Marcello Chang

608 total citations
11 papers, 423 citations indexed

About

Marcello Chang is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Marcello Chang has authored 11 papers receiving a total of 423 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Pulmonary and Respiratory Medicine, 4 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Molecular Biology. Recurrent topics in Marcello Chang's work include Radiomics and Machine Learning in Medical Imaging (4 papers), MRI in cancer diagnosis (2 papers) and Renal cell carcinoma treatment (2 papers). Marcello Chang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (4 papers), MRI in cancer diagnosis (2 papers) and Renal cell carcinoma treatment (2 papers). Marcello Chang collaborates with scholars based in United States, China and Canada. Marcello Chang's co-authors include Fuzheng Guo, Jiho Sohn, David Pleasure, Jordan Lang, Elizabeth Hammond, Zishu Zhang, Paul J. Zhang, Harrison X. Bai, Martin Vallières and Peiman Habibollahi and has published in prestigious journals such as Clinical Cancer Research, PLoS Genetics and Glia.

In The Last Decade

Marcello Chang

11 papers receiving 418 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marcello Chang United States 8 195 132 97 90 52 11 423
Derek Soon Singapore 10 93 0.5× 37 0.3× 56 0.6× 34 0.4× 18 0.3× 21 530
Saki Ito United States 14 173 0.9× 131 1.0× 98 1.0× 27 0.3× 15 0.3× 47 730
Ana Wilson United Kingdom 13 104 0.5× 303 2.3× 34 0.4× 31 0.3× 42 0.8× 43 716
Tai-Chi Lin Taiwan 14 243 1.2× 32 0.2× 141 1.5× 51 0.6× 28 0.5× 30 531
Cuiping Zhang China 8 75 0.4× 79 0.6× 68 0.7× 41 0.5× 41 0.8× 13 416
Mingyu Zhu China 12 78 0.4× 62 0.5× 74 0.8× 55 0.6× 23 0.4× 19 308
Hui‐Ying Liu China 12 132 0.7× 136 1.0× 140 1.4× 17 0.2× 12 0.2× 48 506
Eyal Lotan Israel 10 197 1.0× 35 0.3× 62 0.6× 23 0.3× 22 0.4× 34 571
Andrew Lesniak United States 12 63 0.3× 37 0.3× 92 0.9× 35 0.4× 148 2.8× 17 495
Angela Schulz Australia 10 220 1.1× 101 0.8× 150 1.5× 9 0.1× 9 0.2× 23 497

Countries citing papers authored by Marcello Chang

Since Specialization
Citations

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

Fields of papers citing papers by Marcello Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcello Chang

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

All Works

11 of 11 papers shown
1.
2.
Chang, Marcello, et al.. (2023). Virtual Resection Specimen Interaction Using Augmented Reality Holograms to Guide Margin Communication and Flap Sizing. Otolaryngology. 169(4). 1083–1085. 7 indexed citations
3.
Lee, Jasmine, Christopher Coté, Margaret C. Dunagin, et al.. (2022). Systematically quantifying morphological features reveals constraints on organoid phenotypes. Cell Systems. 13(7). 547–560.e3. 12 indexed citations
4.
Carlson, Jennifer, et al.. (2022). Pediatric Provider Utilization of a Clinical Decision Support Alert and Association with HIV Pre-exposure Prophylaxis Prescription Rates. Applied Clinical Informatics. 13(1). 30–36. 8 indexed citations
5.
Gombar, Saurabh, Marcello Chang, Catherine A. Hogan, et al.. (2020). Persistent detection of SARS-CoV-2 RNA in patients and healthcare workers with COVID-19. Journal of Clinical Virology. 129. 104477–104477. 51 indexed citations
6.
He, Yu, Ian Pan, Kasey Halsey, et al.. (2020). Deep learning-based classification of primary bone tumors on radiographs: A preliminary study. EBioMedicine. 62. 103121–103121. 80 indexed citations
7.
Zhao, Yijun, Marcello Chang, Robin Wang, et al.. (2020). Deep Learning Based on MRI for Differentiation of Low‐ and High‐Grade in Low‐Stage Renal Cell Carcinoma. Journal of Magnetic Resonance Imaging. 52(5). 1542–1549. 38 indexed citations
8.
Zhao, Yijun, Robin Wang, Marcello Chang, et al.. (2020). Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clinical Cancer Research. 26(8). 1944–1952. 108 indexed citations
9.
10.
Symmons, Orsolya, Marcello Chang, Ian A. Mellis, et al.. (2019). Allele-specific RNA imaging shows that allelic imbalances can arise in tissues through transcriptional bursting. PLoS Genetics. 15(1). e1007874–e1007874. 23 indexed citations
11.
Guo, Fuzheng, Jordan Lang, Jiho Sohn, et al.. (2015). Canonical Wnt signaling in the oligodendroglial lineage-puzzles remain. Glia. 63(10). 1671–1693. 88 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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