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Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma
BackgroundXerostomia is one of the most common side effects in nasopharyngeal carcinoma (NPC) patients after chemoradiotherapy. To establish a Delta...
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CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients
BackgroundThis study developed a nomogram model using CT-based delta-radiomics features and clinical factors to predict pathological complete...
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Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study
This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment...
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The CT delta-radiomics based machine learning approach in evaluating multiple primary lung adenocarcinoma
ObjectTo evaluate the difference between multiple primary lung adenocarcinoma (MPLA) and solitary primary lung adenocarcinoma (SPLA) by...
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Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study
BackgroundReliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and...
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Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy
BackgroundTo integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal...
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The Utility of Radiomics in Predicting Response to Cancer Immunotherapy
Immunotherapy, particularly immune checkpoint inhibitor therapy, has made remarkable advancements in the treatment of patients with cancer in the...
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Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME)...
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Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning
ObjectiveIn this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time...
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Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer
BackgroundMagnetic Resonance Image guided Stereotactic body radiotherapy (MRgRT) is an emerging technology that is increasingly used in treatment of...
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Automated breast volume scanner based Radiomics for non-invasively prediction of lymphovascular invasion status in breast cancer
PurposeLymphovascular invasion (LVI) indicates resistance to preoperative adjuvant chemotherapy and a poor prognosis and can only be diagnosed by...
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Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures
PurposeTo construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell...
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Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients
BackgroundIdentifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or...
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A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients
BackgroundThe objective of this paper is to explore the value of a delta-radiomic model of the axillary lymph node (ALN) using dynamic...
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Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN
To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network...
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Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study
BackgroundThe timeliness of diagnostic testing after positive screening remains suboptimal because of limited evidence and methodology, leading to...
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Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancer
The combination of Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) is the standard of care for hormone receptor-positive...
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Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer
BackgroundTo develop and compare delta-radiomics signatures from 2- (2D) and 3-dimensional (3D) features that predict treatment outcomes following...
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A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial
BackgroundThis study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC)...
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MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma
BackgroundRecurrence is the major cause of mortality in patients with resected HCC. However, without a standard approach to evaluate prognosis, it is...
