Literature DB >> 31398144

An update on preoperative assessment of the resectability of advanced ovarian cancer.

Philippe Kadhel1,2, Aurélie Revaux3, Marie Carbonnel3, Iptissem Naoura3, Jennifer Asmar3,4, Jean Marc Ayoubi3,4.   

Abstract

The best prognosis for advanced ovarian cancer is provided by no residual disease after primary cytoreductive surgery. It is thus important to be able to predict resectability that will result in complete cytoreduction, while avoiding unnecessary surgery that may leave residual disease. No single procedure appears to be sufficiently accurate and reliable to predict resectability. The process should include a preoperative workup based on clinical examination, biomarkers, especially tumor markers, and imaging, for which computed tomography, as well as sonography, magnetic resonance imaging and positron-emission tomography, can be used. This workup should provide sufficient information to determine whether complete cytoreduction is possible or if not, to propose neoadjuvant chemotherapy which is preferable in this case. For the remaining patients, laparoscopy is broadly recommended as an ultimate triage step. However, its modalities are still debated, and several scores have been proposed for standardization and improving accuracy. The risk of false negatives requires a final assessment of resectability as the first stage of cytoreductive surgery by laparotomy. Composite models, consisting of several criteria of workup and, sometimes, laparoscopy have been proposed to improve the accuracy of the predictive process. Regardless of the modality, the process appears to be accurate and reliable for predicting residual disease but less so for predicting complete cytoreduction and thus avoiding unnecessary surgery and an inappropriate treatment strategy. Overall, the proposed procedures are heterogeneous, sometimes unvalidated, or do not consider advances in surgery. Future techniques and/or models are still needed to improve the prediction of complete resectability.

Entities:  

Keywords:  cancer staging; complete cytoreduction; ovarian cancer; predictive model

Mesh:

Year:  2019        PMID: 31398144     DOI: 10.1515/hmbci-2019-0032

Source DB:  PubMed          Journal:  Horm Mol Biol Clin Investig        ISSN: 1868-1883


  2 in total

Review 1.  Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.

Authors:  Jasjit S Suri; Mahesh A Maindarkar; Sudip Paul; Puneet Ahluwalia; Mrinalini Bhagawati; Luca Saba; Gavino Faa; Sanjay Saxena; Inder M Singh; Paramjit S Chadha; Monika Turk; Amer Johri; Narendra N Khanna; Klaudija Viskovic; Sofia Mavrogeni; John R Laird; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanase D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Raghu Kolluri; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Meyypan Sockalingam; Ajit Saxena; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Padukode R Krishnan; Tomaz Omerzu; Subbaram Naidu; Andrew Nicolaides; Kosmas I Paraskevas; Mannudeep Kalra; Zoltán Ruzsa; Mostafa M Fouda
Journal:  Diagnostics (Basel)       Date:  2022-06-24

2.  Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.

Authors:  Alexandros Laios; Alexandros Gryparis; Diederick DeJong; Richard Hutson; Georgios Theophilou; Chris Leach
Journal:  J Ovarian Res       Date:  2020-09-29       Impact factor: 4.234

  2 in total

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