Antony, Ajith

Background Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary barrier to improving its otherwise poor rate of survival. Objective To develop and validate the Radiomics-based Early Detection MODel (REDMOD), an AI framework to identify subvisual radiomic signatures of pre-diagnostic PDA on standard-of-care CT. Designs REDMOD was trained on a multi-institutional cohort (n=969; 156 pre-diagnostic, 813 control) and tested on an independent set (n=493; 63 pre-diagnostic, 430 control), simulating a low prevalence (~1:6) early detection paradigm. The fully automated framework couples AI-driven segmentation with a heterogeneous ensemble architecture trained on a 40-feature radiomic signature derived from Synthetic Minority Over-sampling Technique (SMOTE)-balanced data. A tunable Youden Index-optimised classification threshold enables performance calibration without retraining. Validation included direct comparison with radiologists, longitudinal test–retest analysis and external specificity validation across two independent cohorts (n=539 and n=80). Results On an independent test set (n=493), REDMOD identified occult PDA (AUC 0.82; 73.0% sensitivity) at a median 475-day lead time. This represented nearly twofold higher sensitivity than radiologists (38.9%; p<0.001), which grew to nearly threefold (68.0% vs 23.0%) at >24 months lead time. REDMOD showed strong longitudinal stability (90–92% concordance) and generalisable specificity across multi-institutional (81.3%; n=539) and public (87.5%; n=80) datasets. Mechanistic analyses confirmed predictive power derived principally from multi-scale wavelet-filtered textural features (90% of selected signature), which outperformed unfiltered features (AUC 0.82 vs 0.74; p=0.007) in capturing subvisual architectural disruptions. Conclusions REDMOD is an automated, mechanistically grounded, longitudinally stable, externally validated AI that surpasses radiologists for PDA detection at its visually occult pre-diagnostic stage. These attributes position it for prospective validation in high-risk cohorts, a necessary step towards shifting the paradigm from late-stage symptomatic diagnosis to proactive pre-clinical interception.

Abstract Prostate-specific membrane antigen (PSMA) is highly expressed in tumor-associated neovasculature in hepatocellular carcinoma (HCC), highlighting its promise as a theranostic target. This prospective study aimed to evaluate the utility of novel biomarkers derived from PSMA positron emission tomography computed tomography (PET/CT) to assess treatment response in advanced HCC patients (pts) receiving first line immune checkpoint inhibitors (ICI). Methods: This study (NCT05176223) enrolled unresectable, advanced HCC pts eligible for first-line ICI (atezolizumab/bevacizumab [A/B] or durvalumab/tremelimumab [D/T]) with measurable disease according to RECIST criteria. Primary endpoints: progression-free survival (PFS) per RECIST 1.1 and mRECIST; overall survival (OS); and best overall response. The survival distribution was estimated using the Kaplan-Meier method. Observed agreement is defined as the proportion of pts whose responses, as classified by PET/CT or CT, were concordant. Weighted kappa and Cohen’s kappa were used to quantify the strength of agreement between PET/CT and CT. Pts completed up to five 68Ga-PSMA PET/CT scans and blood collections. Results: Twenty-six pts were enrolled (25 evaluable) between Oct 2022 and Nov 2024. Median follow up was 22.1 months (m) (Q1, Q3: 14.5-23.7). Median age 68 yrs, 72% male, 88% White, 68% Child-Pugh [CP] A, 32% CP B, 32% Barcelona Clinic Liver Cancer [BCLC] stage B, 68% BCLC stage C, 36% with microvascular invasion; 56% received A/B and 40% received D/T treatment. PSMA was positive in 96% of pts at baseline with median SUVmax 14.3 (range 5.1-40.5) and median background liver SUVmax 3.9 (range 2.1-6.8). Median (95%CI) PFS was 5.6 m (4.6-12.5) by both RECIST and mRECIST. Median (95%CI) OS was 18.6 m (7.2-not estimable), with 56% deceased. The observed agreement between PET/CT adapted PERCIST criteria compared to RECIST and mRECIST in determining the best response was 46% and 50% and weighted kappa of 0.63 (95% CI: 0.37-0.90) and 0.66 (0.41-0.92), respectively. Observed agreement between PERCIST compared to RECIST and mRECIST for responders’ assessment was 67% and 71% with Cohen’s kappa of 0.36 (0.03-0.68) and 0.43 (0.11-0.76), respectively. Conclusion: In this cohort, PSMA PET/CT avidity was observed in all but one pt with advanced HCC, each demonstrating moderate to high SUVmax values. Median OS and PFS were consistent with outcomes reported in clinical trials. Response assessment using 68Ga-PSMA PET/CT according to PERCIST criteria showed good concordance with both RECIST and mRECIST standards. Analysis of immune biomarkers is ongoing. Citation Format: Nguyen H. Tran, Jacob Hirdler, Ajith Antony, Jacob Teske, Sudhakar K. Venkatesh, Amit Mahipal, Michael S. Torbenson, Scott M. Thompson, Thor R. Halfdanarson, Zhaohui Jin, Lionel Aurelien Kankeu Fonkoua, Leslie A. Washburn, Alexander Revzin, Haidong Dong, Robert R. Mcwilliams, Aminah Jatoi, Hani M. Babiker, Mitesh J. Borad, Tanios Bekaii-Saab, Gregory J. Gores, Lewis R. Roberts, Geoffrey B. Johnson, Ajit H. Goenka. Prospective evaluation of 68Ga-PSMA PET imaging in advanced hepatocellular carcinoma patients undergoing first line immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2611.

Pancreatic cancer (PC), characterized by a dismal 5-year survival rate of approximately 12%, remains one of the most aggressive cancers. Majority of cases are identified at advanced stages, which are beyond the scope of surgical resection. Importantly, while the overall prognosis for PC is poor, localized stages of the disease can significantly enhance survival chances because of the potential for curative interventions. We explore the evolution of early detection strategies, including screening methods for high-risk individuals, the yield of diagnostic tests, and the impact of emerging technologies. Special emphasis is given to the role of artificial intelligence in overcoming the limitations of standard imaging practices, thereby refining diagnostic accuracy, and predicting patient outcomes. Our review synthesizes current controversies and addresses the technological advancements that could potentially shift the paradigm toward improved survival rates for patients with PC.