Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer.

OBJECTIVE: This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability. METHODS: We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients. After segmenting the relevant structures, our model quantifies vascular involvement by measuring the degree of the vessel wall that is in contact with the tumor using AI-segmented CTs. Based on these measurements, the model classifies the resectability stage using the Dutch Pancreatic Cancer Group criteria as either resectable, borderline resectable, or locally advanced (LA). RESULTS: We evaluated the performance of the model using a test set containing 60 CTs from 60 patients, consisting of 20 resectable, 20 borderline resectable, and 20 locally advanced cases, by comparing the automated analysis obtained from the model to expert visual vascular involvement assessments. The model concurred with the radiologists on 227/300 (76%) vessels for determining vascular involvement. The model's resectability classification agreed with the radiologists on 17/20 (85%) resectable, 16/20 (80%) for borderline resectable, and 15/20 (75%) for locally advanced cases. CONCLUSIONS: This study demonstrates that an AI model may allow automatic quantification of vascular involvement and classification of resectability for PDAC. RELEVANCE STATEMENT: This AI model enables automated vascular involvement quantification and resectability classification for pancreatic cancer, aiding radiologists in treatment decisions, and potentially improving patient outcomes. KEY POINTS: • High inter-observer variability exists in determining vascular involvement and resectability for PDAC. • Artificial intelligence accurately quantifies vascular involvement and classifies resectability for PDAC. • Artificial intelligence can aid radiologists by automating vascular involvement and resectability assessments.

Real-world outcomes associated with afatinib use in patients with solid tumors harboring NRG1 gene fusions.

OBJECTIVES: Neuregulin-1 (NRG1) fusions may drive oncogenesis via constitutive activation of ErbB signaling. Hence, NRG1 fusion-driven tumors may be susceptible to ErbB-targeted therapy. Afatinib (irreversible pan-ErbB inhibitor) has demonstrated activity in individual patients with NRG1 fusion-positive solid tumors. This study collected real-world data on demographics, clinical characteristics, and clinical outcomes in this patient population. MATERIALS AND METHODS: In this retrospective, multicenter, non-comparative cohort study, physicians in the US-based Cardinal Health Oncology Provider Extended Network collected data from medical records of patients with NRG1 fusion-positive solid tumors who received afatinib (afatinib cohort) or other systemic therapies (non-afatinib cohort) in any therapy line. Objectives included demographics, clinical characteristics, and outcomes (overall response rate [ORR], progression-free survival [PFS], and overall survival [OS]). RESULTS: Patients (N = 110) with a variety of solid tumor types were included; 72 received afatinib, 38 other therapies. In the afatinib cohort, 70.8 % of patients received afatinib as second-line treatment and Eastern Cooperative Oncology Group performance status (ECOG PS) was 2-4 in 69.4 % at baseline. In the non-afatinib cohort, 94.7 % of patients received systemic therapy as first-line treatment and ECOG PS was 2-4 in 31.6 % at baseline. In the afatinib cohort, ORR was 37.5 % overall (43.8 % when received as first-line therapy); median PFS and OS were 5.5 and 7.2 months, respectively. In the non-afatinib cohort, ORR was 76.3 %; median PFS and OS were 12.9 and 22.6 months, respectively. CONCLUSION: This study provides real-world data on the characteristics of patients with NRG1 fusion-positive solid tumors treated with afatinib or other therapies; durable responses were observed in both groups. However, there were imbalances between the cohorts, and the study was not designed to compare outcomes. Further prospective/retrospective trials are required.

Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9.

OBJECTIVES: To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model. RESULTS: The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort). CONCLUSIONS: The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making. CLINICAL RELEVANCE STATEMENT: We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments. KEY POINTS: • Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Sobrevida del adenocarcinoma ductal de páncreas

El objetivo de este trabajo es analizar la presentación epidemiológica y la sobrevida de los pacientes con adenocarcinoma ductal de páncreas de acuerdo con su estadío clínico y al tipo de intervención realizada, en una cohorte de pacientes atendidos en una clínica en Lima, Perú. Estudio de cohortes retrospectivas que evaluó desde enero del 2015 a febrero del 2021 a pacientes con diagnóstico de adenocarcinoma ductal de páncreas considerando diversos factores epidemiológicos, radiológicos, estadiaje oncológico, haber recibido quimioterapia neoadyuvante o adyuvante, haber sido sometidos a cirugía y la sobrevida posterior a alguna de las intervenciones realizadas. De los 249 pacientes analizados, se encontró que 75 de ellos requerían cirugía resectiva. Entre los principales resultados obtenidos, se observó que aquellos con un nivel de CA 19-9 menor a 200 U/mL presentaban una media de sobrevida más alta en comparación con aquellos cuyo nivel de CA 19-9 era superior a 200 U/mL (HR: 1,96; IC95%: 0,18-0,53; p≤0,001). Asimismo, al comparar a los pacientes según su etapa, se encontró que aquellos con tumores resecables tenían una media de sobrevida de 37,72 meses, mientras que aquellos con tumores localmente avanzados tenían una media de sobrevida de 13,47 meses y aquellos con tumores metastásicos tenían una media de sobrevida de 7,69 meses (HR: 0,87; IC95%: 0,31-0,25; p≤0,001). Igualmente, se observó que recibir tratamiento neoadyuvante se asociaba con un mejor pronóstico de sobrevida para los pacientes (HR: 0,32; IC95%: 0,19-0,53; p≤0,001). Asimismo, se llevaron a cabo 5 pancreatectomías con resección metastásica en pacientes oligometastásicos tratados con quimioterapia de rescate, y se encontró que la media de sobrevida para estos pacientes fue de 22,51 meses. Conclusión: La cirugía resectiva en un estadío clínico temprano , presentar valores de CA 19-9 por debajo de 200 U/mL y haber recibido quimioterapia neoadyuvante se correlaciona estadísticamente con una mayor esperanza de sobrevida.

The objective of this study is to analyze the epidemiological presentation and survival of patients with pancreatic ductal adenocarcinoma according to their clinical stage and the type of intervention performed, in a cohort of patients treated at a clinic in Lima, Peru. A retrospective cohort study evaluated patients diagnosed with pancreatic ductal adenocarcinoma from January 2015 to February 2021, considering various epidemiological factors, radiological findings, oncological staging, receipt of neoadjuvant or adjuvant chemotherapy, undergoing surgery, and post-intervention survival. Out of the 249 patients analyzed, 75 of them required resective surgery. Among the main findings, it was observed that those with a CA 19-9 level below 200 U/mL had a higher median survival compared to those with a CA 19-9 level above 200 U/mL (HR: 1.96; 95% CI: 0.18-0.53; p≤0.001). Furthermore, when comparing patients according to their stage, those with resectable tumors had a median survival of 37.72 months, while those with locally advanced tumors had a median survival of 13.47 months, and those with metastatic tumors had a median survival of 7.69 months (HR: 0.87; 95% CI: 0.31-0.25; p≤0.001). Additionally, receiving neoadjuvant treatment was associated with a better prognosis of survival for patients (HR: 0.32; 95% CI: 0.19-0.53; p≤0.001). Furthermore, 5 pancreatectomies with metastatic resection were performed in oligometastatic patients treated with salvage chemotherapy, and the median survival for these patients was 22.51 months. Conclusion: Resective surgery at an early clinical stage, CA 19-9 levels below 200 U/mL, and receiving neoadjuvant chemotherapy are statistically correlated with a higher overall survival.

Use of individualized 3D-printed models of pancreatic cancer to improve surgeons' anatomic understanding and surgical planning.

OBJECTIVES: Three-dimensional (3D) printing has been increasingly used to create accurate patient-specific 3D-printed models from medical imaging data. We aimed to evaluate the utility of 3D-printed models in the localization and understanding of pancreatic cancer for surgeons before pancreatic surgery. METHODS: Between March and September 2021, we prospectively enrolled 10 patients with suspected pancreatic cancer who were scheduled for surgery. We created an individualized 3D-printed model from preoperative CT images. Six surgeons (three staff and three residents) evaluated the CT images before and after the presentation of the 3D-printed model using a 7-item questionnaire (understanding of anatomy and pancreatic cancer [Q1-4], preoperative planning [Q5], and education for trainees or patients [Q6-7]) on a 5-point scale. Survey scores on Q1-5 before and after the presentation of the 3D-printed model were compared. Q6-7 assessed the 3D-printed model's effects on education compared to CT. Subgroup analysis was performed between staff and residents. RESULTS: After the 3D-printed model presentation, survey scores improved in all five questions (before 3.90 vs. after 4.56, p < 0.001), with a mean improvement of 0.57‒0.93. Staff and resident scores improved after a 3D-printed model presentation (p < 0.05), except for Q4 in the resident group. The mean difference was higher among the staff than among the residents (staff: 0.50‒0.97 vs. residents: 0.27‒0.90). The scores of the 3D-printed model for education were high (trainees: 4.47 vs. patients: 4.60) compared to CT. CONCLUSION: The 3D-printed model of pancreatic cancer improved surgeons' understanding of individual patients' pancreatic cancer and surgical planning. CLINICAL RELEVANCE STATEMENT: The 3D-printed model of pancreatic cancer can be created using a preoperative CT image, which not only assists surgeons in surgical planning but also serves as a valuable educational resource for patients and students. KEY POINTS: • A personalized 3D-printed pancreatic cancer model provides more intuitive information than CT, allowing surgeons to better visualize the tumor's location and relationship to neighboring organs. • In particular, the survey score was higher among staff who performed the surgery than among residents. • Individual patient pancreatic cancer models have the potential to be used for personalized patient education as well as resident education.

Pancreatic ductal adenocarcinoma (PDAC) regional nodal disease at standard lymphadenectomy: is MRI accurate for identifying node-positive patients?

OBJECTIVE: To determine the accuracy of qualitative and quantitative MRI features for the diagnosis of pathologic regional lymph nodes at standard lymphadenectomy in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: All adult patients with pancreatic MRI performed from 2011 to 2021 within 3 months of a pancreaticoduodenectomy were eligible for inclusion in this single-center retrospective cohort study. Regional nodes at standard lymphadenectomy were independently reviewed by two fellowship-trained abdominal radiologists for the following qualitative features: heterogeneous T2 signal, round shape, indistinct margin, peri-nodal fat stranding, and restricted diffusion greater than the spleen. Quantitative characteristics including primary tumor size, largest node short- and long-axes length, number of regional nodes, absolute apparent diffusion coefficient (ADC) values, and ADC node-to-spleen signal index were assessed. Analysis was at the patient-level with surgical pathology as the reference standard. RESULTS: Of 75 patients, 85% (64/75) were positive for regional nodal disease on histopathology. None of the qualitative variables evaluated on MRI was associated with pathologic nodes. Median primary tumor maximum diameter was slightly larger for patients with pathologic nodes compared to those without (18 mm (10-42 mm) vs 16 mm (9-22 mm), p = 0.027). None of the other quantitative features was associated with pathologic nodes. Radiologist opinion was not associated with pathologic nodes (p = 0.520). Interobserver agreement was fair (kappa = 0.257). CONCLUSIONS: Lymph node morphologic features and radiologist opinion using MRI are of limited value for diagnosing PDAC regional nodal disease. Improved diagnostic techniques are needed given the prognostic implications of pathologic lymph nodes in these patients. KEY POINTS: • Multiple lymph node morphologic features routinely assessed on MRI for malignancies elsewhere in the body are likely not applicable when assessing for pancreatic ductal adenocarcinoma nodal disease. • Interobserver agreement for the presence or absence of pancreatic ductal adenocarcinoma lymph node morphologic features on MRI is fair (kappa = 0.257). • Many more lymph nodes are resected at PDAC standard lymphadenectomy than are detectable on MRI, median 25 vs 5 (p < 0.001), suggesting improved diagnostic techniques are needed to identify PDAC nodal disease.

MRI vs. CT for pancreatic adenocarcinoma vascular invasion: comparative diagnostic test accuracy systematic review and meta-analysis.

OBJECTIVES: To perform a systematic review comparing the diagnostic accuracy of MRI vs. CT for assessing pancreatic ductal adenocarcinoma (PDAC) vascular invasion. METHODS: MEDLINE, EMBASE, Cochrane Central, and Scopus were searched until December 2021 for diagnostic accuracy studies comparing MRI vs. CT to evaluate vascular invasion of pathologically confirmed PDAC in the same patients. Findings on resection or exploratory laparotomy were the preferred reference standard. Data extraction, risk of bias, and applicability assessment were performed by two authors using the Quality Assessment of Diagnostic Accuracy Studies-Comparative Tool. Bivariate random-effects meta-analysis and meta-regression were performed with 95% confidence intervals (95% CI). RESULTS: Three studies were included assessing 474 vessels without vascular invasion and 65 with vascular invasion in 107 patients. All patients were imaged using MRI at ≥ 1.5 T and a pancreatic protocol CT. No difference was shown between MRI and CT for diagnosing PDAC vascular invasion: MRI/CT sensitivity (95% CI) were 71% (47-87%)/74% (56-86%), and specificity were 97% (94-99%)/97% (94-98%). Sources of bias included selection bias from only a subset of CT patients undergoing MRI and verification bias from patients with unresectable disease not confirmed on surgery. No patients received neoadjuvant therapy prior to staging. CONCLUSIONS: Based on limited data, no difference was observed between MRI and pancreatic protocol CT for PDAC vascular invasion assessment. MRI may be an adequate substitute for pancreatic protocol CT in some patients, particularly those who have already had a single-phase CT. Larger and more recent cohort studies at low risk of bias, including patients who have received neoadjuvant therapy, are needed. CLINICAL RELEVANCE STATEMENT: Abdominal MRI performed similarly to pancreatic protocol CT at assessing pancreatic ductal adenocarcinoma vascular invasion, suggesting local staging is adequate in some patients using MRI. More data are needed using larger, more recent cohorts including patients with neoadjuvant treatment. KEY POINTS: • Based on limited data, no difference was found between MRI and pancreatic protocol CT sensitivity and specificity for diagnosing PDAC vascular invasion (p = 0.81, 0.73 respectively). • Risk of bias could be reduced in future PDAC MRI vs CT comparative diagnostic test accuracy research by ensuring all enrolled patients undergo both imaging modalities being compared in random order and regardless of the findings on either modality. • More studies are needed that directly compare the diagnostic performance of MRI and CT for PDAC staging after neoadjuvant therapy.

High-resolution pancreatic computed tomography for assessing pancreatic ductal adenocarcinoma resectability: a multicenter prospective study.

OBJECTIVE: This prospective multicenter study aimed to evaluate the diagnostic performance of 80-kVp thin-section pancreatic CT in determining pancreatic ductal adenocarcinoma (PDAC) resectability according to the recent National Comprehensive Cancer Network (NCCN) guidelines. METHODS: We prospectively enrolled surgical resection candidates for PDAC from six tertiary referral hospitals (study identifier: NCT03895177). All participants underwent pancreatic CT using 80 kVp tube voltage with 1-mm reconstruction interval. The local resectability was prospectively evaluated using NCCN guidelines at each center and classified into three categories: resectable, borderline resectable, and unresectable. RESULTS: A total of 138 patients were enrolled; among them, 60 patients underwent neoadjuvant therapy. R0 resection was achieved in 103 patients (74.6%). The R0 resection rates were 88.7% (47/53), 52.4% (11/21), and 0.0% (0/4) for resectable, borderline resectable, and unresectable disease, respectively, in 78 patients who underwent upfront surgery. Meanwhile, the rates were 90.9% (20/22), 76.7% (23/30), and 25.0% (2/8) for resectable, borderline resectable, and unresectable PDAC, respectively, in patients who received neoadjuvant therapy. The area under curve of high-resolution CT in predicting R0 resection was 0.784, with sensitivity, specificity, and accuracy of 87.4% (90/103), 48.6% (17/35), and 77.5% (107/138), respectively. Tumor response was significantly associated with the R0 resection after neoadjuvant therapy (odds ratio [OR] = 38.99, p = 0.016). CONCLUSION: An 80-kVp thin-section pancreatic CT has excellent diagnostic performance in assessing PDAC resectability, enabling R0 resection rates of 88.7% and 90.9% for patients with resectable PDAC who underwent upfront surgery and patients with resectable PDAC after neoadjuvant therapy, respectively. KEY POINTS: • The margin-negative (R0) resection rates were 88.7% (47/53), 52.4% (11/21), and 0.0% (0/4) for resectable, borderline resectable, and unresectable pancreatic ductal adenocarcinoma (PDAC), respectively, on 80-kVp thin-section pancreatic CT in the 78 patients who underwent upfront surgery. • Among the 60 patients who underwent neoadjuvant therapy, the R0 rates were 90.9% (20/22), 76.7% (23/30), and 25.0% (2/8) for resectable, borderline resectable, and unresectable PDAC, respectively. • Tumor response, along with the resectability status on pancreatic CT, was significantly associated with the R0 resection rate after neoadjuvant therapy.

Role of neoadjuvant treatment in resectable pancreatic cancer according to vessel invasion and increase of CA19-9 levels.

BACKGROUND/PURPOSE: The efficacy of neoadjuvant treatment (NAT) for resectable pancreatic cancer remains debatable, particularly in patients with portal vein (PV)/superior mesenteric vein (SMV) contact and elevated serum carbohydrate antigen (CA) 19-9. This study investigated the clinical significance of PV/SMV contact and CA19-9 levels, and the role of NAT in resectable pancreatic cancer. METHODS: A total of 775 patients who underwent surgery for resectable pancreatic cancer between 2007 and 2018 were included. Propensity score-matched (PSM) analysis (1:3) was performed based on tumor size, lymph node enlargement, and PV/SMV contact. Subgroup analyses were performed according to PV/SMV contact and CA19-9 level. RESULTS: Among the patients, 52 underwent NAT and 723 underwent upfront surgery. After PSM, NAT group showed better survival than upfront surgery group (median 30.0 vs 22.0 months, P = .047). In patients with PV/SMV contact, NAT tended to have better survival (30.0 vs 22.0 months, P = .069). CA19-9 >150 U/mL was a poor prognostic factor, with NAT showing a significant survival difference compared with upfront surgery (34.0 vs 18.0 months, P = .004). CONCLUSIONS: Neoadjuvant treatment showed better survival than upfront surgery in resectable pancreatic cancer. In patients with PV/SMV contact or CA19-9 >150 U/mL, NAT showed a survival difference compared to upfront surgery; therefore, NAT could be considered in these patients.

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images.

PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. MATERIALS AND METHODS: This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37-90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32-88 years). Three different methods were discussed to identify PDAC and AIP based on 18F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. RESULTS: The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4-97.3%), 90.1% (95% CI 88.7-91.5%), 87.5% (95% CI 84.3-90.6%), and 93.0% (95% CI 90.3-95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. CONCLUSION: The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on 18F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.

Differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma by CT radiomic and deep learning features / 中华胰腺病杂志

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma.

OBJECTIVES: Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score). METHODS: We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint. RESULTS: The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13-0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12-6.92, p = 0.03) and 4.03 (95% CI: 1.42-11.39, p = 0.01), respectively. CONCLUSIONS: In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor). KEY POINTS: • The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed. • The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers. • The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required.

Prediction of margin-negative resection of pancreatic ductal adenocarcinoma following neoadjuvant therapy: Diagnostic performance of NCCN criteria for resection vs CT-determined resectability.

BACKGROUND: Accurate assessment of pancreatic ductal adenocarcinoma (PDAC) resectability after neoadjuvant therapy (NAT) is crucial. Recently, the NCCN introduced criteria for resection of PDAC following NAT. METHODS: We analyzed 127 patients who underwent NAT and pancreatectomy for PDAC between January 2010 and March 2020. CT-determined resectability according to the NCCN guideline and CA 19-9 level was evaluated before and after NAT. Diagnostic performance of the NCCN criteria for margin-negative (R0) resection was investigated and compared with CT alone. RESULTS: R0 resection was achieved in 104 (81.9%) patients. After NAT, there were 30 (23.6%) resectable, 90 (70.9%) borderline resectable, and seven (5.5%) locally advanced tumors. Significantly decreased or stable CA 19-9 levels were noted in 114 (89.8%) patients. The sensitivity and specificity of the NCCN criteria were 87.5% (91/104) and 21.7% (5/23), respectively, which were significantly different from CT including only resectable PDAC (26.9% [28/104] and 91.3% [21/23]; P < .001), but less prominently different from CT including resectable and borderline resectable PDAC (95.2% [99/104]; P = .022 and 8.7% [2/23]; P = .375). CONCLUSIONS: The NCCN criteria for resection following NAT showed high sensitivity and low specificity for predicting R0 resection. It had supplementary benefit over CT alone, mainly in preventing underestimation of R0 resection.

Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis.

OBJECTIVES: To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP). METHODS: In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility. RESULTS: The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05-0.85 and > 0.05, respectively. CONCLUSIONS: The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients. KEY POINTS: • A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP.

[Surgical treatment of pancreatic cancer-What is new?] / Chirurgische Therapie des Pankreaskarzinoms ­ was gibt es Neues?

The incidence of pancreatic ductal adenocarcinoma is continuously increasing and will become the second leading cause of cancer-related death in Europe and the USA by 2030. With a 5-year overall survival rate of less than 10% the prognosis remains poor. So far surgical tumor resection remains the only curative treatment option, which is now partially supported by multimodal neoadjuvant and adjuvant therapy concepts. Due to the aggressive tumor biology patients with advanced pancreatic cancer in particular can profit from these multimodal therapy concepts. Additionally, in recent years surgical treatment was optimized, the criteria for tumor resectablity were defined and minimally invasive surgery was widely introduced. This review article summarizes the newest developments and the new German S3 guidelines concerning surgery of pancreatic cancer.

[The role of the microbiome in diseases of the pancreas]. / Rolle des Mikrobioms bei Erkrankungen des Pankreas.

BACKGROUND: The human body is inhabited by diverse microorganisms. Together, this so-called microbiome exerts important metabolic functions and contributes to the maintenance of health. At the same time, shifts in the microbiome composition may lead to disease. OBJECTIVES: Review of the current literature about the role of the microbiome in diseases of the pancreas. MATERIALS AND METHODS: Literature search in PubMed and Embase. RESULTS: The exocrine pancreas is a major factor determining the composition and stability of the intestinal microbiome even in healthy people without pancreatic disease. Inflammatory diseases of the pancreas such as acute or chronic pancreatitis lead to reduced microbial diversity, loss of gut barrier stabilizing bacteria and an increase in facultative pathogens like Escherichia or Enterococcus. Even pancreatic cancer tissue harbours microbiota and mice models have shown that the growth of pancreatic cancer can be inhibited by microbiota ablation. CONCLUSIONS: Inflammatory diseases of the pancreas lead to gut microbiome dysbiosis and tumor microbiota probably play a role in the development of pancreatic cancer. Until now, however, there is no proof that therapeutic microbiota modulation in individuals with pancreatic disease can improve mortality or quality of life. At this point, the analysis of the microbiome in pancreatic disease should only be performed in scientific studies.

Fractal analysis improves tumour size measurement on computed tomography in pancreatic ductal adenocarcinoma: comparison with gross pathology and multi-parametric MRI.

OBJECTIVES: Tumour size measurement is pivotal for staging and stratifying patients with pancreatic ductal adenocarcinoma (PDA). However, computed tomography (CT) frequently underestimates tumour size due to insufficient depiction of the tumour rim. CT-derived fractal dimension (FD) maps might help to visualise perfusion chaos, thus allowing more realistic size measurement. METHODS: In 46 patients with histology-proven PDA, we compared tumour size measurements in routine multiphasic CT scans, CT-derived FD maps, multi-parametric magnetic resonance imaging (mpMRI), and, where available, gross pathology of resected specimens. Gross pathology was available as reference for diameter measurement in a discovery cohort of 10 patients. The remaining 36 patients constituted a separate validation cohort with mpMRI as reference for diameter and volume. RESULTS: Median RECIST diameter of all included tumours was 40 mm (range: 18-82 mm). In the discovery cohort, we found significant (p = 0.03) underestimation of tumour diameter on CT compared with gross pathology (Δdiameter3D = -5.7 mm), while realistic diameter measurements were obtained from FD maps (Δdiameter3D = 0.6 mm) and mpMRI (Δdiameter3D = -0.9 mm), with excellent correlation between the two (R2 = 0.88). In the validation cohort, CT also systematically underestimated tumour size in comparison to mpMRI (Δdiameter3D = -10.6 mm, Δvolume = -10.2 mL), especially in larger tumours. In contrast, FD map measurements agreed excellently with mpMRI (Δdiameter3D = +1.5 mm, Δvolume = -0.6 mL). Quantitative perfusion chaos was significantly (p = 0.001) higher in the tumour rim (FDrim = 4.43) compared to the core (FDcore = 4.37) and remote pancreas (FDpancreas = 4.28). CONCLUSIONS: In PDA, fractal analysis visualises perfusion chaos in the tumour rim and improves size measurement on CT in comparison to gross pathology and mpMRI, thus compensating for size underestimation from routine CT. KEY POINTS: • CT-based measurement of tumour size in pancreatic adenocarcinoma systematically underestimates both tumour diameter (Δdiameter = -10.6 mm) and volume (Δvolume = -10.2 mL), especially in larger tumours. • Fractal analysis provides maps of the fractal dimension (FD), which enable a more reliable and size-independent measurement using gross pathology or multi-parametric MRI as reference standards. • FD quantifies perfusion chaos-the underlying pathophysiological principle-and can separate the more chaotic tumour rim from the tumour core and adjacent non-tumourous pancreas tissue.

Diagnostic Accuracy of CT for Evaluating Circumferential Resection Margin Status in Resectable or Borderline Resectable Pancreatic Head Cancer: A Prospective Study Using Axially Sliced Surgical Pathologic Correlation.

OBJECTIVE: CT plays a central role in determining the resectability of pancreatic cancer, which directs the use of neoadjuvant therapy. This study aimed to assess the diagnostic accuracy of CT in predicting circumferential resection margin (CRM) involvement in patients with resectable or borderline resectable pancreatic head cancer. MATERIALS AND METHODS: Seventy-seven patients who were scheduled for upfront surgery for resectable or borderline resectable pancreatic head cancer were prospectively enrolled, and 75 patients (38 male and 37 female; mean age ± standard deviation, 68 ± 11 years) were finally analyzed. The CRM status was evaluated separately for the superior mesenteric artery (SMA) and posterior and superior mesenteric vein/portal vein (SMV/PV) margins. Three independent radiologists reviewed the preoperative CT images and evaluated the resection margin status. The reference standard for CRM status was pathologic examination of pancreaticoduodenectomy specimens in an axial plane perpendicular to the axis of the second portion of the duodenum. The diagnostic accuracy of CT was assessed for overall CRM involvement, defined as involvement of the SMA or posterior margins (per-patient analysis), and involvement of each of the three resection margins (per-margin analysis). The data were pooled using a crossed random effects model. RESULTS: Forty patients had pathologically confirmed overall CRM involvement in pancreatic cancer, while CRM involvement was not seen in 35 patients. For overall CRM involvement, the pooled sensitivity and specificity were 15% (95% confidence interval: 7%-49%) and 99% (96%-100%), respectively. For each of the resection margins, the pooled sensitivity and specificity were 14% (9%-54%) and 99% (38%-100%) for the SMA margin, 12% (8%-46%) and 99% (97%-100%) for the posterior margin; and 37% (29%-53%) and 96% (31%-100%) for the SMV/PV margin, respectively. CONCLUSION: CT showed very high specificity but low sensitivity in predicting pathological CRM involvement in pancreatic cancer.

Identification of intratumoral fluid-containing area by magnetic resonance imaging to predict prognosis in patients with pancreatic ductal adenocarcinoma after curative resection.

OBJECTIVES: To compare the prognosis of pancreatic ductal adenocarcinoma (PDAC) after curative resection according to the type of intratumoral fluid-containing area identified on MRI. METHODS: This retrospective study included 112 consecutive patients who underwent upfront surgery with margin-negative resection between 2012 and 2019. All patients underwent MRI within 1 month before surgery. Three radiologists independently assessed the MRI findings, determined whether intratumoral fluid-containing areas were present, and classified all intratumoral fluid-containing areas by type (i.e., imaging necrosis or neoplastic mucin cysts). Recurrence-free survival (RFS) and overall survival (OS) were evaluated by the Kaplan-Meier method and the Cox proportional hazards model. Histopathological differences according to the type of intratumoral fluid-containing area were assessed. RESULTS: Of the 112 PDAC patients, intratumoral fluid-containing areas were identified on MRI in 33 (29.5%), among which 18 were classified as imaging necrosis and 15 as neoplastic mucin cysts. PDAC patients with imaging necrosis demonstrated significantly shorter RFS (mean 6.1 months versus 47.3 months; p < .001) and OS (18.4 months versus 55.0 months, p = .001) than those with neoplastic mucin cysts. Multivariable analysis showed that only the type of intratumoral fluid-containing area was significantly associated with RFS (hazard ratio, 2.25 and 0.38; p = .009 and p = .046 for imaging necrosis and neoplastic mucin cysts, respectively). PDAC with imaging necrosis had more frequent histological necrosis, more aggressive tumor differentiation, and higher tumor cellularity than PDAC with neoplastic mucin cysts (p ≤ .02). CONCLUSION: The detection and discrimination of intratumoral fluid-containing areas on preoperative MRI may be useful in predicting the prognosis of PDAC patients after curative resection. KEY POINTS: • Pancreatic ductal adenocarcinoma (PDAC) patients with imaging necrosis demonstrated significantly shorter survival than those with neoplastic mucin cysts after curative resection. • Multivariable analysis showed that only the type of intratumoral fluid-containing area identified on MRI was significantly associated with recurrence-free survival. • PDAC with imaging necrosis had more frequent histological necrosis, more aggressive tumor differentiation, and higher tumor cellularity than PDAC with neoplastic mucin cysts.

Outcomes of patients with metastatic pancreatic cancer who progress on first restaging imaging.

BACKGROUND: Objective responses to first-line systemic chemotherapy in metastatic pancreatic cancer patients are seen in less than one third of cases. Unfortunately, a significant amount will have disease progression (PD) on their first restaging imaging. With patients' short life expectancy, it is crucial for clinicians to be prudent when deciding whom and when to treat. Our study aimed to evaluate outcomes of patients that progressed on their first restaging imaging on 1st line therapy. METHODS: We retrospectively analyzed patients diagnosed between 2010-2017 whose first restaging imaging demonstrated PD. The primary outcome was overall survival (OS) from metastatic diagnosis date to death. Patients who were lost to follow-up were excluded. RESULTS: Out of 262 total patients reviewed, 98 patients (37%) were included. Sixty-five (66%) received 2nd line therapy, and 33 (34%) did not. Reasons patients did not pursue 2nd line therapy were performance status (PS) decline, organ dysfunction, or patient choice for alternative therapy. Median ages for patients who did and did not receive 2nd line therapy were 61 and 67, respectively (P<0.001). More patients had a poor PS at the time of initial diagnosis in the non-2nd line therapy group (7.5% vs. 31.0%, P=0.021). Median OS for those receiving 2nd line therapy was 9 months (95% CI: 7-11 months) compared to 4 months (95% CI: 3-5 months) for those not receiving 2nd-line therapy (P<0.001). CONCLUSIONS: Although likely biased due to better performance status and younger age, our patients who progressed rapidly on 1st line therapy showed an OS benefit if they received 2nd line therapy. These results suggest that patients maintaining a good PS after immediate progression on 1st line therapy should be offered 2nd line therapy.