Results 21 to 30 of about 55,768 (244)

Radiomics in Nasopharyngeal Carcinoma [PDF]

open access: yesClinical Medicine Insights: Oncology, 2022
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary
Wenyue Duan   +5 more
openaire   +3 more sources

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. [PDF]

open access: yes, 2016
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in ...
Balagurunathan, Yoganand   +19 more
core   +2 more sources

Radiomics in Oncology III

open access: yesDiagnostics, 2023
In recent years, radiomics has been among the most impactful topics in the research field of quantitative imaging [...]
Zerunian, Marta   +2 more
openaire   +3 more sources

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software. [PDF]

open access: yes, 2017
ObjectiveThe purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software.Materials and methodsMR images of 45 ...
Jamshidi, Neema   +4 more
core   +1 more source

Deep segmentation networks predict survival of non-small cell lung cancer [PDF]

open access: yes, 2019
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed
Allen, Bryan   +16 more
core   +2 more sources

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci

open access: yesCancer Imaging, 2023
Purpose Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often ...
Abdalla Ibrahim   +18 more
doaj   +1 more source

Moddicom: a Complete and Easily Accessible Library for Prognostic Evaluations Relying on Image Features [PDF]

open access: yes, 2015
Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e.
Alitto, Anna Rita   +7 more
core   +1 more source

Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram

open access: yesFrontiers in Oncology, 2021
ObjectiveTo build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC).MethodsTotally, 92 individuals with pathologically confirmed RMAC ...
Zhihui Li   +9 more
doaj   +1 more source

The predictive value of radiomics-based machine learning for peritoneal metastasis in gastric cancer patients: a systematic review and meta-analysis

open access: yesFrontiers in Oncology, 2023
BackgroundFor patients with gastric cancer (GC), effective preoperative identification of peritoneal metastasis (PM) remains a severe challenge in clinical practice. Regrettably, effective early identification tools are still lacking up to now.
Fan Zhang, Guoxue Wu, Nan Chen, Ruyue Li
doaj   +1 more source

The Bionic Radiologist: avoiding blurry pictures and providing greater insights [PDF]

open access: yes, 2019
Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care.
Dewey, Marc, Wilkens, Uta
core   +1 more source

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