[HTML][HTML] The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges
Medical imaging can assess the tumor and its environment in their entirety, which makes it
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
Multiplexed imaging in oncology
In oncology, technologies for clinical molecular imaging are used to diagnose patients,
establish the efficacy of treatments and monitor the recurrence of disease. Multiplexed …
establish the efficacy of treatments and monitor the recurrence of disease. Multiplexed …
Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling
Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …
observing patients' anatomy. However, the interpretation of medical images can be highly …
[HTML][HTML] Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study
Purpose To develop a deep learning-based method to assist radiologists to fast and
accurately identify patients with COVID-19 by CT images. Methods We retrospectively …
accurately identify patients with COVID-19 by CT images. Methods We retrospectively …
Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas
Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish
non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials …
non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials …
[HTML][HTML] Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey
Lung cancer is among the deadliest cancers. Besides lung nodule classification and
diagnosis, develo** non-invasive systems to classify lung cancer histological …
diagnosis, develo** non-invasive systems to classify lung cancer histological …
Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and …
Objective To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from
cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on …
cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on …
Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features
V Giannini, S Mazzetti, I Bertotto, C Chiarenza… - European journal of …, 2019 - Springer
Purpose Pathological complete response (pCR) following neoadjuvant chemoradiotherapy
or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15 …
or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15 …
CT-based radiomic model predicts high grade of clear cell renal cell carcinoma
J Ding, Z **ng, Z Jiang, J Chen, L Pan, J Qiu… - European journal of …, 2018 - Elsevier
Purpose To compare the predictive models that can incorporate a set of CT image features
for preoperatively differentiating the high grade (Fuhrman III–IV) from low grade (Fuhrman I …
for preoperatively differentiating the high grade (Fuhrman III–IV) from low grade (Fuhrman I …
Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients
Objectives The aim of this study was to develop a radiomics nomogram by combining the
optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical …
optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical …