AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics.
TLDR
In this paper, the authors present a review of AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks.Abstract:
Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.read more
Citations
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Interacting with medical artificial intelligence: Integrating self-responsibility attribution, human-computer trust, and personality
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[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications.
Reyhaneh Manafi-Farid,Emran Askari,Isaac Shiri,Christian Pirich,Mahboobeh Asadi,Maziar Khateri,Habib Zaidi,Mohsen Beheshti +7 more
TL;DR: The diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes is focused on.
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Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics
Virginia Liberini,Riccardo Laudicella,M. Balma,Daniele Giovanni Nicolotti,Ambra Buschiazzo,Serena Grimaldi,Leda Lorenzon,Andrea Bianchi,Simona Peano,Tommaso Vincenzo Bartolotta,Mohsen Farsad,Sergio Baldari,Irene A. Burger,Martin Heller,Alberto Papaleo,Désirée Deandreis +15 more
TL;DR: In this paper , the authors describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer and present a systematic review aimed at describing the current state-of-the-art in this area.
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Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction
TL;DR: A prediction framework including a 3D tumor segmentation in positron emission tomography (PET) images, based on a weakly supervised deep learning method, and an outcome prediction based onA 3D-CNN classifier applied to the segmented tumor regions, achieves state-of-the-art prediction results.
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Multi-task multi-scale learning for outcome prediction in 3D PET images
TL;DR: In this paper , a multi-task, multi-scale learning framework was proposed to predict patient's survival and response to treatment and survival in oncology, where the encoder can leverage multiple tasks to extract meaningful and powerful features that improve radiomic performance.
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