68Ga-labeled fibroblast activation protein inhibitor (FAPI) PET/CT for locally advanced or recurrent pancreatic cancer staging and restaging after chemoradiotherapy.

Purpose: 68Ga-labeled fibroblast activation protein inhibitor (FAPI) is a novel PET tracer with great potential for staging pancreatic cancer. Data on locally advanced or recurrent disease is sparse, especially on tracer uptake before and after high dose chemoradiotherapy (CRT). The aim of this study was to evaluate [68Ga]Ga-FAPI-46 PET/CT staging in this setting. Methods: Twenty-seven patients with locally recurrent or locally advanced pancreatic adenocarcinoma (LRPAC n = 15, LAPAC n = 12) in stable disease or partial remission after chemotherapy underwent FAPI PET/CT and received consolidation CRT in stage M0 with follow-up FAPI PET/CT every three months until systemic progression. Quantitative PET parameters SUVmax, SUVmean, FAPI-derived tumor volume and total lesion FAPI-uptake were measured in baseline and follow-up PET/CT scans. Contrast-enhanced CT (ceCT) and PET/CT data were evaluated blinded and staged according to TNM classification. Results: FAPI PET/CT modified staging compared to ceCT alone in 23 of 27 patients in baseline, resulting in major treatment alterations in 52% of all patients (30%: target volume adjustment due to N downstaging, 15%: switch to palliative systemic chemotherapy only due to diffuse metastases, 7%: abortion of radiotherapy due to other reasons). Regarding follow-up scans, major treatment alterations after performing FAPI PET/CT were noted in eleven of 24 follow-up scans (46%) with switch to systemic chemotherapy or best supportive care due to M upstaging and ablative radiotherapy of distant lymph node and oligometastasis. Unexpectedly, in more than 90 % of the follow-up scans, radiotherapy did not induce local fibrosis related FAPI uptake. During the first follow-up, all quantitative PET metrics decreased, and irradiated lesions showed significantly lower FAPI uptake in locally controlled disease (SUVmax p = 0.047, SUVmean p = 0.0092) compared to local failure. Conclusion: Compared to ceCT, FAPI PET/CT led to major therapeutic alterations in patients with LRPAC and LAPAC prior to and after radiotherapy, which might help identify patients benefiting from adjustments in every treatment stage. FAPI PET/CT should be considered a useful diagnostic tool in LRPAC or LAPAC before and after CRT.

A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients. METHODS: Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model's performance was validated in both internal and external test cohorts. RESULTS: Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05). CONCLUSION: A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

Deep-Transfer-Learning-Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer.

PURPOSE: To explore the predictive potential of serial computed tomography (CT) radiology reports for pancreatic cancer survival using natural language processing (NLP). METHODS: Deep-transfer-learning-based NLP models were retrospectively trained and tested with serial, free-text CT reports, and survival information of consecutive patients diagnosed with pancreatic cancer in a Korean tertiary hospital was extracted. Randomly selected patients with pancreatic cancer and their serial CT reports from an independent tertiary hospital in the United States were included in the external testing data set. The concordance index (c-index) of predicted survival and actual survival, and area under the receiver operating characteristic curve (AUROC) for predicting 1-year survival were calculated. RESULTS: Between January 2004 and June 2021, 2,677 patients with 12,255 CT reports and 670 patients with 3,058 CT reports were allocated to training and internal testing data sets, respectively. ClinicalBERT (Bidirectional Encoder Representations from Transformers) model trained on the single, first CT reports showed a c-index of 0.653 and AUROC of 0.722 in predicting the overall survival of patients with pancreatic cancer. ClinicalBERT trained on up to 15 consecutive reports from the initial report showed an improved c-index of 0.811 and AUROC of 0.911. On the external testing set with 273 patients with 1,947 CT reports, the AUROC was 0.888, indicating the generalizability of our model. Further analyses showed our model's contextual interpretation beyond specific phrases. CONCLUSION: Deep-transfer-learning-based NLP model of serial CT reports can predict the survival of patients with pancreatic cancer. Clinical decisions can be supported by the developed model, with survival information extracted solely from serial radiology reports.

Cylindrical TGR as early radiological predictor of RLT progression in GEPNETs: a proof of concept.

This study aims to assess the predictive capability of cylindrical Tumor Growth Rate (cTGR) in the prediction of early progression of well-differentiated gastro-entero-pancreatic tumours after Radio Ligand Therapy (RLT), compared to the conventional TGR. Fifty-eight patients were included and three CT scans per patient were collected at baseline, during RLT, and follow-up. RLT response, evaluated at follow-up according to RECIST 1.1, was calculated as a percentage variation of lesion diameters over time (continuous values) and as four different RECIST classes. TGR between baseline and interim CT was computed using both conventional (approximating lesion volume to a sphere) and cylindrical (called cTGR, approximating lesion volume to an elliptical cylinder) formulations. Receiver Operating Characteristic (ROC) curves were employed for Progressive Disease class prediction, revealing that cTGR outperformed conventional TGR (area under the ROC equal to 1.00 and 0.92, respectively). Multivariate analysis confirmed the superiority of cTGR in predicting continuous RLT response, with a higher coefficient for cTGR (1.56) compared to the conventional one (1.45). This study serves as a proof of concept, paving the way for future clinical trials to incorporate cTGR as a valuable tool for assessing RLT response.

68Ga-NODAGA-Exendin-4 PET/CT Findings in a Case of Metastatic Insulinoma With Refractory Hypoglycemia Controlled by 90Y Transarterial Radioembolization.

ABSTRACT: Metastatic insulinomas can cause recurrent hypoglycemia requiring continuous IV glucose infusion. Various medical and chemotherapeutic treatment options are used to reduce the patient's risk of death due to hypoglycemia. Treatment-resistant hepatic metastatic insulinomas may benefit clinically from 90Y transarterial radioembolization therapy. In this case, we present a case of liver metastatic insulinoma that achieved clinical improvement after 2 cycles of 90Y microspheres transarterial radioembolization, and the presence of active metastases was demonstrated with 68Ga-NODAGA-exendin-4 PET/CT imaging.

Metabolic Tumor Volume on 18-Fluorodeoxyglucose Positron Emission Tomography as a Prognostic Marker of Survival in Patients With Locally Advanced or Metastatic Neuroendocrine Neoplasms Treated With 177Lutetium-DOTA-Octreotate Peptide Receptor Radionuclide Therapy.

OBJECTIVE: We investigated metabolic tumor volume (MTV) and total lesion glycolysis (TLG) on pre-treatment FDG-PET as prognostic markers for survival in patients with metastatic neuroendocrine neoplasms (NENs) receiving peptide receptor radionuclide therapy (PRRT). METHODS: A retrospective review of patients with metastatic NENs receiving PRRT was undertaken. Pre-treatment FDG-PET images were analyzed and variables collected included MTV and TLG (dichotomized by median into high vs low). Main Outcomes were overall survival (OS) and progression-free survival (PFS) by MTV and TLG (high vs low). RESULTS: One hundred five patients were included. Median age was 64 years (50% male). Main primary NEN sites were small bowel (43.8%) and pancreas (40.0%). Median MTV was 3.8 mL and median TLG was 19.9. Dichotomization formed identical cohorts regardless of whether MTV or TLG were used. Median OS was 72 months; OS did not differ based on MTV/TLG high versus low (47.4 months vs not reached; hazard ratio, 0.43; 95% confidence interval [CI], 0.18-1.04; P = 0.0594). Median PFS was 30.4 months; PFS differed based on MTV/TLG high versus low (21.6 months vs 45.7 months; hazard ratio, 0.35; 95% CI, 0.19-0.64; P = 0.007). CONCLUSIONS: Low MTV/TLG on pre-treatment FDG-PET was associated with longer PFS in metastatic NEN patients receiving PRRT.

Extraskeletal osteosarcoma infiltrating pancreas, spleen, gastric, and left kidney: a case report.

BACKGROUND: Extraskeletal osteosarcoma is an extremely rare malignancy that accounts for 1% of soft tissue sarcoma and 4.3% of all osteosarcoma. Extraskeletal osteosarcoma can develop in a patient between the ages of 48 and 60 years. The incidence of extraskeletal osteosarcoma is slightly higher in male patients than in females. CASE PRESENTATION: A 50-year-old Caucasian male patient presented with a 6-month history of intermittent lower-left back pain that limits his activity. Prior ultrasonography and abdominal computed tomography scan showed a diagnosis of kidney stone and tumor in the lower-left abdomen. The computed tomography urography with contrast revealed a mass suspected as a left retroperitoneal malignant tumor. Hence, the tumor was resected through laparotomy and the patient continued with histopathological and immunohistochemistry examination with the result of extraskeletal osteosarcoma. CONCLUSION: Extraskeletal osteosarcoma presents diagnostic challenges requiring multimodal examination, including histological and immunohistochemistry analyses. This case underscores the aggressive nature and poor prognosis despite undergoing the current suggested treatment.

Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial.

Importance: Diagnosing solid lesions in the pancreas via endoscopic ultrasonographic (EUS) images is challenging. Artificial intelligence (AI) has the potential to help with such diagnosis, but existing AI models focus solely on a single modality. Objective: To advance the clinical diagnosis of solid lesions in the pancreas through developing a multimodal AI model integrating both clinical information and EUS images. Design, Setting, and Participants: In this randomized crossover trial conducted from January 1 to June 30, 2023, from 4 centers across China, 12 endoscopists of varying levels of expertise were randomly assigned to diagnose solid lesions in the pancreas with or without AI assistance. Endoscopic ultrasonographic images and clinical information of 439 patients from 1 institution who had solid lesions in the pancreas between January 1, 2014, and December 31, 2022, were collected to train and validate the joint-AI model, while 189 patients from 3 external institutions were used to evaluate the robustness and generalizability of the model. Intervention: Conventional or AI-assisted diagnosis of solid lesions in the pancreas. Main Outcomes and Measures: In the retrospective dataset, the performance of the joint-AI model was evaluated internally and externally. In the prospective dataset, diagnostic performance of the endoscopists with or without the AI assistance was compared. Results: The retrospective dataset included 628 patients (400 men [63.7%]; mean [SD] age, 57.7 [27.4] years) who underwent EUS procedures. A total of 130 patients (81 men [62.3%]; mean [SD] age, 58.4 [11.7] years) were prospectively recruited for the crossover trial. The area under the curve of the joint-AI model ranged from 0.996 (95% CI, 0.993-0.998) in the internal test dataset to 0.955 (95% CI, 0.940-0.968), 0.924 (95% CI, 0.888-0.955), and 0.976 (95% CI, 0.942-0.995) in the 3 external test datasets, respectively. The diagnostic accuracy of novice endoscopists was significantly enhanced with AI assistance (0.69 [95% CI, 0.61-0.76] vs 0.90 [95% CI, 0.83-0.94]; P < .001), and the supplementary interpretability information alleviated the skepticism of the experienced endoscopists. Conclusions and Relevance: In this randomized crossover trial of diagnosing solid lesions in the pancreas with or without AI assistance, the joint-AI model demonstrated positive human-AI interaction, which suggested its potential to facilitate a clinical diagnosis. Nevertheless, future randomized clinical trials are warranted. Trial Registration: ClinicalTrials.gov Identifier: NCT05476978.

A comparison of target volumes drawn on arterial and venous phase scans during radiation therapy planning for patients with pancreatic cancer: the PANCRINJ study.

BACKGROUND: The planification of radiation therapy (RT) for pancreatic cancer (PC) requires a dosimetric computed tomography (CT) scan to define the gross tumor volume (GTV). The main objective of this study was to compare the inter-observer variability in RT planning between the arterial and the venous phases following intravenous contrast. METHODS: PANCRINJ was a prospective monocentric study that included twenty patients with non-metastatic PC. Patients underwent a pre-therapeutic CT scan at the arterial and venous phases. The delineation of the GTV was performed by one radiologist (gold standard) and two senior radiation oncologists (operators). The primary objective was to compare the Jaccard conformity index (JCI) for the GTVs computed between the GS (gold standard) and the operators between the arterial and the venous phases with a Wilcoxon signed rank test for paired samples. The secondary endpoints were the geographical miss index (GMI), the kappa index, the intra-operator variability, and the dose-volume histograms between the arterial and venous phases. RESULTS: The median JCI for the arterial and venous phases were 0.50 (range, 0.17-0.64) and 0.41 (range, 0.23-0.61) (p = 0.10) respectively. The median GS-GTV was statistically significantly smaller compared to the operators at the arterial (p < 0.0001) and venous phases (p < 0.001), respectively. The GMI were low with few tumors missed for all patients with a median GMI of 0.07 (range, 0-0.79) and 0.05 (range, 0-0.39) at the arterial and venous phases, respectively (p = 0.15). There was a moderate agreement between the radiation oncologists with a median kappa index of 0.52 (range 0.38-0.57) on the arterial phase, and 0.52 (range 0.36-0.57) on the venous phase (p = 0.08). The intra-observer variability for GTV delineation was lower at the venous phase than at the arterial phase for the two operators. There was no significant difference between the arterial and the venous phases regarding the dose-volume histogram for the operators. CONCLUSIONS: Our results showed inter- and intra-observer variability in delineating GTV for PC without significant differences between the arterial and the venous phases. The use of both phases should be encouraged. Our findings suggest the need to provide training for radiation oncologists in pancreatic imaging and to collaborate within a multidisciplinary team.

Bifocal solid pseudopapillary tumour of the pancreas.

Solid pseudopapillary neoplasm of the pancreas (SPNP) is a rare entity. In this study, we present a woman in her 20's who presented for evaluation of two separate pancreatic masses. On imaging and biopsy, the tail lesion was thought to be a neuroendocrine tumour and the body lesion was thought to be a metastatic lymph node. The patient was brought to the operating room and underwent a distal pancreatectomy and splenectomy. The patient had an uneventful postoperative course and was discharged home on postoperative day 4. Pathology confirmed both masses were consistent with the diagnosis of well-differentiated SPNP with no signs of malignancy including lymphovascular or perineural invasion, or lymph node involvement.

EUS-FNA Biopsy for Pancreatic Mass.

OBJECTIVE: To assess both solid and cystic pancreatic lesions using endoscopic ultrasound (EUS), and the effect of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in patient management. STUDY DESIGN: Descriptive study. Place and Duration of the Study: Department of Gastroenterology, Division of Internal Diseases, Sivas Cumhuriyet University Hospital, Sivas, Turkiye, from January 2018 to 2022. METHODOLOGY: Patients with pancreatic mass, who underwent EUS-FNA were inducted in the study. EUS-FNA was performed using a 22-gauge needle via both transgastric and transduodenal routes. The size of the pancreatic lesion, its location, and whether there was SMA or CA invasion were evaluated on CT and EUS scans. Biopsy results of 64 patients who received EUS-FNA due to pancreatic lesions were considered. The results were divided into malignancy or benign pathology. RESULTS: A total of 64 cases were compared. Crosstable Chi-square analysis showed a statistically significant difference between CT and EUS (p <0.001). EUS-FNA results revealed that out of the 64 patients with pancreatic mass detected in EUS, 46 had adenocarcinoma, 7 were negative for malignancy, 4 had intraductal papillary mucinous neoplasia (IPMN), 3 had neuroendocrine tumour (NET), 2 had lymphoma, and 2 had solid pseudopapillary neoplasia (SPN). In the 2-year follow-up of the seven patients who were negative for malignancy in EUS-FNA, there were no clinical, laboratory or imaging findings indicating pancreatic malignancy or distant metastasis. CONCLUSION: Tissue sampling through EUS-FNA has minimal side effects and remains useful in managing preoperative patients with resectable or suspicious pancreatic masses. KEY WORDS: Pancreatic cancer, Abdominal CT, Endoscopic ultrasound (EUS), Ultrasound-guided fine-needle aspiration (EUS-FNA).

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

Objective: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors. Methods: 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model. Results: The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility. Conclusion: The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.

Objectives: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs). Methods: A total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed. Results: A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts. Conclusion: A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.