Predicting cancer outcomes with radiomics and artificial intelligence in radiology
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …
application of AI-based cancer imaging analysis to address other, more complex, clinical …
Artificial intelligence and machine learning in cancer imaging
An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …
learning (ML) for cancer imaging. The development of an optimal tool requires …
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer
Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …
Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
KM Boehm, EA Aherne, L Ellenson, I Nikolovski… - Nature cancer, 2022 - nature.com
Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response
to treatment. Known prognostic factors for this disease include homologous recombination …
to treatment. Known prognostic factors for this disease include homologous recombination …
Radiomics in medical imaging—“how-to” guide and critical reflection
JE Van Timmeren, D Cester, S Tanadini-Lang… - Insights into …, 2020 - Springer
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the
existing data available to clinicians by means of advanced mathematical analysis. Through …
existing data available to clinicians by means of advanced mathematical analysis. Through …
Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography
Imaging intact human organs from the organ to the cellular scale in three dimensions is a
goal of biomedical imaging. To meet this challenge, we developed hierarchical phase …
goal of biomedical imaging. To meet this challenge, we developed hierarchical phase …
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis
Early detection of COVID-19 based on chest CT enables timely treatment of patients and
helps control the spread of the disease. We proposed an artificial intelligence (AI) system for …
helps control the spread of the disease. We proposed an artificial intelligence (AI) system for …
An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas
Preoperative MRI is one of the most important clinical results for the diagnosis and treatment
of glioma patients. The objective of this study was to construct a stable and validatable …
of glioma patients. The objective of this study was to construct a stable and validatable …
Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal …
Background Accurate prediction of tumour response to neoadjuvant chemoradiotherapy
enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to …
enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to …
Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images
V Andrearczyk, V Oreiller, S Boughdad… - 3D head and neck tumor …, 2021 - Springer
This paper presents an overview of the second edition of the HEad and neCK TumOR
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …