title: Role of CT scan-based and clinical evaluation in the preoperative prediction of optimal cytoreduction in advanced ovarian cancer: a prospective trial.
abstract: BACKGROUND
  In advanced ovarian cancer, maximal efforts have to be attemptedto achieve optimal cytoreduction, as this represents the keystone in the therapeutic management. This large, prospective study aims at investigating the role of computed tomography (CT) scan in predicting the feasibility of optimal cytoreduction in ovarian cancer.
  
  
  METHODS
  A total of 195 consecutive patients with clinical/radiographic suspicion of advanced ovarian/peritoneal cancer were enrolled at the Gynecologic Oncology Unit, Catholic University of Rome and Campobasso, Italy. Preoperative CT scans were performed with a high-speed scanner (CT Hi Speed Nx/i Pro; 2-slice; GE Medical System). All patients underwent standard laparotomy, and maximal surgical effort was attempted. The following CT parameters were used: peritoneal thickening, peritoneal implants >2 cm, bowel mesentery involvement, omental cake, pelvic sidewall involvement and/or hydroureter, suprarenal aortic lymph nodes >1 cm, infrarenal aortic lymph nodes >2 cm, superficial liver metastases >2 cm and/or intraparenchimal liver metastases any size, large volume ascites (>500 ml). Clinical data included were age, Ca125 serum levels, and ECOG-PS. Radiographic and clinical features exhibiting a specificity >75%, a positive and negative predictive value >50%, an accuracy >60% in predicting surgical outcome were assigned a point value of 2. With this scoring system, a predictive index (PI) was calculated for each patient.
  
  
  RESULTS
  The PI scores ranged from 0 to 6, and from 0 to 8, in Model 1 (including only radiographic parameters) and in Model 2 (including radiographic and clinical data). The AUC was 0.78+0.035 in Model 1, and 0.81+0.031 in Model 2. Therefore, the addition of ECOG-PS data led to the improvement of the diagnostic performances (z=2.41, P-value <0.05).
  
  
  CONCLUSIONS
  Computed scan still represents a valid tool to predict ovarian cancer optimal cytoreduction; the predictive ability of a CT scan-based model is improved by integrating ECOG-PS data.
authors.affiliation: Gynecologic Oncology Unit, Division of Gynecologic Oncology, Department of Oncology, Catholic University of the Sacred Heart, Campobasso, Italy. gabriella.ferrandina@libero.it
authors.forename: G
authors.initials: G
authors.lastname: Ferrandina
authors.forename: G
authors.initials: G
authors.lastname: Sallustio
authors.forename: A
authors.initials: A
authors.lastname: Fagotti
authors.forename: G
authors.initials: G
authors.lastname: Vizzielli
authors.forename: A
authors.initials: A
authors.lastname: Paglia
authors.forename: E
authors.initials: E
authors.lastname: Cucci
authors.forename: A
authors.initials: A
authors.lastname: Margariti
authors.forename: L
authors.initials: L
authors.lastname: Aquilani
authors.forename: G
authors.initials: G
authors.lastname: Garganese
authors.forename: G
authors.initials: G
authors.lastname: Scambia
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging / Q000601:surgery*; D011446:Prospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*