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A Novel Optical Bioimaging Method for Direct Assessment of Ovarian Cancer Chemotherapy Response at Laparoscopy.
Laios, Alexandros; Volpi, Davide; Kumar, Rajeev; Traill, Zoe; Vojnovic, Borivoj; Ahmed, Ahmed A.
Afiliación
  • Laios A; Nuffield Department of Obstetrics and Gynaecology, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
  • Volpi D; CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK.
  • Kumar R; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
  • Traill Z; Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Vojnovic B; CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK.
  • Ahmed AA; Nuffield Department of Obstetrics and Gynaecology, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Cancer Inform ; 15: 243-245, 2016.
Article en En | MEDLINE | ID: mdl-28579738
In patients with advanced ovarian cancer (AOC), additional imaging of disseminated disease at laparoscopy could complement conventional imaging for estimation of chemotherapy response. We developed an image segmentation method and evaluated its use in making accurate and objective measurements of peritoneal metastases in comparison to Response Evaluation Criteria In Solid Tumors (RECIST) criteria. A software tool using a custom ImageJ macro-based approach was employed to estimate lesion size by converting image pixels into unit length. The software tool was tested as a proof-of-principle in an AOC patient with two isolated peritoneal deposits. Image analysis of representative laparoscopic snapshots before and after three cycles of neoadjuvant chemotherapy (NACT) revealed an average tumor nodule response ratio (TNRR) of 40% (partial response), which was in concordance with RECIST evaluation by computed tomography (CT). We demonstrated the feasibility of using this novel anatomical analysis for direct assessment of chemotherapy response in an AOC patient as an adjunct to RECIST criteria.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Cancer Inform Año: 2016 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Cancer Inform Año: 2016 Tipo del documento: Article