Christopher W. Schacherer

1.4k total citations
18 papers, 845 citations indexed

About

Christopher W. Schacherer is a scholar working on Oncology, Biomedical Engineering and Immunology. According to data from OpenAlex, Christopher W. Schacherer has authored 18 papers receiving a total of 845 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Oncology, 6 papers in Biomedical Engineering and 4 papers in Immunology. Recurrent topics in Christopher W. Schacherer's work include Cutaneous Melanoma Detection and Management (8 papers), Immunotherapy and Immune Responses (4 papers) and SAS software applications and methods (4 papers). Christopher W. Schacherer is often cited by papers focused on Cutaneous Melanoma Detection and Management (8 papers), Immunotherapy and Immune Responses (4 papers) and SAS software applications and methods (4 papers). Christopher W. Schacherer collaborates with scholars based in United States. Christopher W. Schacherer's co-authors include Jeffrey E. Gershenwald, Merrick I. Ross, Janice N. Cormier, Jeffrey E. Lee, Paul F. Mansfield, Marcella M. Johnson, Timothy M. Pawlik, Víctor G. Prieto, Ellen R. Gritz and Cindy L. Carmack and has published in prestigious journals such as Journal of Clinical Oncology, Cancer and International Journal of Cancer.

In The Last Decade

Christopher W. Schacherer

14 papers receiving 809 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Christopher W. Schacherer United States 9 706 235 235 124 90 18 845
Harry Wanebo United States 6 611 0.9× 195 0.8× 250 1.1× 105 0.8× 56 0.6× 7 707
Vicki Viar United States 7 617 0.9× 120 0.5× 332 1.4× 75 0.6× 10 0.1× 10 688
Christer Lindholm Sweden 11 500 0.7× 184 0.8× 159 0.7× 71 0.6× 7 0.1× 17 775
K.W. Schulte Germany 13 550 0.8× 225 1.0× 310 1.3× 124 1.0× 5 0.1× 37 817
Adi Nosrati United States 13 671 1.0× 210 0.9× 124 0.5× 354 2.9× 6 0.1× 22 911
John V. Kiluk United States 22 500 0.7× 96 0.4× 57 0.2× 43 0.3× 14 0.2× 74 1.3k
Aaron R. Mangold United States 15 256 0.4× 102 0.4× 267 1.1× 102 0.8× 10 0.1× 104 755
Hank Schmidt United States 17 433 0.6× 194 0.8× 54 0.2× 235 1.9× 6 0.1× 43 978
D. Verver Netherlands 12 355 0.5× 148 0.6× 146 0.6× 51 0.4× 5 0.1× 26 479
A. Tejera‐Vaquerizo Spain 15 516 0.7× 147 0.6× 321 1.4× 68 0.5× 4 0.0× 51 654

Countries citing papers authored by Christopher W. Schacherer

Since Specialization
Citations

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

Fields of papers citing papers by Christopher W. Schacherer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christopher W. Schacherer. 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 Christopher W. Schacherer. The network helps show where Christopher W. Schacherer may publish in the future.

Co-authorship network of co-authors of Christopher W. Schacherer

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

All Works

18 of 18 papers shown
1.
Fang, Shenying, Yuling Wang, Yun Shin Chun, et al.. (2015). Association of Common Genetic Polymorphisms with Melanoma Patient IL-12p40 Blood Levels, Risk, and Outcomes. Journal of Investigative Dermatology. 135(9). 2266–2272. 5 indexed citations
2.
Fang, Shenying, Yuling Wang, Dawen Sui, et al.. (2015). C-Reactive Protein As a Marker of Melanoma Progression. Journal of Clinical Oncology. 33(12). 1389–1396. 58 indexed citations
3.
Fang, Shenying, Yuling Wang, Yun Shin Chun, et al.. (2014). The relationship between blood IL‐12p40 level and melanoma progression. International Journal of Cancer. 136(8). 1874–1880. 4 indexed citations
4.
Callender, Glenda G., Jeffrey E. Gershenwald, Michael E. Egger, et al.. (2012). A Novel and Accurate Computer Model of Melanoma Prognosis for Patients Staged by Sentinel Lymph Node Biopsy: Comparison with the American Joint Committee on Cancer Model. Journal of the American College of Surgeons. 214(4). 608–617. 21 indexed citations
5.
Oh, Jeong Hoon, Kevin B. Kim, Jeffrey E. Gershenwald, et al.. (2012). Impact of comorbidities on overall survival of high-risk and advanced melanoma.. Journal of Clinical Oncology. 30(15_suppl). 8536–8536. 2 indexed citations
6.
Lee, Ian, Patricia S. Fox, Sherise D. Ferguson, et al.. (2012). The Expression of p-STAT3 in Stage IV Melanoma: Risk of CNS Metastasis and Survival. Oncotarget. 3(3). 336–344. 20 indexed citations
7.
Schacherer, Christopher W.. (2012). SAS® Data Management Techniques: Cleaning and transforming data for delivery of analytic datasets.
8.
Schacherer, Christopher W., et al.. (2010). Building an Extract, Transform, and Load (ETL) Server Using Base SAS®, SAS/SHARE®, SAS/CONNECT®, and SAS/ACCESS®. 4 indexed citations
9.
Schacherer, Christopher W., et al.. (2010). A SAS ® Primer for Healthcare Data Analysts.
10.
Schacherer, Christopher W.. (2010). Base SAS ® Methods for Building Dimensional Data Models. 1 indexed citations
11.
Gershenwald, Jeffrey E., Robert H.I. Andtbacka, Víctor G. Prieto, et al.. (2008). Microscopic Tumor Burden in Sentinel Lymph Nodes Predicts Synchronous Nonsentinel Lymph Node Involvement in Patients With Melanoma. Journal of Clinical Oncology. 26(26). 4296–4303. 155 indexed citations
12.
Wang, Yinghong, Jeffrey E. Gershenwald, Merrick I. Ross, et al.. (2007). HLA class II alleles predict recurrence and pattern of failure in early-stage melanoma patients. Journal of Clinical Oncology. 25(18_suppl). 8503–8503. 1 indexed citations
13.
Aloia, Thomas A., Jeffrey E. Gershenwald, Robert H.I. Andtbacka, et al.. (2006). Utility of Computed Tomography and Magnetic Resonance Imaging Staging Before Completion Lymphadenectomy in Patients With Sentinel Lymph Node–Positive Melanoma. Journal of Clinical Oncology. 24(18). 2858–2865. 60 indexed citations
14.
Andtbacka, Robert H.I., Jeffrey E. Gershenwald, Víctor G. Prieto, et al.. (2006). Microscopic tumor burden in sentinel lymph nodes (SLNs) best predicts nonsentinel lymph node (NSLN) involvement in patients with melanoma. Journal of Clinical Oncology. 24(18_suppl). 8004–8004. 1 indexed citations
15.
Pawlik, Timothy M., Merrick I. Ross, Marcella M. Johnson, et al.. (2005). Predictors and Natural History of In-Transit Melanoma After Sentinel Lymphadenectomy. Annals of Surgical Oncology. 12(8). 587–596. 147 indexed citations
16.
Pawlik, Timothy M., Merrick I. Ross, Christopher W. Schacherer, et al.. (2004). Predictors and natural history of in-transit melanoma after sentinel lymphadenectomy. Annals of Surgical Oncology. 11(S2). S61–S61. 134 indexed citations
17.
Sumner, William E., Merrick I. Ross, Paul F. Mansfield, et al.. (2002). Implications of lymphatic drainage to unusual sentinel lymph node sites in patients with primary cutaneous melanoma. Cancer. 95(2). 354–360. 64 indexed citations
18.
Gritz, Ellen R., Cindy L. Carmack, Carl de Moor, et al.. (1999). First Year After Head and Neck Cancer: Quality of Life. Journal of Clinical Oncology. 17(1). 352–352. 168 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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