Toward precision prognosis: Predicting recurrence-free survival in high-grade serous ovarian cancer patients using multi-time point clinical and computed tomography radiomics data.
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| Title: | Toward precision prognosis: Predicting recurrence-free survival in high-grade serous ovarian cancer patients using multi-time point clinical and computed tomography radiomics data. |
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| Authors: | Roller L; Department of Radiology, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Radiology, Brigham and Women's Hospital, Boston, MA, United States of America., Zhou N; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States of America., Atre I; Department of Radiology, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Radiology, Brigham and Women's Hospital, Boston, MA, United States of America., Xiong N; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States of America., Li J; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States of America., Cheng SC; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States of America., Liu N; Division of Gynecologic Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America., Lacad D; Division of Gynecologic Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America., Adjei M; Division of Gynecologic Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America., Matulonis UA; Division of Gynecologic Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Medicine, Harvard Medical School, Boston, MA, United States of America., Konstantinopoulos PA; Division of Gynecologic Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Medicine, Harvard Medical School, Boston, MA, United States of America., Liu JF; Division of Gynecologic Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Medicine, Harvard Medical School, Boston, MA, United States of America., Tayob N; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Medicine, Harvard Medical School, Boston, MA, United States of America., Stover EH; Division of Gynecologic Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Medicine, Harvard Medical School, Boston, MA, United States of America. Electronic address: elizabeth_stover@dfci.harvard.edu., Shinagare AB; Department of Radiology, Dana-Farber Cancer Institute, Boston, MA, United States of America; Department of Radiology, Brigham and Women's Hospital, Boston, MA, United States of America. |
| Source: | Gynecologic oncology [Gynecol Oncol] 2026 Jul 02; Vol. 211, pp. 98-107. Date of Electronic Publication: 2026 Jul 02. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Academic Press Country of Publication: United States NLM ID: 0365304 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-6859 (Electronic) Linking ISSN: 00908258 NLM ISO Abbreviation: Gynecol Oncol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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