Baseline Characteristics and Use of Pretherapeutic 18 F-Fluorodeoxyglucose-PET for Pancreatic Cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy. Surgical resection is the only curative modality combined with neoadjuvant chemotherapy to improve survival. Given the limitations of traditional responses such as cross-sectional imaging (CT/MRI) or tumor markers, carbohydrate antigen 19-9 (CA19-9), the 2023 National Comprehensive Cancer Network guidelines included 18 F-fluorodeoxyglucose (FDG)-PET as an adjunct to assess response to neoadjuvant chemotherapy. There are common misconceptions on the metabolic activity (tumor avidity) in PDAC so we aimed to describe the baseline characteristics and use of FDG-PET in a cohort of treatment-naive patients with PDAC. STUDY DESIGN: A single-center retrospective study was conducted capturing all biopsy-proven, treatment-naive patients with PDAC who underwent either baseline FDG-PET/CT or FDG-PET/MRI imaging between 2008 and 2023. Baseline FDG-PET characteristics were collected, including primary tumors' maximum standardized uptake value defined as metabolic activity (FDG uptake) of tumor compared with surrounding pancreatic parenchymal background, and the identification of extrapancreatic metastatic disease. RESULTS: We identified 1,095 treatment-naive patients with PDAC who underwent baseline FDG-PET imaging at diagnosis. CA19-9 was elevated in 76% of patients. Overall, 96.3% (1,054) of patients had FDG-avid tumors with a median maximum standardized uptake value of 6.4. FDG-PET also identified suspicious extrapancreatic metastatic lesions in 50% of patients, with a higher proportion (p < 0.001) in PET/MRI (59.9%) vs PET/CT (44.3%). After controlling for CA19-9 elevation, PET/MRI was superior in detection of extrapancreatic lesions compared with PET/CT. CONCLUSIONS: FDG-PET has significant use in PDAC as a baseline imaging modality earlier neoadjuvant therapy given the majority of tumors are FDG-avid. FDG-PET can identify additional extrapancreatic suspicious lesions allowing for optimal initial staging, with PET/MRI having increased sensitivity over PET/CT.

Preliminary study on the dynamic positron emission tomography imaging with 11C-ER176 to delineate macrophage activation in diabetic gastroparesis.

BACKGROUND: Animal models and human data have suggested macrophage-driven immune dysregulation in diabetic gastroparesis (DG). Translocator protein (TSPO) upregulation has been suggested to indicate activated state of macrophages and ER176 is a high affinity third generation TSPO-specific radioligand. The aim of this study was to determine feasibility of dynamic 11C-ER 176 PET to identify macrophage activation in DG. METHODS: Twelve patients, all females, were recruited (4 DG, 4 diabetics, and 4 healthy volunteers) for 11C-ER 176 PET/CT scanning. The standardized uptake value (SUVmax) in the gastric fundus, body, pylorus, and descending part of the duodenum were compared between three groups using Kruskal-Wallis test to perform the comparisons, and a p-value of 0.05 was considered statistically significant. KEY RESULTS: Age was comparable among the three groups with a median of 53 years. The uptake was higher in pylorus in diabetics compared to DG and healthy (SUVmax healthy 4.6 ± 0.2, diabetics 8.4 ± 4.1, DG 5.5 ± 1.0, p = 0.04). The uptake was similar in gastric fundus (9.0 ± 1.6, 13.1 ± 8.3, 7.8 ± 1.9 respectively, p = 0.3), body (7.7 ± 1.9, 13 ± 9.2, 7.8 ± 1.9 respectively, p = 0.8), and duodenum (6.2 ± 2.1, 9.5 ± 6.8, 7.0 ± 1.8 respectively, p = 0.6). No correlation was observed between SUVmax uptake and either HbA1C or fasting blood glucose. CONCLUSIONS AND INFERENCES: Female diabetic gastroparesis patients did not demonstrate increased TSPO ligand 11C-ER 176 uptake in the stomach. Possible explanations include lack of specificity of ligand for specific macrophage phenotypes in DG, sex effect, or small sample size. Further studies investigating non-invasive ways of analyzing immune dysregulation in neurogastrointestinal disorders are warranted.

Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods.

PURPOSE: To evaluate robustness of a radiomics-based support vector machine (SVM) model for detection of visually occult PDA on pre-diagnostic CTs by simulating common variations in image acquisition and radiomics workflow using image perturbation methods. METHODS: Eighteen algorithmically generated-perturbations, which simulated variations in image noise levels (σ, 2σ, 3σ, 5σ), image rotation [both CT image and the corresponding pancreas segmentation mask by 45° and 90° in axial plane], voxel resampling (isotropic and anisotropic), gray-level discretization [bin width (BW) 32 and 64)], and pancreas segmentation (sequential erosions by 3, 4, 6, and 8 pixels and dilations by 3, 4, and 6 pixels from the boundary), were introduced to the original (unperturbed) test subset (n = 128; 45 pre-diagnostic CTs, 83 control CTs with normal pancreas). Radiomic features were extracted from pancreas masks of these additional test subsets, and the model's performance was compared vis-a-vis the unperturbed test subset. RESULTS: The model correctly classified 43 out of 45 pre-diagnostic CTs and 75 out of 83 control CTs in the unperturbed test subset, achieving 92.2% accuracy and 0.98 AUC. Model's performance was unaffected by a three-fold increase in noise level except for sensitivity declining to 80% at 3σ (p = 0.02). Performance remained comparable vis-a-vis the unperturbed test subset despite variations in image rotation (p = 0.99), voxel resampling (p = 0.25-0.31), change in gray-level BW to 32 (p = 0.31-0.99), and erosions/dilations up to 4 pixels from the pancreas boundary (p = 0.12-0.34). CONCLUSION: The model's high performance for detection of visually occult PDA was robust within a broad range of clinically relevant variations in image acquisition and radiomics workflow.

Gallium-68-labeled fibroblast activation protein inhibitor-46 PET in patients with resectable or borderline resectable pancreatic ductal adenocarcinoma: A phase 2, multicenter, single arm, open label non-randomized study protocol.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease prone to widespread metastatic dissemination and characterized by a desmoplastic stroma that contributes to poor outcomes. Fibroblast activation protein (FAP)-expressing Cancer-Associated Fibroblasts (CAFs) are crucial components of the tumor stroma, influencing carcinogenesis, fibrosis, tumor growth, metastases, and treatment resistance. Non-invasive tools to profile CAF identity and function are essential for overcoming CAF-mediated therapy resistance, developing innovative targeted therapies, and improved patient outcomes. We present the design of a multicenter phase 2 study (clinicaltrials.gov identifier NCT05262855) of [68Ga]FAPI-46 PET to image FAP-expressing CAFs in resectable or borderline resectable PDAC. METHODS: We will enroll up to 60 adult treatment-naïve patients with confirmed PDAC. These patients will be eligible for curative surgical resection, either without prior treatment (Cohort 1) or after neoadjuvant therapy (NAT) (Cohort 2). A baseline PET scan will be conducted from the vertex to mid-thighs approximately 15 minutes after administering 5 mCi (±2) of [68Ga]FAPI-46 intravenously. Cohort 2 patients will undergo an additional PET after completing NAT but before surgery. Histopathology and FAP immunohistochemistry (IHC) of initial diagnostic biopsy and resected tumor samples will serve as the truth standards. Primary objective is to assess the sensitivity, specificity, and accuracy of [68Ga]FAPI-46 PET for detecting FAP-expressing CAFs. Secondary objectives will assess predictive values and safety profile validation. Exploratory objectives are comparison of diagnostic performance of [68Ga]FAPI-46 PET to standard-of-care imaging, and comparison of pre- versus post-NAT [68Ga]FAPI-46 PET in Cohort 2. CONCLUSION: To facilitate the clinical translation of [68Ga]FAPI-46 in PDAC, the current study seeks to implement a coherent strategy to mitigate risks and increase the probability of meeting FDA requirements and stakeholder expectations. The findings from this study could potentially serve as a foundation for a New Drug Application to the FDA. TRIAL REGISTRATION: @ClinicalTrials.gov identifier NCT05262855.

Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans.

BACKGROUND & AIMS: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs. METHODS: A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls. RESULTS: Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis. CONCLUSIONS: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.

MRI in rectal cancer patients on 'watch and wait': patterns of response and their evolution.

PURPOSE: Evaluate MR patterns of response and their evolution in rectal cancer patients on watch and wait (WW). METHODS: We retrospectively reviewed 337 MRIs of 60 patients (median follow-up: 12 months; range: 6-49 months). Baseline MRIs (available in 34/60 patients) were evaluated for tumor morphology, location, thickness, circumferential involvement, nodal status and EMVI. First post-treatment MRIs (in all patients) were additionally evaluated for pattern of response on T2 and DWI. Change in post-treatment scar thickness and scar depth angle between the first and second post-treatment scans was also evaluated. Evolution of the response pattern/recurrence were evaluated till the last available scan. RESULTS: On the baseline scans, 20/34 (59%) patients had polypoidal tumor with 12/20 having ≤ 25% circumferential wall involvement. We saw five patterns of response-normalized rectal wall (2/60-3%), minimal fibrosis (23/60-38%), full thickness fibrosis (16/60-27%), irregular fibrosis (11/60-18%) and split scar (6/60-10%), with 2/60 (3%) showing possible residual disease. On the first post-treatment scans, 12/60 (20%) had restricted diffusion, with 3/12 having persistent restriction till last follow-up. Post-treatment fibrosis/split scar remained stable in 44/60 (73%) cases and improved further in the rest. 9/60 (15%) patients developed regrowth/recurrence. Patients with recurrence had < 10 mm scar thickness and < 21° change in scar angle between the first and second post-treatment MRIs. CONCLUSION: Most patients on WW protocol developed minimal or full thickness fibrosis, majority of which remained stable on follow-up.

Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.

OBJECTIVES: To develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. METHODS: Reference segmentations were obtained on CTs (2006-2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets. RESULTS: Total 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1-12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08). CONCLUSION: A computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors. CLINICAL RELEVANCE: AI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor.