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Clear cell renal cell carcinoma (ccRCC) is a prevalent kidney disease, accounting for more than 75% of renal cell carcinoma (RCC) and approximately 3.8% of human malignancies. Early grading of ccRCC is crucial in guiding personalized treatment ...

• Leveraging degraded SDCTs for screened lung nodule classification.
• Application of radiomics on lung nodules for developing a more explainable model.
• Potential of shape and size features to enhance lung nodule classification ...

Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these ...

• PDA prognosis with AI is a challenging task.
• Combining radiomic, clinical and mutational data can improve risk stratification.
• Time-dependent XAI is underutilized in multi-omics approaches.
• Investigation on how radiomics can ...

In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (...

Diabetic retinopathy (DR), a progressive condition due to diabetes that can lead to blindness, is typically characterized by a number of stages, including non-proliferative (mild, moderate and severe) and proliferative DR. These stages are marked ...

• Proposed an automated method for accurate grading of DR severity on UW-OCTA images.
• Performs quality assessment, vascular abnormality segmentation and DR grading.
• Our method achieved recall values of 0.88 for vascular abnormality ...

New machine learning methods and techniques are frequently introduced in radiomics, but they are often tested on a single dataset, which makes it challenging to assess their true benefit. Currently, there is a lack of a larger, ...

• New methods are introduced in radiomics, but often not properly benchmarked.
• We collected 50 radiomics dataset in tabular form and with binary outcome.
• We illustrate its use by showing that decorrelating features has no large ...

Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infections (CAUTIs) and exhibits increased antibiotic resistance. Traditional diagnostics still rely on ...

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• There is a need for a new approach to tackle the rising issue of antimicrobial resistance.
• Wavelet-based Radiomics analysis of light microscopy images can identify antibiotic-resistant of P. mirabilis strains.
• The Random Forest ...

Ultrasound is a widely used diagnostic modality that generates substantial amounts of ultrasonic medical data. With the rapid development of deep learning and effective utilization of medical data, computer-aided intelligent diagnosis methods have ...

This study aims to evaluate the repeatability and reproducibility of radiomics features (RFs) under image perturbations and examine their generalizability across computed tomography (CT) and magnetic resonance (MR) images among nasopharyngeal ...

18F-FDG PET/CT images have been proven promising for the prognosis of Diffuse Large B-cell Lymphoma (DLBCL) patients. However, the implicit drawbacks of images constrain their wide applications. In this paper, we propose a fusion solution which ...

Radiomics has emerged as a promising tool for early diagnosis of cancer using Computerised Tomography (CT) scans. However, a main concern is the reproducibility of results, particularly in cases unseen by the network. Although overfitting can ...

Accurately differentiating indeterminate pulmonary nodules remains a significant challenge in clinical practice. This challenge becomes increasingly formidable when dealing with the vast radiomic features obtained from low-dose computed ...

• Altruistic mechanism is integrated into the Seagull Optimization Algorithm.
• The proposed multi-objective function accommodates to class-imbalanced datasets.
• A radiomic feature panel is selected to predict the malignant risk of lung ...

Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such ...

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• AI based techniques cane used in for various diagnostic procedures like prediction of disease, decision for medication and prognosis of a disease.
• One of the major requirements of development of AI in healthcare is large quantity of ...

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging ...

• PET Radiomics offers unique insights into tumor biology and treatment responses.
• Despite its potential, PET radiomics faces several challenges and limitations that need to be addressed to facilitate its clinical translation.
• This ...

• Predicting radiation pneumonitis in lung cancer patients undergoing radiotherapy helps improve the management of lung cancer patients.
• Combining dosiomics, radiomics, and deep learning enhances the prediction of radiation pneumonitis ...

To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve ...

Objectives: The preoperative diagnose of epidermal growth factor receptor (EGFR) is crucial for targeted therapy in non-small cell lung cancer (NSCLC) patients with brain metastases. This study aimed to assess the feasibility of using deep learning ...

Tourette Syndrome(TS) is a hereditary condition characterized by involuntary motor and vocal actions. Although there is no cure for TS, various prescription medications are typically provided to patients to help alleviate symptoms. Incorporating ...

Magnetic resonance imaging (MRI) has become an essential and a frontline technique in the detection of brain tumor. However, segmenting tumors manually from scans is laborious and time-consuming. This has led to an increasing trend towards fully ...

• Precision in brain tumor segmentation is pivotal for accurate diagnosis and treatment planning.
• Benchmarking the performance of four widely used CNN methods i.e., CaPTk, 2DVNet, EnsembleUNets, and ResNet50 on BraTS2021 dataset and ...

Breast cancer is the most prevalent disease that poses a significant threat to women’s health. Despite the Dynamic Contrast-Enhanced MRI (DCE-MRI) has been widely used for breast cancer classification, its diagnostic performance is still ...

• Modeling of breast cancer classification in DCE-MRI through time series algorithms.
• Radiomics enables the extraction of intelligible features also in small-dataset.
• The intelligibility of radiomic features enables accurate and ...

The metabolic syndrome induced by obesity is closely associated with cardiovascular disease, and the prevalence is increasing globally, year by year. Obesity is a risk marker for detecting this disease. However, current ...

• Build abdominal adipose tissue dataset containing 13,732 CT images of 300 subjects.
• Validate the dataset applied value in denoising, segmentation and radiomics research.
• This dataset is published as open source for noncommercial ...

To investigate the value of machine learning-based radiomics for predicting disease-free survival (DFS) and overall survival (OS) undergoing concurrent chemoradiotherapy (CCRT) for patients with locally advanced cervical cancer (LACC).

• A multicentre large sample study has built a stable and reliable predictive tool that can efficiently predict the progression and prognosis of cervical cancer patients after concurrent chemoradiotherapy.
• Complementary of radiomics ...