Immuno-genomic-radiomics to predict response of biliary tract cancer to camrelizumab plus GEMOX in a single-arm phase II trial

Liu, Qiuping; Tang, Jie; Chen, Yizhang; Guo, Fang; Ma, Ling; Pan, Long; Tian, Yitong; Wu, Xiaofeng; Zhang, Yu-Dong; Chen, Xiaofeng

doi:10.1016/j.jhepr.2023.100763

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Fifthly, data-driven radiomics methods are inherently challenging to interpret in the context of their underlying biological and immunological mechanisms, which constrains the explainability and broader clinical adoption of radiomics models. In the future, there are many possibilities for exploration in the radiomics practice of schistosomiasis-related liver diseases [40,[43][44][45][46][47][48][49].…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection

Guo,

Zhao,

Liu

et al. 2024

PLoS Negl Trop Dis

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Background Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. Methods From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People’s Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). Results This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779–0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713–0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762–0.885) for LFSI grade 2 vs. LFSI grade 3. Conclusion Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.

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Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection

Guo,

Zhao,

Liu

et al. 2024

PLoS Negl Trop Dis

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Research hotspots and trends in immunotherapy for cholangiocarcinoma: a bibliometric analysis (2014-2023)

Qu,

Wang,

Zhu

et al. 2024

Front. Immunol.

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BackgroundCholangiocarcinoma (CCA) is a malignant tumor of the gastrointestinal tract with a poor prognosis. Immunotherapy plays an important role in the treatment of CCA. This study aimed to investigate the research hotspots and trends in immunotherapy for CCA.MethodsThe Web of Science Core Collection was searched for literature related to CCA immunotherapy research from January 1, 2014, to December 31, 2023, and features such as country, institution, authors, references, and keywords in the included literature were quantitatively and visually analyzed using the VOS viewer and CiteSpace software.ResultsA total of 252 English publications published between 2014 and 2023 were included. The publications were mainly from China and the United States, with Fudan University being the institution that published the most papers. The highest number of publications came from Frontiers in Oncology. The most prolific authors were Jia Fan, Jian Zhou from China and Pa-Thai Yenchitsomanus from Thailand, while the Journal of Clinical Oncology ranked first in the number of citations among the co-cited journals. In recent years, the focus of research has shifted from “immune checkpoint” and “chemotherapy” to “immunotherapy combined therapy.” Currently, the research frontiers are “microenvironment,” “immune cells,” and “macrophages.”ConclusionOur study analyzes the research hotspots and trends in CCA to provide a knowledge map of immunotherapy research, which will serve as a reference and direction for future research.

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Immuno-genomic-radiomics to predict response of biliary tract cancer to camrelizumab plus GEMOX in a single-arm phase II trial

Cited by 2 publications

References 24 publications

Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection

Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection

Research hotspots and trends in immunotherapy for cholangiocarcinoma: a bibliometric analysis (2014-2023)

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