Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …
The era of radiogenomics in precision medicine: an emerging approach to support diagnosis, treatment decisions, and prognostication in oncology
L Shui, H Ren, X Yang, J Li, Z Chen, C Yi, H Zhu… - Frontiers in …, 2021 - frontiersin.org
With the rapid development of new technologies, including artificial intelligence and genome
sequencing, radiogenomics has emerged as a state-of-the-art science in the field of …
sequencing, radiogenomics has emerged as a state-of-the-art science in the field of …
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 …
Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation
E Lanza, R Muglia, I Bolengo, OG Santonocito, C Lisi… - European …, 2020 - Springer
Abstract Objective Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March
2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support …
2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support …
Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell …
Z Feng, P Rong, P Cao, Q Zhou, W Zhu, Z Yan, Q Liu… - European …, 2018 - Springer
Objective To evaluate the diagnostic performance of machine-learning based quantitative
texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible …
texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible …
Lung tumor segmentation methods: impact on the uncertainty of radiomics features for non-small cell lung cancer
Purpose To evaluate the uncertainty of radiomics features from contrast-enhanced breath-
hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic …
hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic …
OFF-eNET: An optimally fused fully end-to-end network for automatic dense volumetric 3D intracranial blood vessels segmentation
Intracranial blood vessels segmentation from computed tomography angiography (CTA)
volumes is a promising biomarker for diagnosis and therapeutic treatment in …
volumes is a promising biomarker for diagnosis and therapeutic treatment in …
PET/CT radiomics in lung cancer: an overview
Quantitative extraction of imaging features from medical scans ('radiomics') has attracted a
lot of research attention in the last few years. The literature has consistently emphasized the …
lot of research attention in the last few years. The literature has consistently emphasized the …
SlicerVR for medical intervention training and planning in immersive virtual reality
Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety
of medical applications. Currently, however, no free open-source software platform exists …
of medical applications. Currently, however, no free open-source software platform exists …
Association of AI quantified COVID-19 chest CT and patient outcome
Purpose Severity scoring is a key step in managing patients with COVID-19 pneumonia.
However, manual quantitative analysis by radiologists is a time-consuming task, while …
However, manual quantitative analysis by radiologists is a time-consuming task, while …