Goenka, Ajit Harishkumar

ABSTRACT Background Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high‐risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region‐of‐interest–based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non‐HRI controls using an Artificial intelligence (AI)‐based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI). Methods We developed and validated a deep‐learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1‐weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non‐HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement. Linear models adjusting for matching variables were used to assess associations. Results Model‐expert agreement for whole‐pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98–0.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4]; p = 0.32). PDFF increased with age ( p = 0.015), male sex ( p < 0.001), presence of diabetes ( p = 0.009), BMI ( p < 0.001). IPF distribution patterns did not differ by risk group. Conclusion An AI‐based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.

Abstract Background Optimal treatment following first-line immunotherapy for advanced hepatocellular carcinoma (HCC) remains uncertain. This study evaluated futibatinib, a pan-FGFR inhibitor, combined with pembrolizumab in previously treated patients with FGF19-expressing HCC. Methods Eligible patients were ≥18 years old with advanced HCC, prior systemic therapy, Child-Pugh A-B7 liver function, ECOG 0–2, and FGF19 overexpression. Patients received futibatinib 20 mg orally daily plus pembrolizumab 200 mg intravenously every 3 weeks. The primary endpoint was progression-free survival at 6 months (PFS6). Secondary endpoints included overall survival (OS), objective response rate (ORR), quality of life, and safety. Results Between August 2021 and March 2024, 14 patients enrolled and 13 were evaluable. Median age was 72 years (range, 60–87), 53.8% were male, 92.3% had Child-Pugh A liver function, and all had received prior immunotherapy. Median progression-free survival was 15 weeks (95% CI, 9.0–NE), with a PFS6 rate of 23.1% (95% CI, 8.6–62.3%). ORR was 0% (95% CI, 0–24.7%), while stable disease was observed in 53.9% of patients. Median OS was 55.7 weeks (95% CI, 47.3–NE). Grade ≥3 adverse events occurred in 69.2% of patients, and grade ≥4 adverse events in 15.4%. One grade 5 event (acute-on-chronic kidney injury) was deemed unrelated to treatment. Common adverse events included hyperphosphatemia, fatigue, diarrhea, elevated transaminases, dry eye, nail changes, rash, and oral mucositis. Conclusions Futibatinib plus pembrolizumab demonstrated an acceptable safety profile but did not meet efficacy thresholds supporting further study in FGF19-expressing HCC. Effective post-immunotherapy treatment strategies remain an unmet need.

Abstract Purpose: Fibroblast activation protein (FAP) is an attractive target for radiopharmaceutical therapy. Phase I of the LuMIERE study (ClinicalTrials.gov, NCT04939610) investigated the safety of [177Lu]Lu-FAP-2286 (177Lu-FAP-2286) in heavily pretreated patients with advanced solid tumors and identified the recommended phase II dosage (RP2D). Patients and Methods: LuMIERE is a prospective, open-label, nonrandomized, phase I/II, multicenter study. Phase I followed a Bayesian optimal interval design evaluating four escalating activity levels of 177Lu-FAP-2286 (3.70, 5.55, 7.40, and 9.25 GBq). Patients were selected by positive [68Ga]Ga-FAP-2286 (68Ga-FAP-2286, also known as [68Ga]Ga-HKG301) PET/CT imaging on all target lesions [maximum standardized uptake value (SUVmax) ≥1.5× SUVmean of mediastinal blood pool]. 177Lu-FAP-2286 was administered intravenously every 6 weeks for ≤6 cycles. The primary endpoint was dosage-limiting toxicities (DLT) and treatment-emergent adverse events (TEAE). Results: Of 35 patients imaged with 68Ga-FAP-2286, 27 received 177Lu-FAP-2286 (3.70 GBq, n = 3; 5.55 GBq, n = 6; 7.40 GBq, n = 7; 9.25 GBq, n = 11). Two DLTs were observed: one patient who received 5.55 GBq of 177Lu-FAP-2286 experienced grade 4 lymphopenia, and one patient who received 9.25 GBq experienced grade 3 hemoptysis; these were the only treatment-related grade ≥3 TEAEs. All-cause grade ≥3 TEAEs were reported for 11 patients (40.7%). The RP2D for 177Lu-FAP-2286 monotherapy was determined to be 9.25 GBq. Preliminary Response Evaluation Criteria in Solid Tumors efficacy findings demonstrated a partial response in 1 patient and stable disease in 10 patients (maintained for at least two assessments in 6 patients). Conclusions: 177Lu-FAP-2286 was well tolerated in patients with advanced solid tumors. The safety and preliminary efficacy findings support its continued development in phase II.

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.