Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.

PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening. MATERIALS AND METHODS: Patients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network. A total of 1223 radiomics features were independently extracted from both segmentation volumes, in the arterial and venous phases separately. Ten final features were selected to train classifiers to identify PanNETs and controls. The cohort was divided into training and testing sets, and performance of classifiers was assessed using area under the receiver operator characteristic curve (AUC), specificity and sensitivity, and compared against two radiologists blinded to the diagnoses. RESULTS: A total of 135 patients with 142 PanNETs, and 135 healthy controls were included. There were 168 women and 102 men, with a mean age of 55.4 ± 11.6 (standard deviation) years (range: 20-85 years). Median PanNET size was 1.3 cm (Q1, 1.0; Q3, 1.5; range: 0.5-1.9). The arterial phase LightGBM model achieved the best performance in the test set, with 90 % sensitivity (95 % confidence interval [CI]: 80-98), 76 % specificity (95 % CI: 62-88) and an AUC of 0.87 (95 % CI: 0.79-0.94). Using features from the automated segmentations, this model achieved an AUC of 0.86 (95 % CI: 0.79-0.93). In comparison, the two radiologists achieved a mean 50 % sensitivity and 100 % specificity using arterial phase CT images. CONCLUSION: Radiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners.

Misdiagnosis of pancreatic intraductal papillary mucinous neoplasms and the challenge of mimicking lesions: imaging diagnosis and differentiation strategies.

The rising prevalence of pancreatic cystic lesions (PCLs), particularly intraductal papillary neoplasms (IPMNs), has been attributed to increased utilization of advanced imaging techniques. Incidental detection of PCLs is frequent in abdominal CT and MRI scans, with IPMNs representing a significant portion of these lesions. Surveillance of IPMNs is recommended due to their malignant potential; however, their overlapping imaging features with benign entities can lead to misdiagnosis, overtreatment, and overutilization of healthcare resources. This paper aims to highlight and differentiate lesions often mistaken for IPMNs, providing insight into their imaging characteristics, diagnostic challenges, and distinctive features while highlighting the incidence of wrong diagnosis for these lesions. These lesions include serous cystadenomas, cystic pancreatic neuroendocrine tumors, mucinous cystic neoplasms, lymphoepithelial cysts, duodenal diverticula, pancreatic schwannomas, chronic pancreatitis, retention cysts, intrapancreatic accessory spleens, pancreatic lipomas, choledochal cysts, and others. Utilizing various imaging modalities, including contrast-enhanced CT, MRI, and EUS, alongside histological and molecular analyses, can aid in accurate diagnosis and appropriate management. Understanding these mimicry scenarios is crucial to avoid unnecessary surveillance, interventions, and the burden they place on both patients and healthcare systems. Improved recognition of these lesions can lead to better patient outcomes and resource allocation.

Diminishing calcifications as a potential predictor of pancreatic ductal adenocarcinoma arising in association with IPMN in patients with chronic pancreatitis.

Chronic pancreatitis (CP) is a progressive benign fibroinflammatory condition involving repeated episodes of pancreatic inflammation, which lead to fibrotic tissue replacement and subsequent pancreatic insufficiency. A lifetime risk of developing pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis is reported to be 1.5%-4%. However, diagnosis of PDAC in patients with CP can be challenging, in part due to overlapping imaging features. In rare instances, pancreatic parenchymal calcifications that are typically associated with chronic pancreatitis may diminish in the case of a developing PDAC. In this article, we present a patient with chronic pancreatitis in whom calcifications decreased at the time of pancreatic ductal adenocarcinoma diagnosis, as compared to prior CT imaging. The unique imaging features of "diminishing calcifications" associated with a hypoattenuating lesion can potentially be a useful sign of pancreatic ductal adenocarcinoma and may aid in early diagnosis and prompt treatment intervention.

A Confirmed Extrarenal Birt-Hogg-Dubé-Associated Oncocytic Neoplasm.

Birt-Hogg-Dubé (BHD) syndrome is a rare inherited disease characterized by a variety of renal epithelial tumors and oncocytosis, with extrarenal manifestations primarily consisting of pulmonary cysts and cutaneous fibrofolliculomas. Here we report a unique case of a primary extrarenal BHD-associated oncocytic epithelial neoplasm which arose between the duodenum and head of the pancreas. The unusual morphology and immunoprofile of this lesion defied classification as any previously reported entity, despite an extensive diagnostic workup. The immunohistochemical and molecular features indicate the tumor was driven by FLCN loss, and thus a consequence of the underlying germline mutation with a somatic second hit. This tumor is the first reported example of an extrarenal BHD-associated oncocytic epithelial tumor driven by FLCN loss.

Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst.

BACKGROUND/OBJECTIVES: Pancreatic cyst management can be distilled into three separate pathways - discharge, monitoring or surgery- based on the risk of malignant transformation. This study compares the performance of artificial intelligence (AI) models to clinical care for this task. METHODS: Two explainable boosting machine (EBM) models were developed and evaluated using clinical features only, or clinical features and cyst fluid molecular markers (CFMM) using a publicly available dataset, consisting of 850 cases (median age 64; 65 % female) with independent training (429 cases) and holdout test cohorts (421 cases). There were 137 cysts with no malignant potential, 114 malignant cysts, and 599 IPMNs and MCNs. RESULTS: The EBM and EBM with CFMM models had higher accuracy for identifying patients requiring monitoring (0.88 and 0.82) and surgery (0.66 and 0.82) respectively compared with current clinical care (0.62 and 0.58). For discharge, the EBM with CFMM model had a higher accuracy (0.91) than either the EBM model (0.84) or current clinical care (0.86). In the cohort of patients who underwent surgical resection, use of the EBM-CFMM model would have decreased the number of unnecessary surgeries by 59 % (n = 92), increased correct surgeries by 7.5 % (n = 11), identified patients who require monitoring by 122 % (n = 76), and increased the number of patients correctly classified for discharge by 138 % (n = 18) compared to clinical care. CONCLUSIONS: EBM models had greater sensitivity and specificity for identifying the correct management compared with either clinical management or previous AI models. The model predictions are demonstrated to be interpretable by clinicians.

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.

Pancreatic Cancer Surveillance and Survival of High-Risk Individuals.

Importance: Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with increasing incidence. The majority of PDACs are incurable at presentation, but population-based screening is not recommended. Surveillance of high-risk individuals for PDAC may lead to early detection, but the survival benefit is unproven. Objective: To compare the survival of patients with surveillance-detected PDAC with US national data. Design, Setting, and Participants: This comparative cohort study was conducted in multiple US academic medical centers participating in the Cancer of the Pancreas Screening program, which screens high-risk individuals with a familial or genetic predisposition for PDAC. The comparison cohort comprised patients with PDAC matched for age, sex, and year of diagnosis from the Surveillance, Epidemiology, and End Results (SEER) program. The Cancer of the Pancreas Screening program originated in 1998, and data collection was done through 2021. The data analysis was performed from April 29, 2022, through April 10, 2023. Exposures: Endoscopic ultrasonography or magnetic resonance imaging performed annually and standard-of-care surgical and/or oncologic treatment. Main Outcomes and Measures: Stage of PDAC at diagnosis, overall survival (OS), and PDAC mortality were compared using descriptive statistics and conditional logistic regression, Cox proportional hazards regression, and competing risk regression models. Sensitivity analyses and adjustment for lead-time bias were also conducted. Results: A total of 26 high-risk individuals (mean [SD] age at diagnosis, 65.8 [9.5] years; 15 female [57.7%]) with PDAC were compared with 1504 SEER control patients with PDAC (mean [SD] age at diagnosis, 66.8 [7.9] years; 771 female [51.3%]). The median primary tumor diameter of the 26 high-risk individuals was smaller than in the control patients (2.5 [range, 0.6-5.0] vs 3.6 [range, 0.2-8.0] cm, respectively; P < .001). The high-risk individuals were more likely to be diagnosed with a lower stage (stage I, 10 [38.5%]; stage II, 8 [30.8%]) than matched control patients (stage I, 155 [10.3%]; stage II, 377 [25.1%]; P < .001). The PDAC mortality rate at 5 years was lower for high-risk individuals than control patients (43% vs 86%; hazard ratio, 3.58; 95% CI, 2.01-6.39; P < .001), and high-risk individuals lived longer than matched control patients (median OS, 61.7 [range, 1.9-147.3] vs 8.0 [range, 1.0-131.0] months; 5-year OS rate, 50% [95% CI, 32%-80%] vs 9% [95% CI, 7%-11%]). Conclusions and Relevance: These findings suggest that surveillance of high-risk individuals may lead to detection of smaller, lower-stage PDACs and improved survival.

Adrenal cysts less than 10 cm can be safely observed.

BACKGROUND: Adrenal cysts are rare and appropriate management is unclear due to a lack of data on their natural history. Understanding adrenal cyst growth patterns would assist in clinical management. METHODS: This single-institution study included all adult patients diagnosed with simple adrenal cysts between 2004 and 2021. Baseline characteristics and outcomes of those who underwent resection (ADX) or observation (OBS) were compared using the chi-squared test, student's t-test, and Wilcoxon rank-sum test. Growth curves and sensitivity analysis were plotted for all patients who had follow-up imaging. RESULTS: We identified 77 patients with imaging-confirmed adrenal cysts. The majority were female (75.3%) and more than half were white (55.8%). One-third of patients underwent ADX, and the remaining were observed. ADX patients were younger (median age [IQR]: 55.5 y [45.0-68.2 y] vs. 44.2 y [38.7-55.0 y], p = 0.01) and more likely to be Hispanic (12% vs. 0%, p = 0.05). ADX patients presented with larger cysts (5.6 vs. 2.6 cm, p = 0.002). The median time from diagnosis to last follow-up was 1.1 y for ADX and 4.1 y for OBS. Average growth for OBS was 0.3 cm/y, while average growth for ADX was 3.9 cm/y. In ADX patients, cysts >10 cm grew significantly faster than cysts <10 cm (median growth rate 13.2 cm/y vs. 0.3 cm/y, p < 0.05). There was no adrenal malignancy diagnosis, hyperfunctionality, or observation-related complications (e.g., rupture). CONCLUSION: While size >4-6 cm has guided surgical referral for solid adrenal masses, this study demonstrates a size threshold of 10 cm, below which asymptomatic, simple adrenal cysts can safely be observed.

Outcomes Following a False-Positive Multi-Cancer Early Detection Test: Results from DETECT-A, the First Large, Prospective, Interventional MCED Study.

Guideline recommended standard of care screening is available for four cancer types; most cancer-related deaths are caused by cancers without standard of care screening. DETECT-A is the first prospective interventional trial evaluating a multi-cancer early detection (MCED) blood test (CancerSEEK) in women without a history of cancer, providing the first opportunity to assess the long-term outcomes of individuals with false-positive (FP) MCED results. This prospective analysis of DETECT-A participants with FP results evaluates the performance of an imaging-based diagnostic workflow and examines cancer risk following a FP result. This analysis included all DETECT-A participants with a positive CancerSEEK test and subsequent flourine-18 fluorodeoxyglucose positron emission tomography-IV contrast-enhanced computed tomography (18-F-FDG PET-CT) imaging and clinical workup indicating no evidence of cancer within 1 year of enrollment (n = 98). Medical records, study interactions, and study surveys were used to assess cancer incidence, treatments, and clinical outcomes through August 2023. Ninety-five of 98 participants with a FP result remained cancer-free with a median follow-up of 3.6 years (IQR: 2.5-4.1) from determination of FP status. Three incident cancers were observed over the follow-up period. One bilateral stage IIIC ovarian cancer was diagnosed 1.9 years after determination of FP status; two stage I breast cancers were diagnosed 0.1 and 1.6 years from determination of FP status. The annual incidence rate of cancer during follow-up from FP determination was 1.0% (95% confidence interval, 0.2%-2.8%). Participants with a positive CancerSEEK test who underwent 18-F-FDG PET-CT and clinical workup without cancer findings had low risk for cancer over the following several years. Prevention Relevance: This study provides multiyear clinical outcomes data following a false-positive multi-cancer early detection test for individuals participating in a prospective interventional trial. It provides a preliminary performance assessment of an imaging-based diagnostic workflow following a false-positive multi-cancer early detection test.

Cross-sectional imaging of gastric cancer: pearls, pitfalls and lessons learned from multidisciplinary conference.

Gastric cancer is rising in prevalence associated with high mortality, primarily due to late-stage detection, underscoring the imperative for early and precise diagnosis. Etiology involves an interplay of genetic susceptibilities and environmental factors with a prominent role of Helicobacter pylori infection. Due to its often-delayed symptom presentation, prompt and accurate diagnosis is necessary. A multimodal imaging approach, including endoscopic ultrasound (EUS), multi-detector computed tomography (MDCT), and magnetic resonance imaging (MRI) is critical for accurate staging. Each modality contributes unique advantages and limitations, highlighting the importance of integrating diagnostic strategy. Moreover, multidisciplinary conferences offer a vital collaborative platform, bringing together specialists from diverse fields for treatment planning. This synergistic approach not only enhances diagnostic precision but also improves patient outcome. This review highlights the critical role of imaging in diagnosis, staging, and management and advocates for interdisciplinary collaboration in early detection and comprehensive management of gastric cancer, aiming to reduce mortality.

Cross-sectional imaging of mimics of inflammatory bowel disease: not everything is Crohn's disease or ulcerative colitis.

Acute and chronic bowel pathologies can often be mistaken for manifestations of inflammatory bowel disease (IBD), and there are many entities with imaging and clinical features that overlap with IBD, making diagnosis difficult. We describe multiple inflammatory, infectious, neoplastic, and vascular entities with imaging and clinical features that may mimic IBD, and highlight differentiating features to assist in diagnosis.

Early detection of pancreatic cancer in the era of precision medicine.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

Hidden in plain sight: commonly missed early signs of pancreatic cancer on CT.

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis mostly due to the advanced stage at which disease is diagnosed. Early detection of disease at a resectable stage is, therefore, critical for improving outcomes of patients. Prior studies have demonstrated that pancreatic abnormalities may be detected on CT in up to 38% of CT studies 5 years before clinical diagnosis of PDAC. In this review, we highlight commonly missed signs of early PDAC on CT. Broadly, these commonly missed signs consist of small isoattenuating PDAC without contour deformity, isolated pancreatic duct dilatation and cutoff, focal pancreatic enhancement and focal parenchymal atrophy, pancreatitis with underlying PDAC, and vascular encasement. Through providing commentary on demonstrative examples of these signs, we demonstrate how to reduce the risk of missing or misinterpreting radiological features of early PDAC.

Outcomes following a false positive multi-cancer early detection (MCED) test: Results from DETECT-A, the first large, prospective, interventional MCED study.

Guideline recommended standard of care (SoC) screening is available for four cancer types; most cancer-related deaths are caused by cancers without SoC screening. DETECT-A is the first prospective interventional trial evaluating an MCED blood test (CancerSEEK) in women without a history of cancer, providing the first opportunity to assess the long-term outcomes of individuals with false positive (FP) MCED results. This prospective analysis of DETECT-A participants with FP results evaluates the performance of an imaging-based diagnostic workflow and examines cancer risk following a FP result. This analysis included all DETECT-A participants with a positive CancerSEEK test and subsequent flourine-18 fluorodeoxyglucose positron emission tomography-IV contrast enhanced computed tomography (18-F-FDG PET-CT) imaging and clinical workup indicating no evidence of cancer within one year of enrollment (n=98). Medical records, study interactions, and study surveys were used to assess cancer incidence, treatments, and clinical outcomes through August 2023. Ninety-five of 98 participants with a FP result remained cancer-free with a median follow-up of 3.6 years (IQR: 2.5-4.1) from determination of FP status. Three incident cancers were observed over the follow-up period. One bilateral stage IIIC ovarian cancer was diagnosed 1.9 years after determination of FP status; two stage I breast cancers were diagnosed 0.1 and 1.6 years from determination of FP status. The annual incidence rate of cancer during follow-up from FP determination was 1.0% (95% CI: 0.2%-2.8%). Participants with a positive CancerSEEK test who underwent 18-F-FDG PET-CT and clinical workup without cancer findings had low risk for cancer over the following several years.

PanIN or IPMN? Redefining Lesion Size in 3 Dimensions.

Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size. IPMNs are defined as macroscopic: generally >1.0 cm and visible in CT, and PanINs are defined as microscopic: generally <0.5 cm and not identifiable in CT. As 2D evaluation fails to take into account 3D structures, we hypothesized that this classification would fail in evaluation of high-resolution, 3D images. To characterize the size and prevalence of PanINs in 3D, 47 thick slabs of pancreas were harvested from grossly normal areas of pancreatic resections, excluding samples from individuals with a diagnosis of an IPMN. All patients but one underwent preoperative CT scans. Through construction of cellular resolution 3D maps, we identified >1400 ductal precursor lesions that met the 2D histologic size criteria of PanINs. We show that, when 3D space is considered, 25 of these lesions can be digitally sectioned to meet the 2D histologic size criterion of IPMN. Re-evaluation of the preoperative CT images of individuals found to possess these large precursor lesions showed that nearly half are visible on imaging. These findings demonstrate that the clinical classification of PanIN and IPMN fails in evaluation of high-resolution, 3D images, emphasizing the need for re-evaluation of classification guidelines that place significant weight on 2D assessment of 3D structures.

3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

Cinematic rendering of primary adrenal lymphoma.

Primary adrenal lymphoma (PAL) is a particularly rare subset of malignant adrenal neoplasms, accounting for ∼1% of all non-Hodgkin's lymphomas. Reported outcomes of PAL, though limited, are dismal, with a 12-month survival rate of ∼20%. PAL is treated with polychemotherapy and early tissue diagnosis to allow initiation of chemotherapy is associated with improved outcomes. Early and accurate radiological diagnosis of PAL is therefore essential in improving outcomes through informing decisions to biopsy and thereby facilitating timely initiation of chemotherapy. To date, however, imaging features of PAL have not been conclusively defined, and a range of divergent imaging appearances have been reported. Cinematic rendering (CR) is a 3D post-processing technique that simulates the propagation and interaction of photons as they pass through the imaged volume. This results in the generation of more photorealistic images that may allow for more comprehensive visualization, description and interpretation of anatomical structures. This manuscript presents the first characterization of the various CR appearances of PAL in the reported literature and provides commentary on the clinical opportunities afforded by CR in the workup of these heterogenous tumors.