Deep Learning and XGBoost Based Prediction Algorithm for Esophageal Varices

BackgroundIdentifying predictive markers for immunotherapy in non-small cell lung cancer (NSCLC) is critical for personalized treatment. This study aimed to construct a predictive model that integrates clinical features, enhanced computed tomography (CT)-radiomics, and deep learning (DL) features for the assessment of durable clinical benefit (DCB) from immunotherapy in patients with advanced NSCLC and to provide biological interpretability to predictions by integrating radiogenomic data.MethodsWe conducted a retrospective analysis of 201 advanced NSCLC patients who underwent immunotherapy with CT images, with data supplemented from The Cancer Imaging Archive (TCIA). Radiomics features (RFs) were extracted from enhanced CT images, and DL features were derived using a pre-trained ResNet-34 model. DCB-related signatures were constructed using the least absolute shrinkage and selection operator (LASSO) algorithm, and fusion nomogram models were developed by integrating significant clinical variables, radiomics, and DL features. Shapley additive explanations were employed to quantify the impact of radiomics-DL features on model predictions. Gene set enrichment and biological correlation analyses based on transcriptomic TCIA data were performed to explore the biological significance of radiomics-DL score.ResultsStatistically significant clinical predictors included initial efficacy, brain metastases, programmed death-ligand 1 (PD-L1) expression, and hemoglobin levels. The fusion nomogram model demonstrated the highest predictive accuracy for DCB, with area under the curve (AUC) values of 0.843 in the train cohort and 0.894 in the test cohort, surpassing individual feature sets. Biological exploration revealed associations between radiomics-DL score and biological characteristics, including immune responses and immunoregulation.ConclusionsThis integrated approach shows the potential of combining clinical, radiomics and deep learning features (DLFs) as a noninvasive biomarker for predicting immunotherapy efficacy in NSCLC, assisting in patient selection and clinical decision-making. Radiotranscriptomic analysis may reveal key cellular and immune patterns associated with radiomics-DL signature.

The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathologically confirmed placenta invasion and 40 pregnant women with normal placenta. After 24 gestational weeks, all participants underwent magnetic resonance imaging. The uterus and placenta regions were segmented in magnetic resonance images on sagittal T2WI. Ninety-three radiomics features were extracted from the placenta region, and 128 deep features were extracted from the uterus region using a deep neural network. The least absolute shrinkage and selection operator (LASSO) algorithm was used to filter these 221 features and to form the combined signature. Then the combined signature (CS) and clinical factors were combined to construct a nomogram. The accuracy, sensitivity, specificity and AUC of the nomogram were compared with four machine learning methods. The model NDRC was trained on the dataset of 78 pregnant women in the training cohort. Finally, the model NDRC was compared with four machine learning methods on the independent validation cohort of 34 pregnant women. The results showed that the prediction accuracy, sensitivity, specificity and AUC of the NDRC model were 0.941, 0.952, 0.923 and 0.985 respectively, which outperforms the traditional machine learning methods which rely on radiomics features and deep learning features alone.

PurposeTo differentiate complicated appendicitis (CA) from uncomplicated appendicitis (UA) in children, we developed and validated an artificial intelligence (AI) model using a multimodal approach integrating ultrasound images and clinical data.MethodsA retrospective analysis was performed on 372 pathologically confirmed pediatric appendicitis cases (230 male, 142 female) from three centers, all with preoperative abdominal ultrasound. Deep learning (DL) features and radiomic features were extracted from appendiceal ultrasound images using deep transfer learning (DTL) and conventional radiomic methods, respectively. We selected 12 radiomic features, 9 DL features, and 3 clinical features—namely, white blood cell count (WBC), neutrophil count (NEU), and C-reactive protein (CRP)—for building the machine learning classification model. Based on these features, four distinct models were constructed: the Rad model (radiomic features only), the DL model (DL features only), the DTL model (combined radiomic and DL features), and the Combine model (integrating all three feature types: radiomic, DL, and clinical features). Model performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the DeLong test. Finally, the combined model was compared to the performance of clinicians with varying levels of experience.ResultsThe combined model demonstrated consistently favorable performance across all cohorts (AUC: 0.940, 0.895, 0.866, and 0.783 for training and validation sets, respectively). The model's accuracy (0.862) and positive predictive value (0.896) were comparable to senior surgeons (0.741, 0.970) and superior to junior surgeons (0.672, 0.865) in the internal validation cohort. DCA confirmed the clinical utility of the combined model over conventional strategies.ConclusionOur ultrasound-based AI model provides reliable differentiation between CA and UA in children, offering potential value as a diagnostic support tool for clinical decision making.

Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients’ quality of life. This study aimed to establish a combined model of radiomics, dosiomics, deep learning (DL) based on simulated location CT and dosimetry images combining with clinical parameters to improve the predictive ability of ≥ 2 grade RP (RP2) in patients with non-small cell lung cancer (NSCLC). This study retrospectively collected 245 patients with NSCLC who received radiotherapy from three hospitals. 162 patients from Hospital I were randomly divided into training cohort and internal validation cohort according to 7:3. 83 patients from two other hospitals served as an external validation cohort. Multivariate analysis was used to screen independent clinical predictors and establish clinical model (CM). The radiomic and dosiomics (RD) features and DL features were extracted from simulated location CT and dosimetry images based on the region of interest (ROI) of total lung-PTV (TL-PTV). The features screened by the t-test and least absolute shrinkage and selection operator (LASSO) were used to construct the RD and DL model, and RD-score and DL-score were calculated. RD-score, DL-score and independent clinical features were combined to establish deep learning radiomics and dosiomics nomogram (DLRDN). The model performance was evaluated by area under the curve (AUC). Three clinical factors, including V20, V30, and mean lung dose (MLD), were used to establish the CM. 7 RD features including 4 radiomics features and 3 dosiomics features were selected to establish RD model. 10 DL features were selected to establish DL model. Among the different models, DLRDN showed the best predictions, with the AUCs of 0.891 (0.826–0.957), 0.825 (0.693–0.957), and 0.801 (0.698–0.904) in the training cohort, internal validation cohort and external validation cohort, respectively. DCA showed that DLRDN had a higher overall net benefit than other models. The calibration curve showed that the predicted value of DLRDN was in good agreement with the actual value. Overall, radiomics, dosiomics, and DL features based on simulated location CT and dosimetry images have the potential to help predict RP2. The combination of multi-dimensional data produced the optimal predictive model, which could provide guidance for clinicians.

To explore and compare the potential value of radiomics models based on contrast-enhanced computed tomography (CT) for noninvasive preoperative prediction of lymphovascular invasion (LVI) in laryngeal squamous cell carcinoma (LSCC). This multicenter diagnostic study retrospectively enrolled patients with LSCC from three tertiary hospitals who underwent surgical treatment. Standardized preprocessing was performed on the CT images, followed by region-of-interest segmentation and extraction of traditional radiomics features and deep learning (DL) features. Features were selected using least absolute shrinkage and selection operator (LASSO) regression. Traditional radiomics models and deep learning radiomics (DLR) models were established using logistic regression, random forest, and multilayer perceptron algorithms, respectively. A transformer-based hybrid model was developed by integrating radiomics and DL features. The predictive performance of the three types of models was evaluated and compared using the area under the curve (AUC), decision curve analysis (DCA), sample probability distribution histograms, confusion matrices, calibration curves, net reclassification index (NRI), and integrated discrimination improvement (IDI). A total of 1024 patients were allocated to the training set (center1, n =291), internal validation set ( n =126), and external test sets (Center 2, n =437; Center 3, n =170). Three radiomics models and three DLR models were constructed, and the optimal performance was observed in the DLR_ Random Forest model (AUC: 0.812-0.867). The transformer hybrid model demonstrated superior predictive performance, with AUC values of 0.881, 0.843, 0.833, and 0.836 in the training, internal validation, and external test sets, respectively. DCA indicated a higher net benefit for the Transformer model, along with an improved NRI and IDI. Radiomics models based on CT images exhibit potential for noninvasive prediction of LVI in LSCC, with the transformer hybrid model achieving the highest diagnostic performance. This approach may provide clinicians with a preoperative decision support tool to optimize treatment strategies for patients with LSCC.

BackgroundAnalyzing apparent diffusion coefficient (ADC) images before chemoradiotherapy (CRT) can effectively predict the treatment response of patients with hypopharyngeal squamous cell carcinoma (HPSCC), thereby reducing the treatment risks. This study aimed to develop a predictive model by combining deep-learning features and radiomics features derived from ADC images to predict the CRT sensitivity of HPSCC patients, providing effective guidance for treatment strategy selection.MethodsThis study retrospectively analyzed the data of 120 HPSCC patients. Deep-learning features were extracted from ADC images using a vision transformer (ViT)-based deep-learning model, while radiomics features were extracted using the PyRadiomics feature extractor. Among the 1,288 extracted radiomics features, the most significant ones were selected using the Spearman’s correlation coefficient, intraclass correlation coefficient (ICC), and least absolute shrinkage and selection operator (LASSO) method. These features were fused through a concatenation approach, and a classification prediction was performed using a convolutional neural network with three fully connected layers.ResultsThe accuracy, sensitivity, specificity, and area under the curve (AUC) values of the feature fusion model were 0.99 vs. 0.875, 0.988 vs. 0.842, 1.000 vs. 1.000, and 1.000 vs. 0.947, for the training and validation datasets, respectively. The feature fusion model performed optimally in comparison to the other models. In the validation dataset, the accuracy of the feature fusion model improved by 16.7% and 4.2% compared to the clinical and radiomic models, respectively.ConclusionsThe model developed in this study, which integrates deep-learning features with traditional radiomics features, can accurately predict the CRT sensitivity of HPSCC patients using pre-treatment ADC images. This model provides an effective reference for selecting optimal treatment strategies for patients.

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma

Treatment Response Prediction Using a Combination of Hematological Biomarkers and CT Deep Learning Radiomics in Locally Advanced Esophageal Squamous Cell Carcinoma

BackgroundAccurate preoperative evaluation of cT1N0M0 papillary thyroid carcinoma (PTC) is essential for guiding appropriate treatment strategies. Although ultrasound is widely used for clinical staging, it has limitations in detecting lymph node metastasis (LNM) and capsular invasion (CI), which may lead to misclassification of high-risk patients. Such undetected risks pose safety concerns for those undergoing radiofrequency ablation. This study aimed to develop an artificial intelligence (AI)-assisted predictive model that integrates ultrasound radiomics and deep learning features to improve the identification of LNM and CI, thereby enhancing risk stratification and optimizing treatment strategies for cT1N0M0 PTC patients.MethodsA total of 203 PTC patients were divided into high-risk (CI or LNM) and low-risk groups, with 142 assigned to the training set and 61 to the internal test set. Regions of interest delineation was performed using ITK-Snap. Radiomic features were extracted with PyRadiomics, and embedding features were obtained through the Vision Transformer (ViT) model. Risk-related features were selected using least absolute shrinkage and selection operator (LASSO), variance thresholding, and recursive feature elimination (RFE). Single-modal and multimodal models were developed using feature-level and decision-level fusion. Feature importance was assessed using Shapley Additive exPlanations (SHAP). Model performance was evaluated using recall, accuracy, and area under curve (AUC).ResultsAmong 1,001 radiomics features, 47 were selected via LASSO and RFE, and 15 relevant features from 768 ViT features. In the internal test set, NeuralNet models based on radiomics and 2D deep learning achieved AUCs of 0.756 and 0.708, respectively, and 0.829 and 0.840 in the training set. The multimodal RandomForest model outperformed single-modality models, with an AUC of 0.763 in the test set and 0.992 in the training set. Decision-level fusion models, such as DLRad_LF_Avg and DLRad_LF_Max, improved the external test set AUC to 0.843. SHAP analysis identified key features linked to tumor heterogeneity.ConclusionThe multimodal AI model effectively predicts high-risk cT1N0M0 PTC, outperforming single-modality models and aiding clinical decision-making.

An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning

BackgroundLymphovascular invasion (LVI) is a critical factor in breast cancer (BC) prognosis and treatment planning, yet preoperative non-invasive assessment remains challenging. This research proposes the design and validation of a comprehensive artificial intelligence (AI) system that combines intratumoral and peritumoral radiomic analysis, deep learning (DL)-derived features, and clinical risk indicators extracted from dynamic contrast-enhanced MRI (DCE-MRI), with the goal of predicting LVI status in patients with BC.MethodsThis multi-institutional retrospective study included 496 IBC patients (training cohort: n = 344; validation cohort: n = 152). DCE-MRI scans were acquired preoperatively, and intratumoral/peritumoral (0-1, 1-3, 3-5 mm) radiomics features were extracted. A ResNet-50-based DL model was applied to 2.5D tumor slices, and clinical risk factors were identified via logistic regression. The least absolute shrinkage and selection operator (LASSO) method was employed to identify the most relevant features. The ensemble model was created by combining the Intra- Peri Fusion model with the clinically independent risk factors. Model performance was evaluated by sensitivity, specificity, AUC, and decision curve analysis (DCA).ResultsLVI was present in 33.8% and 32.7% of the training and validation cohorts. The SVM (Support Vector Machine) Intra-Peri Fusion model reached AUCs of 0.921 and 0.906, showing enhanced discriminative performance over single-region approaches. The ensemble model, derived from integrating a fusion model with clinical risk factors, demonstrated superior performance with AUCs of 0.951 (training) and 0.929 (validation) and high net benefit in DCA. Calibration curves confirmed excellent agreement between predicted and observed outcomes.ConclusionThe AI-driven ensemble model combining radiomics, DL, and clinical features enables accurate preoperative prediction of LVI in IBC, which holds potential for optimizing surgical planning and adjuvant therapy strategies.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

ObjectiveThis study aims to create a reliable framework for grading esophageal cancer. The framework combines feature extraction, deep learning with attention mechanisms, and radiomics to ensure accuracy, interpretability, and practical use in tumor analysis.Materials and methodsThis retrospective study used data from 2,560 esophageal cancer patients across multiple clinical centers, collected from 2018 to 2023. The dataset included CT scan images and clinical information, representing a variety of cancer grades and types. Standardized CT imaging protocols were followed, and experienced radiologists manually segmented the tumor regions. Only high-quality data were used in the study. A total of 215 radiomic features were extracted using the SERA platform. The study used two deep learning models—DenseNet121 and EfficientNet-B0—enhanced with attention mechanisms to improve accuracy. A combined classification approach used both radiomic and deep learning features, and machine learning models like Random Forest, XGBoost, and CatBoost were applied. These models were validated with strict training and testing procedures to ensure effective cancer grading.ResultsThis study analyzed the reliability and performance of radiomic and deep learning features for grading esophageal cancer. Radiomic features were classified into four reliability levels based on their ICC (Intraclass Correlation) values. Most of the features had excellent (ICC > 0.90) or good (0.75 < ICC ≤ 0.90) reliability. Deep learning features extracted from DenseNet121 and EfficientNet-B0 were also categorized, and some of them showed poor reliability. The machine learning models, including XGBoost and CatBoost, were tested for their ability to grade cancer. XGBoost with Recursive Feature Elimination (RFE) gave the best results for radiomic features, with an AUC (Area Under the Curve) of 91.36%. For deep learning features, XGBoost with Principal Component Analysis (PCA) gave the best results using DenseNet121, while CatBoost with RFE performed best with EfficientNet-B0, achieving an AUC of 94.20%. Combining radiomic and deep features led to significant improvements, with XGBoost achieving the highest AUC of 96.70%, accuracy of 96.71%, and sensitivity of 95.44%. The combination of both DenseNet121 and EfficientNet-B0 models in ensemble models achieved the best overall performance, with an AUC of 95.14% and accuracy of 94.88%.ConclusionsThis study improves esophageal cancer grading by combining radiomics and deep learning. It enhances diagnostic accuracy, reproducibility, and interpretability, while also helping in personalized treatment planning through better tumor characterization.Clinical trial numberNot applicable.

BACKGROUND Timely identification of local failure after Stereotactic Radiosurgery (SRS) offers the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life. Previous studies showed that the addition of either radiomics or deep learning features to clinical features increased the accuracy of the models in predicting local control (LC) of brain metastases after SRS. To date, however, no study combined both radiomics and deep learning features together with clinical features to develop machine learning algorithms to predict LC of brain metastases. In this study, we examined whether a model trained with a combination of all these features could predict LC better than models trained with only a subset of these features. MATERIAL AND METHODS Pre-treatment brain MRIs and clinical data were collected retrospectively for 129 patients at the Gamma Knife Center of Elisabeth-TweeSteden Hospital (ETZ), Tilburg, The Netherlands. The patients were split into 103 patients for training and 26 patients for testing. The segment-based radiomics features were extracted using the radiomics feature extractor of the python radiomics package. The deep learning features were extracted using a fine-tuned 3D ResNet model and then combined with the clinical and radiomics features. A Random Forest classifier was trained with the training data set and then tested with the test data set. The performance was compared across 4 different models trained with clinical features only, clinical and radiomics features, clinical and deep learning features, and clinical, radiomics and deep learning features. RESULTS The prediction model with only clinical variables demonstrated an area under the receiver operating characteristic curve (AUC) of 0.82 and an accuracy of 75.6%. The prediction model with the combination of clinical and radiomics features demonstrated an AUC of 0.880 and an accuracy of 83.3% whereas the prediction model with the combination of clinical and deep learning features demonstrated an AUC of 0.863 and an accuracy of 78.3%. The best prediction performance was associated with the model that combined the clinical, radiomics and deep learning features with an AUC of 0.886 and 87% accuracy. CONCLUSION Machine learning models built on radiomics features and deep learning features combined with patient characteristics show potential to predict LC after SRS with high accuracy. The promising findings from this study demonstrate the potential for early prediction of SRS outcome for brain metastasis prior to treatment initiation and might offer the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life.

To investigate the value of multimodal MRI radiomics model in predicting Neoadjuvant chemotherapy (NAC) response in Nasopharyngeal carcinoma (NPC) and assess the performance differences between machine learning and deep learning models. 370 NPC patients were retrospectively enrolled, with 126 patients receiving additional Dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI). Radiomics features were extracted from T1WI, PDWI, CE-T1WI and DCE-MRI sequences. Feature selection was performed through Mutual Information Criterion (MIC), Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, and feature selection network. Machine learning models based on logistic regression, support vector machines, random forests, and extreme gradient boosting, as well as deep learning prediction models of multi-layer perceptrons were established. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves and interpreted through SHapley Additive exPlanations (SHAP) values. The deep learning model incorporating DCE-MRI features showed superior performance with an Area under the curve(AUC) of 0.885, sensitivity of 79.6%, and specificity of 83.7%. SHAP analysis revealed that DCE-MRI features, particularly from late enhancement phases, contributed most significantly to model predictions. The multimodal MRI radiomics model, especially when combined with DCE-MRI, can effectively predict NAC response in NPC patients. The model's interpretability through SHAP analysis demonstrates its potential for clinical application.