Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status.
Saved in:
| Title: | Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status. |
|---|---|
| Authors: | Chen KA; Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Kirchoff KE; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Butler LR; Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Holloway AD; Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Kapadia MR; Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Kuzmiak CM; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Downs-Canner SM; Department of Surgery, Breast Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Spanheimer PM; Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Gallagher KK; Division of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. kristalyn_gallagher@med.unc.edu., Gomez SM; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. smgomez@unc.edu. |
| Source: | Annals of surgical oncology [Ann Surg Oncol] 2023 Nov; Vol. 30 (12), pp. 7107-7115. Date of Electronic Publication: 2023 Aug 10. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer Country of Publication: United States NLM ID: 9420840 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1534-4681 (Electronic) Linking ISSN: 10689265 NLM ISO Abbreviation: Ann Surg Oncol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 1534-4681 |
|---|---|
| DOI: | 10.1245/s10434-023-14083-1 |
