Results 161 to 170 of about 73,359 (292)

Development and validation of the risk stratification based on deep learning and radiomics to predict survival of advanced cervical cancer [PDF]

Mutangala Muloye Guy, Zhonghao Mao, Yi Yu, Yi Liu, Tingting Bian, Qingqing Liu, Ningfeng Li, Yiping Hao, Baoxia Cui   +8 more
openalex   +1 more source

Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm [PDF]

, 2023
Zhihao Yang, Yijing Han, Ming Cheng, Rui Wang, Jing Li, Huiping Zhao, Jian-Bo Gao   +6 more
openalex   +1 more source

Anatomical Sublobar Resection for Multi‐Intersegmental Pulmonary Nodules

Thoracic Cancer, Volume 17, Issue 3, February 2026.
Anatomical sublobar resection achieved a median surgical margin of 2.0 cm and preserved seven more subsegments than lobectomy without increasing air leak or postoperative hospital stay. In addition, anatomical sublobar resection yielded oncological outcomes comparable to lobectomy. ABSTRACT Background Anatomical sublobar resection (ASR) is non‐inferior
Xianglong Pan, Shengzhe Zhou, Zhihua Li, Wenzheng Xu, Liang Chen, Weibing Wu   +5 more
wiley   +1 more source

Emerging Role of ctDNA Fragmentomics and Epigenetic Signatures in the Early Detection, Minimal Residual Disease Assessment, and Precision Monitoring of Renal Cell Carcinoma

Journal of Cellular and Molecular Medicine, Volume 30, Issue 3, February 2026.
ABSTRACT Renal cell carcinoma (RCC) presents a significant global health challenge, with a substantial proportion of patients diagnosed with advanced or metastatic disease due to the limitations of current diagnostic imaging and the lack of validated non‐invasive biomarkers.
Hossam Kamli, Najeeb Ullah Khan
wiley   +1 more source

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study [PDF]

, 2020
Ahmad Algohary, Rakesh Shiradkar, Shivani Pahwa, Andrei S. Purysko, Sadhna Verma, Daniel Moses, R Shnier, Anne‐Maree Haynes, Warick Delprado, James Thompson, Sreeharsha Tirumani, Amr Mahran, Ardeshir R. Rastinehad, Lee Ponsky, Phillip D. Stricker, Anant Madabhushi   +15 more
openalex   +1 more source

Machine learning‐based integration of radiomics and dosiomics for early prediction of radiation‐induced temporal lobe injury in nasopharyngeal carcinoma: A multicenter study

Journal of Applied Clinical Medical Physics, Volume 27, Issue 1, January 2026.
Abstract Purpose This study developed and validated a multimodal machine learning model integrating clinical, radiomics, and dosiomics characteristics to predict radiation‐induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma patients. Methods This study utilized dual‐center retrospective cohorts comprising 213 patients. From pre‐treatment 3D
Xiaoming Sun, Huijun Zhu, Jian Qiao, Yingying Liang, Zhipeng Zhu, Sixue Dong, Jiazhou Wang   +6 more
wiley   +1 more source

A framework for artificial intelligence in cancer research and precision oncology

npj Precision Oncology, 2023
Raquel Perez-Lopez, Jorge S. Reis-Filho, Jakob Nikolas Kather   +2 more
doaj   +1 more source

CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions [PDF]

Shuanbao Yu, Yang Yang, Zeyuan Wang, Haoke Zheng, Jinshan Cui, Yonghao Zhan, Junxiao Liu, Peng Li, Yafeng Fan, Wendong Jia, Meng Wang, Bo Chen, Tao Jin, Yuhong Li, Xuepei Zhang   +14 more
openalex   +1 more source

Influence of scan mode, tilt, and radiation dose on CT radiomic metrics

Journal of Applied Clinical Medical Physics, Volume 27, Issue 1, January 2026.
Abstract Background Radiomic features derived from computed tomography (CT) are highly susceptible to variations in acquisition parameters, which can introduce confounding effects in multicenter research and reduce diagnostic accuracy. While the effects of parameters such as scanning mode and dose have been studied, the impact of gantry tilt—despite ...
Neha Yadav, Xiaomeng Lei, Steven Y. Cen, Joshua Levy, Kristin Jensen, Bino A. Varghese   +5 more
wiley   +1 more source

Quantification of head and neck cancer patients' anatomical changes during radiotherapy: Toward the prediction of replanning need

Journal of Applied Clinical Medical Physics, Volume 27, Issue 1, January 2026.
Abstract Background Head and neck cancer (HNC) patients undergoing radiotherapy (RT) may experience anatomical changes during treatment, which can compromise the validity of the initial treatment plan, necessitating replanning. However, ad hoc replanning disrupts clinical workflows and increases workload.
Odette Rios‐Ibacache, James Manalad, Kayla O'Sullivan‐Steben, Emily Poon, Luc Galarneau, Julia Khriguian, George Shenouda, John Kildea   +7 more
wiley   +1 more source