The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
HER2 Testing by Local, Central, and Reference Laboratories in Specimens From the North Central Cancer Treatment Group N9831 Intergroup Adjuvant Trial
Open accessAbstract
Purpose: To evaluate concordance between local and central laboratory HER2 testing results in patients from the North Central Cancer Treatment Group (NCCTG) N9831 adjuvant trial of trastuzumab. Patients and Methods: NCCTG N9831 is a randomized, phase III clinical trial comparing three drug regimens: doxorubicin/cyclophosphamide followed by paclitaxel with trastuzumab added concurrently, sequentially, or not at all as adjuvant therapy for women with HER2-positive resected breast cancer. Originally, patients were eligible if their tumors were HER2 positive by either local laboratory immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH). A protocol modification in 2002 made central laboratory testing mandatory, with additional testing of discordant cases conducted by a reference laboratory. Concordance between local and central laboratory, and level of agreement between central and reference laboratory HER2 findings in discordant cases were examined. Results: HER2 positivity was confirmed in 85.8% of 2,535 patients registered since March 2002. When local and central evaluation used the same methodology, concordance was 88.1% for FISH and 81.6% for a diagnostic test for presence of the HER2 protein. Among discordant cases examined at the reference laboratory, there was 94.3% agreement for IHC (0, 1+, 2+) and 95.2% agreement for FISH (not gene amplified). Conclusion: There was a high degree of discordance between local and central testing for IHC and FISH, but a high degree of agreement between central and reference laboratories. These findings support the importance of using high-volume, experienced laboratories for HER2 testing to improve the process of selecting patients likely to benefit from trastuzumab therapy.
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Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.026 · Cited by 334
Mittendorf, E. A., Wu, Y., Scaltriti, M., Meric-Bernstam, F., Hunt, K. K., Dawood, S., Esteva, F. J., Buzdar, A. U., Chen, H., Eksambi, S., et al. (2009) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-09-1735 · Cited by 330
Metadata
- Authors
- Perez, E. A., Suman, V. J., Davidson, N. E., Martino, S., Kaufman, P. A., Lingle, W. L., Flynn, P. J., Ingle, J. N., Visscher, D., & Jenkins, R. B.
- Venue
- Journal of Clinical Oncology
- Published
- 2006-07-01
- Volume
- 24
- Issue
- 19
- Pages
- 3032-3038
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W2099630695
- Cited by
- 438
- References
- 27
- FWCI
- 17.21
- Type
- journal article
- Language
- en
- Open access
- bronze
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