Sanja Antic

799 total citations
28 papers, 385 citations indexed

About

Sanja Antic is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Sanja Antic has authored 28 papers receiving a total of 385 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Pulmonary and Respiratory Medicine, 20 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Oncology. Recurrent topics in Sanja Antic's work include Lung Cancer Diagnosis and Treatment (21 papers), Radiomics and Machine Learning in Medical Imaging (18 papers) and Medical Imaging Techniques and Applications (6 papers). Sanja Antic is often cited by papers focused on Lung Cancer Diagnosis and Treatment (21 papers), Radiomics and Machine Learning in Medical Imaging (18 papers) and Medical Imaging Techniques and Applications (6 papers). Sanja Antic collaborates with scholars based in United States, China and Czechia. Sanja Antic's co-authors include Pierre P. Massion, Bennett A. Landman, Gary T. Smith, Ronald C. Walker, Riqiang Gao, Yuankai Huo, Kim L. Sandler, Qian Li, Robert J. Gillies and Thomas Atwater and has published in prestigious journals such as PLoS ONE, American Journal of Respiratory and Critical Care Medicine and Cancer Research.

In The Last Decade

Sanja Antic

26 papers receiving 381 citations

Peers

Sanja Antic

RNMI

PRM

ONCOL

Zhuangsheng Liu China

Junfeng Xiong China

Pranjal Vaidya United States

Mostafa Nazari Iran

E Linning China

Yehang Chen China

Bao Feng China

Wei-Lin Chen Taiwan

Amirhossein Mohammadian Bajgiran United States

Zhuangsheng Liu China

Sanja Antic

269 ×0.9

RNMI

133 ×0.5

PRM

71 ×1.3

61 ×1.1

35 ×1.0

ONCOL

Citations per year, relative to Sanja Antic Sanja Antic (= 1×) peers Zhuangsheng Liu

Countries citing papers authored by Sanja Antic

Since Specialization

Citations

This map shows the geographic impact of Sanja Antic's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sanja Antic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanja Antic more than expected).

Fields of papers citing papers by Sanja Antic

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sanja Antic. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sanja Antic. The network helps show where Sanja Antic may publish in the future.

Co-authorship network of co-authors of Sanja Antic

This figure shows the co-authorship network connecting the top 25 collaborators of Sanja Antic. A scholar is included among the top collaborators of Sanja Antic based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sanja Antic. Sanja Antic is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Li, Thomas, Kaiwen Xu, Riqiang Gao, et al.. (2025). Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules. PubMed Central. 7(2). e230506–e230506. 1 indexed citations

Arimura, Ken, Michael N. Kammer, Saifur Rahman, et al.. (2024). Elucidating the role of EPPK1 in lung adenocarcinoma development. BMC Cancer. 24(1). 441–441. 1 indexed citations

Li, Thomas, Kaiwen Xu, Heidi Chen, et al.. (2024). Curating retrospective multimodal and longitudinal data for community cohorts at risk for lung cancer. Cancer Biomarkers. 42(1). CBM230340–CBM230340.

Xu, Kaiwen, Thomas Li, Riqiang Gao, et al.. (2023). Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective. Medical Image Analysis. 88. 102852–102852. 11 indexed citations

Xu, Kaiwen, Ho Hin Lee, Leon Y. Cai, et al.. (2023). Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures from Routine EHRs for Pulmonary Nodule Classification. Lecture notes in computer science. 14221. 649–659. 8 indexed citations

Craig, Daniel J., Erin L. Crawford, Heidi Chen, et al.. (2023). TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk. BMC Cancer. 23(1). 3 indexed citations

Zou, Yong, Sanja Antic, Katherine Young, et al.. (2023). Cancer-associated fibroblasts in early-stage lung adenocarcinoma correlate with tumor aggressiveness. Scientific Reports. 13(1). 17604–17604. 6 indexed citations

Kammer, Michael N., Hidetoshi Mori, Sheau‐Chiann Chen, et al.. (2023). Tumoral Densities of T-Cells and Mast Cells Are Associated With Recurrence in Early-Stage Lung Adenocarcinoma. JTO Clinical and Research Reports. 4(9). 100504–100504. 4 indexed citations

Xu, Kaiwen, Riqiang Gao, Yucheng Tang, et al.. (2022). Extending the value of routine lung screening CT with quantitative body composition assessment. PubMed. 12032. 6 indexed citations

10.

Gao, Riqiang, Thomas Li, Yucheng Tang, et al.. (2022). Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model. Computers in Biology and Medicine. 150. 106113–106113. 12 indexed citations

11.

Gao, Riqiang, Yucheng Tang, Kaiwen Xu, et al.. (2021). Deep multi-path network integrating incomplete biomarker and chest CT data for evaluating lung cancer risk. PubMed. 11596. 46–46. 6 indexed citations

12.

Antic, Sanja, et al.. (2021). A 56-Year-Old Man With Chronic Cough, Hemoptysis, and a Left Lower Lobe Infiltrate. CHEST Journal. 159(1). e53–e56. 1 indexed citations

13.

Maldonado, Fabien, Srinivasan Rajagopalan, Fenghai Duan, et al.. (2020). Validation of the BRODERS classifier (BenignversusaggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules. European Respiratory Journal. 57(4). 2002485–2002485. 22 indexed citations

14.

Massion, Pierre P., Sanja Antic, Sarim Ather, et al.. (2020). Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules. American Journal of Respiratory and Critical Care Medicine. 202(2). 241–249. 109 indexed citations

15.

Gao, Riqiang, Yuankai Huo, Shunxing Bao, et al.. (2020). Multi-path x-D recurrent neural networks for collaborative image classification. Neurocomputing. 397. 48–59. 10 indexed citations

16.

Yang, Yiyuan, Riqiang Gao, Yucheng Tang, et al.. (2020). Internal-transfer weighting of multi-task learning for lung cancer detection. PubMed. 11313. 74–74. 5 indexed citations

17.

Gao, Riqiang, Lingfeng Li, Yucheng Tang, et al.. (2020). Deep multi-task prediction of lung cancer and cancer-free progression from censored heterogenous clinical imaging. PubMed. 11313. 12–12. 6 indexed citations

18.

Gao, Riqiang, Yuankai Huo, Shunxing Bao, et al.. (2019). Lung cancer detection using co-learning from chest CT images and clinical demographics. PubMed. 10949. 122–122. 15 indexed citations

19.

Nakajima, Erica C., Tucker F. Johnson, Sanja Antic, et al.. (2018). Assessing the inter-observer variability of Computer-Aided Nodule Assessment and Risk Yield (CANARY) to characterize lung adenocarcinomas. PLoS ONE. 13(6). e0198118–e0198118. 10 indexed citations

20.

Liu, Ying, Yoganand Balagurunathan, Thomas Atwater, et al.. (2016). Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules. Clinical Cancer Research. 23(6). 1442–1449. 80 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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