A rat-based preclinical platform facilitating transcatheter hepatic arterial infusion in immunodeficient rats with liver xenografts of patient-derived pancreatic ductal adenocarcinoma.

Liver metastases from pancreatic ductal adenocarcinoma (PDAC) are highly fatal. A rat-based patient-derived tumor xenograft (PDX) model is available for transcatheter therapy. This study aimed to create an immunodeficient rat model with liver xenografts of patient-derived primary PDAC and evaluate efficacy of hepatic arterial infusion chemotherapy with cisplatin in this model. Three patient-derived PDACs were transplanted into the livers of 21 rats each (totally, 63 rats), randomly assigned into hepatic arterial infusion, systemic venous infusion, and control groups (n = 7 each) four weeks post-implantation. Computed tomography evaluated tumor volumes before and four weeks after treatment. Post-euthanasia, resected tumor specimens underwent histopathological examination. A liver-implanted PDAC PDX rat model was established in all 63 rats, with first CT identifying all tumors. Four weeks post-treatment, arterial infusion groups exhibited significantly smaller tumor volumes than controls for all three tumors on second CT. Xenograft tumors histologically maintained adenocarcinoma features compared to original patient tumors. Ki67 expression was significantly lower in arterial infusion groups than in the other two for the three tumors, indicating reduced tumor growth in PDX rats. A liver-implanted PDAC PDX rat model was established as a rat-based preclinical platform. Arterial cisplatin infusion chemotherapy represents a potential therapy for PDAC liver metastasis.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

BACKGROUND: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Oil blotting paper for formalin fixation increases endoscopic ultrasound-guided tissue acquisition-collected sample volumes on glass slides.

OBJECTIVES: Endoscopic ultrasound-guided tissue acquisition (EUS-TA) is used for pathological diagnosis and obtaining samples for molecular testing, facilitating the initiation of targeted therapies in patients with pancreatic cancer. However, samples obtained via EUS-TA are often insufficient, requiring more efforts to improve sampling adequacy for molecular testing. Therefore, this study investigated the use of oil blotting paper for formalin fixation of samples obtained via EUS-TA. METHODS: This prospective study enrolled 42 patients who underwent EUS-TA for pancreatic cancer between September 2020 and February 2022 at the Osaka International Cancer Institute. After a portion of each sample obtained via EUS-TA was separated for routine histological evaluation, the residual samples were divided into filter paper and oil blotting paper groups for analysis. Accordingly, filter paper and oil blotting paper were used for the formalin fixation process. The total tissue, nuclear, and cytoplasm areas of each sample were quantitatively evaluated using virtual slides, and the specimen volume and histological diagnosis of each sample were evaluated by an expert pathologist. RESULTS: All cases were cytologically diagnosed as adenocarcinoma. The area ratios of the total tissue, nuclear, and cytoplasmic portions were significantly larger in the oil blotting paper group than in the filter paper group. The frequency of cases with large amount of tumor cells was significantly higher in the oil blotting paper group (33.3%) than in the filter paper group (11.9%) (p = 0.035). CONCLUSIONS: Oil blotting paper can increase the sample volume obtained via EUS-TA on glass slides and improve sampling adequacy for molecular testing.

[A case of R0 resection in conversion surgery after duodenal stent placement for locally advanced unresectable pancreatic cancer with duodenal stenosis].

A 67-year-old man presented to our hospital with vomiting. Esophagogastroduodenoscopy revealed duodenal stenosis and atypical epithelium. A tumor in the pancreatic head, about 30mm in size, involving the superior mesenteric artery and a superior mesenteric vein was identified using abdominal contrast computed tomography (CT). Locally advanced pancreatic cancer was diagnosed in the patient through an endoscopic biopsy. Due to the duodenal stenosis complication, duodenal stent placement was conducted. After stent placement, oral intake was resumed, and improvement of the systemic condition led to chemotherapy (modified FOLFIRINOX). After chemotherapy, CT revealed decreased carcinoma progression and vascular invasion. Conversion surgery was improved, and R0 resection was achieved. Our study showed that duodenal stent placement could enhance prognosis;as a result, it was regarded as a good choice for multidisciplinary therapy.

[A rare case of intraductal tumor of the pancreas in which an intraductal tubulopapillary neoplasm was mixed with a widely spreading gastric-type intraductal papillary-mucinous neoplasm].

A 70-year-old man receiving treatment for diabetes mellitus presented with a cystic mass in the border area of the pancreatic body and tail on plain computed tomography (CT) due to impaired glucose intolerance. Contrast-enhanced CT showed a faint hyperattenuated nodular mass extending from the dilated main pancreatic duct (MPD) to the branch duct. Endoscopic retrograde cholangiopancreatography revealed a mildly dilated orifice of the papilla of Vater and MPD stenosis with entire upstream and immediate downstream dilatations. The patient underwent distal pancreatectomy due to the suspicion of mixed-type intraductal papillary-mucinous carcinoma. A pathological examination showed an intraductal solid-nodular mass measuring 25mm in length, consisting of two types of neoplasms. One showed tubulopapillary growth with entirely high-grade (HG) atypical cuboidal epithelium, in which immunohistochemical examinations were positive for MUC6 but negative for human gastric mucin (HGM), MUC1, MUC2, and MUC5AC, fitting the concept of intraductal tubulopapillary neoplasm (ITPN). The other showed the same growth of low-grade (LG) atypical columnar cells positive for HGM and MUC5AC and negative for MUC1 and MUC2, which corresponded to gastric-type intraductal papillary-mucinous neoplasm (IPMN) -LG. The tumor had not invaded the duct walls, and no metastatic lymph nodes were observed. The ITPN was adjacent to the IPMN mainly composed of tubular glands mimicking pyloric glands with LG dysplasia that corresponded to the so-called IPMN-pyloric gland variant. Moreover, the proliferation of low-papillary gastric-type IPMN spread around the intraductal tumors. Consequently, the patient was diagnosed with an intraductal tubular neoplasm comprising a noninvasive ITPN and gastric-type IPMN-LG. ITPN is a recently identified intraductal neoplasm of the pancreas proposed by Yamaguchi et al. and is distinguished by intraductal tubulopapillary growth with HG cellular atypia without overt mucin production, in contrast to IPMN. To date, no cases of intraductal nodular tumors comprising ITPN and IPMN have been reported. We report this original case with imaging and pathological observations and discuss potential processes via which ITPN and IPMN may arise adjacent to each other in the same pancreatic duct.

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review.

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.

Detection of uridine diphosphate glucuronosyltransferase 1A1 for pancreatic cancer imaging and treatment via a "turn-on" fluorescent probe.

Uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) is expressed ubiquitously in cancer cells and can metabolize exogenous substances. Studies show higher UGT1A1 levels in pancreatic cancer cells than normal cells. Therefore, we need a method to monitor the activity level of UGT1A1 in pancreatic cancer cells and in vivo. Here, we report a fluorescent probe, BCy-panc, for UGT1A1 imaging in cells and in vivo. Compared with other molecular probes, this probe is readily prepared, with high selectivity and sensitivity for the detection of UGT1A1. Our results show that BCy-panc rapidly detects UGT1A1 in pancreatic cancer. In addition, there is an urgent need for evidence to clarify the relationship between UGT1A1 and pancreatic cancer development. The present investigation found that the increase of UGT1A1 by chrysin was effective in inducing apoptosis in pancreatic cancer cells. These results indicate that the synergistic effect of chrysin and cisplatin at the cellular level is superior to that of cisplatin alone. The UGT1A1 level may be a biomarker for early diagnosis of cancer. Meanwhile, UGT1A1 plays a crucial role in pancreatic cancer, and the combination of chrysin and cisplatin may provide effective ideas for pancreatic cancer treatment.

[Applications of Artificial Intelligence in Pancreatic Cystic Lesion Imaging].

As the detection rate of pancreatic cystic lesions(PCL)increases,artificial intelligence(AI)has made breakthroughs in the imaging workflow of PCL,including image post-processing,lesion detection,segmentation,diagnosis and differential diagnosis.AI-based image post-processing can optimize the quality of medical images and AI-assisted models for lesion detection,segmentation,diagnosis and differential diagnosis significantly enhance the work efficiency of radiologists.This article reviews the application progress of AI in PCL imaging and provides prospects for future research directions.

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

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

CBCT-DRRs superior to CT-DRRs for target-tracking applications for pancreatic SBRT.

Objective.In current radiograph-based intra-fraction markerless target-tracking, digitally reconstructed radiographs (DRRs) from planning CTs (CT-DRRs) are often used to train deep learning models that extract information from the intra-fraction radiographs acquired during treatment. Traditional DRR algorithms were designed for patient alignment (i.e.bone matching) and may not replicate the radiographic image quality of intra-fraction radiographs at treatment. Hypothetically, generating DRRs from pre-treatment Cone-Beam CTs (CBCT-DRRs) with DRR algorithms incorporating physical modelling of on-board-imagers (OBIs) could improve the similarity between intra-fraction radiographs and DRRs by eliminating inter-fraction variation and reducing image-quality mismatches between radiographs and DRRs. In this study, we test the two hypotheses that intra-fraction radiographs are more similar to CBCT-DRRs than CT-DRRs, and that intra-fraction radiographs are more similar to DRRs from algorithms incorporating physical models of OBI components than DRRs from algorithms omitting these models.Approach.DRRs were generated from CBCT and CT image sets collected from 20 patients undergoing pancreas stereotactic body radiotherapy. CBCT-DRRs and CT-DRRs were generated replicating the treatment position of patients and the OBI geometry during intra-fraction radiograph acquisition. To investigate whether the modelling of physical OBI components influenced radiograph-DRR similarity, four DRR algorithms were applied for the generation of CBCT-DRRs and CT-DRRs, incorporating and omitting different combinations of OBI component models. The four DRR algorithms were: a traditional DRR algorithm, a DRR algorithm with source-spectrum modelling, a DRR algorithm with source-spectrum and detector modelling, and a DRR algorithm with source-spectrum, detector and patient material modelling. Similarity between radiographs and matched DRRs was quantified using Pearson's correlation and Czekanowski's index, calculated on a per-image basis. Distributions of correlations and indexes were compared to test each of the hypotheses. Distribution differences were determined to be statistically significant when Wilcoxon's signed rank test and the Kolmogorov-Smirnov two sample test returnedp≤ 0.05 for both tests.Main results.Intra-fraction radiographs were more similar to CBCT-DRRs than CT-DRRs for both metrics across all algorithms, with allp≤ 0.007. Source-spectrum modelling improved radiograph-DRR similarity for both metrics, with allp< 10-6. OBI detector modelling and patient material modelling did not influence radiograph-DRR similarity for either metric.Significance.Generating DRRs from pre-treatment CBCT-DRRs is feasible, and incorporating CBCT-DRRs into markerless target-tracking methods may promote improved target-tracking accuracies. Incorporating source-spectrum modelling into a treatment planning system's DRR algorithms may reinforce the safe treatment of cancer patients by aiding in patient alignment.

Al18F-NOTA-Octreotide outperforms 18F-FDG in identifying rare renal metastases from pancreatic neuroendocrine tumor.

We presented a case involving a 56-year-old man who had been experiencing shoulder and back pain for over a year, with extensive bone metastases revealed by a bone scan. To identify the primary source of these issues, the patients underwent a fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) scan, which indicated moderate uptake in the right renal soft mass and low uptake in multiple osteolytic lesions. Pathological examination and immunohistochemical staining of the renal mass supported the diagnosis of neuroendocrine tumors. Subsequently, a novel somatostatin receptor imaging agent, Al18F-NOTA-octreotide (18F-OC), was performed to further investigate the source of metastatic lesions and to stage the tumor. The 18F-OC scan revealed a high-uptake lesion in the pancreatic head, as well as additional lymph node and bone metastases lesions. Compared to 18F-FDG, the 18F-OC demonstrated superior imaging capabilities and a significantly higher tumor-to-background ratio in neuroendocrine neoplasms, which contributed to improving the staging and treatment management.

The Pancreatic Cancer Early Detection (PRECEDE) Study is a Global Effort to Drive Early Detection: Baseline Imaging Findings in High-Risk Individuals.

BACKGROUND: Pancreatic adenocarcinoma (PC) is a highly lethal malignancy with a survival rate of only 12%. Surveillance is recommended for high-risk individuals (HRIs), but it is not widely adopted. To address this unmet clinical need and drive early diagnosis research, we established the Pancreatic Cancer Early Detection (PRECEDE) Consortium. METHODS: PRECEDE is a multi-institutional international collaboration that has undertaken an observational prospective cohort study. Individuals (aged 18-90 years) are enrolled into 1 of 7 cohorts based on family history and pathogenic germline variant (PGV) status. From April 1, 2020, to November 21, 2022, a total of 3,402 participants were enrolled in 1 of 7 study cohorts, with 1,759 (51.7%) meeting criteria for the highest-risk cohort (Cohort 1). Cohort 1 HRIs underwent germline testing and pancreas imaging by MRI/MR-cholangiopancreatography or endoscopic ultrasound. RESULTS: A total of 1,400 participants in Cohort 1 (79.6%) had completed baseline imaging and were subclassified into 3 groups based on familial PC (FPC; n=670), a PGV and FPC (PGV+/FPC+; n=115), and a PGV with a pedigree that does not meet FPC criteria (PGV+/FPC-; n=615). One HRI was diagnosed with stage IIB PC on study entry, and 35.1% of HRIs harbored pancreatic cysts. Increasing age (odds ratio, 1.05; P<.001) and FPC group assignment (odds ratio, 1.57; P<.001; relative to PGV+/FPC-) were independent predictors of harboring a pancreatic cyst. CONCLUSIONS: PRECEDE provides infrastructure support to increase access to clinical surveillance for HRIs worldwide, while aiming to drive early PC detection advancements through longitudinal standardized clinical data, imaging, and biospecimen captures. Increased cyst prevalence in HRIs with FPC suggests that FPC may infer distinct biological processes. To enable the development of PC surveillance approaches better tailored to risk category, we recommend adoption of subclassification of HRIs into FPC, PGV+/FPC+, and PGV+/FPC- risk groups by surveillance protocols.

Short- and long-term outcomes after distal pancreatectomy with radiologic infiltration of splenic vessels for pancreatic ductal adenocarcinoma.

BACKGROUND: The effect of radiologic splenic vessels involvement (RSVI) on the survival of patients with pancreatic adenocarcinoma (PAC) located in the body and tail of the pancreas is controversial, and its influence on postoperative morbidity after distal pancreatectomy (DP) is unknown. This study aimed to determine the influence of RSVI on postoperative complications, overall survival (OS), and disease-free survival (DFS) in patients undergoing DP for PAC. METHODS: A multicenter retrospective study of DP was conducted at 7 hepatopancreatobiliary units between January 2008 and December 2018. Patients were classified according to the presence of RSVI. A Clavien-Dindo grade of >II was considered to represent a major complication. RESULTS: A total of 95 patients were included in the analysis. Moreover, 47 patients had vascular infiltration: 4 had arterial involvement, 10 had venous involvement, and 33 had both arterial and venous involvements. The rates of major complications were 20.8% in patients without RSVI, 40.0% in those with venous RSVI, 25.0% in those with arterial RSVI, and 30.3% in those with both arterial and venous RSVIs (P = .024). The DFS rates at 3 years were 56% in the group without RSVI, 50% in the group with arterial RSVI, and 16% in the group with both arterial and venous RSVIs (P = .003). The OS rates at 3 years were 66% in the group without RSVI, 50% in the group with arterial RSVI, and 29% in the group with both arterial and venous RSVIs (P < .0001). CONCLUSION: RSVI increased the major complication rates after DP and reduced the OS and DFS. Therefore, it may be a useful prognostic marker in patients with PAC scheduled to undergo DP and may help to select patients likely to benefit from neoadjuvant treatment.

Predicting postoperative prognosis of pancreatic cancer using a computed tomography-based radio-clinical model: exploring biologic functions.

Computed tomography (CT) imaging has the potential to assist in predicting the prognosis and treatment strategies for pancreatic cancer (PC). This study aimed to develop and validate a radio-clinical model based on preoperative multiphase CT assessments to predict the overall survival (OS) of PC and identify differentially expressed genes associated with OS. METHODS: Patients with PC who had undergone radical pancreatectomy (R0 resection) were divided into development and external validation sets. Independent predictors of OS were identified using Cox regression analyses and included in the nomogram, which was externally validated. The area under the curve was used to measure the model's accuracy in estimating OS probability. RNA sequencing data from The Cancer Genome Atlas were used for gene expression analysis. RESULTS: In the development and external validation sets, survival was estimated respectively for 132 and 27 patients. Multivariate Cox regression analysis identified 5 independent OS predictors: age (P = .049), sex (P = .001), bilirubin level (P = .005), tumor size (P = .020), and venous invasion (P = .041). These variables were incorporated into the nomogram. Patients were divided into high- and low-risk groups for OS and survival curves showed that all patients in the low-risk group had better OS than that of those in the high-risk group (P < .001). Differentially expressed genes in patients with a poor prognosis were involved in neuroactive ligand-receptor interaction. CONCLUSION: The radio-clinical model may be clinically useful for successfully predicting PC prognosis.

Prevalence of metastases outside the liver and abdominal lymph nodes on 68Ga-DOTATOC-PET/CT in patients with small intestinal and pancreatic neuroendocrine tumours.

Metastases outside the liver and abdominal/retroperitoneal lymph nodes are nowadays detected frequently in patients with neuroendocrine tumours (NETs), owing to the high sensitivity of positron emission tomography (PET) with Gallium-68-DOTA-somatostatin analogues (68Ga-SSA) and concomitant diagnostic computed tomography (CT). Our aim was to determine the prevalence of extra-abdominal metastases on 68Ga-DOTATOC-PET/CT in a cohort of patients with small intestinal (Si-NET) and pancreatic NET (Pan-NET), as well as that of pancreatic metastasis in patients with Si-NET. Among 2090 patients examined by 68Ga-DOTATOC-PET/CT at two tertiary referral centres, a total of 1177 patients with a history of Si- or Pan-NET, were identified. The most recent 68Ga-DOTATOC-PET/CT report for each patient was reviewed, and the location and number of metastases of interest were recorded. Lesions outside the liver and abdominal nodes were found in 26% of patients (n = 310/1177), of whom 21.5% (255/1177) were diagnosed with Si-NET and 4.5% (55/1177) Pan-NET. Bone metastases were found in 18.4% (215/1177), metastases to Virchow's lymph node in 7.1% (83/1177), and lung/pleura in 4.8% (56/1177). In the subset of 255 Si-NET patients, 5.4% (41/255) manifested lesions in the pancreas, 1.5% in the breast (18/255), 1.3% in the heart (15/255) and 1% in the orbita (12/255). In Si-NET patients, the Ki-67 proliferation index was higher in those with ≥2 metastatic sites of interest, than with 1 metastatic site, (p <0.001). Overall, extra-abdominal or pancreatic metastases were more often found in patients with Si-NET (34%) than in those with Pan-NET (13%) (p <0.001). Bone metastases were 2.6 times more frequent in patients with Si-NET compared to Pan-NET patients (p <0.001). Lesions to the breast and orbita were encountered in almost only Si-NET patients. In conclusion, lesions outside the liver and abdominal nodes were detected in as many as 26% of the patients, with different prevalence and metastatic patterns in patients with Si-NET compared to Pan-NET. The impact of such metastases on overall survival and clinical decision-making needs further evaluation.

Deep causal learning for pancreatic cancer segmentation in CT sequences.

Segmenting the irregular pancreas and inconspicuous tumor simultaneously is an essential but challenging step in diagnosing pancreatic cancer. Current deep-learning (DL) methods usually segment the pancreas or tumor independently using mixed image features, which are disrupted by surrounding complex and low-contrast background tissues. Here, we proposed a deep causal learning framework named CausegNet for pancreas and tumor co-segmentation in 3D CT sequences. Specifically, a causality-aware module and a counterfactual loss are employed to enhance the DL network's comprehension of the anatomical causal relationship between the foreground elements (pancreas and tumor) and the background. By integrating causality into CausegNet, the network focuses solely on extracting intrinsic foreground causal features while effectively learning the potential causality between the pancreas and the tumor. Then based on the extracted causal features, CausegNet applies a counterfactual inference to significantly reduce the background interference and sequentially search for pancreas and tumor from the foreground. Consequently, our approach can handle deformable pancreas and obscure tumors, resulting in superior co-segmentation performance in both public and real clinical datasets, achieving the highest pancreas/tumor Dice coefficients of 86.67%/84.28%. The visualized features and anti-noise experiments further demonstrate the causal interpretability and stability of our method. Furthermore, our approach improves the accuracy and sensitivity of downstream pancreatic cancer risk assessment task by 12.50% and 50.00%, respectively, compared to experienced clinicians, indicating promising clinical applications.

Impact of data transfer between treatment planning systems on dosimetric parameters.

PURPOSE: In radiotherapy it is often necessary to transfer a patient's DICOM (Digital Imaging and COmmunications in Medicine) dataset from one system to another for re-treatment, plan-summation or registration purposes. The aim of the study is to evaluate effects of dataset transfer between treatment planning systems. MATERIALS AND METHODS: Twenty-five patients treated in a 0.35T MR-Linac (MRidian, ViewRay) for locally-advanced pancreatic cancer were enrolled. For each patient, a nominal dose distribution was optimized on the planning MRI. Each plan was daily re-optimized if needed to match the anatomy and exported from MRIdian-TPS (ViewRay Inc.) to Eclipse-TPS (Siemens-Varian). A comparison between the two TPSs was performed considering the PTV and OARs volumes (cc), as well as dose coverages and clinical constraints. RESULTS: From the twenty-five enrolled patients, 139 plans were included in the data comparison. The median values of percentage PTV volume variation are 10.8 % for each fraction, while percentage differences of PTV coverage have a mean value of -1.4 %. The median values of the percentage OARs volume variation are 16.0 %, 7.0 %, 10.4 % and 8.5 % for duodenum, stomach, small and large bowel, respectively. The percentage variations of the dose constraints are 41.0 %, 52.7 % and 49.8 % for duodenum, stomach and small bowel, respectively. CONCLUSIONS: This study has demonstrated a non-negligible variation in size and dosimetric parameters when datasets are transferred between TPSs. Such variations should be clinically considered. Investigations are focused on DICOM structure algorithm employed by the TPSs during the transfer to understand the cause of such variations.

Imaging Intratumoral Heterogeneity of Diffuse Pancreatic Neuroendocrine Tumor With Multitracer PET/CT.

ABSTRACT: Diffuse involvement of pancreatic neuroendocrine tumor (PNET) is a rare presentation. Here, we report a case of suspected autoimmune pancreatitis with 18 F-FDG and 18 F-FAPI-42 PET/CT showing increased tracer uptake in the entire pancreas, which was eventually confirmed by biopsy pathologic analysis as diffuse PNET. 18 F-AlF-NOTA-octreotide PET/CT imaging showed heterogeneous tracer uptake in the entire pancreas.