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Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening. A deep learning model provides high accuracy in detecting pancreatic lesions in multicenter data, outperforming radiology specialists.

ObjectivePancreatic ductal adenocarcinoma (PDAC) is the most aggressive type of GI tumour, and it possesses deregulated cellular energetics. Although recent advances in PDAC biology have led to the discovery of recurrent genetic mutations in Kras, TP53 and SMAD4, which are related to this disease, clinical application of the molecular phenotype of PDAC remains challenging.DesignWe combined molecular imaging technology (positron emission tomography/CT) and immunohistochemistry to evaluate the correlation between the maximum standardised uptake value and SMAD4 expression and examined the effect of SMAD4 on glycolysis through in vitro and in vivo experiments. Furthermore, we identified the effect of SMAD4 on metabolic reprogramming by metabolomics and glucose metabolism gene expression analyses. Dual luciferase reporter assays and chromatin immunoprecipitation were performed to identify whether SMAD4 functioned as a transcription factor for phosphoglycerate kinase 1 (PGK1) in PDAC cells. Proliferative and metastatic assays were performed to examine the effect of PGK1 on the malignant behaviour of PDAC.ResultsWe provide compelling evidence that the glycolytic enzyme PGK1 is repressed by transforming growth factor-β/SMAD4. Loss of SMAD4 induces PGK1 upregulation in PDAC, which enhances glycolysis and aggressive tumour behaviour. Notably, in SMAD4-negative PDAC, nuclear PGK1 preferentially drives cell metastasis via mitochondrial oxidative phosphorylation induction, whereas cytoplasmic PGK1 preferentially supports proliferation by functioning as a glycolytic enzyme. The PDAC progression pattern and distinct PGK1 localisation combine to predict overall survival and disease-free survival.ConclusionPGK1 is a decisive oncogene in patients with SMAD4-negative PDAC and can be a target for the development of a therapeutic strategy for SMAD4-negative PDAC.

Desmoplasia describes the deposition of extensive extracellular matrix and defines primary pancreatic ductal adenocarcinoma (PDA). The acellular component of this stroma has been implicated in PDA pathogenesis and is being targeted therapeutically in clinical trials. By analyzing the stromal content of PDA samples from numerous annotated PDA data sets and correlating stromal content with both anatomic site and clinical outcome, we found PDA metastases in the liver, the primary cause of mortality to have less stroma, have higher tumor cellularity than primary tumors. Experimentally manipulating stromal matrix with an anti-lysyl oxidase like-2 (anti-LOXL2) antibody in syngeneic orthotopic PDA mouse models significantly decreased matrix content, led to lower tissue stiffness, lower contrast retention on computed tomography, and accelerated tumor growth, resulting in diminished overall survival. These studies suggest an important protective role of stroma in PDA and urge caution in clinically deploying stromal depletion strategies.

The management of small and incidental branch duct intraductal papillary mucinous neoplasms (BD-IPMNs) still is of concern. The aim is assessing the safety of a surveillance protocol through the evaluation of their progression to malignancy. All presumed BD-IPMNs observed from 2000 to 2016 were included. Only patients presenting without worrisome features (WFs) and high-risk stigmata (HRS) at diagnosis were included. Development of WF, HRS, pancreatic cancer (PC), and survival were analyzed. BD-IPMNs were defined as trivial in the continuing absence of WF/HRS after 5 years of surveillance. The age-specific standardized incidence ratio of PC in the general population was used for comparison. A total of 1,036 BD-IPMNs without WF/HRS at diagnosis were included, 4.2% developed WF or HRS, and 1.1% developed PC after a median of 62 months. The median cyst growth rate was 0 mm/yr. A growth rate ≥2.5 mm/yr and the development of WF resulted independent predictors of PC. The standardized incidence ratio of PC for trivial BD-IPMN (n = 378) was 22.45 (95% confidence interval 8.19-48.86), but considering only patients aged >65 years (n = 198), it decreased to 3.84 (95% confidence interval 0.77-11.20). Surveillance of the vast majority of presumed BD-IPMNs is safe, as the risk of PC is comparable to postoperative mortality of pancreatic surgery. A growth rate ≥2.5 mm/yr is the main predictor of PC, reinforcing the role of repeated observations. A trivial BD-IPMN in patients aged >65 years might not increase the risk of developing PC compared with general population, identifying potential targets for follow-up discontinuation.

BackgroundOperative management of pancreatic ductal adenocarcinoma (PDAC) is complicated by several key decisions during the procedure. Identification of metastatic disease at the outset and, when none is found, complete (R0) resection of primary tumor are key to optimizing clinical outcomes. The use of tumor-targeted molecular imaging, based on photoacoustic and fluorescence optical imaging, can provide crucial information to the surgeon. The first-in-human use of multimodality molecular imaging for intraoperative detection of pancreatic cancer is reported using cetuximab-IRDye800, a near-infrared fluorescent agent that binds to epidermal growth factor receptor.MethodsA dose-escalation study was performed to assess safety and feasibility of targeting and identifying PDAC in a tumor-specific manner using cetuximab-IRDye800 in patients undergoing surgical resection for pancreatic cancer. Patients received a loading dose of 100 mg of unlabeled cetuximab before infusion of cetuximab-IRDye800 (50 mg or 100 mg). Multi-instrument fluorescence imaging was performed throughout the surgery in addition to fluorescence and photoacoustic imaging ex vivo.ResultsSeven patients with resectable pancreatic masses suspected to be PDAC were enrolled in this study. Fluorescence imaging successfully identified tumor with a significantly higher mean fluorescence intensity in the tumor (0.09 ± 0.06) versus surrounding normal pancreatic tissue (0.02 ± 0.01), and pancreatitis (0.04 ± 0.01; p < 0.001), with a sensitivity of 96.1% and specificity of 67.0%. The mean photoacoustic signal in the tumor site was 3.7-fold higher than surrounding tissue.ConclusionsThe safety and feasibilty of intraoperative, tumor-specific detection of PDAC using cetuximab-IRDye800 with multimodal molecular imaging of the primary tumor and metastases was demonstrated.

Abundant fibrotic stroma is a typical feature of pancreatic ductal adenocarcinoma (PDAC) in humans. It is becoming clear that this stromal tissue is not just a bystander in PDAC, but has a crucial role in tumorigenesis, angiogenesis and resistance to therapy. Targeting the stroma for diagnostic and therapeutic purposes opens a new avenue of research in the management of PDAC. Pancreatic ductal adenocarcinoma (PDAC) is one of the five most lethal malignancies worldwide and survival has not improved substantially in the past 30 years. Desmoplasia (abundant fibrotic stroma) is a typical feature of PDAC in humans, and stromal activation commonly starts around precancerous lesions. It is becoming clear that this stromal tissue is not a bystander in disease progression. Cancer–stroma interactions effect tumorigenesis, angiogenesis, therapy resistance and possibly the metastatic spread of tumour cells. Therefore, targeting the tumour stroma, in combination with chemotherapy, is a promising new option for the treatment of PDAC. In this Review, we focus on four issues. First, how can stromal activity be used to detect early steps of pancreatic carcinogenesis? Second, what is the effect of perpetual pancreatic stellate cell activity on angiogenesis and tissue perfusion? Third, what are the (experimental) antifibrotic therapy options in PDAC? Fourth, what lessons can be learned from Langton's Ant (a simple mathematical model) regarding the unpredictability of genetically engineered mouse models? Key Points Desmoplastic stroma is a typical feature of pancreatic ductal adenocarcinoma (PDAC); it is either absent or much less prominent in other tumours of the pancreas Precursor lesions of PDAC (such as pancreatic intraepithelial neoplasms and atypical flat lesions) elicit stromal activation and extracellular matrix deposition around them Precursor lesions of PDAC are beneath the detection limit of conventional diagnostic methods; late diagnosis of PDAC leads to a closed therapeutic time window, thus hindering curative treatment The activated stroma of pancreatic cancer has an effect on the aggressiveness of the tumour as well as its resistance to therapy Activated stroma is a new diagnostic and therapeutic target in the treatment of PDAC

BackgroundPancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients.MethodsA prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation.ResultsA total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data.ConclusionWe present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has become the mainstay of preoperative pathological diagnosis. However, on EUS-FNB specimens, accurate histopathological evaluation is difficult due to low specimen volume with isolated cancer cells and high contamination of blood, inflammatory and digestive tract cells. In this study, we performed annotations for training sets by expert pancreatic pathologists and trained a deep learning model to assess PDAC on EUS-FNB of the pancreas in histopathological whole-slide images. We obtained a high receiver operator curve area under the curve of 0.984, accuracy of 0.9417, sensitivity of 0.9302 and specificity of 0.9706. Our model was able to accurately detect difficult cases of isolated and low volume cancer cells. If adopted as a supportive system in routine diagnosis of pancreatic EUS-FNB specimens, our model has the potential to aid pathologists diagnose difficult cases.

PurposeDiagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment.MethodsFrom August 2016 to October 2020, 148 PDAC patients underwent regional lymph node dissection and scanned preoperatively DECT were enrolled. The virtual monoenergetic image at 40 keV was reconstructed from 100 and 150 keV of DECT. By setting January 1, 2021, as the cut-off date, 113 patients were assigned into the primary set, and 35 were in the test set. DLR models using VMI 40 keV, 100 keV, 150 keV, and 100 + 150 keV images were developed and compared. The best model was integrated with key clinical features selected by multivariate Cox regression analysis to achieve the most accurate prediction.ResultsDLR based on 100 + 150 keV DECT yields the best performance in predicting LNM status with the AUC of 0.87 (95% confidence interval [CI]: 0.85–0.89) in the test cohort. After integrating key clinical features (CT-reported T stage, LN status, glutamyl transpeptadase, and glucose), the AUC was improved to 0.92 (95% CI: 0.91–0.94). Patients at high risk of LNM portended significantly worse overall survival than those at low risk after surgery (P = 0.012).ConclusionsThe DLR model showed outstanding performance for predicting LNM in PADC and hold promise of improving clinical decision-making.

SignificanceCancers, fibroses, and inflammatory disorders are characterized by increased deposition of the extracellular matrix (ECM). ECM biomarkers that are selectively expressed at these disease sites are attractive targets for imaging and therapeutic approaches. Nanobodies against these biomarkers would be pertinent vehicles for the accumulation of imaging and therapeutic cargo at disease sites, potentially increasing specificity and reducing background. We demonstrate the specificity of one such anti-ECM nanobody by using immuno-PET/CT and show that it detects multiple models of cancer, including early lesions and metastases, and also fibroses, with excellent specificity and clarity. Thus, novel strategies for delivering imaging and therapeutic probes specifically to the ECM in disease sites may prove particularly valuable for detection and treatment of cancer in patients. Extracellular matrix (ECM) deposition is a hallmark of many diseases, including cancer and fibroses. To exploit the ECM as an imaging and therapeutic target, we developed alpaca-derived libraries of “nanobodies” against disease-associated ECM proteins. We describe here one such nanobody, NJB2, specific for an alternatively spliced domain of fibronectin expressed in disease ECM and neovasculature. We showed by noninvasive in vivo immuno-PET/CT imaging that NJB2 detects primary tumors and metastatic sites with excellent specificity in multiple models of breast cancer, including human and mouse triple-negative breast cancer, and in melanoma. We also imaged mice with pancreatic ductal adenocarcinoma (PDAC) in which NJB2 was able to detect not only PDAC tumors but also early pancreatic lesions called pancreatic intraepithelial neoplasias, which are challenging to detect by any current imaging modalities, with excellent clarity and signal-to-noise ratios that outperformed conventional 2-fluorodeoxyglucose PET/CT imaging. NJB2 also detected pulmonary fibrosis in a bleomycin-induced fibrosis model. We propose NJB2 and similar anti-ECM nanobodies as powerful tools for noninvasive detection of tumors, metastatic lesions, and fibroses. Furthermore, the selective recognition of disease tissues makes NJB2 a promising candidate for nanobody-based therapeutic applications.