Self-assembling dendrimer nanosystems for specific fluorine magnetic resonance imaging and effective theranostic treatment of tumors.

Fluorine magnetic resonance imaging (19F-MRI) is particularly promising for biomedical applications owing to the absence of fluorine in most biological systems. However, its use has been limited by the lack of safe and water-soluble imaging agents with high fluorine contents and suitable relaxation properties. We report innovative 19F-MRI agents based on supramolecular dendrimers self-assembled by an amphiphilic dendrimer composed of a hydrophobic alkyl chain and a hydrophilic dendron. Specifically, this amphiphilic dendrimer bears multiple negatively charged terminals with high fluorine content, which effectively prevented intra- and intermolecular aggregation of fluorinated entities via electrostatic repulsion. This permitted high fluorine nuclei mobility alongside good water solubility with favorable relaxation properties for use in 19F-MRI. Importantly, the self-assembling 19F-MRI agent was able to encapsulate the near-infrared fluorescence (NIRF) agent DiR and the anticancer drug paclitaxel for multimodal 19F-MRI and NIRF imaging of and theranostics for pancreatic cancer, a deadly disease for which there remains no adequate early detection method or efficacious treatment. The 19F-MRI and multimodal 19F-MRI and NIRF imaging studies on human pancreatic cancer xenografts in mice confirmed the capability of both imaging modalities to specifically image the tumors and demonstrated the efficacy of the theranostic agent in cancer treatment, largely outperforming the clinical anticancer drug paclitaxel. Consequently, these dendrimer nanosystems constitute promising 19F-MRI agents for effective cancer management. This study offers a broad avenue to the construction of 19F-MRI agents and theranostics, exploiting self-assembling supramolecular dendrimer chemistry.

Targeted Cyclo[8]pyrrole-Based NIR-II Photoacoustic Tomography Probe for Suppression of Orthotopic Pancreatic Tumor Growth and Intra-abdominal Metastases.

Pancreatic cancer is highly lethal. New diagnostic and treatment modalities are desperately needed. We report here that an expanded porphyrin, cyclo[8]pyrrole (CP), with a high extinction coefficient (89.16 L/g·cm) within the second near-infrared window (NIR-II), may be formulated with an αvß3-specific targeting peptide, cyclic-Arg-Gly-Asp (cRGD), to form cRGD-CP nanoparticles (cRGD-CPNPs) with promising NIR-II photothermal (PT) therapeutic and photoacoustic (PA) imaging properties. Studies with a ring-array PA tomography system, coupled with analysis of control nanoparticles lacking a targeting element (CPNPs), revealed that cRGD conjugation promoted the delivery of the NPs through abnormal vessels around the tumor to the solid tumor core. This proved true in both subcutaneous and orthotopic pancreatic tumor mice models, as confirmed by immunofluorescent studies. In combination with NIR-II laser photoirradiation, the cRGD-CPNPs provided near-baseline tumor growth inhibition through PTT both in vitro and in vivo. Notably, the combination of the present cRGD-CPNPs and photoirradiation was found to inhibit intra-abdominal metastases in an orthotopic pancreatic tumor mouse model. The cRGD-CPNPs also displayed good biosafety profiles, as inferred from PA tomography, blood analyses, and H&E staining. They thus appear promising for use in combined PA imaging and PT therapeutic treatment of pancreatic cancer.

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.

JS-K Combined with a Melanin-Based Theranostic Agent: A Novel Sequential Delivery Strategy to Enhance the Near-Infrared Fluorescence Imaging of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5 year survival rate less than 12%. This malignancy is closely related to the unique tumor microenvironment (TME), which is characterized by a hypovascular and hyperdense extracellular matrix, making it difficult for drugs to permeate the tumor center. Near-infrared fluorescence (NIRF) imaging, which has high sensitivity and resolution, may improve the survival rate of PDAC patients. In this study, we first used JS-K (O2-(2,4-dinitrophenyl) 1-[(4-ethoxycarbonyl) piperazine-1-yl] diazene-1-ium-1,2-diolate) to specifically dilate blood vessels within the TME of PDAC patients and subsequently injected IR820-PEG-MNPs (IPM NPs) to diagnose and treat orthotopic PDAC. We found that JS-K promoted the accumulation of IPM NPs in orthotopic Pan02 tumor-bearing mice and was able to increase the tumor signal-to-background ratio (SBR) in the orthotopic PDAC area by 41.5%. In addition, surgical navigation in orthotopic Pan02 tumor-bearing mice and complete tumor resection based on fluorescence imaging were achieved with a detection sensitivity of 81.0%. Moreover, we verified the feasibility of the combination of laparoscopy and photothermal ablation (PTA) for the treatment of PDAC. Finally, we demonstrated that IPM NPs had greater affinity for human PDAC tissues than for normal pancreatic tissues ex vivo, preliminarily highlighting the potential for clinical translation of these NPs. In conclusion, we developed and validated a novel sequential delivery strategy that promotes the accumulation of nanoagents in the tumor area and can be used for the diagnosis and treatment of PDAC.

NIR Fluorescent/Photoacoustic Bimodal Imaging of Ferroptosis in Pancreatic Cancer Using Biothiols-Activable Probes.

Ferroptosis modulation is a powerful therapeutic option for pancreatic ductal adenocarcinoma (PDAC) with a low 5-year survival rate and lack of effective treatment methods. However, due to the dual role of ferroptosis in promoting and inhibiting pancreatic tumorigenesis, regulating the degree of ferroptosis is very important to obtain the best therapeutic effect of PDAC. Biothiols are suitable as biomarkers of imaging ferroptosis due to the dramatic decreases of biothiol levels in ferroptosis caused by the inhibited synthesis pathway of glutathione (GSH) and the depletion of biothiol by reactive oxygen species. Moreover, a very recent study reported that cysteine (Cys) depletion can lead to pancreatic tumor ferroptosis in mice and may be employed as an effective therapeutic strategy for PDAC. Therefore, visualization of biothiols in ferroptosis of PDAC will be helpful for regulating the degree of ferroptosis, understanding the mechanism of Cys depletion-induced pancreatic tumor ferroptosis, and further promoting the study and treatment of PDAC. Herein, two biothiol-activable near-infrared (NIR) fluorescent/photoacoustic bimodal imaging probes (HYD-BX and HYD-DX) for imaging of pancreatic tumor ferroptosis were reported. These two probes show excellent bimodal response performances for biothiols in solution, cells, and tumors. Subsequently, they have been employed successfully for real-time visualization of changes in concentration levels of biothiols during the ferroptosis process in PDAC cells and HepG2 cells. Most importantly, they have been further applied for bimodal imaging of ferroptosis in pancreatic cancer in mice, with satisfactory results. The development of these two probes provides new tools for monitoring changes in concentration levels of biothiols in ferroptosis and will have a positive impact on understanding the mechanism of Cys depletion-induced pancreatic tumor ferroptosis and further promoting the study and treatment of PDAC.

The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.

OBJECTIVE: To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy. METHODS: 105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model. RESULT: The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively. CONCLUSION: The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients.

Outcomes of a 3-Year Prospective Surveillance in Individuals at High Risk of Pancreatic Cancer.

INTRODUCTION: Pancreatic cancer (PC) surveillance of high-risk individuals (HRI) is becoming more common worldwide, aiming at anticipating PC diagnosis at a preclinical stage. In 2015, the Italian Registry of Families at Risk of Pancreatic Cancer was created. We aimed to assess the prevalence and incidence of pancreatic findings, oncological outcomes, and harms 7 years after the Italian Registry of Families at Risk of Pancreatic Cancer inception, focusing on individuals with at least a 3-year follow-up or developing events before. METHODS: HRI (subjects with a family history or mutation carriers with/without a family history were enrolled in 18 centers). They underwent annual magnetic resonance with cholangiopancreatography or endoscopic ultrasound (NCT04095195). RESULTS: During the study period (June 2015-September 2022), 679 individuals were enrolled. Of these, 524 (77.2%) underwent at least baseline imaging, and 156 (29.8%) with at least a 3-year follow-up or pancreatic malignancy/premalignancy-related events, and represented the study population. The median age was 51 (interquartile range 16) years. Familial PC cases accounted for 81.4% of HRI and individuals with pathogenic variant for 18.6%. Malignant (n = 8) and premalignant (1 PanIN3) lesions were found in 9 individuals. Five of these 8 cases occurred in pathogenic variant carriers, 4 in familial PC cases (2 tested negative at germline testing and 2 others were not tested). Three of the 8 PC were stage I. Five of the 8 PC were resectable, 3 Stage I, all advanced cases being prevalent. The 1-, 2-, and 3-year cumulative hazard of PC was 1.7%, 2.5%, and 3%, respectively. Median overall and disease-free survival of patients with resected PC were 18 and 12 months (95% CI not computable). Considering HRI who underwent baseline imaging, 6 pancreatic neuroendocrine neoplasms (1 resected) and 1 low-yield surgery (low-grade mixed-intraductal papillary mucinous neoplasm) were also reported. DISCUSSION: PC surveillance in a fully public health care system is feasible and safe, and leads to early PC or premalignant lesions diagnoses, mostly at baseline but also over time.

Somatic Mutational Analysis in Endoscopic Ultrasound-Guided Biopsy of Pancreatic Adenocarcinoma: Assessing Yield and Impact.

INTRODUCTION: We sought to determine the yield of somatic mutational analysis from endoscopic ultrasound (EUS)-guided biopsies of pancreatic adenocarcinoma compared with that of surgical resection and to assess the impact of these results on oncologic treatment. METHODS: We determined the yield of EUS sampling and surgical resection. We evaluated the potential impact of mutational analysis by identifying actionable mutations and its direct impact by reviewing actual treatment decisions. RESULTS: Yield of EUS sampling was 89.5%, comparable with the 95.8% yield of surgical resection. More than a quarter in the EUS cohort carried actionable mutations, and of these, more than 1 in 6 had treatment impacted by mutational analysis. DISCUSSION: EUS sampling is nearly always adequate for somatic testing and may have substantial potential and real impact on treatment decisions.

Large Language Models for Automated Synoptic Reports and Resectability Categorization in Pancreatic Cancer.

Background Structured radiology reports for pancreatic ductal adenocarcinoma (PDAC) improve surgical decision-making over free-text reports, but radiologist adoption is variable. Resectability criteria are applied inconsistently. Purpose To evaluate the performance of large language models (LLMs) in automatically creating PDAC synoptic reports from original reports and to explore performance in categorizing tumor resectability. Materials and Methods In this institutional review board-approved retrospective study, 180 consecutive PDAC staging CT reports on patients referred to the authors' European Society for Medical Oncology-designated cancer center from January to December 2018 were included. Reports were reviewed by two radiologists to establish the reference standard for 14 key findings and National Comprehensive Cancer Network (NCCN) resectability category. GPT-3.5 and GPT-4 (accessed September 18-29, 2023) were prompted to create synoptic reports from original reports with the same 14 features, and their performance was evaluated (recall, precision, F1 score). To categorize resectability, three prompting strategies (default knowledge, in-context knowledge, chain-of-thought) were used for both LLMs. Hepatopancreaticobiliary surgeons reviewed original and artificial intelligence (AI)-generated reports to determine resectability, with accuracy and review time compared. The McNemar test, t test, Wilcoxon signed-rank test, and mixed effects logistic regression models were used where appropriate. Results GPT-4 outperformed GPT-3.5 in the creation of synoptic reports (F1 score: 0.997 vs 0.967, respectively). Compared with GPT-3.5, GPT-4 achieved equal or higher F1 scores for all 14 extracted features. GPT-4 had higher precision than GPT-3.5 for extracting superior mesenteric artery involvement (100% vs 88.8%, respectively). For categorizing resectability, GPT-4 outperformed GPT-3.5 for each prompting strategy. For GPT-4, chain-of-thought prompting was most accurate, outperforming in-context knowledge prompting (92% vs 83%, respectively; P = .002), which outperformed the default knowledge strategy (83% vs 67%, P < .001). Surgeons were more accurate in categorizing resectability using AI-generated reports than original reports (83% vs 76%, respectively; P = .03), while spending less time on each report (58%; 95% CI: 0.53, 0.62). Conclusion GPT-4 created near-perfect PDAC synoptic reports from original reports. GPT-4 with chain-of-thought achieved high accuracy in categorizing resectability. Surgeons were more accurate and efficient using AI-generated reports. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Chang in this issue.

Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer.

BACKGROUND: To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features. METHODS: Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features. RESULTS: Patients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77-0.91) and validation (AUC = 0.85, 95% CI = 0.73-0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features. CONCLUSIONS: Machine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.

A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients. METHODS: Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model's performance was validated in both internal and external test cohorts. RESULTS: Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05). CONCLUSION: A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

MR signature matching (MRSIGMA) implementation for true real-time free-breathing volumetric imaging with sub-200 ms latency on an MR-Linac.

PURPOSE: Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac. METHODS: MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance. RESULTS: Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk. CONCLUSION: The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion.

Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

PURPOSE: For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. METHODS: The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. RESULTS: The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. CONCLUSION: Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients.

BACKGROUND: Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions. METHODS: We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS. RESULTS: The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69). CONCLUSIONS: Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.

Accuracy and Prognostic Impact of Nodal Status on Preoperative Imaging for Management of Pancreatic Neuroendocrine Tumors: A Multi-Institutional Study.

BACKGROUND: We sought to define the accuracy of preoperative imaging to detect lymph node metastasis (LNM) among patients with pancreatic neuroendocrine tumors (pNETs), as well as characterize the impact of preoperative imaging nodal status on survival. METHODS: Patients who underwent curative-intent resection for pNETs between 2000 and 2020 were identified from eight centers. Sensitivity and specificity of computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)-CT, and OctreoScan for LNM were evaluated. The impact of preoperative lymph node status on lymphadenectomy (LND), as well as overall and recurrence-free survival was defined. RESULTS: Among 852 patients, 235 (27.6%) individuals had LNM on final histologic examination (hN1). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 12.4%, 98.1%, 71.8%, and 74.4% for CT, 6.3%, 100%, 100%, and 80.1% for MRI, 9.5%, 100%, 100%, and 58.7% for PET, 11.3%, 97.5%, 66.7%, and 70.8% for OctreoScan, respectively. Among patients with any combination of these imaging modalities, overall sensitivity, specificity, PPV, and NPV was 14.9%, 97.9%, 72.9%, and 75.1%, respectively. Preoperative N1 on imaging (iN1) was associated with a higher number of LND (iN1 13 vs. iN0 9, p = 0.003) and a higher frequency of final hN1 versus preoperative iN0 (iN1 72.9% vs. iN0 24.9%, p < 0.001). Preoperative iN1 was associated with a higher risk of recurrence versus preoperative iN0 (median recurrence-free survival, iN1→hN1 47.5 vs. iN0→hN1 92.7 months, p = 0.05). CONCLUSIONS: Only 4% of patients with LNM on final pathologic examine had preoperative imaging that was suspicious for LNM. Traditional imaging modalities had low sensitivity to determine nodal status among patients with pNETs.

Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model.

BACKGROUND: PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs). PATIENTS AND METHODS: An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally. RESULTS: Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm). CONCLUSIONS: Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.

Larger Tumor Size and Elevated Serum Chromogranin A Levels Predict Metastatic Disease on DOTATATE Imaging in Patients with Gastroenteropancreatic Neuroendocrine Tumors.

PURPOSE: DOTATATE PET/CT (DOTATATE) is superior to conventional imaging in detecting metastasis for gastroenteropancreatic neuroendocrine tumors (GEP-NETs). However, limited availability, high-cost, and additive radiation exposure necessitate guidelines for its use. This study seeks to investigate the relationship between clinical characteristics and metastasis on DOTATATE. METHODS: This was a retrospective analysis of 815 patients who underwent DOTATATE at UCLA from 2014 to 2022. After applying inclusion and exclusion criteria, the study cohort consisted of 163 patients with pathologically diagnosed GEP-NETs, who either underwent primary tumor resection within 1-year prior, or had not undergone resection at the time of DOTATATE imaging. The presence of metastasis was determined using DOTATATE. Fisher's exact test, chi-squared test, and Mann-Whitney test were conducted to compare intergroup difference. Multivariate analysis was performed to identify clinical characteristics associated with metastasis on DOTATATE. RESULTS: Of patients with GEP-NETs, 40.5% (n = 66) were diagnosed with metastases by using DOTATATE. Those with metastatic disease were more likely to exhibit a larger primary tumor size (median 3.4 vs. 1.2, cm, P < 0.001), elevated serum chromogranin A level (CgA, median 208 vs. 97, mg/ml, P = 0.005), and higher tumor grade (P < 0.001). Primary tumor size ≥2 cm and serum CgA level ≥150 ng/mL for metastatic disease had a sensitivity and specificity of 64% and 89%, and 72% and 59%, respectively. Multivariate analysis demonstrated that primary tumor size (≥2/<2, cm, odds ratio [OR] 47.90, P < 0.001), tumor functionality (functional/nonfunctional, adjusted OR 10.17 P = 0.008), serum CgA level (≥150/<150, ng/ml, OR 6.25, P = 0.005), and tumor grade G2 (G2/G1, OR 9.6, P < 0.001) were independently associated with metastases on DOTATATE. CONCLUSIONS: Among patients with GEP-NETs, primary tumor size ≥2 cm, serum CgA level ≥150 ng/mL, and tumor grade G2 are associated with an increased risk of metastases on DOTATATE, and these predictors may be helpful to identify patients where DOTATATE is indicated for complete staging.

Robotic Ultrasound-Guided Central Pancreatectomy with Main Pancreatic Duct Endoscopy Evaluation for High-Risk, Mixed-Type Intraductal Papillary Mucinous Neoplasm.

BACKGROUND: Central pancreatectomy (CP) is a parenchymal-sparing technique indicated for the resection of selected lesions of the neck or proximal body of the pancreas.1,2 The risk of postoperative complications is theoretically doubled because the surgeon has to manage two cut surfaces of the pancreas. The video shows a fully robotic CP to treat a 62-year-old male patient with a mixed-type intraductal papillary mucinous neoplasm (IPMN) of the pancreatic neck, using ultrasound (US) and Wirsung endoscopic evaluation to guide the pancreatic resection and ensure optimal resection margins. MATERIALS AND METHODS: A US-guided robotic CP was carried out, and an intraoperative endoscopic evaluation of the MPD was performed to determine the distal transection level. A transmesocolic, end-to-side, robot-sewn Wirsung-jejunostomy with internal MPD stenting was then created. The procedure was completed with a side-to-side jejunojejunostomy. RESULTS: The operative time was 290 min, with negligible blood loss. During the postoperative course, the patient experienced bleeding from a branch of the gastroduodenal artery with subsequent fluid collection, which was successfully treated with angioembolization and percutaneous drainage. He was discharged home on postoperative day 22. Final pathology revealed a non-invasive IPMN with low-grade dysplasia and free surgical margins. At 12 months of follow-up, the patient was doing well, with no evidence of local recurrence and endocrine or exocrine pancreatic insufficiency. CONCLUSIONS: The combination of robotic surgery with intraoperative US and Wirsungoscopy may offer distinct technical advantages for challenging pancreatectomies that follow the principles of parenchymal-sparing surgery.

Cadherin-17 as a target for the immunoPET of adenocarcinoma.

PURPOSE: Cadherin-17 (CDH17) is a calcium-dependent cell adhesion protein that is overexpressed in several adenocarcinomas, including gastric, colorectal, and pancreatic adenocarcinoma. High levels of CDH17 have been linked to metastatic disease and poor prognoses in patients with these malignancies, fueling interest in the protein as a target for diagnostics and therapeutics. Herein, we report the synthesis, in vitro validation, and in vivo evaluation of a CDH17-targeted 89Zr-labeled immunoPET probe. METHODS: The CDH17-targeting mAb D2101 was modified with an isothiocyanate-bearing derivative of desferrioxamine (DFO) to produce a chelator-bearing immunoconjugate - DFO-D2101 - and flow cytometry and surface plasmon resonance (SPR) were used to interrogate its antigen-binding properties. The immunoconjugate was then radiolabeled with zirconium-89 (t1/2 ~ 3.3 days), and the serum stability and immunoreactive fraction of [89Zr]Zr-DFO-D2101 were determined. Finally, [89Zr]Zr-DFO-D2101's performance was evaluated in a trio of murine models of pancreatic ductal adenocarcinoma (PDAC): subcutaneous, orthotopic, and patient-derived xenografts (PDX). PET images were acquired over the course of 5 days, and terminal biodistribution data were collected after the final imaging time point. RESULTS: DFO-D2101 was produced with a degree of labeling of ~ 1.1 DFO/mAb. Flow cytometry with CDH17-expressing AsPC-1 cells demonstrated that the immunoconjugate binds to its target in a manner similar to its parent mAb, while SPR with recombinant CDH17 revealed that D2101 and DFO-D2101 exhibit nearly identical KD values: 8.2 × 10-9 and 6.7 × 10-9 M, respectively. [89Zr]Zr-DFO-D2101 was produced with a specific activity of 185 MBq/mg (5.0 mCi/mg), remained >80% stable in human serum over the course of 5 days, and boasted an immunoreactive fraction of >0.85. In all three murine models of PDAC, the radioimmunoconjugate yielded high contrast images, with high activity concentrations in tumor tissue and low uptake in non-target organs. Tumoral activity concentrations reached as high as >60 %ID/g in two of the cohorts bearing PDXs. CONCLUSION: Taken together, these data underscore that [89Zr]Zr-DFO-D2101 is a highly promising probe for the non-invasive visualization of CDH17 expression in PDAC. We contend that this radioimmunoconjugate could have a significant impact on the clinical management of patients with both PDAC and gastrointestinal adenocarcinoma, most likely as a theranostic imaging tool in support of CDH17-targeted therapies.

Preoperative assessment of lymph nodal metastases with [68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours.

PURPOSE: Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [68Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. METHODS: Seventy-two patients who performed preoperative [68Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [68Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney). RESULTS: LN metastases were detected in 29/72 patients at histology. [68Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*). CONCLUSION: In this study, [68Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.