC value. These findings highlight the added predictive contribution of the mesorectum as a key peritumoral structure in radiomics-based assessment. Currently, the response of locally advanced rectal tumors to neoadjuvant therapy cannot be reliably predicted using conventional methods. Recently, the significance of the mesorectum in predicting treatment response has gained attention, although the number of studies focusing on this area remains limited. In our study, we performed radiomics analyses of both the tumor tissue and the mesorectum to predict neoadjuvant treatment response.

Postoperative papillary thyroid cancer (PTC) patients often have enlarged cervical lymph nodes due to inflammation or hyperplasia, which complicates the assessment of recurrence or metastasis. This study aimed to explore the diagnostic capabilities of computed tomography (CT) imaging and radiomic analysis to distinguish the recurrence of cervical lymph nodes in patients with PTC postoperatively. A retrospective analysis of 194 PTC patients who underwent total thyroidectomy was conducted, with 98 cases of cervical lymph node recurrence and 96 cases without recurrence. Using 3D Slicer software, Regions of Interest (ROI) were delineated on enhanced venous phase CT images, analyzing 302 positive and 391 negative lymph nodes. These nodes were randomly divided into training and validation sets in a 3:2 ratio. Python was used to extract radiomic features from the ROIs and to develop radiomic models. Univariate and multivariate analyses identified statistically significant risk factors for cervical lymph node recurrence from clinical data, which, when combined with radiomic scores, formed a nomogram to predict recurrence risk. The diagnostic efficacy and clinical utility of the models were assessed using ROC curves, calibration curves, and Decision Curve Analysis (DCA). This study analyzed 693 lymph nodes (302 positive and 391 negative) and identified 35 significant radiomic features through dimensionality reduction and selection. The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed.

In this review, a comprehensive overview of the current state of artificial intelligence (AI) research in Inflammatory Bowel Disease (IBD) diagnostics in the domains of endoscopy, radiology and histology is presented. Moreover, key considerations for development of AI algorithms in medical image analysis are discussed. AI presents a potential breakthrough in real-time, objective and rapid endoscopic assessment, with implications for predicting disease progression. It is anticipated that, by harmonising multimodal data, AI will transform patient care through early diagnosis, accurate patient profiling and therapeutic response prediction. The ability of AI in cross-sectional medical imaging to improve diagnostic accuracy, automate and enable objective assessment of disease activity and predict clinical outcomes highlights its transformative potential. AI models have consistently outperformed traditional methods of image interpretation, particularly in complex areas such as differentiating IBD subtypes, identifying disease progression and complications. The use of AI in histology is a particularly dynamic research field. Implementation of AI algorithms in clinical practice is still lagging, a major hurdle being the lack of a digital workflow in many pathology institutes. Adoption is likely to start with implementation of automatic disease activity scoring. Beyond matching pathologist performance, algorithms may teach us more about IBD pathophysiology. While AI is set to substantially advance IBD diagnostics, various challenges such as heterogeneous datasets, retrospective designs and assessment of different endpoints must be addressed. Implementation of novel standards of reporting may drive an increase in research quality and overcome these obstacles.

Cervical 2 pedicle screw (C2PS) fixation is widely used in posterior cervical surgery but carries risks of vertebral artery injury (VAI), a rare yet severe complication. This study aimed to identify risk factors for VAI during C2PS placement and develop a machine learning (ML)-based predictive model to enhance preoperative risk assessment. Clinical and radiological data from 280 patients undergoing head and neck CT angiography were retrospectively analyzed. Three-dimensional reconstructed images simulated C2PS placement, classifying patients into injury (n = 98) and non-injury (n = 182) groups. Fifteen variables, including characteristic of patients and anatomic variables were evaluated. Eight ML algorithms were trained (70% training cohort) and validated (30% validation cohort). Model performance was assessed using AUC, sensitivity, specificity, and SHAP (SHapley Additive exPlanations) for interpretability. Six key risk factors were identified: pedicle diameter, high-riding vertebral artery (HRVA), intra-axial vertebral artery (IAVA), vertebral artery diameter (VAD), distance between the transverse foramen and the posterior end of the vertebral body (TFPEVB) and distance between the vertebral artery and the vertebral body (VAVB). The neural network model (NNet) demonstrated optimal predictive performance, achieving AUCs of 0.929 (training) and 0.936 (validation). SHAP analysis confirmed these variables as primary contributors to VAI risk. This study established an ML-driven predictive model for VAI during C2PS placement, highlighting six critical anatomical and radiological risk factors. Integrating this model into clinical workflows may optimize preoperative planning, reduce complications, and improve surgical outcomes. External validation in multicenter cohorts is warranted to enhance generalizability.

<b>Objective:</b> To develop a machine learning diagnostic model for T-shaped uterus based on quantitative parameters from 3D transvaginal ultrasound. <b>Methods:</b> A retrospective cross-sectional study was conducted, recruiting 304 patients who visited the hysteroscopy centre of Fuxing Hospital, Beijing, China, between July 2021 and June 2024 for reasons such as "infertility or recurrent pregnancy loss" and other adverse obstetric histories. Twelve experts, including seven clinicians and five sonographers, from Fuxing Hospital and Beijing Obstetrics and Gynecology Hospital of Capital Medical University, Peking University People's Hospital, and Beijing Hospital, independently and anonymously assessed the diagnosis of T-shaped uterus using a modified Delphi method. Based on the consensus results, 56 cases were classified into the T-shaped uterus group and 248 cases into the non-T-shaped uterus group. A total of 7 clinical features and 14 sonographic features were initially included. Features demonstrating significant diagnostic impact were selected using 10-fold cross-validated LASSO (Least Absolute Shrinkage and Selection Operator) regression. Four machine learning algorithms [logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM)] were subsequently implemented to develop T-shaped uterus diagnostic models. Using the Python random module, the patient dataset was randomly divided into five subsets, each maintaining the original class distribution (T-shaped uterus: non-T-shaped uterus ≈ 1∶4) and a balanced number of samples between the two categories. Five-fold cross-validation was performed, with four subsets used for training and one for validation in each round, to enhance the reliability of model evaluation. Model performance was rigorously assessed using established metrics: area under the curve (AUC) of receiver operator characteristic (ROC) curve, sensitivity, specificity, precision, and F1-score. In the RF model, feature importance was assessed by the mean decrease in Gini impurity attributed to each variable. <b>Results:</b> A total of 304 patients had a mean age of (35±4) years, and the age of the T-shaped uterus group was (35±5) years; the age of the non-T-shaped uterus group was (34±4) years.. Eight features with non-zero coefficients were selected by LASSO regression, including average lateral wall indentation width, average lateral wall indentation angle, upper cavity depth, endometrial thickness, uterine cavity area, cavity width at level of lateral wall indentation, angle formed by the bilateral lateral walls, and average cornual angle (coefficient: 0.125, -0.064,-0.037,-0.030,-0.026,-0.025,-0.025 and -0.024, respectively). The RF model showed the best diagnostic performance: in training set, AUC was 0.986 (95%<i>CI</i>: 0.980-0.992), sensitivity was 0.978, specificity 0.946, precision 0.802, and F1-score 0.881; in testing set, AUC was 0.948 (95%<i>CI</i>: 0.911-0.985), sensitivity was 0.873, specificity 0.919, precision 0.716, and F1-score 0.784. RF model feature importance analysis revealed that average lateral wall indentation width, upper cavity depth, and average lateral wall indentation angle were the top three features (over 65% in total), playing a decisive role in model prediction. <b>Conclusion:</b> The machine learning models developed in this study, particularly the RF model, are promising for the diagnosis of T-shaped uterus, offering new perspectives and technical support for clinical practice.

Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weighted contrast-enhanced (T1-CE) magnetic resonance imaging (MRI) sequences to predict glioma recurrence within specific quadrants of the surgical margin. In this retrospective study, 149 patients with confirmed glioma recurrence were included. 23 cases of data from Guizhou Medical University were used as a test set, and the remaining data were randomly used as a training set (70%) and a validation set (30%). Two radiologists from the research group established a Cartesian coordinate system centred on the tumour, based on FLAIR and T1-CE MRI sequences, dividing the tumour into four quadrants. Recurrence in each quadrant after surgery was assessed, categorising preoperative tumour quadrants as recurrent and non-recurrent. Following the division of tumours into quadrants and the removal of outliers, These quadrants were assigned to a training set (105 non-recurrence quadrants and 226 recurrence quadrants), a verification set (45 non-recurrence quadrants and 97 recurrence quadrants) and a test set (16 non-recurrence quadrants and 68 recurrence quadrants). Imaging features were extracted from preoperative sequences, and feature selection was performed using least absolute shrinkage and selection operator. Machine learning models included support vector machine, random forest, extra trees, and XGBoost. Clinical efficacy was evaluated through model calibration and decision curve analysis. The fusion model, which combines features from FLAIR and T1-CE sequences, exhibited higher predictive accuracy than single-modality models. Among the models, the LightGBM model demonstrated the highest predictive accuracy, with an area under the curve of 0.906 in the training set, 0.832 in the validation set and 0.805 in the test set. The study highlights the potential of a multimodal radiomics approach for predicting glioma recurrence, with the fusion model serving as a robust tool for clinical decision-making.

Medicine is one of the most sensitive fields in which artificial intelligence (AI) is extensively used, spanning from medical image analysis to clinical support. Specifically, in medicine, where every decision may severely affect human lives, the issue of ensuring that AI systems operate ethically and produce results that align with ethical considerations is of great importance. In this work, we investigate the combination of several key parameters on the performance of artificial neural networks (ANNs) used for medical image analysis in the presence of data corruption or errors. For this purpose, we examined five different ANN architectures (AlexNet, LeNet 5, VGG16, ResNet-50, and Vision Transformers - ViT), and for each architecture, we checked its performance under varying combinations of training dataset sizes and percentages of images that are corrupted through mislabeling. The image mislabeling simulates deliberate or nondeliberate changes to the dataset, which may cause the AI system to produce unreliable results. We found that the five ANN architectures produce different results for the same task, both for cases with and without dataset modification, which implies that the selection of which ANN architecture to implement may have ethical aspects that need to be considered. We also found that label corruption resulted in a mixture of performance metrics tendencies, indicating that it is difficult to conclude whether label corruption has occurred. Our findings demonstrate the relation between ethics in AI and ANN architecture implementation and AI computational parameters used therefor, and raise awareness of the need to find appropriate ways to determine whether label corruption has occurred.

Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer. In this study, we developed deep radiomic models to enhance the prediction of chemotherapy response after the first treatment cycle. 18F-Fludeoxyglucose PET/CT imaging data and clinical record from 60 breast cancer patients were retrospectively obtained from the Cancer Imaging Archive. PET/CT scans were conducted at three distinct stages of treatment; prior to the initiation of chemotherapy (T1), following the first cycle of chemotherapy (T2), and after the full chemotherapy regimen (T3). The patient's primary gross tumor volume (GTV) was delineated on PET images using a 40% threshold of the maximum standardized uptake value (SUVmax). Radiomic features were extracted from the GTV based on the PET/CT images. In addition, a squeeze-and-excitation network (SENet) deep learning model was employed to generate additional features from the PET/CT images for combined analysis. A XGBoost machine learning model was developed and compared with the conventional machine learning algorithm [random forest (RF), logistic regression (LR) and support vector machine (SVM)]. The performance of each model was assessed using receiver operating characteristics area under the curve (ROC AUC) analysis, and prediction accuracy in a validation cohort. Model performance was evaluated through fivefold cross-validation on the entire cohort, with data splits stratified by treatment response categories to ensure balanced representation. The AUC values for the machine learning models using only radiomic features were 0.85(XGBoost), 0.76 (RF), 0.80 (LR), and 0.59 (SVM), with XGBoost showing the best performance. After incorporating additional deep learning-derived features from SENet, the AUC values increased to 0.92, 0.88, 0.90, and 0.61, respectively, demonstrating significant improvements in predictive accuracy. Predictions were based on pre-treatment (T1) and post-first-cycle (T2) imaging data, enabling early assessment of chemotherapy response after the initial treatment cycle. Integrating deep learning-derived features significantly enhanced the performance of predictive models for chemotherapy response in breast cancer patients. This study demonstrated the superior predictive capability of the XGBoost model, emphasizing its potential to optimize personalized therapeutic strategies by accurately identifying patients unlikely to respond to chemotherapy after the first treatment cycle.

This study aims to integrate CT imaging with occupational health surveillance data to construct a multimodal model for preclinical CWP identification and individualized risk evaluation. CT images and occupational health surveillance data were retrospectively collected from 874 coal workers, including 228 Stage I and 4 Stage II pneumoconiosis patients, along with 600 healthy and 42 subcategory 0/1 coal workers. First, the YOLOX was employed for automated 3D lung extraction to extract radiomics features. Second, two feature selection algorithms were applied to select critical features from both CT radiomics and occupational health data. Third, three distinct feature sets were constructed for model training: CT radiomics features, occupational health data, and their multimodal integration. Finally, five machine learning models were implemented to predict the preclinical stage of CWP. The model's performance was evaluated using the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity. SHapley Additive exPlanation (SHAP) values were calculated to determine the prediction role of each feature in the model with the highest predictive performance. The YOLOX-based lung extraction demonstrated robust performance, achieving an Average Precision (AP) of 0.98. 8 CT radiomic features and 4 occupational health surveillance data were selected for the multimodal model. The optimal occupational health surveillance feature subset comprised the Length of service. Among 5 machine learning algorithms evaluated, the Decision Tree-based multimodal model showed superior predictive capacity on the test set of 142 samples, with an AUC of 0.94 (95% CI 0.88-0.99), accuracy 0.95, specificity 1.00, and Youden's index 0.83. SHAP analysis indicated that Total Protein Results, original shape Flatness, diagnostics Image original Mean were the most influential contributors. Our study demonstrated that the multimodal model demonstrated strong predictive capability for the preclinical stage of CWP by integrating CT radiomic features with occupational health data.

Considerable socioeconomic disparities exist among pediatric traumatic brain injury (TBI) patients. This study aims to analyze the effects of social determinants of health on head injury outcomes and to create a novel machine-learning algorithm (MLA) that incorporates socioeconomic factors to predict the likelihood of a positive or negative trauma-related finding on head computed tomography (CT). A cohort of blunt trauma patients under age 15 who presented to the largest safety net hospital in New England between January 2006 and December 2013 (n=211) was included in this study. Patient socioeconomic data such as race, language, household income, and insurance type were collected alongside other parameters like Injury Severity Score (ISS), age, sex, and mechanism of injury. Multivariable analysis was performed to identify significant factors in predicting a positive head CT outcome. The cohort was split into 80% training (168 samples) and 20% testing (43 samples) datasets using stratified sampling. Twenty-two multi-parametric MLAs were trained with 5-fold cross-validation and hyperparameter tuning via GridSearchCV, and top-performing models were evaluated on the test dataset. Significant factors associated with pediatric head CT outcome included ISS, age, and insurance type (p<0.05). The age of the subjects with a clinically relevant trauma-related head CT finding (median= 1.8 years) was significantly different from the age of patients without such findings (median= 9.1 years). These predictors were utilized to train the machine learning models. With ISS, the Fine Gaussian SVM achieved the highest test AUC (0.923), with accuracy=0.837, sensitivity=0.647, and specificity=0.962. The Coarse Tree yielded accuracy=0.837, AUC=0.837, sensitivity=0.824, and specificity=0.846. Without ISS, the Narrow Neural Network performed best with accuracy=0.837, AUC=0.857, sensitivity=0.765, and specificity=0.885. Key predictors of clinically relevant head CT findings in pediatric TBI include ISS, age, and social determinants of health, with children under 5 at higher risk. A novel Fine Gaussian SVM model outperformed other MLA, offering high accuracy in predicting outcomes. This tool shows promise for improving clinical decisions while minimizing radiation exposure in children. TBI = Traumatic Brain Injury; ISS = Injury Severity Score; MLA = Machine Learning Algorithm; CT = Computed Tomography; AUC = Area Under the Curve.