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.

External validation of the Brain Tumour Reporting and Data System (BT-RADS) in the multidisciplinary managementof post-treatment gliomas.

Purpose: To independently and externally validate the Brain Tumour Reporting and Data System (BT-RADS) for post-treatment gliomas and assess interobserver variability. Material and methods: In this retrospective observational study, consecutive MRIs of 100 post-treatment glioma patients were reviewed by two independent radiologists (RD1 and RD2) and assigned a BT-RADS score. Inter-observer agreement statistics were determined by kappa statistics. The BT-RADS-linked management recommendations per score were compared with the multidisciplinary meeting (MDM) decisions. Results: The overall agreement rate between RD1 and RD2 was 62.7% (κ = 0.67). The agreement rate between RD1 and consensus was 83.3% (κ = 0.85), while the agreement between RD2 and consensus was 69.3% (κ = 0.79). Among the radiologists, agreement was highest for score 2 and lowest for score 3b. There was a 97.9% agreement between BT-RADS-linked management recommendations and MDM decisions. Conclusions: BT-RADS scoring led to improved consistency, and standardised language in the structured MRI reporting of post-treatment brain tumours. It demonstrated good overall agreement among the reporting radiologists at both extremes; however, variation rates increased in the middle part of the spectrum. The interpretation categories linked to management decisions showed a near-perfect match with MDM decisions.

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.

Cross-sectional imaging review of common to uncommon lung cancer mimickers in a tertiary care oncology center.

Lung cancer is the most diagnosed cancer worldwide. Many non-malignant pulmonary lesions, such as tuberculosis, fungal infection, organizing pneumonia, inflammatory myofibroblastic tumor, and IgG4 disease, can mimic lung cancer due to their overlapping morphological appearance on imaging. These benign entities with minor differentiating imaging clues may go unnoticed in a high-volume cancer institution, leading to over-investigation that may result in repeated biopsies, pointless wedge resections, and related morbidities. However, with a thorough medical history, laboratory diagnostic work-up, and careful analysis of imaging findings, one can occasionally restrict the range of possible diagnoses or arrive at a definitive conclusion. When imaging features overlap, image-guided lung sampling is crucial since histopathological analysis is the gold standard.

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.

Subpial en bloc resection improves extent of resection in infiltrating gliomas - A propensity matched comparative cohort analysis.

BACKGROUND: Surgery remains the mainstay of glioma therapy and extent of resection is an important prognostic factor. Optimization of surgical outcomes is essential and to this end the technique of resection can potentially play an important role. Based on patterns of glioma growth and extrapolating from other solid cancer surgical principles, a subpial dissection combined with an en-bloc resection (SPER) technique appears to have advantages METHODS: We performed a propensity matched analysis comparing gliomas that were resected using SPER versus a standard piecemeal debulking technique at our centre. Potentially confounding factors (including eloquent location, use of intraoperative imaging, surgeon experience) were adjusted for in the matching of the two cohorts. Outcomes included postoperative morbidity and blinded radiological review documented postoperative ischemia (on diffusion weighted MR imaging - DWI) as well as extent of resection. RESULTS: In 57 gliomas (23 SPER and 34 standard), the gross total resection (GTR) rates were significantly higher with SPER (91 vs 65%). Postoperative DWI revealed significant ischemia in almost 50% of cases in either group, though many did not have postoperative deficits. Arterial ischemia was higher in the standard surgery group and this was associated with a significantly higher risk (seven times) of resulting in prolonged neurological deficits. CONCLUSIONS: SPER is a useful technique which increases the GTR rates in gliomas undergoing resection. It is associated with lower incidence of arterial ischemia in the postoperative period and this can result in improved long term functional outcomes.