Hypoxia is associated with resistance to radiotherapy and chemotherapy in malignant gliomas, and it can be imaged by positron emission tomography with F-fluoromisonidazole (F-FMISO). Previous results for patients with brain cancer imaged with F-FMISO at a single center before conventional chemoradiotherapy showed that tumor uptake via T/Bmax (tissue SUVmax/blood SUV) and hypoxic volume (HV) was associated with poor survival. However, in a multicenter clinical trial (ACRIN 6684), traditional uptake parameters were not found to be prognostically significant, but tumor SUVpeak did predict survival at 1 year. The present analysis considered both study cohorts to reconcile key differences and examine the potential utility of adding radiomic features as prognostic variables for outcome prediction on the combined cohort of 72 patients with brain cancer (30 University of Washington and 42 ACRIN 6684). We used both F-FMISO intensity metrics (T/Bmax, HV, SUV, SUVmax, SUVpeak) and assessed radiomic measures that determined first-order (histogram), second-order, and higher-order radiomic features of F-FMISO uptake distributions. A multivariate model was developed that included age, HV, and the intensity of F-FMISO uptake. HV and SUVpeak were both independent predictors of outcome for the combined data set ( < .001) and were also found significant in multivariate prognostic models ( < .002 and < .001, respectively). Further model selection that included radiomic features showed the additional prognostic value for overall survival of specific higher order texture features, leading to an increase in relative risk prediction performance by a further 5%, when added to the multivariate clinical model..
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http://dx.doi.org/10.18383/j.tom.2019.00023 | DOI Listing |
Jpn J Radiol
January 2025
Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece.
Objective: Calcific tendinopathy, predominantly affecting rotator cuff tendons, leads to significant pain and tendon degeneration. Although US-guided percutaneous irrigation (US-PICT) is an effective treatment for this condition, prediction of patient' s response and long-term outcomes remains a challenge. This study introduces a novel radiomics-based model to forecast patient outcomes, addressing a gap in the current predictive methodologies.
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
Glioma is characterized by high heterogeneity and poor prognosis. Attempts have been made to understand its diversity in both genetic expressions and radiomic characteristics, while few integrated the two omics in predicting survival of glioma. This study was intended to investigate the connection between glioma imaging and genome, and examine its predictive value in glioma mortality risk and tumor immune microenvironment (TIME).
View Article and Find Full Text PDFCancer Biol Med
January 2025
Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In immunogenomics, AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis, thus providing strong support for personalized treatments. In radiomics, AI can analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) images to discover imaging biomarkers associated with tumor heterogeneity, treatment response, and disease progression, thereby enabling non-invasive, real-time assessments for personalized therapy.
View Article and Find Full Text PDFAlzheimers Dement (N Y)
October 2024
Eli Lilly and Company Indianapolis Indiana USA.
Introduction: Alzheimer's disease is partially characterized by the progressive accumulation of aggregated tau-containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir-positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.
View Article and Find Full Text PDFBMC Med Imaging
January 2025
Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong, Road, Nanning, Guangxi Zhuang Autonomous Region, China.
Objectives: To develop ultrasound-based radiomics models and a clinical model associated with inflammatory markers for predicting intrahepatic cholangiocarcinoma (ICC) lymph node (LN) metastasis. Both are integrated for enhanced preoperative prediction.
Methods: This study retrospectively enrolled 156 surgically diagnosed ICC patients.
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