Cristian Barrera

576 total citations
17 papers, 239 citations indexed

About

Cristian Barrera is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Cristian Barrera has authored 17 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Oncology and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Cristian Barrera's work include Radiomics and Machine Learning in Medical Imaging (13 papers), Cancer Immunotherapy and Biomarkers (8 papers) and AI in cancer detection (4 papers). Cristian Barrera is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (13 papers), Cancer Immunotherapy and Biomarkers (8 papers) and AI in cancer detection (4 papers). Cristian Barrera collaborates with scholars based in United States, Colombia and Switzerland. Cristian Barrera's co-authors include Vamsidhar Velcheti, Anant Madabhushi, Kurt A. Schalper, Germán Corredor, David L. Rimm, Xiangxue Wang, Kaustav Bera, Priya Velu, Cheng Lu and Roy S. Herbst and has published in prestigious journals such as Journal of Clinical Oncology, Science Advances and Cancer Letters.

In The Last Decade

Cristian Barrera

17 papers receiving 235 citations

Peers

Cristian Barrera

ONCOL

PRM

RNMI

IMMUN

Julia Naso Canada

Vidya Sankar Viswanathan United States

Charlotte Syrykh France

Ines P. Nearchou Japan

Paula Toro United States

Kyunghyun Paeng South Korea

Alexander J. Fletcher United Kingdom

Nicholas Meti Canada

Kaitlin E. Sundling United States

Soo‐Youn Ham South Korea

Julia Naso Canada

Cristian Barrera

87 ×0.7

ONCOL

159 ×1.7

PRM

57 ×0.7

RNMI

50 ×0.9

13 ×0.3

IMMUN

Citations per year, relative to Cristian Barrera Cristian Barrera (= 1×) peers Julia Naso

Countries citing papers authored by Cristian Barrera

Since Specialization

Citations

This map shows the geographic impact of Cristian Barrera'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 Cristian Barrera with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cristian Barrera more than expected).

Fields of papers citing papers by Cristian Barrera

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cristian Barrera. 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 Cristian Barrera. The network helps show where Cristian Barrera may publish in the future.

Co-authorship network of co-authors of Cristian Barrera

This figure shows the co-authorship network connecting the top 25 collaborators of Cristian Barrera. A scholar is included among the top collaborators of Cristian Barrera 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 Cristian Barrera. Cristian Barrera is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Vaidya, Pranjal, Mohammadhadi Khorrami, Kaustav Bera, et al.. (2025). Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer. npj Precision Oncology. 9(1). 161–161. 2 indexed citations

Barrera, Cristian, et al.. (2025). AI-based radiomic features predict outcomes and the added benefit of chemoimmunotherapy over chemotherapy in extensive stage small cell lung cancer: A multi-institutional study. Cancer Letters. 628. 217872–217872. 2 indexed citations

Wang, Xiangxue, Cristian Barrera, Kaustav Bera, et al.. (2022). Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. Science Advances. 8(22). eabn3966–eabn3966. 48 indexed citations

Prasanna, Prateek, Germán Corredor, Cristian Barrera, et al.. (2022). Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome. npj Precision Oncology. 6(1). 33–33. 32 indexed citations

Corredor, Germán, Prateek Prasanna, Cristian Barrera, et al.. (2022). Density patterns of tumor-infiltrating lymphocytes and association with objective response to nivolumab in patients with lung adenocarcinoma from CheckMate 057.. Journal of Clinical Oncology. 40(16_suppl). 2662–2662. 1 indexed citations

Ravi, Arvind, Ila Datar, Mari Mino–Kenudson, et al.. (2022). Role of tumor infiltrating lymphocytes and spatial immune heterogeneity in sensitivity to PD-1 axis blockers in non-small cell lung cancer. Journal for ImmunoTherapy of Cancer. 10(6). e004440–e004440. 84 indexed citations

Wang, Xiangxue, Kaustav Bera, Cristian Barrera, et al.. (2021). A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer. EBioMedicine. 69. 103481–103481. 15 indexed citations

Jain, Prantesh, Cristian Barrera, Abdullah Ösme, et al.. (2021). P68.02 Computer Extracted Morphology Features of Tumor Nuclei Predict Response to Chemotherapy and Prognostic of OS in Small Cell Lung Cancer. Journal of Thoracic Oncology. 16(3). S560–S560. 1 indexed citations

Barrera, Cristian, Mohammadhadi Khorrami, Prantesh Jain, et al.. (2021). Combination of quantitative features from H&E biopsies and CT scans predicts response to chemotherapy and overall survival in small cell lung cancer (SCLC).. Journal of Clinical Oncology. 39(15_suppl). 8572–8572. 1 indexed citations

10.

Wang, Xinglong, Cristian Barrera, Kaustav Bera, et al.. (2019). MA25.02 Arrangement and Architecture of Tumor-Infiltrating Lymphocyte on H&E Slides Predict OS in Nivolumab Treated Non-Small Cell Lung Cancer. Journal of Thoracic Oncology. 14(10). S350–S351. 1 indexed citations

11.

Barrera, Cristian, Germán Corredor, Xiangxue Wang, et al.. (2019). Phenotyping tumor infiltrating lymphocytes (PhenoTIL) on H&E tissue images: predicting recurrence in lung cancer. 6388. 5–5. 1 indexed citations

12.

Barrera, Cristian, et al.. (2019). Localization of Small Neoplastic Lesions in Colonoscopy by Estimating Edge, Texture and Motion Saliency. PubMed. 58. 5945–5948. 2 indexed citations

13.

Barrera, Cristian, et al.. (2019). Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI. PubMed. 10. 2695–2698. 3 indexed citations

14.

Wang, Xinglong, Cristian Barrera, Cheng Lu, Vamsidhar Velcheti, & Anant Madabhushi. (2018). MA11.03 Interaction of Tumor Infiltrating Lymphocytes and Cancer Nuclei Predicts Response to Nivolumab in Non-Small Cell Lung Cancer (NSCLC). Journal of Thoracic Oncology. 13(10). S393–S393. 1 indexed citations

15.

Wang, Xiangxue, Cristian Barrera, Priya Velu, et al.. (2018). Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer.. Journal of Clinical Oncology. 36(15_suppl). 12061–12061. 9 indexed citations

16.

Barrera, Cristian, Priya Velu, Kaustav Bera, et al.. (2018). Computer-extracted features relating to spatial arrangement of tumor infiltrating lymphocytes to predict response to nivolumab in non-small cell lung cancer (NSCLC).. Journal of Clinical Oncology. 36(15_suppl). 12115–12115. 11 indexed citations

17.

Barrera, Cristian, et al.. (1999). Internal carotid artery transposition: another cause of widening of the retropharyngeal space. European Radiology. 9(2). 347–348. 25 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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