Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis

Tumor immune microenvironment in pancreatic ductal adenocarcinoma revisited - Exploring the "Space"

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most deadly malignancies with a highly immunosuppressive tumor immune microenvironment (TIME) that hinders effective therapy. PDAC is characterized by significant heterogeneity in immune cell composition, spatial distribution and activation states, which impacts tumor progression and treatment response. Tumour-infiltrating lymphocytes (TILs), including CD4+ T-helper cells, CD8+ cytotoxic T-cells and FOXP3+ regulatory T-cells, play a key role in immune regulation, yet PDAC is largely an immunologically "cold" tumour with limited effector T-cell infiltration. The surrounding cellular microenvironment, particularly Cancer Associated Fibroblasts (CAFs) and macrophages, contributes to immune evasion by promoting a fibrotic and desmoplastic barrier that limits TIL infiltration. The prognostic significance of TILs is increasingly recognized, with higher densities correlating with improved survival, whereas regulatory T-cell infiltration and immunosuppressive stromal interactions are associated with poor outcomes. Emerging therapeutic strategies targeting the TIME (e.g., CAFs), immune checkpoint inhibitors, and TIL-based therapies offer the potential to overcome resistance. Future research must focus on optimizing immunotherapy strategies and unravelling the complex stromal-immune interactions to improve clinical translation.

Interpretable Prognostic Modeling for Postoperative Pancreatic Cancer Using Multi-machine Learning and Habitat Radiomics: A Multi-center Study

RATIONALE AND OBJECTIVES

Accurate risk stratification is critical for guiding personalized treatment in resectable pancreatic cancer (RPC). This retrospective study assessed the utility of habitat radiomics for predicting recurrence-free survival (RFS) in RPC patients.

MATERIALS AND METHODS

A total of 455 RPC patients were divided into training and external test sets from January 2018 to July 2024. Tumors were segmented into subregions using habitat radiomics to capture localized heterogeneity. Seven machine learning models, including random survival forest (RSF), were compared using Harrell's C-index. The optimal model underwent further validation through time-dependent ROC and Kaplan-Meier (KM) analyses. Shapley additive explanations (SHAP) and survival local interpretable model-agnostic explanations (SurvLIME) were applied to enhance model interpretability.

RESULTS

The RSF model based on habitat radiomics achieved a C-index of 0.828 in the training cohort and 0.702, 0.680 in external test sets, outperforming whole-tumor radiomics (p<0.05). Time-dependent ROC analysis showed AUCs of 0.71, 0.83, and 0.79 at 0.5, 1, and 2 years in the first test set, and 0.65, 0.79, and 0.75 in the second test set. KM analysis revealed that the predicted low-risk groups had significantly longer RFS compared to the predicted high-risk groups in both external test sets (all p<0.05). Interpretability analysis identified key variables, including Feature 1, Feature 5, Feature 2, and Feature 4 from Habitat Subregion 1, and Feature 3 from Habitat Subregion 3.

CONCLUSION

The habitat radiomics RSF machine learning model improves prognostic accuracy and interpretability for postoperative RPC, providing a promising tool for personalized management.

Conditions at risk of pancreatic cancer: The radiology perspective

Pancreatic cancer remains one for the most aggressive cancer worldwide, with pancreatic ductal adenocarcinoma being the most common malignant pancreatic lesion, associated with poor prognosis. While surgical resection is the only curative treatment, only a minority of patients is eligible for surgery due to its diagnosis at advanced stages. Therefore, strategies for early detection of pancreatic cancer are needed. This article aims to provide a state-of-the-art review of the most common conditions associated to an increased risk of pancreatic cancer. Conditions linked to risk of pancreatic cancer development include certain pancreato-biliary anatomical variants, intraductal papillary mucinous neoplasms, mucinous cystic neoplasm, and familial pancreatic cancer with specific genetic mutations. Early imaging signs of pancreatic cancer can also be incidentally encountered on CT or MRI performed for other indications and they should be promptly recognized by the radiologists in order to avoid delays in the diagnosis. The features include focal pancreatic atrophy, contour deformity, dilation of the main pancreatic duct (MPD), changes in the caliber of the MPD, abrupt interruption of the MPD, and biliary tree dilation. MRI with the adoption of abbreviated protocols has been increasingly evaluated for the follow-up of cystic lesions. Although screening of the general population is not recommended due to the low incidence and high costs, surveillance with MRI can be considered in selected high-risk individuals.

The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease

Artificial intelligence (AI) is a rapidly evolving presence in all fields and industries, with the ability to both improve quality and reduce the burden of human effort. Gastroenterology is a field with a focus on diagnostic techniques and procedures, and AI and big data have established and growing roles to play. Alongside these opportunities are challenges, which will evolve in parallel.

Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and developing imaging biomarkers, through novel imaging techniques, and most notably, artificial intelligence (AI) technology. This review provides an overview of these advancements, with a focus on deep learning methods for early detection of PDA.

Enhancing diagnostic frameworks in pancreatic cancer imaging: A critical appraisal

This letter to the editor critically appraises the study by Luo et al. While the study provides valuable insights into imaging-pathology correlations in pancreatic cancer, we identify several opportunities for enhancing its clinical relevance. Notably, the exclusion of magnetic resonance cholangiopancreatography and positron emission tomography/computed tomography imaging limits the study's diagnostic scope, as these modalities offer superior capabilities in differentiating benign from malignant lesions and assessing metabolic tumor activity. Additionally, the retrospective, cross-sectional design restricts the potential for dynamic insights into disease progression. We also highlight the untapped potential of radiomics-based analyses, which could significantly improve diagnostic accuracy and prognostic assessments. We recommend integrating these advanced imaging modalities, adopting longitudinal study designs, and leveraging radiomics approaches in future research to enhance the diagnostic frameworks in pancreatic cancer imaging.

Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning

BACKGROUND

This study analyzed different classifier models for differentiating pancreatic adenocarcinoma from surrounding healthy pancreatic tissue based on radiomic analysis of magnetic resonance (MR) images.

METHODS

We observed T2W-FS and ADC images obtained by 1.5T-MR of 87 patients with histologically proven pancreatic adenocarcinoma for training and validation purposes and then tested the most accurate predictive models that were obtained on another group of 58 patients. The tumor and surrounding pancreatic tissue were segmented on three consecutive slices, with the largest area of interest (ROI) of tumor marked using MaZda v4.6 software. This resulted in a total of 261 ROIs for each of the observed tissue classes in the training-validation group and 174 ROIs in the testing group. The software extracted a total of 304 radiomic features for each ROI, divided into six categories. The analysis was conducted through six different classifier models with six different feature reduction methods and five-fold subject-wise cross-validation.

RESULTS

In-depth analysis shows that the best results were obtained with the Random Forest (RF) classifier with feature reduction based on the Mutual Information score (all nine features are from the co-occurrence matrix): an accuracy of 0.94/0.98, sensitivity of 0.94/0.98, specificity of 0.94/0.98, and F1-score of 0.94/0.98 were achieved for the T2W-FS/ADC images from the validation group, retrospectively. In the testing group, an accuracy of 0.69/0.81, sensitivity of 0.86/0.82, specificity of 0.52/0.70, and F1-score of 0.74/0.83 were achieved for the T2W-FS/ADC images, retrospectively.

CONCLUSIONS

The machine learning approach using radiomics features extracted from T2W-FS and ADC achieved a relatively high sensitivity in the differentiation of pancreatic adenocarcinoma from healthy pancreatic tissue, which could be especially applicable for screening purposes.

Diagnosis of parotid gland tumors using a ternary classification model based on ultrasound radiomics

Objective

This study aimed to evaluate the diagnostic value of two-step ultrasound radiomics models in distinguishing parotid malignancies from pleomorphic adenomas (PAs) and Warthin's tumors (WTs).

Methods

A retrospective analysis was conducted on patients who underwent parotidectomy at our institution between January 2015 and December 2022. Radiomics features were extracted from two-dimensional (2D) ultrasound images using 3D Slicer. Feature selection was performed using the Mann-Whitney U test and seven additional selection methods. Two-step LASSO-BNB and voting ensemble learning modeling algorithm with recursive feature elimination feature selection method (RFE-Voting) models were then applied for classification. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), and internal validation was conducted through fivefold cross-validation.

Results

A total of 336 patients were included in the study, comprising 73 with malignant tumors and 263 with benign lesions (118 WT and 145 PA). The LASSO-NB model demonstrated excellent performance in distinguishing between benign and malignant parotid lesions, achieving an AUC of 0.910 (95% CI, 0.907-0.914), with an accuracy of 86.8%, sensitivity of 92.5%, and specificity of 66.7%, significantly outperforming experienced sonographers (accuracy of 61.90%). The RFE-Voting model also showed outstanding performance in differentiating PA from WT, with an AUC of 0.962 (95% CI, 0.959-0.963), accuracy of 83.0%, sensitivity of 84.0%, and specificity of 92.1%, exceeding the diagnostic capability of experienced sonographers (accuracy of 65.39%).

Conclusion

The two-step LASSO-BNB and RFE-Voting models based on ultrasound imaging performed well in distinguishing glandular malignant tumors from PA and WT and have good predictive capabilities, which can provide more useful information for non-invasive differentiation of parotid gland tumors before surgery.

Magnetic MOF-based sensing platform integrated with graphene field-effect transistors for ultrasensitive detection of infectious disease

The development of highly sensitive methods for detecting infectious diseases is crucial for preventing disease spread. In this study, a novel sensing platform for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogens was developed by combining a magnetic metal-organic framework (Fe3O4@MIL-100) with graphene field-effect transistors (GFET). The Fe3O4@MIL-100 magnetic MOF was functionalized with SARS-CoV-2-specific antibodies, enabling highly selective pathogen capture in a phosphate-buffered solution. Following magnetic separation, the captured pathogens were detected using GFETs, with a linear detection range of 1 ag/mL to 10 ng/mL and a detection limit as low as 8.60 ag/mL. Furthermore, the platform has been successfully applied to human serum samples, highlighting its remarkable potential for practical application.

Diagnostic Accuracy of Radiomics in the Early Detection of Pancreatic Cancer: A Systematic Review and Qualitative Assessment Using the Methodological Radiomics Score (METRICS)

BACKGROUND/OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with increasing incidence and low survival rate, primarily due to the late detection of the disease. Radiomics has demonstrated its utility in recognizing patterns and anomalies not perceptible to the human eye. This systematic literature review aims to assess the application of radiomics in the analysis of pancreatic parenchyma images to identify early indicators predictive of PDAC.

METHODS

A systematic search of original research papers was performed on three databases: PubMed, Embase, and Scopus. Two reviewers applied the inclusion and exclusion criteria, and one expert solved conflicts for selecting the articles. After extraction and analysis of the data, there was a quality assessment of these articles using the Methodological Radiomics Score (METRICS) tool. The METRICS assessment was carried out by two raters, and conflicts were solved by a third reviewer.

RESULTS

Ten articles for analysis were retrieved. CT scan was the diagnostic imaging used in all the articles. All the studies were retrospective and published between 2019 and 2024. The main objective of the articles was to generate radiomics-based machine learning models able to differentiate pancreatic tumors from healthy tissue. The reported diagnostic performance of the model chosen yielded very high results, with a diagnostic accuracy between 86.5% and 99.2%. Texture and shape features were the most frequently implemented. The METRICS scoring assessment demonstrated that three articles obtained a moderate quality, five a good quality, and, finally, two articles yielded excellent quality. The lack of external validation and available model, code, and data were the major limitations according to the qualitative assessment.

CONCLUSIONS

There is high heterogeneity in the research question regarding radiomics and pancreatic cancer. The principal limitations of the studies were mainly due to the nature of the trials and the considerable heterogeneity of the radiomic features reported. Nonetheless, the work in this field is promising, and further studies are still required to adopt radiomics in the early detection of PDAC.

CT Imaging of the Pancreas: A Review of Current Developments and Applications

Pancreatic cancer continues to pose daily challenges to clinicians, radiologists, and researchers. These challenges are encountered at each stage of pancreatic cancer management, including early detection, definite characterization, accurate assessment of tumour burden, preoperative planning when surgical resection is possible, prediction of tumour aggressiveness, response to treatment, and detection of recurrence. CT imaging of the pancreas has made major advances in recent years through innovations in research and clinical practice. Technical advances in CT imaging, often in combination with imaging biomarkers, hold considerable promise in addressing such challenges. Ongoing research in dual-energy and spectral photon-counting computed tomography, new applications of artificial intelligence and image rendering have led to innovative implementations that allow now a more precise diagnosis of pancreatic cancer and other diseases affecting this organ. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of CT imaging of the pancreas. By highlighting key contributions in diagnostic imaging, artificial intelligence, and image rendering, this article provides a comprehensive overview of how these innovations are enhancing diagnostic precision and improving outcome in patients with pancreatic diseases.

Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging

Ensemble learning can effectively mitigate the risk of model overfitting during training. This study aims to evaluate the performance of ensemble learning models in predicting tumor deposits in rectal cancer (RC) and identify the optimal model for preoperative clinical decision-making. A total of 199 RC patients were analyzed, with radiomic features extracted from T2-weighted and apparent diffusion coefficient images and selected through advanced statistical methods. After that, the bagging-ensemble learning model (random forest), boosting-ensemble learning model (XGBoost, AdaBoost, LightGBM, and CatBoost), and voting-ensemble learning model (integrating 5 classifiers) were applied and optimized using grid search with tenfold cross-validation. The area under the receiver operator characteristic curve, calibration curve, t-distributed stochastic neighbor embedding (t-SNE), and decision curve analysis were adopted to evaluate the performance of each model. The voting-ensemble learning model (VELM) performs best in the testing cohort, with an AUC of 0.875 and an accuracy of 0.800. Notably, Calibration plots confirmed VELM's stability and t-SNE visualization illustrated clear clustering of radiomic features. Decision curve analysis further validated the VELM's superior net benefit across a range of clinical thresholds, underscoring its potential as a reliable tool for clinical decision-making in RC.

Artificial Intelligence in Pancreatic Imaging: A Systematic Review

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

A predictive clinical-radiomics nomogram for early diagnosis of mesenteric arterial embolism based on non-contrast CT and biomarkers

PURPOSE

Mesenteric artery embolism (MAE) is a relatively uncommon abdominal surgical emergency, but it can lead to catastrophic clinical outcomes if the diagnosis is delayed. This study aims to build a prediction model of clinical-radiomics nomogram for early diagnosis of MAE based on non-contrast computed tomography (CT) and biomarkers.

METHOD

In this retrospective study, a total of 364 patients confirmed as MAE (n = 131) or non-MAE (n = 233) who were randomly divided into a training cohort (70%) and a validation cohort (30%). In the training cohort, the minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms were used to select optimal radiomics features from non-contrast CT images for calculating Radscore which was utilized to establish the radiomics model. Logistic regression analysis was performed to screen clinical factors, and then generate the clinical model. A predictive nomogram model was built using Radscore and the selected clinical risk factors, which was evaluated through the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA).

RESULTS

Thirteen radiomics features were chosen to calculate Radscore. Age, white blood cell (WBC) count, creatine kinase (CK) and D-dimer were determined as the independent clinical factors. The clinical-radiomics nomogram model showed the best performance in training cohort. The nomogram model was with higher area under curve (AUC) value of 0.93, compared to radiomics model with AUC value of 0.90 or clinical model with AUC value of 0.78 in the validation cohort. The calibration curve showed that nomogram model achieved a good fit in both cohorts (P  = 0.59 and 0.92, respectively). The DCA indicated that nomogram model was significantly favorable for clinical usefulness of MAE diagnosis.

CONCLUSIONS

The nomogram provides an effective tool for the early diagnosis of MAE, which may play a crucial role in shortening the time for therapeutic decision-making, thereby reducing the risk of intestinal necrosis and death.

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.

Development and validation of a CT-based radiomics model to predict survival-graded fibrosis in pancreatic ductal adenocarcinoma

BACKGROUND

Tumor fibrosis plays an important role in chemotherapy resistance in pancreatic ductal adenocarcinoma (PDAC); however, there remains a contradiction in the prognostic value of fibrosis. The authors aimed to investigate the relationship between tumor fibrosis and survival in patients with PDAC, classify patients into high- and low-fibrosis groups, and develop and validate a CT-based radiomics model to non-invasively predict fibrosis before treatment.

MATERIALS AND METHODS

This retrospective, bicentric study included 295 patients with PDAC without any treatments before surgery. Tumor fibrosis was assessed using the collagen fraction (CF). Cox regression analysis was used to evaluate the associations of CF with overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) analyses were used to determine the rounded threshold of CF. An integrated model (IM) was developed by incorporating selected radiomic features and clinical-radiological characteristics. The predictive performance was validated in the test cohort (Center 2).

RESULTS

The CFs were 38.22±6.89% and 38.44±8.66% in center 1 (131 patients, 83 males) and center 2 (164 patients, 100 males), respectively ( P =0.814). Multivariable Cox regression revealed that CF was an independent risk factor in the OS and DFS analyses at both centers. ROCs revealed that 40% was the rounded cut-off value of CF. IM predicted CF with areas under the curves (AUCs) of 0.829 (95% CI: 0.753-0.889) and 0.751 (95% CI: 0.677-0.815) in the training and test cohorts, respectively. Decision curve analyses revealed that IM outperformed radiomics model and clinical-radiological model for CF prediction in both cohorts.

CONCLUSIONS

Tumor fibrosis was an independent risk factor for survival of patients with PDAC, and a rounded cut-off value of 40% provided a good differentiation of patient prognosis. The model combining CT-based radiomics and clinical-radiological features can satisfactorily predict survival-grade fibrosis in patients with PDAC.

AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year survival rate and frequent late-stage diagnosis. A significant barrier to early detection even in high-risk cohorts is that the pancreas often appears morphologically normal during the pre-diagnostic phase. Yet, the disease can progress rapidly from subclinical stages to widespread metastasis, undermining the effectiveness of screening. Recently, artificial intelligence (AI) applied to cross-sectional imaging has shown significant potential in identifying subtle, early-stage changes in pancreatic tissue that are often imperceptible to the human eye. Moreover, AI-driven imaging also aids in the discovery of prognostic and predictive biomarkers, essential for personalized treatment planning. This article uniquely integrates a critical discussion on AI's role in detecting visually occult PDAC on pre-diagnostic imaging, addresses challenges of model generalizability, and emphasizes solutions like standardized datasets and clinical workflows. By focusing on both technical advancements and practical implementation, this article provides a forward-thinking conceptual framework that bridges current gaps in AI-driven PDAC research.

Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis

PURPOSE

To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images.

METHOD

Included studies assessed adults diagnosed with PDAC, confirmed by histopathology. Metrics of tests were interpreted by ML algorithms. Studies provided data on sensitivity and specificity. Studies that did not meet the inclusion criteria, segmentation-focused studies, multiple classifiers or non-diagnostic studies were excluded. PubMed, Cochrane Central Register of Controlled Trials, and Embase were searched without restrictions. Risk of bias was assessed using QUADAS-2, methodological quality was evaluated using Radiomics Quality Score (RQS) and a Checklist for AI in Medical Imaging (CLAIM). Bivariate random-effects models were used for meta-analysis of sensitivity and specificity, I2 values and subgroup analysis used to assess heterogeneity.

RESULTS

Nine studies were included and 12,788 participants were evaluated, of which 3,997 were included in the meta-analysis. AI models based on CT scans showed an accuracy of 88.7% (IC 95%, 87.7%-89.7%), sensitivity of 87.9% (95% CI, 82.9%-91.6%), and specificity of 92.2% (95% CI, 86.8%-95.5%). The average score of six radiomics studies was 17.83 RQS points. Nine ML methods had an average CLAIM score of 30.55 points.

CONCLUSIONS

Our study is the first to quantitatively interpret various independent research, offering insights for clinical application. Despite favorable sensitivity and specificity results, the studies were of low quality, limiting definitive conclusions. Further research is necessary to validate these models before widespread adoption.

Early detection of pancreatic cancer: Study design and analytical considerations in biomarker discovery and early phase validation studies

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that is challenging to detect at an early stage. Biomarkers are needed that can detect PDAC early in the course of disease when interventions lead to the best outcomes. We highlight study design and statistical considerations that inform pancreatic cancer early detection biomarker evaluation.

METHODS

We describe experimental design strategies in this setting useful for streamlining biomarker evaluation at each Early Detection Research Network (EDRN) phase of biomarker development. We break the early EDRN phases into sub-phases, proposing objectives, study design strategies, and biomarker performance benchmarks.

RESULTS

The goal of early detection in populations at high-risk of PDAC is described. Evaluating biomarker behavior in patients under surveillance without disease can winnow candidate biomarkers. Potential resources for biomarker validation studies are described.

CONCLUSIONS

Multisite and multidisciplinary collaboration can facilitate study design strategies in this lethal but low incidence disease and streamline the path from biomarker discovery to clinical use. Improvements in analytical and experimental design methods could help accelerate biomarker evaluation through the phases of biomarker development.

Opportunity and Opportunism in Artificial Intelligence-Powered Data Extraction: A Value-Centered Approach

Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions to be made. This charge is rapidly evolving due to forces such as artificial intelligence (AI), big data (opportunistic imaging, imaging prognostication), and advanced diagnostic technologies. A new modernistic paradigm is emerging whereby radiologists, in conjunction with computer algorithms, will be tasked with extracting as much information from imaging data as possible, often without a specific clinical question being posed and independent of any stated clinical need. In addition, AI algorithms are increasingly able to predict long-term outcomes using data from seemingly normal examinations, enabling AI-assisted prognostication. As these algorithms become a standard component of radiology practice, the sheer amount of information they demand will increase the need for streamlined workflows, communication, and data management techniques. In addition, the provision of such information raises reimbursement, liability, and access issues. Guidelines will be needed to ensure that all patients have access to the benefits of this new technology and guarantee that mined data do not inadvertently create harm. In this Review, we discuss the challenges and opportunities relevant to radiologists in this changing landscape, with an emphasis on ensuring that radiologists provide high-value care.

Early prediction of acute pancreatitis with acute kidney injury using abdominal contrast-enhanced CT features

Early prediction of acute pancreatitis (AP) with acute kidney injury (AKI) using abdominal contrast-enhanced CT could effectively reduce the mortality and the economic burden on patients and society. However, this challenge is limited by the imaging manifestations of early-stage AP that are not clearly visible to the naked eye. To address this, we developed a machine learning model using imperceptible variations in the structural changes of pancreas and peripancreatic region, extracted by radiomics and artificial intelligence technology, to screen and stratify the high-risk AP patients at the early stage of AP. The results demonstrate that the machine learning model could screen the high-risk AP with AKI patients with an area under the curve (AUC) of 0.82 for the external cohort, superior to the human radiologists. This finding confirms the significant potential of machine learning in the screening of acute pancreatitis and contributes to personalized treatment and management for AP patients.

Canadian National Pancreas Conference 2023: A Review of Multidisciplinary Engagement in Pancreatic Cancer Care

Pancreatic cancer is a complex malignancy associated with poor prognosis and high symptom burden. Optimal patient care relies on the integration of various sectors in the healthcare field as well as innovation through research. The Canadian National Pancreas Conference (NPC) was co-organized and hosted by Craig's Cause Pancreatic Cancer Society and The Royal College of Physicians and Surgeons in November 2023 in Montreal, Canada. The conference sought to bridge the gap between Canadian healthcare providers and researchers who share the common goal of improving the prognosis, quality of life, and survival for patients with pancreatic cancer. The accredited event featured discussion topics including diagnosis and screening, value-based and palliative care, pancreatic enzyme replacement therapy, cancer-reducing treatment, and an overview of the current management landscape. The present article reviews the NPC sessions and discusses the presented content with respect to the current literature.

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.

The utility of axial imaging among selected patients in the early postoperative period after pancreatectomy

BACKGROUND

Postoperative computed tomography imaging has been shown to play an important role in avoiding failure-to-rescue. We sought to examine the impact of the timing of such imaging studies on outcomes after pancreatectomy.

METHODS

Patients who underwent pancreatic resection at our institution from 2017 to 2022 were reviewed retrospectively to identify those undergoing computed tomography for any indication before discharge. Patients were subdivided by the postoperative day that the first computed tomography scan was obtained: immediate (postoperative day <3), early (postoperative day 3-7), and delayed (postoperative day >7).

RESULTS

Of 370 patients, 110 (30%) had a computed tomography during the initial surgical stay. The 3 timing groups were similar in age, comorbidities, pathology, operative time, and number of scans. When comparing the early with the delayed group, we found that antibiotic usage, percutaneous drainage, and overall invasive interventions during surgical stay were all similar. However, those patients who were scanned in the early period had significantly shorter length of stay (17.05 vs 22.82, P = .0008) and fewer composite days hospitalized (20.1 vs 24.9, P = .01) relative to the delayed group. Importantly, early computed tomography imaging was found to be the only independent predictor of a postoperative length of stay ≤15 days on multivariate analysis. Surgical stay mortality rates were significantly lower in the early compared with delayed group (0% vs 11%, P = .02). A change in treatment was observed in 59% after computed tomography, with 15% undergoing invasive interventions, 27% treated medically, and 16% with expectant management.

CONCLUSION

In our cohort, patients imaged early after pancreatectomy experienced shorter hospital stays and lower inpatient mortality relative to those scanned after the first postoperative week.

MicroRNAs as Bile-based biomarkers in pancreaticobiliary cancers (MIRABILE): a cohort study

BACKGROUND

Biliary obstruction can be due to both malignant and benign pancreaticobiliary disease. Currently, there are no biomarkers that can accurately help make this distinction. MicroRNAs (miRNAs) are stable molecules in tissue and biofluids that are commonly deregulated in cancer. The MIRABILE study aimed to identify miRNAs in bile that can differentiate malignant from benign pancreaticobiliary disease.

MATERIALS AND METHODS

There were 111 patients recruited prospectively at endoscopic retrograde cholangiopancreatography (ERCP) or percutaneous transhepatic cholangiography (PTC) for obstructive jaundice, and bile was aspirated for cell-free RNA (cfRNA) extraction and analysis. In a discovery cohort of 78 patients (27 with pancreatic ductal adenocarcinoma (PDAC), 14 cholangiocarcinoma (CCA), 37 benign disease), cfRNA was subjected to small-RNA sequencing. LASSO regression was used to define bile miRNA signatures, and NormFinder to identify endogenous controls. In a second cohort of 87 patients (34 PDAC, 14 CCA, 39 benign disease), RT-qPCR was used for validation.

RESULTS

LASSO regression identified 14 differentially-expressed bile miRNAs of which 6 were selected for validation. When comparing malignant and benign pancreaticobiliary disease, bile miR-340 and miR-182 were validated and significantly differentially expressed ( P <0.05 and P <0.001, respectively). This generated an AUC of 0.79 (95% CI: 0.70-0.88, sensitivity 65%; specificity 82%) in predicting malignant disease.

CONCLUSION

Bile collected during biliary drainage contains miRNAs able to differentiate benign from malignant pancreaticobiliary diseases in patients with obstructive jaundice. These bile miRNAs have the potential to increase diagnostic accuracy.

Pancreatic incidentaloma: incidental findings from history towards the era of liquid biopsy

This report provides an overview of the most common diagnostic methods that bring to light incidental findings of pancreatic cancer. It reviews the impact of medical imaging and genetic assessment on the definitions of incidental findings and incidentaloma of the pancreas. For different diagnostic approaches (eg, MRI and CT) and for different affections (cysts/intraductal papillary mucinous neoplasia, solid lesions), specific guidelines have been proposed and some are established. Based on this, we summarise the differences between the traditional methods with those applied in the PANCAID project. Biomarkers, genetic predispositions, mutations and circulating tumour cells give rise to different levels of concern. The final part of the report discusses the risks and the opportunities associated with further diagnostic procedures and surgical interventions. From the ethical perspective, the most urging question is, can a screening based on liquid biopsy and blood samples open a gateway for the prevention of pancreatic cancer-even if morbidity and lethality of today's surgical interventions is still very high?

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy

PURPOSE

Predicting nonalcoholic fatty liver disease (NAFLD) following pancreaticoduodenectomy (PD) is challenging, which delays therapeutic intervention and makes its prevention difficult. We conducted this study to assess the potential application of preoperative computed tomography (CT) radiomics for predicting NAFLD.

METHODS

The subjects of this retrospective study were 186 patients with PD from a single institution. We extracted the predictors of NAFLD after PD statistically from conventional clinical and radiomic features of the estimated remnant pancreas and whole liver region on preoperative nonenhanced CT images. Based on these predictors, we developed a machine-learning predictive model, which integrated clinical and radiomic features. A comparative model used only clinical features as predictors.

RESULTS

The incidence of NAFLD after PD was 43.5%. The variables of the clinicoradiomic model included one shape feature of the pancreas, two texture features of the liver, and sex; the variables of the clinical model were age, sex, and chemoradiotherapy. The accuracy%, precision%, recall%, F1 score, and area under the curve of the two models were 75.0, 72.7, 66.7, 69.6, and 0.80; and 69.6, 68.4, 54.2, 60.5, and 0.69, respectively.

CONCLUSIONS

Preoperative CT-derived radiomic features from the pancreatic and liver regions are promising for the prediction of NAFLD post-PD. Using these features enhances the predictive model, enabling earlier intervention for high-risk patients.

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.

Imaging for Early Detection of Pancreatic Ductal Adenocarcinoma: Updates and Challenges in the Implementation of Screening and Surveillance Programs

Pancreatic ductal adenocarcinoma (PDA) is one of the most aggressive cancers. It has a poor 5-year survival rate of 12%, partly because most cases are diagnosed at advanced stages, precluding curative surgical resection. Early-stage PDA has significantly better prognoses due to increased potential for curative interventions, making early detection of PDA critically important to improved patient outcomes. We examine current and evolving early detection concepts, screening strategies, diagnostic yields among high-risk individuals, controversies, and limitations of standard-of-care imaging.

The role of biomarkers in the early detection of pancreatic cancer

Pancreatic surveillance can detect early-stage pancreatic cancer and achieve long-term survival, but currently involves annual endoscopic ultrasound and MRI/MRCP, and is recommended only for individuals who meet familial/genetic risk criteria. To improve upon current approaches to pancreatic cancer early detection and to expand access, more accurate, inexpensive, and safe biomarkers are needed, but finding them has remained elusive. Newer approaches to early detection, such as using gene tests to personalize biomarker interpretation, and the increasing application of artificial intelligence approaches to integrate complex biomarker data, offer promise that clinically useful biomarkers for early pancreatic cancer detection are on the horizon.

Artificial Intelligence in Pancreatic Image Analysis: A Review

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era

Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host-microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.

Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer

Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach.

Preliminary study on the ability of the machine learning models based on 18F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma

PURPOSE

The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to distinguish mass-forming pancreatic lymphoma from pancreatic carcinoma using machine learning.

METHODS

A total of 88 lesions from 86 patients diagnosed as mass-forming pancreatic lymphoma or pancreatic carcinoma were included and randomly divided into a training set and a validation set at a 4-to-1 ratio. The segmentation of regions of interest was performed using ITK-SNAP software, PET metabolic parameters and radiomics features were extracted using 3Dslicer and PYTHON. Following the selection of optimal metabolic parameters and radiomics features, Logistic regression (LR), support vector machine (SVM), and random forest (RF) models were constructed for PET metabolic parameters, CT radiomics, PET radiomics, and PET/CT radiomics. Model performance was assessed in terms of area under the curve (AUC), accuracy, sensitivity, and specificity in both the training and validation sets.

RESULTS

Strong discriminative ability observed in all models, with AUC values ranging from 0.727 to 0.978. The highest performance exhibited by the combined PET and CT radiomics features. AUC values for PET/CT radiomics models in the training set were LR 0.994, SVM 0.994, RF 0.989. In the validation set, AUC values were LR 0.909, SVM 0.883, RF 0.844.

CONCLUSION

Machine learning models utilizing the metabolic parameters and radiomics of 18F-FDG PET/CT show promise in distinguishing between pancreatic carcinoma and mass-forming pancreatic lymphoma. Further validation on a larger cohort is necessary before practical implementation in clinical settings.

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.

Development and validation of a preoperative radiomics-based nomogram to identify patients who can benefit from splenic hilar lymphadenectomy: a pooled analysis of three prospective trials

BACKGROUND

The authors aimed to use preoperative computed tomography images to develop a radiomic nomogram to select patients who would benefit from spleen-preserving splenic hilar (No.10) lymphadenectomy (SPSHL).

METHODS

A pooled analysis of three distinct prospective studies was performed. The splenic hilar lymph node (SHLN) ratio (sLNR) was established as the quotient of the number of metastatic SHLN to the total number of SHLN. Radiomic features reflecting the phenotypes of the primary tumor (RS1) and SHLN region (RS2) were extracted and used as predictive factors for sLNR.

RESULTS

This study included 733 patients: 301 in the D2 group and 432 in the D2+No.10 group. The optimal sLNR cutoff value was set at 0.4, and the D2+No.10 group was divided into three groups: sLNR=0, sLNR ≤0.4, and sLNR >0.4. Patients in the D2+No. 10 group were randomly divided into the training ( n =302) and validation ( n =130) cohorts. The AUCs value of the nomogram, including RS1 and RS2, were 0.952 in the training cohort and 0.888 in the validation cohort. The entire cohort was divided into three groups based on the nomogram scores: low, moderate, and high SHLN metastasis burden groups (LMB, MMB, and HMB, respectively). A similar 5-year OS rate was found between the D2 and D2+No. 10 groups in the LMB and HMB groups. In the MMB group, the 5-year OS of the D2+No. 10 group (73.4%) was significantly higher than that of the D2 group (37.6%) ( P <0.001).

CONCLUSIONS

The nomogram showed good predictive ability for distinguishing patients with various SHLN metastasis burdens. It can accurately identify patients who would benefit from SPSHL.

Total Neoadjuvant Therapy in Localized Pancreatic Cancer: Is More Better?

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge in oncology due to its advanced stage upon diagnosis and limited treatment options. Surgical resection, the primary curative approach, often results in poor long-term survival rates, leading to the exploration of alternative strategies like neoadjuvant therapy (NAT) and total neoadjuvant therapy (TNT). While NAT aims to enhance resectability and overall survival, there appears to be potential for improvement, prompting consideration of alternative neoadjuvant strategies integrating full-dose chemotherapy (CT) and radiotherapy (RT) in TNT approaches. TNT integrates chemotherapy and radiotherapy prior to surgery, potentially improving margin-negative resection rates and enabling curative resection for locally advanced cases. The lingering question: is more always better? This article categorizes TNT strategies into six main groups based on radiotherapy (RT) techniques: (1) conventional chemoradiotherapy (CRT), (2) the Dutch PREOPANC approach, (3) hypofractionated ablative intensity-modulated radiotherapy (HFA-IMRT), and stereotactic body radiotherapy (SBRT) techniques, which further divide into (4) non-ablative SBRT, (5) nearly ablative SBRT, and (6) adaptive ablative SBRT. A comprehensive analysis of the literature on TNT is provided for both borderline resectable pancreatic cancer (BRPC) and locally advanced pancreatic cancer (LAPC), with detailed sections for each.

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence

Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.

A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer

OBJECTIVE

Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans.

METHODS

Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set.

RESULTS

Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749.

CONCLUSION

Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.

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.

[Early detection of sporadic pancreatic cancer]

The incidence of pancreatic cancer is rising. At present, pancreatic cancer is the third most common cancer-causing death in Germany, but it is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at advanced stages, and 5-year-survival has remained poor. Early detection of sporadic PC at stage IA, however, can yield a 5-year-survival rate of about 80%. Early detection initiatives aim at identifying persons at high risk. People with new-onset diabetes at age 50 or older have attracted much interest. Novel strategies regarding how to detect sporadic PC at an early stage are being discussed.

Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images

Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.