Paulina Cybulska

Clinical outcomes of patients with endometrioid epithelial ovarian cancer following surgical treatment

AbstractBackgroundEndometrioid epithelial ovarian cancer (EEOC) is rare, and its management poorly defined. We examined factors associated with 5‐year progression‐free survival (PFS) after surgery for EEOC.MethodsRetrospective study: treatment and outcomes of all EEOC patients undergoing initial surgery at, or presenting to, our institution within 3 months of initial surgery, 1/2002‐9/2017.ResultsIn total, 212 patients were identified. Median follow‐up, 63.9 months (range, 0.7–192); median age at diagnosis, 52 years (range, 20–88); disease stage: I, n = 145 (68%); II, n = 47 (22%); III/IV, n = 20 (9%); FIGO grade: 1, 127 (60%); 2, 66 (31%); 3, 17 (8%); unknown, 2 (1%). One hundred twenty‐eight (60%) had endometriosis; 75 (35%), synchronous endometrioid endometrial cancer (80%, IA); 101 (48%), complete surgical staging; 8 (5%), positive pelvic lymph nodes (LNs); 6 (4%), positive para‐aortic LNs; 176 (97%), complete gross resection; 123 (60%), postoperative chemotherapy; 56(28%), no additional treatment. Five‐year PFS, 83% (95% confidence interval [CI]: 76.6%–87.8%); 5‐year overall survival (OS), 92.7% (95% CI: 87.7%–95.8%). Age, stage, and surgical staging were associated with improved 5‐year PFS, and younger age at diagnosis with improved 5‐year OS (p < 0.001). Chemotherapy did not improve 5‐year PFS in IA/IB versus observation, but improved survival in IC (hazard ratio [HR]: 1.01, 95% CI: 0.22–4.59, p = 0.99; HR: 0.17, 95% CI: 0.04–0.7, p = 0.006).ConclusionsAge, stage, and full surgical staging were associated with improved 5‐year PFS. Chemotherapy showed no benefit in IA/IB disease.

Treatment outcomes and predictive factors in patients ≥70 years old with advanced ovarian cancer

AbstractObjectiveTo evaluate treatment outcomes, survival, and predictive factors in patients ≥70 with advanced epithelial ovarian cancer (AEOC).MethodsA retrospective single institution cohort study of women ≥70 with Stage III–IV AEOC between 2010 and 2018. Patients had either primary cytoreductive surgery (PCS), neoadjuvant chemotherapy (NACT) with interval cytoreductive surgery (ICS), chemotherapy alone, or no treatment. Demographics, surgical outcome, complications, and survival outcome were compared between groups.ResultsAmong 248 patients, 69 (27.7%) underwent PCS, 99 (39.9%) had ICS, 56 (22.5%) had chemotherapy alone. Twenty‐four (9.6%) remained untreated. Optimal cytoreduction (≤1 cm) was achieved in 72.4% of PCS and 77.8% of NACT/ICS (p = 0.34), without difference in grade ≥3 postoperative complications (15.9% vs. 9.1%, p = 0.37). Progression‐free survival (PFS) was 23.5 months in PCS and 15.0 months in ICS patients (hazard ratio [HR]: 1.4, p = 0.041). Patients in the surgical arms, PCS or ICS, had better 2‐year overall survival (OS) compared to chemotherapy alone (79%, 68%, 41%, respectively, HR: 3.58, p < 0.001). In a subgroup analysis, patients ≥80 had improved 2‐year OS when treated with NACT compared to PCS (82% vs. 57%) and a trend toward improved PFS. Age, stage, and CA‐125 were determinants of undergoing PCS.ConclusionIn patients ≥70 with AEOC, surgery should not be deferred based on age alone. Fit, well selected patients ≥70 can benefit from PCS, while patients ≥80 might benefit from NACT over PCS.

Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer

AbstractObjectiveTo develop a machine learning (ML) algorithm to predict outcome of primary cytoreductive surgery (PCS) in patients with advanced ovarian cancer (AOC)MethodsThis retrospective cohort study included patients with AOC undergoing PCS between January 2017 and February 2021. Using radiologic criteria, patient factors (age, CA‐125, performance status, BRCA) and surgical complexity scores, we trained a random forest model to predict the dichotomous outcome of optimal cytoreduction (<1 cm) and no gross residual (RD = 0 mm) using JMP‐Pro 15 (SAS). This model is available at https://ipm-ml.ccm.sickkids.ca.ResultsOne hundred and fifty‐one patients underwent PCS and randomly assigned to train (n = 92), validate (n = 30), or test (n = 29) the model. The median age was 58 (27–83). Patients with suboptimal cytoreduction were more likely to have an Eastern Cooperative Oncology Group 3–4 (11% vs. 0.75%, p = 0.004), lower albumin (38 vs. 41, p = 0.02), and higher CA125 (1126 vs. 388, p = 0.012) than patients with optimal cytoreduction (n = 133). There were no significant differences in age, histology, stage, or BRCA status between groups. The bootstrap random forest model had AUCs of 99.8% (training), 89.6%(validation), and 89.0% (test). The top five contributors were CA125, albumin, diaphragmatic disease, age, and ascites. For RD = 0 mm, the AUCs were 94.4%, 52%, and 84%, respectively.ConclusionOur ML algorithm demonstrated high accuracy in predicting optimal cytoreduction in patients with AOC selected for PCS and may assist decision‐making.

Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening

Significance The optimal timing of surgery/chemotherapy and the benefits of earlier diagnosis of HGSC remain controversial. We developed a mathematical framework of tumor dynamics, populated the model with primary clinical data, and reliably recapitulated clinical observations. Our model predicts that 1) PDS is superior to NACT with relatively small tumor burden and when complete debulking is feasible, 2) timely adjuvant chemotherapy is critical for the outcome of PDS with <1-mm residual tumors, 3) earlier detection of relapse is unlikely beneficial with current therapies, and 4) earlier detection of primary HGSC could have substantial benefit. These results provide insights into the evolutionary dynamics of HGSC, argue for new clinical trials to optimize therapy, and are potentially applicable to other tumor types.