• This meta-analysis assesses the role of PET/CT in breast cancer during NAC. • Pathological responses were based on both primary tumour and lymph node. • F-FDG PET/CT has a moderate accuracy in predicting the pathological response.
Background: 18-fluorodeoxyglucose positron emission tomography/computed tomography (
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F-PET/CT) has been widely applied for the imaging of head and neck squamous cell carcinoma (HNSCC). This study examined whether pre- and post-treatment
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F-PET/CT features can help predict the survival of HNSCC patients.
Results: Three radiomics features were identified as prognostic factors. Radiomics score calculated from these features significantly predicted overall survival (OS) and disease-free disease (DFS). The clinicopathological characteristics combined with pre- or post-treatment nomograms showed better ROC curves and decision curves than the nomogram based only on clinicopathological characteristics.
Conclusions: Combining clinicopathological characteristics with radiomics features of pre-treatment PET/CT or post-treatment PET/CT assessment of primary tumor sites as positive or negative may substantially improve prediction of OS and DFS of HNSCC patients.
Methods: 171 patients who received pre-treatment
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F-PET/CT scans and 154 patients who received post-treatment
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F-PET/CT scans with HNSCC in the Cancer Imaging Achieve (TCIA) were included. Nomograms that combined clinicopathological features with either pre-treatment PET/CT radiomics features or post-treatment assessment of primary tumor sites were constructed using data from 154 HNSCC patients. Receiver operating characteristic (ROC) curves and decision curves were used to compare the predictions of these models with those of a model incorporating only clinicopathological features.
Alzheimer's disease (AD) is a high mortality and high disability rates neurodegenerative disease characterized by irreversible progression and poses a significant social and economic burden throughout the world. However, currently approved AD therapeutic agents only alleviate symptoms and there is still a lack of practical therapeutic regimens to stop or slow the progression of this disease. Thus, there is urgently needed novel diagnosis tools and drugs for early diagnosis and treatment of AD. Among several AD pathological hallmarks, amyloid-β (Aβ) peptide deposition is considered a critical initiating factor in AD. In recent years, with the advantages of excellent sensitivity and high resolution, near-infrared fluorescence (NIRF) imaging has attracted the attention of many researchers to develop Aβ plaque probes. This review mainly focused on different NIRF probe design strategies for imaging Aβ species to pave the way for the future design of novel NIRF probes for early diagnosis AD.
Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.
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