Results 51 to 60 of about 55,768 (244)

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer. [PDF]

, 2018
Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung ...
Benedict, Stanley, He, Yaoyao, Qiu, Jianfeng, Rong, Yi, Shi, Liting, Valicenti, Richard, Yuan, Zilong   +6 more
core   +1 more source

Highly accurate model for prediction of lung nodule malignancy with CT scans [PDF]

, 2018
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer.
Causey, Jason L., Huang, Xiuzhen, Jiang, Bo, Ma, Shiqian, Politte, David G., Prior, Fred, Qualls, Jake A., Zhang, Junyu, Zhang, Shuzhong   +8 more
core   +3 more sources

Radiomics and Deep Radiomics for precision medicine

, 2021
{"references": ["E Bertelli, L Mercatelli, C Marzi, E Pachetti, M Baccini, et al. Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI., Fron in Oncology, 5515, 2021, https://doi.org/10.3389/fonc.2021.802964", "F Gioia, MA Pascali, A Greco, S Colantonio, EP Scilingo.
openaire   +1 more source

Risk Prediction Models for Recurrence After Curative Treatment of Early‐Stage or Locally Advanced Lung Cancer: A Systematic Review

Aging and Cancer, EarlyView.
This systematic review synthesizes prognostic models for survival and recurrence in resected non‐small cell lung cancer. While many models demonstrate moderate to good discrimination, few are externally validated and reporting quality is variable, limiting clinical applicability and highlighting the need for robust, transparent model development ...
Evangeline Samuel, S. Tissera, E. Paul, J. Zalcberg, R. G. Stirling   +4 more
wiley   +1 more source

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. [PDF]

, 2018
Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization ...
Arasu, Vignesh A, Behr, Spencer C, Copeland, Timothy P, Esserman, Laura, Franc, Benjamin L, Harnish, Roy J, Huang, Shih-Ying, Hylton, Nola M, Jones, Ella F, Kornak, John, Liu, Gengbo, Mitra, Debasis, Price, Elissa R, Seo, Youngho   +13 more
core  

Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography

, 2016
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and ...
Bradley, Andrew P., Carneiro, Gustavo, Nascimento, Jacinto, Oakden-Rayner, Luke, Palmer, Lyle   +4 more
core   +2 more sources

4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

Physics in Medicine & Biology, 2021
Abstract Purpose. To investigate the impact of 4D-CBCT image quality on radiomic analysis and the efficacy of using deep learning based image enhancement to improve the accuracy of radiomic features of 4D-CBCT. Material and Methods.
Zeyu Zhang, Mi Huang, Zhuoran Jiang, Yushi Chang, Jordan Torok, Fang-Fang Yin, Lei Ren   +6 more
openaire   +3 more sources

Development of a Prediction Model for Progression Risk in High‐Grade Gliomas Based on Habitat Radiomics and Pathomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu, Yuxi Gong, Weilin Xu, Xingjian Sun, Gefei Jiang, Lei Qiu, Kexin Shi, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou   +14 more
wiley   +1 more source

Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning

Journal of Imaging
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer. Extensive efforts have been made to utilize radiomics from computed tomography (CT) imaging to predict tumor immune microenvironment (TIME) measurements. This study proposes
Yixing Wu, Alexander Shieh, Steven Cen, Darryl Hwang, Xiaomeng Lei, S. J. Pawan, Manju Aron, Inderbir Gill, William D. Wallace, C.-C. Jay Kuo, Vinay Duddalwar   +10 more
doaj   +1 more source

Towards reproducible radiomics research: introduction of a database for radiomics studies

European Radiology, 2023
Abstract Objectives To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database. Methods A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading ...
Akinci D'Antonoli, Tugba, Cuocolo, Renato, Baessler, Bettina, Pinto Dos Santos, Daniel   +3 more
openaire   +4 more sources