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139 results on '"Li, Butuo"'

9. Co-graph Attention Reasoning Based Imaging and Clinical Features Integration for Lymph Node Metastasis Prediction

Author

Cui, Hui, Xuan, Ping, Jin, Qiangguo, Ding, Mingjun, Li, Butuo, Zou, Bing, Xu, Yiyue, Fan, Bingjie, Li, Wanlong, Yu, Jinming, Wang, Linlin, Duh, Been-Lirn, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, de Bruijne, Marleen, editor, Cattin, Philippe C., editor, Cotin, Stéphane, editor, Padoy, Nicolas, editor, Speidel, Stefanie, editor, Zheng, Yefeng, editor, and Essert, Caroline, editor

Published
2021
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14. Development and Validation of Nomograms for Predicting Pneumonia in Patients with COVID-19 and Lung Cancer

Author

Xu,Yiyue, Li,Haoqian, Wang,Xiaoqing, Li,Butuo, Gao,Aiqin, Zhao,Qian, Yang,Linlin, Qin,Wenru, Wang,Linlin, Xu,Yiyue, Li,Haoqian, Wang,Xiaoqing, Li,Butuo, Gao,Aiqin, Zhao,Qian, Yang,Linlin, Qin,Wenru, and Wang,Linlin

Abstract

Yiyue Xu,1,* Haoqian Li,1,* Xiaoqing Wang,2 Butuo Li,1 Aiqin Gao,1 Qian Zhao,1 Linlin Yang,1 Wenru Qin,1 Linlin Wang1 1Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People’s Republic of China; 2Department of Portal Hypertension, Shandong Public Health Clinical Center, Shandong University, Jinan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Linlin Wang, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 JiYan Road, Jinan, 250117, People’s Republic of China, Tel +86-13793187739, Email wanglinlinatjn@163.comBackground: COVID-19 has spread worldwide, becoming a global threat to public health and can lead to complications, especially pneumonia, which can be life-threatening. However, in lung cancer patients, the prediction of pneumonia and severe pneumonia has not been studied. We aimed to develop effective models to assess pneumonia after SARS-CoV-2 infection in lung cancer patients to guide COVID-19 management.Methods: We retrospectively recruited 621 lung cancer patients diagnosed with COVID-19 via SARS-CoV-2 RT-PCR analysis in two medical centers and divided into training and validation group, respectively. Univariate and multivariate logistic regression analysis were used to identify independent risk factors of all-grade pneumonia and ≥ grade 2 pneumonia in the training group. Nomograms were established based on independent predictors and verified in the validation group. C-index, ROC curves, calibration curve, and DCA were used to evaluate the nomograms. Subgroup analyses in immunotherapy or thoracic radiotherapy patients were then conducted.Results: Among 621 lung cancer patients infected with SARS-CoV-2, 203 (32.7%) developed pneumonia, and 66 (10.6%) were ≥ grade 2. Multivari

Published
2024

23. Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.

Author

Jiao, Tianyu, Li, Fuyan, Cui, Yi, Wang, Xiao, Li, Butuo, Shi, Feng, Xia, Yuwei, Zhou, Qing, and Zeng, Qingshi

Subjects
SMALL cell lung cancer, NON-small-cell lung carcinoma, BRAIN metastasis, DEEP learning, MAGNETIC resonance imaging
Abstract

Background: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. Purpose: To distinguish primary site of BM and identify the best DL models. Study Type: Retrospective. Population: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non‐small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. Field Strength/Sequence: A 3‐T, T1 turbo spin echo (T1‐TSE), T2‐TSE, T2FLAIR‐TSE, DWI echo‐planar imaging (DWI‐EPI) and contrast‐enhanced T1‐TSE (CE T1‐TSE). Assessment: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three‐dimensional residual network (3D‐ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2‐FLAIR + DWI, CE‐T1WI + DWI, CE‐T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non‐lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non‐lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient‐weighted class activation mapping (Grad‐CAM) was used to visualize the model by heatmaps. Statistical Tests: The area under the receiver operating characteristics curve (AUC) assess each classification performance. Results: 3D ResNet18 with Grad‐CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non‐lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE‐T1WI performed best for lung cancer vs. non‐lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE‐T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2‐FLAIR + DWI, CE‐T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non‐lung cancer, SCLC vs. NSCLC. Grad‐CAM helped for model visualization by heatmaps that focused on tumor regions. Data Conclusion: DL models may help to distinguish the origins of BM based on MRI data. Evidence Level: 3 Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Prognostic Value of a Nomogram Based on the Dynamic Albumin-to-Alkaline Phosphatase Ratio for Patients with Extensive-Stage Small-Cell Lung Cancer

Author

Li,Butuo, Jiang,Chao, Wang,Ruiqing, Zou,Bing, Xie,Peng, Li,Wanlong, Sun,Xindong, Yu,Jinming, Wang,Linlin, Li,Butuo, Jiang,Chao, Wang,Ruiqing, Zou,Bing, Xie,Peng, Li,Wanlong, Sun,Xindong, Yu,Jinming, and Wang,Linlin

Abstract

Butuo Li,1 Chao Jiang,2 Ruiqing Wang,3 Bing Zou,1 Peng Xie,1 Wanlong Li,1 Xindong Sun,1 Jinming Yu,1 Linlin Wang1 1Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250017, Shandong Province, People’s Republic of China; 2Department of Otorhinolaryngology, Head and Neck Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province 250021, People’s Republic of China; 3Department of Breast Surgery, Linyi People’s Hospital, Linyi 276000, Shandong Province, People’s Republic of ChinaCorrespondence: Linlin Wang Tel +86-531-67626142Fax +86-531-67626141Email wanglinlinatjn@163.comPurpose: Small-cell lung cancer (SCLC) is known as the characteristics of high invasion, rapid progression, and poor prognosis. Therefore, identification of patients with high risk of progression and death is critical to improve the survival of patients with extensive-stage SCLC (ES-SCLC). This study was designed to determine the prognostic importance of the albumin-to-alkaline phosphatase ratio (AAPR) in the survival of patients with ES-SCLC and to develop a nomogram based on AAPR dynamics for ES-SCLC prognosis.Patients and Methods: Characteristics were reviewed from 300 patients with ES-SCLC. Training and validation cohorts included 200 and 100 patients, respectively. We applied univariate and multivariate Cox models to assess the prognostic value of AAPR for ES-SCLC. The nomogram for progression-free survival (PFS) and overall survival (OS) of ES-SCLC patients was developed based on the multivariate survival analysis of the training cohort. External validation of the established nomogram was performed using the validation cohort.Results: N3 stage, thoracic radiotherapy, and post-AAPR were the independent factors identified for PFS. T stage, thoracic radiotherapy, and high post-AAPR were the independent

Published
2020

41. Extramedullary hematopoiesis secondary to malignant solid tumors: a case report and literature review

Author

Bao,Youting, Liu,Zhichao, Guo,Meiying, Li,Butuo, Sun,Xindong, and Wang,Linlin

Subjects
Cancer Management and Research
Abstract

Youting Bao,1,2,* Zhichao Liu,2,3,* Meiying Guo,2,4 Butuo Li,2,5 Xindong Sun,2 Linlin Wang2 1Department of Oncology, Clinical College, Weifang Medical University, Weifang 261053, 2Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan 250117, 3Department of Oncology, School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan 250200, 4Medical College of Shandong University, Jinan 250012, 5Department of Oncology, Tianjin Medical University, Tianjin 300070, People’s Republic of China *These authors contributed equally to this work Abstract: Extramedullary hematopoiesis (EMH) usually occurs in hematological disease, but more rarely develops in cases of malignant solid tumors. Due to its features on computed tomography (CT) and magnetic resonance imaging (MRI) that are atypical, EMH in tumor patients might easily be misdiagnosed as metastasis leading to the improper TNM staging and inappropriate therapy. Here, we reported the first case of pleural EMH occurring in a patient with esophageal carcinoma whose pleural lesion was first diagnosed as metastasis and confirmed EMH after the needle biopsy. In addition, a retrospective review was conducted by analyzing patients presented with EMH with malignant solid tumors from PubMed and Medline databases. A total of 42 solid tumor patients with EMH were enrolled, and breast cancer was the most common (n=13, 31.0%), followed by renal carcinoma (n=7, 16.7%) and lung cancer (n=6, 14.3%). A wide variety of body sites may be affected by EMH in malignant solid tumor patients, of which the lymph nodes (n=8, 19.0%) and liver (n=7, 16.7%) were the most common, followed by the kidney (n=6, 14.3%). All patients were diagnosed with EMH by excision, biopsy, or autopsy. Treatment strategies for EMH included surgery (n=25, 59.5%), hydroxyurea (n=1, 2.4%), and blood transfusions (n=2, 4.8%); a further 14 patients (33.3%) were subjected to clinical observation without intervention. Of the patients for whom outcome was reported, 10 patients maintained a good performance status (23.8%) and a further six patients died from the malignant tumor. This was the first study to summarize the presentations of EMH in malignant solid tumors, and our findings might provide some useful guidance for clinical practice, especially for treating patients harboring nonresponse lesions during the antitumor treatment. Keywords: extramedullary hematopoiesis, cancer, sarcoma, imaging features, biopsy

Published
2018

46. Predictive value of LDH kinetics in bevacizumab treatment and survival of patients with advanced NSCLC

Author

Li,Butuo, Li,Cheng, Guo,Meiying, Shang,Shuheng, Li,Xiaogang, Xie,Peng, Sun,Xindong, Yu,Jinming, Wang,Linlin, Li,Butuo, Li,Cheng, Guo,Meiying, Shang,Shuheng, Li,Xiaogang, Xie,Peng, Sun,Xindong, Yu,Jinming, and Wang,Linlin

Abstract

Butuo Li,1,2,* Cheng Li,3,* Meiying Guo,2,4 Shuheng Shang,2,4 Xiaogang Li,5 Peng Xie,2 Xindong Sun,2 Jinming Yu,1,2 Linlin Wang2 1Department of Radiation Oncology and Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University, Tianjin, People’s Republic of China; 2Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People’s Republic of China; 3Department of Dean’s Office, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, Jinan, People’s Republic of China; 4Department of Radiation Oncology, School of Medicine, Shandong University, Jinan, People’s Republic of China; 5Department of Radiation Oncology, School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, People’s Republic of China *These authors contributed equally to this work Background: The combination of bevacizumab and chemotherapy is still one of the standard treatments for advanced non-small-cell lung cancer (NSCLC) patients in the new era of targeted therapy. Although a high level of baseline lactate dehydrogenase (LDH) was found to predict survival benefit from bevacizumab in patients with metastatic colorectal cancer, the predictive value of serum level of LDH in NSCLC patients treated with bevacizumab has not been investigated yet. Moreover, dynamic evaluation of serum level of LDH changes may be more informative and promising in predicting patients’ prognosis. We thus sought to analyze LDH kinetics and evaluate its predictive role in the response and survival of advanced NSCLC patients treated with bevacizumab. Method: We retrospectively collected and analyzed a total of 161 advanced NSCLC patients who had undergone treatment with bevacizumab. Univariate and multivariate logistic regression analyses of serum level of LDH were used for response

Published
2018

50. Prognostic Value of TP53 Alteration in Patients with Head and Neck Squamous Cell Carcinoma Receiving Immunotherapy.

Author

Jiang, Chao, Zhou, Xuanchen, Han, Jie, Yue, Zhiyong, and Li, Butuo

Subjects
GENETIC mutation, HEAD & neck cancer, INDIVIDUALIZED medicine, SQUAMOUS cell carcinoma
Abstract

In the article, the author presents their study which examined the predictive value of the alteration of TP53 gene in patients with head and neck squamous cell carcinoma (HNSCC) receiving immunotherapy. Topics include the link between TP53 alteration and inferior survival of said patients, and the approved treatments of HNSCC like pembrolizumab and nivolumab.

Published
2022
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