Development of Web-Based Nomograms to Predict Treatment Response and Prognosis of Epithelial Ovarian Cancer

Abstract

Purpose Discovery of models predicting the exact prognosis of epithelial ovarian cancer (EOC) is necessary as the first step of implementation of individualized treatment. This study aimed to develop nomograms predicting treatment response and prognosis in EOC. Materials and Methods We comprehensively reviewed medical records of 866 patients diagnosed with and treated for EOC at two tertiary institutional hospitals between 2007 and 2016. Patients’ clinico-pathologic characteristics, details of primary treatment, intra-operative surgical findings, and survival outcomes were collected. To construct predictive nomograms for platinum sensitivity, 3-year progression-free survival (PFS), and 5-year overall survival (OS), we performed stepwise variable selection by measuring the area under the receiver operating characteristic curve (AUC) with leave-one-out cross-validation. For model validation, 10-fold cross-validation was applied. Results The median length of observation was 42.4 months (interquartile range, 25.7 to 69.9 months), during which 441 patients (50.9%) experienced disease recurrence. The median value of PFS was 32.6 months and 3-year PFS rate was 47.8% while 5-year OS rate was 68.4%. The AUCs of the newly developed nomograms predicting platinum sensitivity, 3-year PFS, and 5-year OS were 0.758, 0.841, and 0.805, respectively. We also developed predictive nomograms confined to the patients who underwent primary debulking surgery. The AUCs for platinum sensitivity, 3-year PFS, and 5-year OS were 0.713, 0.839, and 0.803, respectively. Conclusion We successfully developed nomograms predicting treatment response and prognosis of patients with EOC. These nomograms are expected to be useful in clinical practice and designing clinical trials.

Can we predict who lives long with ovarian cancer?

“…There was no distinction between HGSC and low-grade serous cancer. 6 With the prognostic importance of optimal cytoreductive surgery and the growing acceptance of NACT, the decision process for an individual patient remains largely empiric and incorporates disease distribution, vital organ function, comorbidities, nutritional status, and largevolume ascites or pleural effusions. 4 In terms of surgery, the extent and distribution of disease before cytoreduction as well as the complexity of the surgery required to achieve optimal cytoreduction are now recognized as important prognostic factors, as noted in a retrospective analysis of 2655 patients from the GOG 0182 phase 3 trial, which enrolled patients with optimal and suboptimal cytoreduction in the same study.…”

“…5 Investigators from Korea at 2 tertiary care centers evaluated 866 patients and developed nomograms for 3-year progression-free survival and 5-year overall survival that incorporated specific patterns of tumor distribution (rather than stage) as well as the assigned treatment plan (NACT with interval cytoreductive surgery vs PCS) and a subset of biological parameters (cancer antigen 125, monocytes, and lymphocytes). 6 With the prognostic importance of optimal cytoreductive surgery and the growing acceptance of NACT, the decision process for an individual patient remains largely empiric and incorporates disease distribution, vital organ function, comorbidities, nutritional status, and largevolume ascites or pleural effusions. Molecular markers related to the activation of transforming growth factor β (TGF-β) are associated with suboptimal cytoreductive surgical outcomes but are not yet sufficiently robust to guide primary surgical decisions.…”

M2 Macrophage-based Prognostic Nomogram for Gastric Cancer After Surgical Resection: Original Article

“…5,10,11 M2 macrophage surface markers include CD163, Stabilin-1, and CD206. 4,9,10 Although some studies have explored the correlation between intertumoral macrophages and prognosis, [2][3][4][5] the results were heterogeneous. Based on the characteristics of the immune state of polarized macrophages, immune status can provide a strati cation method to more accurately predict prognosis.…”

“…Currently, nomograms are widely applied in major types of cancer. [6][7][8][9][10][11] In addition, nomograms are preferred compared with traditional staging systems for many types of cancer. It is because of the intuitive and easy-to-understand features of nomograms that they have gradually received more and more attention in medical research and clinical practice.…”

“…Thus, the value of nomograms to predict tumor prognosis is promising. 9,12 This study aimed to create a postoperative survival model for patients with cancer to predict survival time after resection and to simultaneously explore the prognostic value of macrophage markers using gastric cancer samples. We used a nomogram to transform the complex regression equation into a visual graph, making the results of the prediction model more readable and convenient for evaluation.…”