AIMC Topic: Radiomics
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Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.
BMC cancer
Jan 9, 2025
OBJECTIVES: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 8, 2025
Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment tra...
Rad4XCNN: A new agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics.
Computer methods and programs in biomedicine
Jan 7, 2025
BACKGROUND AND OBJECTIVE: In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI mode...
The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer.
BMC medical imaging
Jan 7, 2025
OBJECTIVE: This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status.
Neural Network and Logistic Regression Models Based on Ultrasound Radiomics and Clinical-Pathological Features to Predict Occult Level II Lymph Node Metastasis in Papillary Thyroid Carcinoma.
Academic radiology
Jan 4, 2025
RATIONALE AND OBJECTIVES: Papillary thyroid carcinoma (PTC) often metastasizes to lateral cervical lymph nodes, especially in level II. This study aims to develop predictive models to identify level II lymph node metastasis (LNM), guiding selective n...
Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.
Abdominal radiology (New York)
Jan 2, 2025
OBJECTIVE: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assess...
Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk.
Academic radiology
Jan 2, 2025
RATIONALE AND OBJECTIVES: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.
An Automatic Deep-Radiomics Framework for Prostate Cancer Diagnosis and Stratification in Patients with Serum Prostate-Specific Antigen of 4.0-10.0 ng/mL: A Multicenter Retrospective Study.
Academic radiology
Jan 2, 2025
RATIONALE AND OBJECTIVES: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL.
Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.
Scientific reports
Dec 30, 2024
Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing ...
Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.
Radiation oncology (London, England)
Dec 30, 2024
BACKGROUND AND PURPOSE: Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learni...
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