Results 61 to 70 of about 42,977 (292)

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

Combination Immunotherapy as a Promising Strategy to Overcome Immunotherapy Resistance: From Emergence to Next‐Generation Approaches

Advanced Science, EarlyView.
This review examines emerging combination immunotherapy strategies tailored to distinct tumor microenvironments and highlights next‐generation biomarkers that guide response prediction and treatment personalization. It integrates lessons from unsuccessful trials, addresses toxicity challenges, and outlines approaches for early biomarker discovery and ...
Asmita Pandey, Sudhir Kumar Rai, Li Ma, Lauren Higa, Vedbar Khadka, Hua Yang, Youping Deng   +6 more
wiley   +1 more source

Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram

Frontiers in Oncology, 2022
ObjectivesTo construct a contrast-enhanced CT-based radiomics nomogram that combines clinical factors and a radiomics signature to distinguish papillary renal cell carcinoma (pRCC) type 1 from pRCC type 2 tumours.MethodsA total of 131 patients with 60 in
Yankun Gao, Xingwei Wang, Shihui Wang, Yingying Miao, Chao Zhu, Cuiping Li, Guoquan Huang, Yan Jiang, Jianying Li, Xiaoying Zhao, Xingwang Wu   +10 more
doaj   +1 more source

Delta-radiomics Entropy Based on Tumor Heterogeneity Concept – Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma [PDF]

, 2022
Although adjuvant radiotherapy in combination with Temozolomide administration has clearly demonstrated the benefit in improving the prognosis of patients with multiforme glioblastoma, radiotherapy as only treatment or in combination with systemic ...
Camil Ciprian MIRESTEAN, Roxana Irina IANCU, Dragos Teodor IANCU   +2 more
core   +1 more source

Full‐Body AI Agent: A Perspective on Multi‐Scale Collaborative AI for Systemic Biology and Precision Medicine

Advanced Science, EarlyView.
We propose the Full‐Body AI Agent, a multi‐scale collaborative framework with 7 biological‐layer agents. It unifies multi‐omics/clinical data via standardized protocols, enabling phenotype‐guided closed‐loop reasoning, quantitative evaluation, and LLM safeguards, with promising applications in tumor metastasis modeling and precision drug development ...
Aoqi Wang, Jiajia Liu, Jianguo Wen, Yangyang Luo, Zhiwei Fan, Liren Yang, Xi Hu, Ruihan Luo, Yankai Yu, Sophia Li, Weiling Zhao, Xiaobo Zhou   +11 more
wiley   +1 more source

Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancerResearch in context

EBioMedicine, 2018
To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in ...
Yuming Jiang, Chuanli Chen, Jingjing Xie, Wei Wang, Xuefan Zha, Wenbing Lv, Hao Chen, Yanfeng Hu, Tuanjie Li, Jiang Yu, Zhiwei Zhou, Yikai Xu, Guoxin Li   +12 more
doaj   +1 more source

Development of a Risk Prediction Model for Bone Metastasis in Lung Adenocarcinoma With a T1 Primary Tumor Based on Intratumoral and Peritumoral Radiomics. [PDF]

Cancer Med
ABSTRACT Purpose Bone metastasis significantly affects the prognosis of lung adenocarcinoma (LUAD) patients. This study aims to construct and validate a risk prediction model for bone metastasis in LUAD with a T1 primary tumor based on intratumoral and peritumoral radiomics features.
Li T, Zheng H, Guo Y, Zhang K, Liao M.
europepmc   +2 more sources

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer

Technology in Cancer Research & Treatment
Objective Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening immune-related adverse event. Efficient strategies to select patients at risk are still required.
François Cousin MD, PhD, Thomas Louis MS, PhD, Pierre Frères MD, PhD, Julien Guiot MD, PhD, Mariaelena Occhipinti MD, Fabio Bottari MS, Wim Vos MS, Roland Hustinx MD, PhD   +7 more
doaj   +1 more source

Compact Tabletop Magnetic Resonance Elastography for Mapping Soft Tissue Viscoelasticity

Advanced Science, EarlyView.
This work introduces a compact, low‐cost tabletop magnetic resonance elastography platform for high‐resolution viscoelastic mapping in soft‐tissue specimens. Using this method in human colorectal liver metastases, we demonstrate fully automated biomechanical profiling of treatment response and show that heterogeneity‐based metrics outperform ...
Weijie Zhao, Lisa‐Marie Skrip, Heiko Tzschätzsch, Tom Meyer, Hossein S. Aghamiry, Alexander Arnold, Lene Änne Böhne, Jakob Jordan, Oliver Boehm, Agnes K. Boehm, Steffen Görner, Jakob Schattenfroh, Yanglei Wu, Pedro Augusto Dantas de Moraes, Johann Pratschke, Jürgen Braun, Igor M. Sauer, Jing Guo, Ingolf Sack, Karl H. Hillebrandt   +19 more
wiley   +1 more source