PURPOSE:This multicenter study aimed to develop and validate a multiscale deep learning radiomics nomogram for predicting recurrence-free survival (RFS) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS:A total of 469 PDAC patients from four hospitals were divided into training and test sets. Handcrafted radiomics and deep learning (DL) features were extracted from optimal regions of interest, encompassing both intratumoral and peritumoral areas. Univariate Cox regression, LASSO regression, and multivariate Cox regression selected features for three image signatures (intratumoral, peritumoral radiomics, and DL). A multiscale nomogram was constructed and validated against the 8th AJCC staging system. RESULTS:The 4 mm peritumoral VOI yielded the best radiomics prediction, while a 15 mm expansion was optimal for deep learning. The multiscale nomogram demonstrated a C-index of 0.82 (95 % CI: 0.78-0.85) in the training set and 0.70 (95 % CI: 0.64-0.76) in the external test 1 (high-volume hospital), with the external test 2 (low-volume hospital) showing a C-index of 0.78 (95 % CI: 0.65-0.91). These outperformed the AJCC system's C-index (0.54-0.57). The area under the curve (AUC) for recurrence prediction at 0.5, 1, and 2 years was 0.89, 0.94, and 0.89 in the training set, with AUC values ranging from 0.75 to 0.97 in the external validation sets, consistently surpassing the AJCC system across all sets. Kaplan-Meier analysis confirmed significant differences in prognosis between high- and low-risk groups (P < 0.01 across all cohorts). CONCLUSION:The multiscale nomogram effectively stratifies recurrence risk in PDAC patients, enhancing presurgical clinical decision-making and potentially improving patient outcomes.

PURPOSE:To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS:The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise-insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES:Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS:This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.

OBJECTIVES:To use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer. METHODS:We retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity. RESULTS:Early recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort. CONCLUSIONS:The CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.

To identify risk factors and biomarker for early recurrence in patients diagnosed with pancreatic cancer who undergo curative resection. Early recurrence after curative resection of pancreatic cancer is an obstacle to long-term survival. We retrospectively reviewed 162 patients diagnosed with pancreatic cancer who underwent curative resection. Early recurrence was defined as recurrence within 12 months of surgery. We selected S100A2 as a biomarker and investigated its expression using immunohistochemistry. Of the total, 79.6% (n = 129) of patients received adjuvant chemotherapy after surgery and 117 (72.2%) experienced recurrence, of which 73 (45.1%) experience early recurrence. In multivariate analysis, age < 60 years, presence of lymph node metastasis, and no adjuvant chemotherapy were significantly associated with early recurrence (all P < 0.05). The proportion of patients with high S100A2 expression (H-score > 5) was significantly lower in the early recurrence group (41.5% vs. 63.3%, P = 0.020). The cumulative incidence rate of early recurrence was higher in patients with an S100A2 H-score < 5 (41.5% vs. 63.3%, P = 0.012). The median overall survival of patients with higher S100A2 expression was longer than those with lower S100A2 expression (median 30.1 months vs. 24.2 months, P = 0.003). High-risk factors for early recurrence after surgery for pancreatic cancer include young age, lymph node metastasis, and no adjuvant therapy. Neoadjuvant treatment or intensive adjuvant therapy after surgery may improve the prognosis of patients with high-risk signatures. In patients who receive adjuvant therapy, high S100A2 expression is a good predictor.

BACKGROUND:Poor grain plumpness (GP) is one of the main constraints to reaching the yield potential of hybrid rice. RESULTS:In this study, the GP of 177 rice varieties was investigated in three locations across 2 years. By combining the genotype data of 261 simple sequence repeat (SSR) markers, association mapping was conducted to identify the marker-GP association loci. Among 31 marker-GP association loci detected in two or more environments and determined using general linear model (GLM) analyses, seven association loci were also detected using mixed linear model (MLM) analyses. The seven common loci detected by the two analytical methods were located on chromosomes 2, 3 (2), 7, 8 and 12 (2) and explained 7.24~22.28% of the variance. Of these 7 association loci, five markers linked to GP were newly detected: RM5340 on Chr2, RM5480 and RM148 on Chr3, RM1235 on Chr8, and RM5479 on Chr12. CONCLUSIONS:Five marker-GP association loci were newly detected using both the GLM and MLM analytical methods. Elite allele RM505-170 bp had the highest average phenotypic effect on increasing the GP, and the typical carrier variety was 'Maozitou'. Based on the distribution of the elite alleles among the carrier varieties, the top 10 parental combinations for improving the GP in rice via cross-breeding were predicted.

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.