Xu, Qinmei

560 Background: Patients (pts) with early stage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer (BC) remain at both short and long term risk of recurrence. While clinico-pathological and gene-based biomarkers provide insight into early and late recurrence risk, additional prognostic tools are needed. We previously developed (Saha et al. Clin Cancer Res , 2025) a new breast MRI image-based artificial intelligence prognostic tool (TumorSight Risk) composed of both radiographic and clinical information. Here we sought to validate this tool in an independent cohort. Methods: In this multi-center, blinded, prospective/retrospective validation study, we sought to validate the 5-year recurrence-free survival (RFS) according to pre-defined risk scores (0-100) and categories (“high” or “low”) via pre-specified cutoff (≥47) for pts with HR+/HER2- BC. Kaplan-Meier and Cox regression analyses were then used to assess the unadjusted and adjusted association between risk category/score and the 5-year RFS. Results: 2,129 pts with HR+/HER2- BC diagnosed between January 1, 2010, and December 31, 2024, who underwent breast MRI prior to surgery (no neoadjuvant therapy) were included from four institutions. Median age was 57 years, 16% were Black, 15% had grade 3 disease, 93% were clinical T1 or T2, and 85%/15% were clinical N0/N1. At a median follow up of 6.24 years, the unadjusted hazard ratio (HR) for RFS comparing low (n=1808) and high risk (n=321) pts was 2.71 (95% CI = 1.95 – 3.76, p = 3x10 -9 ). The estimated 5 year RFS rate according to risk score (low and high) was 95.5% (95% CI: 94.63 – 96.3%) and 89.6% (95% CI = 86.4 – 92.6%) while the 10 year RFS estimates were 88.9% (95% CI = 86.5 - 90.9%) and 69.6% (95% CI = 59.1 – 77.9%). Univariate subgroup analysis demonstrated that the risk score was prognostic by age (< 50 vs. ≥50 years), pathologic nodal status, grade, and surgery type, regardless of chemo-, endocrine, or radiation therapy administration. The observed continuous risk score was strongly associated with risk of recurrence, with a 22% increase in relative risk per 10-unit change in risk score (0-100), translating to a 2.4% (95% CI = 1.2 – 4.5%) and 25.5% (95% CI = 15.4 – 40.4%) risk of recurrence at 5-years for pts at the lowest (0-9) versus highest (90-100) risk score. The prognostic association between risk score and RFS remained after adjusting for pathologic T- and N-stage, grade, age, race, and treatment (p = 0.002). Conclusions: TumorSight Risk is a new image-based biomarker for HR+/HER2- BC that provides prognostic information independent of standard clinico-pathological parameters. Given the ready availability of breast MRI in most pts with BC, TumorSight Risk may be a convenient tool for estimating risk for recurrence.

e16457 Background: Patients with inoperable stage II-III pancreatic cancer commonly undergo sequential stereotactic body radiation therapy (SBRT) and chemotherapy. However, there is variability in clinical outcomes, and the predictors of high-risk patients with rapid tumor progression (within 3 months) and poor overall survival (OS) despite treatment are not well characterized. We developed a radiomic (imaging-derived features) signature (RS) that predicts rapid progression. We then investigated clinical characteristics, including pathologic features and treatments received, and RS in a combined analysis to develop and validate the highest-performing model to predict OS from the time of SBRT initiation. We identified the most predictive feature from the combined model that best predicts OS. Methods: In our retrospective study, we examined a cohort of 124 stage II-III pancreatic cancer patients who underwent sequential SBRT and systemic chemotherapy and had pre-treatment pancreatic protocol computed tomography (CT) imaging. We examined 10 clinical features and extracted 900 radiomic features from each segmented tumor per patient using PyRadiomics. Dividing our cohort into training and test sets (60:40), we built a prediction model for rapid tumor progression using radiomic data on the training set (n = 74), applying a LASSO-based algorithm for feature selection and 5-fold cross-validation for parameter optimization. We validated the model performance on the held-out test set (n = 50) and generated the RS for predicting rapid progression. We examined the performance of clinical features and RS in predicting OS in univariate and multivariate Cox regression models. Results: Analysis on our cohort (57 men, 67 women; mean age 67 ± 11 years) generated a 43-feature RS that predicted rapid tumor progression (AUC 0.83, 95% CI: 0.70–0.94) in the test set. High RS was a significant predictor of mortality with hazard ratio (HR) 2.22 (95% CI: 1.32–3.73, p = 0.003) within the first year and 2.85 (95% CI: 1.35–6.03, p = 0.006) thereafter. Non-intensive chemotherapy increased only early mortality risk (HR 1.95, 95% CI: 1.08–3.53, p = 0.03), while older age was significant in later years (HR 1.80, 95% CI: 1.02–3.15, p = 0.04). Conclusions: CT-derived RS accurately predicted rapid tumor progression in stage II-III pancreatic cancer patients undergoing SBRT sequentially with chemotherapy. High RS was the strongest prognostic indicator for increased mortality risk, suggesting its utility for guiding treatment or selection for clinical trials.

74 Background: We identified computed tomography (CT)-derived radiomic features predictive of tumor progression within three months, then examined their ability to prognosticate overall survival (OS) along with clinical features in pancreatic cancer. We evaluated these features in patients with unresected pancreatic cancer who underwent stereotactic body radiation therapy (SBRT) in sequence with chemotherapy, but not surgery. Methods: In this retrospective study, we examined a cohort of 101 patients with stage II-III pancreatic cancer who underwent SBRT with sequential chemotherapy at a single institution (Stanford Health Care) between 1999-2020. From their pre-SBRT contrast-enhanced CT images with segmented tumors, delineating regions-of-interest, we extracted 900 radiomic (quantitative pixel-level imaging characteristic) features. In the first phase, we identified radiomic features that predicted rapid tumor progression within three months following SBRT. We divided the dataset into a training set (n = 53) for model development and a test set (n = 48) for evaluation. Using logistic regression with the Least Absolute Shrinkage and Selection Operator algorithm for feature selection and classification, we built a binary prediction model on the training set to identify patients at risk of progression within three months of SBRT. To fine-tune parameters, we performed five-fold cross-validation (CV) on the training set, repeating each set of parameters five times. We assessed model performance on the test set using the area under the curve (AUC). We selected the model with the best AUC, generating the predictive radiomic feature set. In the second phase, we conducted univariate and multivariate Cox regression analyses to assess the relationship between OS and individual clinical variables (age, sex, stage, vessel involvement, tumor location, performance status, body mass index, biological equivalent dose of radiation) and the radiomic feature set as high versus low risk. Results: Our cohort consisted of 48 men (mean age, 70 years ± 11 [SD]) and 53 women (mean age, 67 years ± 13 [SD]). From the first phase, 32 textural features comprised the radiomic feature set that best predicted rapid tumor progression, with mean AUCs of 0.852 (CV, n=53) and 0.814 (test, n=48). In the univariate Cox model, only the radiomic feature set was predictive of OS (hazard ratio, HR, 1.724, p=0.011). In the multivariate Cox model, radiomic features and age were significant predictors of OS, with HR of 1.819 (p=0.007) and 1.024 (p=0.024), respectively. Conclusions: CT-derived radiomic features predict rapid tumor progression following SBRT, confer nearly a twofold increase in mortality risk, and, along with patient age, enhance the identification of patients with stage II-III pancreatic cancer with poor OS. [Table: see text]

56 Background: We sought to identify the clinical determinants of overall survival (OS) in patients diagnosed with stage II-III pancreatic cancer not undergoing surgery. We curated a relatively homogenous cohort of patients who did not undergo surgical resection, but instead underwent chemotherapy in sequence with stereotactic body radiation therapy (SBRT) to examine the features that differentiated those with poor OS. Methods: In a retrospective study, we examined a cohort of 259 patients with stage II-III pancreatic cancer who underwent chemotherapy in sequence with SBRT without surgery at a single institution (Stanford Health Care). We conducted both univariate and multivariate Cox regression analyses to assess the relationships between OS and individual clinical and tumoral variables, including: age, sex, stage, Karnofsky performance status (KPS), body mass index, vessel involvement, tumor location, and biological equivalent dose [BED] of SBRT as potential determinants. Results: The study cohort consisted of 136 men (52.5%) and 123 women (47.5%) with a median age of 69 (range 38-94). Tumor histology was predominantly ductal adenocarcinoma (n=249, 96.1%), with tumor sites of involvement spanning: uncinate (n=35, 13.5%), head (n=160, 61.8%), body (n=59, 22.8%), and tail (n=5,1.9%). More patients had stage III (n=171, 66%) than stage II (n=88, 34%) disease, and their tumor resectability by NCCN criteria were rated as: unresectable (n=145, 56%), borderline (n=28, 10.8%), resectable (n=11, 4.2%), or unrecorded (n=75, 29%). Tumor progression was observed in local (n=71, 27.4%), regional (n=29, 11.2%), and distant (n=150, 57.9%) sites during the median follow up period of 17 months (range 15-18 months) from diagnosis and 12 months (10-13 months) from SBRT. Death (n=213, 82.2%) during this period was more common than survival (n=46, 17.8%). In univariate analyses, clinical features impacting OS included: KPS (hazard ratio [HR] 0.14, p=0.007); male sex (HR 1.44, p=0.01), pancreatic head tumors (HR 1.44, p=0.011), BED (HR 1.33, p=0.035), and age (HR 1.013, p=0.044). In the multivariate analysis, which incorporated only features significant in the univariate analyses, tumor location at the pancreatic head (HR 1.412, p=0.021) and male sex (HR 1.360, p=0.03) conferred the highest risks for poor OS, with BED bordering on significance (p=0.059) (Table). Conclusions: Among patients with stage II-III pancreatic cancer who underwent chemotherapy in sequence with SBRT, but not surgery, the most significant clinical determinants of poor OS were location of the tumor in the pancreatic head and male sex. Prioritizing additional disease monitoring and alternative therapies for this subset of patients at higher risk of poor survival may be warranted. [Table: see text]

706 Background: We sought to assess the ability of a computed tomography (CT)-derived radiomic signature (RS) feature set to predict rapid tumor progression and overall survival (OS) in patients diagnosed with stage II-III pancreatic cancer undergoing stereotactic body radiation therapy (SBRT) in sequence with chemotherapy. Methods: We conducted a retrospective study on a cohort of 240 stage II-III pancreatic cancer patients who underwent SBRT in sequence with chemotherapy at a single institution (Stanford Hospital and Clinics). Among them, 83 had pre-SBRT contrast-enhanced CT images with segmented tumors, forming the imaging set, from which we extracted 900 radiomic (quantitative pixel-level imaging characteristic) features from delineated tumor regions-of-interest. For RS development, we divided the imaging set into a training set (n = 53) for model development and a test set (n = 30) for evaluation. We built a binary prediction model on the training set to identify patients at risk of rapid tumor progression within three months after SBRT. We utilized logistic regression with the Least Absolute Shrinkage and Selection Operator algorithm for feature selection and classification. To fine-tune parameters, we performed five-fold cross-validation on the training set, repeating each set of parameters five times. Finally, we assessed the model's performance on the test set (n = 30) using the area under the curve (AUC) value. We selected the model with the best AUC, while also generating the predictive radiomic feature set. For OS prognostication, we conducted both univariate and multivariate Cox proportional-hazards analyses using RS and clinical features (age, sex, stage, vessel involvement, tumor location, performance status, body mass index, biological equivalent dose of radiation) as potential predictors. Results: The enrolled cohort consisted of 127 men (mean age, 69 years ± 12 [SD]) and 113 women (mean age, 69 years ± 13 [SD]). Seventeen textural radiomic features were identified as the RS, which demonstrated a high AUC in the test set for the prediction of rapid progression (AUC 0.850; 95% CI: 0.725, 0.975). Age was associated with OS in the multivariate model while high RS (>0.5) was independently associated with OS in both univariate and multivariate models. Conclusions: CT-derived RS, combined with age, represent the most prognostic factors for stage II-III pancreatic cancer. RS, which likely reflects underlying biology or molecular alterations, contributes to the highest HR of poor OS and provide the strongest indicator of outcome following commonly used treatments in this patient population. [Table: see text]