Outcomes of hyperthermic intraperitoneal chemotherapy (HIPEC) with cytoreduction in peritoneal metastatic pancreatic adenocarcinoma: Survival benefit and impact of germline genetic mutations.
e16435 Background: Peritoneal metastasis in pancreatic ductal adenocarcinoma (PDAC) is historically associated with a dismal prognosis and limited response to systemic therapy alone. The role of locoregional therapy remains controversial. This study evaluated the survival impact and safety of HIPEC, with or without cytoreductive surgery (CRS), compared to standard-of-care systemic therapy in patients with peritoneal metastatic PDAC. Methods: We conducted a comparative analysis of 130 patients with peritoneal metastatic PDAC, comprising a study cohort treated with HIPEC (n = 33) and a control group treated with systemic chemotherapy alone (Non-HIPEC, n = 97). HIPEC patients received induction chemotherapy (predominantly FOLFIRINOX) followed by HIPEC ± CRS. Co-primary endpoints included 12 months survival, and 6 months progression free survival (PFS). Secondary endpoints evaluated Overall Survival (OS ), safety of CRS, and impact of germline mutation status on survival. Study was approved under IRB protocol 25-008762. Results: The 12-month OS rate was significantly higher in the HIPEC group compared to the chemotherapy-only control group (100% vs. 52.6%, P < 0.001). The 6-month PFS rate in the HIPEC group was 63.0% vs. 47.94 in-only control group (P < 0.001). For secondary endpoints, the HIPEC group demonstrated a significantly longer median OS of 33.0 months vs. 17.0 months for controls (P < 0.001). Within the HIPEC cohort, the addition of CRS (n = 25) significantly improved outcomes compared to HIPEC alone (n = 8): median OS improved from 21.5 to 36.0 months (P = 0.03). Germline testing was performed in 61% of HIPEC patients and 48% of controls, identifying pathogenic mutations in 25% and 17%, respectively. Identified pathogenic mutations in both groups included ATM (n = 4), MUTYH (n = 2), BRCA2 (n = 1), CHEK2 (n = 1), PALB2 (n = 1), and KIT (n = 1). In the control group, specific mutations drove divergent outcomes; ATM median OS: 23.5 months vs. MUTYH : 8.0 months (P = 0.04). Conversely, in the HIPEC+CRS cohort, survival was excellent regardless of genetic profile: mutation carriers achieved a median OS of 33.0 months compared to 31.0 months in wild-type patients (P = 0.85). Despite a 34.8% postoperative complication rate in the HIPEC group, there was no perioperative mortality. Conclusions: In selected patients with peritoneal metastatic PDAC, HIPEC provides a substantial survival advantage over systemic therapy alone, achieving a 100% 12-month survival rate and more than doubling the median overall survival. Importantly, germline mutation status did not preclude survival benefit, with mutation carriers deriving equal efficacy from the multimodal approach. These findings support HIPEC+CRS as a highly effective strategy that may overcome adverse biologic drivers.
Dynamic disease trajectories, not treatment timing, as a driver of outcomes in intrahepatic cholangiocarcinoma: A machine learning analysis.
e16018 Background: Intrahepatic cholangiocarcinoma (iCCA) exhibits marked heterogeneity in outcomes that is incompletely explained by stage or treatment timing. Although treatment delay is frequently cited as a modifiable risk factor, its prognostic significance in real-world practice remains unclear. We applied machine learning (ML) to model iCCA as a dynamic disease process and to evaluate whether longitudinal disease trajectories better explain outcomes than treatment timing alone. Methods: We conducted a population-based retrospective analysis of adults diagnosed with iCCA using the Surveillance, Epidemiology, and End Results (SEER) database (2000–2022). Time from diagnosis to first cancer-directed therapy was categorized as ≤30 days, 31–60 days, or > 60 days. Associations between treatment delay, disease characteristics, and overall survival (OS) were assessed using multivariable logistic and Cox regression models. To capture real-world disease evolution, each patient’s clinical course was encoded as an ordered longitudinal sequence of diagnosis, treatment states (surgery, chemotherapy, radiation), and outcome. Unsupervised ML was used to identify latent disease-course trajectories. A supervised ML model using variables available at diagnosis was developed to predict membership in high-risk trajectories. Results: The cohort included 18,312 patients with iCCA. Treatment initiation beyond 30 days occurred in more than half of patients and was more common in localized than advanced disease. In adjusted survival analyses, shorter time to treatment was paradoxically associated with worse OS, consistent with confounding by disease severity rather than a protective effect of delay. Unsupervised ML identified four distinct and reproducible disease trajectories with markedly different outcomes. Median OS ranged from approximately 2 months in early-failure trajectories to approximately 15 months in favorable multimodality trajectories. Trajectory membership was not fully explained by stage or treatment timing. A supervised ML model predicted high-risk trajectory membership with good discrimination (AUC 0.82). Key predictors included tumor grade, age, tumor burden, and incomplete staging, whereas treatment timing had minimal influence. Conclusions: In iCCA, treatment delay reflects underlying disease biology and care prioritization rather than independently determining survival. ML-based trajectory modeling reveals clinically meaningful disease courses that better explain outcome heterogeneity than treatment timing or stage alone and enables early identification of patients at highest risk for unfavorable real-world outcomes.