Dataset Information

A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study.

ABSTRACT:

Objective

This study assessed the predictive value of preoperative computed tomography (CT) scans and clinical factors for optimal debulking surgery (ODS) in patients with advanced ovarian cancer (AOC).

Methods

Patients with AOC in International Federation of Gynecology and Obstetrics (FIGO) stage III-IV who underwent primary debulking surgery (PDS) between 2016 and 2019 from nine tertiary Chinese hospitals were included. Large-volume ascites, diffuse peritoneal thickening, omental cake, retroperitoneal lymph node enlargement (RLNE) below and above the inferior mesenteric artery (IMA), and suspected pelvic bowel, abdominal bowel, liver surface, liver parenchyma and portal, spleen, diaphragm and pleural lesions were evaluated on CT. Preoperative factors included age, platelet count, and albumin and CA125 levels.

Results

Overall, 296 patients were included, and 250 (84.5%) underwent ODS. The prediction model included age >60 years (P=0.016; prediction index value, PIV=1), a CA125 level >800 U/ml (P=0.033, PIV=1), abdominal bowel metastasis (P=0.034, PIV=1), spleen metastasis (P<0.001, PIV=2), diaphragmatic metastasis (P=0.014, PIV=2), and an RLNE above the IMA (P<0.001, PIV=2). This model had superior discrimination (AUC=0.788>0.750), and the Hosmer-Lemeshow test indicated its stable calibration (P=0.600>0.050). With the aim of maximizing the accuracy of prediction and minimizing the rate of inappropriate explorations, a total PIV ?5 achieved the highest accuracy of 85.47% and identified patients who underwent suboptimal PDS with a specificity of 100%.

Conclusions

We developed a prediction model based on two preoperative clinical factors and four radiological criteria to predict unsatisfactory debulking surgery in patients with AOC. The accuracy of this prediction model needs to be validated and adjusted in further multicenter prospective studies.

SUBMITTER: Gu Y

PROVIDER: S-EPMC7819136 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study.

Gu Yu Y Qin Meng M Jin Ying Y Zuo Jing J Li Ning N Bian Ce C Zhang Yu Y Li Rong R Wu Yu-Mei YM Wang Chun-Yan CY Zhang Ke-Qiang KQ Yue Ying Y Wu Ling-Ying LY Pan Ling-Ya LY

Frontiers in oncology 20210107

<h4>Objective</h4>This study assessed the predictive value of preoperative computed tomography (CT) scans and clinical factors for optimal debulking surgery (ODS) in patients with advanced ovarian cancer (AOC).<h4>Methods</h4>Patients with AOC in International Federation of Gynecology and Obstetrics (FIGO) stage III-IV who underwent primary debulking surgery (PDS) between 2016 and 2019 from nine tertiary Chinese hospitals were included. Large-volume ascites, diffuse peritoneal thickening, omenta ...[more]

PMID: 33489921

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Project description:BackgroundSarcopenia is commonly observed in patients with advanced-stage epithelial ovarian cancer (EOC). However, the effect of body composition changes-during primary debulking surgery (PDS) and adjuvant platinum-based chemotherapy-on outcomes of patients with advanced-stage EOC is unknown. This study aimed to evaluate the association between body composition changes and outcomes of patients with stage III EOC treated with PDS and adjuvant platinum-based chemotherapy.MethodsPre-treatment and post-treatment computed tomography (CT) images of 139 patients with stage III EOC were analysed. All CT images were contrast-enhanced scans and were acquired according to a standardized protocol. The skeletal muscle index (SMI), skeletal muscle radiodensity (SMD), and total adipose tissue index were measured using CT images obtained at the L3 vertebral level. Predictors of overall survival were identified using Cox regression models.ResultsThe median follow-up was 37.9 months. The median duration between pre-treatment and post-treatment CT was 182 days (interquartile range: 161-225 days). Patients experienced an average SMI loss of 1.8%/180 days (95% confidence interval: -3.1 to -0.4; P = 0.01) and SMD loss of 1.7%/180 days (95% confidence interval: -3.3 to -0.03; P = 0.046). SMI and SMD changes were weakly correlated with body mass index changes (Spearman ? for SMI, 0.15, P = 0.07; ? for SMD, 0.02, P = 0.82). The modified Glasgow prognostic score was associated with SMI loss (odds ratio: 2.42, 95% confidence interval: 1.03-5.69; P = 0.04). The median time to disease recurrence was significantly shorter in patients with SMI loss ?5% after treatment than in those with SMI loss <5% or gain (5.4 vs. 11.2 months, P = 0.01). Pre-treatment SMI (1 cm2 /m2 decrease; hazard ratio: 1.08, 95% confidence interval: 1.03-1.11; P = 0.002) and SMI change (1%/180 days decrease; hazard ratio: 1.04, 95% confidence interval: 1.01-1.08; P = 0.002) were independently associated with poorer overall survival. SMD, body mass index, and total adipose tissue index at baseline and changes were not associated with overall survival.ConclusionsSkeletal muscle index decreased significantly during treatment and was independently associated with poor overall survival in patients with stage III EOC treated with PDS and adjuvant platinum-based chemotherapy. The modified Glasgow prognostic score might be a predictor of SMI loss during treatment.

Dataset Information

A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study.

Objective

Methods

Results

Conclusions

Publications

A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study.

Similar Datasets

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