Identification Of Tumors Research Articles

Identification of differentiating sonographic features between fibroadenomas and malignant tumors of the breast mimicking fibroadenomas: 10-year experience in 421 histologically verified cases.

Ultrasound is a highly effective imaging tool for assessing abnormalities within the breast. However, especially the identification of malignant tumors of the breast mimicking fibroadenomas (MTMF) by means of breast ultrasound can be challenging. This study aimed to identify reliable imaging characteristics of MTMF.This retrospective study was approved by the local ethics review board. After screening 623 patients, 421 cases with histologically verified fibroadenomas and MTMF between 2011 and 2021 were included. Sonographic features were compared to histopathological results and an algorithm-based quantitative ranking of predictors contributing most to the correct classification of malignant tumors was conducted.A total of 363 benign, 18 intermediate, and 40 malignant lesions were analyzed. Algorithm-based quantitative ranking showed that the most predictive features indicating malignancy were a hyperechoic rim (gain ratio merit 0.135 ± 0.004), an irregular border (0.057 ± 0.002), perilesional stiffening (0.054 ± 0.002), pectoral contact (0.051 ± 0.003), an irregular shape (0.029 ± 0.001), and irregular vasculature (0.027 ± 0.002).Ultrasound findings for fibroadenomas vary, making identification of MTMF challenging. Features such as indistinct margins and increased perilesional echogenicity are predictors for malignancy and should be considered during sonographic evaluation of fibroadenomas and MTMF.

Fiber Bragg Grating Sensors Combined With Artificial Intelligence for Noninvasive Breast Tumor Identification

Fiber Bragg Grating Sensors Combined With Artificial Intelligence for Noninvasive Breast Tumor Identification

Stoichiometric constraints for detection of EV-borne biomarkers in blood.

Stochiometric issues, encompassing both the quantity and heterogeneity of extracellular vesicles (EVs) derived from tumour or other tissues in blood, pose important challenges across various stages of biomarker discovery and detection, affecting the integrity of data, introducing losses and artifacts during blood processing, EV purification and analysis. These challenges shape the diagnostic utility of EVs especially within the framework of established and emerging methodologies. By addressing these challenges, we aim to delineate crucial parameters and requirements for tumour-specific EV detection, or more precisely, for tumour identification via EV based assays. Our endeavour involves a comprehensive examination of the layers that mask or confound the traceability of EV markers such as nucleic acids and proteins, and focus on 'low prevalence-low concentration' scenario. Finally, we evaluate the advantages versus limitations of single-particle analysers over more conventional bulk assays, suggesting that the combined use of both to capture and interpret the EV signals, in particular the EV surface displayed proteins, may ultimately provide quantitative information on their absolute abundance and distribution.

Identification of Intracranial Germ Cell Tumors Based on Facial Photos: Exploratory Study on the Use of Deep Learning for Software Development.

Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life. This study aimed to investigate the application of facial recognition technology in the early detection of iGCTs in children and adolescents. Early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life. A multicenter, phased approach was adopted for the development and validation of a deep learning model, GVisageNet, dedicated to the screening of midline brain tumors from normal controls (NCs) and iGCTs from other midline brain tumors. The study comprised the collection and division of datasets into training (n=847, iGCTs=358, NCs=300, other midline brain tumors=189) and testing (n=212, iGCTs=79, NCs=70, other midline brain tumors=63), with an additional independent validation dataset (n=336, iGCTs=130, NCs=100, other midline brain tumors=106) sourced from 4 medical institutions. A regression model using clinically relevant, statistically significant data was developed and combined with GVisageNet outputs to create a hybrid model. This integration sought to assess the incremental value of clinical data. The model's predictive mechanisms were explored through correlation analyses with endocrine indicators and stratified evaluations based on the degree of hypothalamic-pituitary-target axis damage. Performance metrics included area under the curve (AUC), accuracy, sensitivity, and specificity. On the independent validation dataset, GVisageNet achieved an AUC of 0.938 (P<.01) in distinguishing midline brain tumors from NCs. Further, GVisageNet demonstrated significant diagnostic capability in distinguishing iGCTs from the other midline brain tumors, achieving an AUC of 0.739, which is superior to the regression model alone (AUC=0.632, P<.001) but less than the hybrid model (AUC=0.789, P=.04). Significant correlations were found between the GVisageNet's outputs and 7 endocrine indicators. Performance varied with hypothalamic-pituitary-target axis damage, indicating a further understanding of the working mechanism of GVisageNet. GVisageNet, capable of high accuracy both independently and with clinical data, shows substantial potential for early iGCTs detection, highlighting the importance of combining deep learning with clinical insights for personalized health care.

A TaqMan-Based qRT-PCR Assay for Accurate Evaluation of the Oncogenic TrkAIII Splice Variant in Tumor cDNAs

Background: Alternative NTRK1/TrkA splicing resulting in TrkAIII expression, originally discovered in advanced-stage metastatic neuroblastomas, is also pronounced in prostate, medullary thyroid, glioblastoma multiforme, MCPyV-positive Merkel cell, cutaneous malignant melanoma, and pituitary neuroendocrine tumor subsets. In tumor models, TrkAIII exhibits actionable oncogenic activity equivalent to the TrkT3-fused oncogene, and in tumor cell lines, alternative TrkAIII splicing is promoted by hypoxia, nutrient deprivation, endoplasmic reticulum stress, and SV40 large T antigen, implicating tumor microenvironmental conditions and oncogenic polyoma viruses in tumor-associated TrkAIII expression. Collectively, these observations characterize TrkAIII as a potentially frequent, actionable oncogenic alternative to TrkA gene fusion in different tumor types. Currently, therapeutic approval for efficacious Trk inhibitors is restricted to Trk-fused gene positive tumors and not for tumors potentially driven by TrkAIII. Methods: With the therapeutically relevant aim of improving the identification of tumors potentially driven by TrkAIII, we have developed a TaqMan-based qRT-PCR assay for evaluating TrkAIII expression in tumor cDNAs. Results: This assay, validated using gel-purified fs-TrkA and TrkAIII cDNAs alone and in complex cDNA mixtures, employs primers and probes designed from fs-TrkA and TrkAIII sequences, with specificity provided by a TaqMan probe spanning the TrkAIII exon 5–8 splice junction. It is highly efficient, reproducible, and specific and can detect as few as 10 TrkAIII copies in complex RNAs extracted from either fresh or FFPE tumor tissues. Conclusions: Inclusion of this assay into precision oncology algorithms, when paired with fs-TrkA qRT-PCR and TrkA immune histochemistry, will make it easier to identify patients with therapy-resistant, advanced-stage metastatic Trk-fused gene-negative tumors potentially driven by TrkAIII, for whom approval of third-line effective Trk inhibitors could be extended.

Detection of bone and soft tissue tumors using Mueller matrix polarimetry: a non-invasive approach for unfixed tumor identification

Detection of bone and soft tissue tumors using Mueller matrix polarimetry: a non-invasive approach for unfixed tumor identification

CTCs Detection Methods In Vivo and In Vitro and Their Application in Tumor Immunotherapy.

Circulating tumor cells (CTCs) are tumor cells that detach from the primary tumor site and enter the bloodstream. They hold significant value for the early detection, diagnosis, and treatment of tumors. CTC detection methods can be classified into in vivo and in vitro techniques. Compared to traditional tumor detection methods, CTC detection causes less harm to the body and allows for earlier identification of malignant tumors, thereby enabling timely treatment and improving patient survival rates. In recent years, CTC detection has been widely used to assess the effectiveness of tumor immunotherapy. Dynamic monitoring of CTCs can help optimize clinical treatment strategies. This review summarizes the latest in vivo and in vitro detection methods for CTCs and discusses their applications in immunotherapy for various common tumors, aiming to provide better guidance and support for clinical practice.

YOLO-CPC: a breast tumor detection and identification algorithm based on improved YOLOv7

YOLO-CPC: a breast tumor detection and identification algorithm based on improved YOLOv7

Dilated SE-DenseNet for brain tumor MRI classification

In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks’ attention mechanisms. This novel approach aims to improve upon state-of-the-art methods of tumor identification. Our model, trained and evaluated on a comprehensive Kaggle brain tumor dataset, demonstrated superior performance over established convolution-based and transformer-based models: ResNet-101, VGG-19, original DenseNet-121, MobileNet-V2, ViT-L/16, and Swin-B across key metrics: F1-score, accuracy, precision, and recall. The results underscore the effectiveness of our architectural enhancements in medical image analysis. Future research directions include optimizing dilation layers and exploring various architectural configurations. The study highlights the significant role of machine learning in improving diagnostic accuracy in medical imaging, with potential applications extending beyond brain tumor detection to other medical imaging tasks.

A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification

Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background. In a Chinese seven-center cohort, data from 347 subjects were analyzed. Our one-step model incorporated normal tissue/organ labels to provide contextual information, offering a solution for tumors with complex backgrounds. To address privacy concerns, we utilized a lightweight deep neural network suitable for hospital deployment. The final model achieved an accuracy of 85.71% for MPNST diagnosis in the validation cohort and 84.75% accuracy in the independent test set, outperforming another classic two-step model. This success suggests potential for AI models in screening other whole-body primary/metastatic tumors.

Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction

Accurate identification and localisation of brain tumours from medical images remain challenging due to tumour variability and structural complexity. Convolutional Neural Networks (CNNs), particularly ResNet and Unet, have made significant progress in medical image processing, offering robust capabilities for image segmentation. However, limited research has explored their integration with human-computer interaction (HCI) to enhance usability, interpretability, and clinical applicability. This paper introduces ResUnet++, an advanced hybrid model combining ResNet and Unet++, designed to improve tumour detection and localisation while fostering seamless interaction between clinicians and medical imaging systems. ResUnet++ integrates residual blocks in both the downsampling and upsampling phases, ensuring critical image features are preserved. By incorporating HCI principles, the model provides intuitive, real-time feedback, enabling clinicians to visualise and interact with tumour localisation results effectively. This fosters informed decision-making and supports workflow efficiency in clinical settings. We evaluated ResUnet++ on the LGG Segmentation Dataset, achieving a Jaccard Loss of 98.17%. The results demonstrate its strong segmentation performance and potential for real-world applications. By bridging advanced medical imaging techniques with HCI, ResUnet++ offers a foundation for developing interactive diagnostic tools, improving clinician trust, decision accuracy, and patient outcomes, and advancing the integration of AI in healthcare workflows.

A punção aspirativa por agulha fina como ferramenta diagnóstica de tumor de células melanocíticas em cavalo tordilho

Melanocytic cell tumors, commonly known as melanomas, are frequently observed in grey horses, especially in older animals. These tumors can range from benign to malignant and are often diagnosed in areas such as the base of the tail, perineum, lips, and parotid glands. Fine-Needle Aspiration (FNA) has proven to be a valuable diagnostic tool in the evaluation of neoplasms, allowing for the collection of tumor cells for cytological analysis in a minimally invasive and cost-effective manner for many owners. This case report describes the use of FNA in diagnosing a benign melanocytic tumor in a 15-year-old grey horse that presented a subcutaneous mass in the submandibular region, as well as formations at the base of the tail and lips. After collecting the material via FNA, the samples were subjected to cytological analysis, where no malignancy criteria were observed. Since surgical intervention and histopathological evaluation were not opted for, treatment with Cimetidine was initiated to reduce the tumor masses. The application of FNA proved to be an efficient and practical technique in obtaining diagnostic material, allowing for early tumor identification and aiding in the clinical management of the patient. This report reinforces the importance of FNA as a diagnostic tool in veterinary medicine, especially in cases of cutaneous neoplasms in horses, where quick and accurate interventions are essential for successful treatment.

Dual-Labeled Small Peptides in Cancer Imaging and Fluorescence-Guided Surgery: Progress and Future Perspectives

Dual-labeled compounds that combine radiolabeling and fluorescence labeling represent a significant advancement in precision oncology. Their clinical implementation enhances patient care and outcomes by leveraging the high sensitivity of radioimaging for tumor detection and taking advantage of fluorescence-based optical visualization for surgical guidance. Non-invasive radioimaging facilitates immediate identification of both primary tumors and metastases, while fluorescence imaging assists in decision-making during surgery by offering a spatial distinction between malignant and non-malignant tissue. These advancements hold promise for enhancing patient outcomes and personalization of cancer treatment. The development of dual-labeled molecular probes targeting various cancer biomarkers is crucial in addressing the heterogeneity inherent in cancer pathology and recent studies had already demonstrated the impact of dual-labeled compounds in surgical decision-making (NCT03699332, NCT03407781). This review focuses on the development and application of small dual-labeled peptides in the imaging and treatment of various cancer types. It summarizes the biomarkers targeted to date, tracing their development from initial discovery to the latest advancements in peptidomimetics. Through comprehensive analysis of recent preclinical and clinical studies, the review demonstrates the potential of these dual-labeled peptides to improve tumor detection, localization, and resection. Additionally, it highlights the evolving landscape of dual-modality imaging, emphasizing its critical role in advancing personalized and effective cancer therapy. This synthesis of current research underscores the promise of dual-labeled peptides in enhancing diagnostic accuracy and therapeutic outcomes in oncology.

CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.

Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost. To address these challenges, a novel U-Net model for tumour segmentation in magnetic resonance images(MRI) is proposed. Initially, images are claimed from the dataset and pre-processed with the Probabilistic Hybrid Wiener filter (PHWF) to remove unwanted noise and improve image quality. To reduce model complexity, the pre-processed images are submitted to a feature extraction procedure known as 3D Convolutional Vision Transformer (3D-VT). To perform the segmentation approach usingchaotic optimization assisted Dilated Channel Gate attention U-Net (CDCG-UNet)model to segment brain tumour regions effectively. The proposed approach segments tumour portions as whole tumour (WT), tumour Core (TC), and Enhancing Tumour (ET) positions. The optimization loss function can be performed using the Chaotic Harris Shrinking Spiral optimization algorithm (CHSOA). The proposed CDCG-UNet model is evaluated with three datasets: BRATS 2021, BRATS 2020, and BRATS 2023. For the BRATS 2021 dataset, the proposed CDCG-UNet model obtained a dice score of 0.972 for ET, 0.987 for CT, and 0.98 for WT. For the BRATS 2020 dataset, the proposed CDCG-UNet model produced a dice score of 98.87% for ET, 98.67% for CT, and 99.1% for WT. The CDCG-UNet model is further evaluated using the BRATS 2023 dataset, which yields 98.42% for ET, 98.08% for CT, and 99.3% for WT.

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network

ABSTRACT Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).

The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights. VGG16, known for its depth and high performance, is utilized for this purpose. The study demonstrates the model's potential for precise and effective diagnosis by examining how well it can differentiate between areas of normal brain tissue and cancerous regions, leveraging both MRI and microscopy data. We describe in full the pre-processing actions taken to improve the quality of input data and maximize model efficiency. A carefully selected dataset, incorporating diverse tumor sizes and types from both microscopy and MRI sources, is used during the training phase to ensure representativeness. The proposed modified VGG19 model achieved 98.81% validation accuracy. Despite good accuracy, interpretation of the result still questionable. The proposed methodology integrates explainable AI (XAI) for brain tumor detection to interpret system decisions. The proposed study uses a gradient explainer to interpret classification results. Comparative statistical analysis highlights the effectiveness of the proposed explainer model over other XAI techniques.

Identification and characterization of tumor and stromal derived liquid biopsy analytes in pancreatic ductal adenocarcinoma

BackgroundThe lack of predictive biomarkers contributes notably to the poor outcomes of patients with pancreatic ductal adenocarcinoma (PDAC). Cancer-associated fibroblasts (CAFs) are the key components of the prominent PDAC stroma. Data on clinical relevance of CAFs entering the bloodstream, known as circulating CAFs (cCAFs) are scarce. Here, we developed a combined liquid biopsy assay to detect cCAFs and circulating tumor cells (CTCs) in metastatic PDAC (mPDAC) and other metastatic gastrointestinal malignancies (mGI). In addition, we evaluated plasma hyaluronan (HA) levels as a complementary surrogate biomarker of the stromal extent in patients with PDAC.MethodsA sequential liquid biopsy assay based on a two step-enrichment, combining marker dependent and independent cell enrichment, was established for cCAF and CTC detection and validated in mPDAC and mGI patients. The enriched cells were identified by multiplex immunofluorescence. HA measurement was performed by ELISA on blood samples from healthy blood donors (HD), localized and late-stage PDAC patients.ResultscCAFs (≥ 1cCAFs/7.5 mL blood) were detected in 95.4% of mPDAC and in 78.2% of mGI patients, with significantly higher numbers in mPDAC compared to mGI patients (mean number 22.7 vs. 11.0; P = 0.0318). mPDAC patients with ≥ 15 cCAFs/7.5 mL blood had a significant shorter median overall survival (mOS 3.2 months (95% confidence interval (CI) 0.801–5.855) vs. 14.2 months (95% CI 6.055–22.332); P = 0.013), whereby CTC levels were not associated with mOS. In mGI neither cCAFs nor CTCs had a significant impact on OS. HA plasma levels in mPDAC patients were significantly higher compared to HD (mean 123.0 ng/mL vs. 74.45 ng/mL, P = 0.015). High HA in localized and late-stage PDAC were associated with a significantly shorter mOS (mOSlocalized PDAC: 12.6 months vs. 23.5 months (P = 0.008); mOSmPDAC: 1.8 months vs. 5.3 months (P = 0.004)).ConclusionsOur liquid biopsy assay provides robust detection of cCAFs in mPDAC and mGI patients. The measurement of both circulatory stromal parameters, cCAFs and HA, adds valuable clinical information as they are associated with an unfavorable outcome in PDAC. These results highlight that stromal characteristics unique to PDAC could be leveraged to fill the current gap in discovering predictive biomarkers.

Automated CAD system for early detection and classification of pancreatic cancer using deep learning model

Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems. In the preprocessing stage, the input image resizes into 227 × 227 dimensions then converts the RGB image into a grayscale image, and enhances the image by removing noise without blurring edges by applying anisotropic diffusion filtering. In the segmentation stage, the preprocessed grayscale image a binary image is created based on a threshold, highlighting the edges by Sobel filtering, and watershed segmentation to segment the tumor region and we also implement the U-Net method for segmentation. Then refine the geometric structure of the image using morphological operation and extracting the texture features from the image using a gray-level co-occurrence matrix computed by analyzing the spatial relationship of pixel intensities in the refined image, counting the occurrences of pixel pairs with specific intensity values and spatial relationships. The detection stage analyzes the tumor region’s extracted features characteristics by labeling the connected components and selecting the region with the highest density to locate the tumor area, achieving a good accuracy of 99.64%. In the classification stage, the system classifies the detected tumor into the normal, pancreatic tumor, then into benign, pre-malignant, or malignant using a proposed reduced 11-layer AlexNet model. The classification stage attained an accuracy level of 98.72%, an AUC of 0.9979, and an overall system average processing time of 1.51 seconds, demonstrating the capability of the system to effectively and efficiently identify and classify pancreatic cancers.

Enhanced CATBraTS for Brain Tumour Semantic Segmentation.

The early and precise identification of a brain tumour is imperative for enhancing a patient's life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation.

Application of MRI-based tumor heterogeneity analysis for identification and pathologic staging of breast phyllodes tumors.

To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors. Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO). The efficacy of each model was assessed using the subject operating characteristic (ROC) curve, The DeLong test was used to assess the differences in the AUC values of the different models, and the clinical benefit of each model was assessed using the decision curve (DCA), and the predictive accuracy of the model was assessed using the calibration curve (CCA). Among the constructed models for classification of breast phyllodes tumors, the fusion model (AUC: 0.97) had the best diagnostic efficacy and highest clinical benefit. The traditional radiomics model (AUC: 0.81) had better diagnostic efficacy compared with subregion radiomics model (AUC: 0.70). De-Long test, there is a statistical difference between the fusion model traditional radiomics model, and subregion radiomics model in the training group. Among the models constructed to distinguish phyllodes tumors from fibroadenomas in the breast, the TDT_CIDL model (AUC: 0.974) had the best predictive efficacy and the highest clinical benefit. De-Long test, the TDT_CI combination model was statistically different from the remaining five models in the training group. Traditional radiomics models, subregion radiomics models and deep learning models based on MRI sequences can help to differentiate benign from junctional phyllodes tumors, phyllodes tumors from fibroadenomas, and provide personalized treatment for patients.