scholarly journals Is early-stage pancreatic adenocarcinoma truly early: stage migration on final pathology with surgery-first vs. neoadjuvant therapy sequencing

HPB ◽  
2018 ◽  
Vol 20 ◽  
pp. S176
Author(s):  
A. Lee ◽  
E. Beaudry Simoneau ◽  
Y.-J. Chiang ◽  
J. Lee ◽  
M. Kim ◽  
...  
HPB ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 1203-1210 ◽  
Author(s):  
Andrew J. Lee ◽  
Eve Simoneau ◽  
Yi-Ju Chiang ◽  
Jeffrey E. Lee ◽  
Michael P. Kim ◽  
...  

2020 ◽  
Vol 123 (1) ◽  
pp. 245-251
Author(s):  
Ibrahim Nassour ◽  
Mohamed A. Adam ◽  
Stacy Kowalsky ◽  
Samer Al Masri ◽  
Nathan Bahary ◽  
...  

2017 ◽  
Vol 35 (5) ◽  
pp. 515-522 ◽  
Author(s):  
Ali A. Mokdad ◽  
Rebecca M. Minter ◽  
Hong Zhu ◽  
Mathew M. Augustine ◽  
Matthew R. Porembka ◽  
...  

Purpose To compare overall survival between patients who received neoadjuvant therapy (NAT) followed by resection and those who received upfront resection (UR)—as well as a subgroup of UR patients who also received adjuvant therapy—for early-stage resectable pancreatic adenocarcinoma. Patients and Methods Adult patients with resected, clinical stage I or II adenocarcinoma of the head of the pancreas were identified in the National Cancer Database from 2006 to 2012. Patients who underwent NAT followed by curative-intent resection were matched by propensity score with patients whose tumors were resected upfront. Overall survival was compared by using a Cox proportional hazards regression model. Early postoperative and oncologic outcomes were evaluated. Results We identified 15,237 patients with clinical stage I or II resected pancreatic head adenocarcinoma. From the NAT group, 2,005 patients (95%) were matched with 6,015 patients who underwent UR. The NAT group was associated with improved survival compared with UR (median survival, 26 months v 21 months, respectively; stratified log-rank P < .01; hazard ratio, 0.72; 95% CI, 0.68 to 0.78). Patients in the UR group had higher pathologic T stage (pT3 and T4: 86% v 73%; P < .01), higher positive lymph nodes (73% v 48%; P < .01), and higher positive resection margin (24% v 17%; P < .01). Compared with a subset of UR patients who received adjuvant therapy, NAT patients had a better survival (adjusted hazard ratio, 0.83; 95% CI, 0.73 to 0.89). Conclusion NAT followed by resection has a significant survival benefit compared with UR in early-stage, resected pancreatic head adenocarcinoma. These findings support the use of NAT, particularly as a patient selection tool, in the management of resectable pancreatic adenocarcinoma.


HPB ◽  
2020 ◽  
Vol 22 ◽  
pp. S77
Author(s):  
J.F. Griffin ◽  
T.E. Newhook ◽  
T.J. Vreeland ◽  
L. Prakash ◽  
M.P. Kim ◽  
...  

2020 ◽  
Vol 27 (S3) ◽  
pp. 965-965
Author(s):  
Amr I. Al Abbas ◽  
Mazen Zenati ◽  
Caroline J. Rieser ◽  
Ahmad Hamad ◽  
Jae Pil Jung ◽  
...  

2020 ◽  
pp. 000313482098255
Author(s):  
Michael D. Watson ◽  
Maria R. Baimas-George ◽  
Keith J. Murphy ◽  
Ryan C. Pickens ◽  
David A. Iannitti ◽  
...  

Background Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning model can predict tumor response to NAC. Methods Patients with pancreatic cancer receiving neoadjuvant therapy prior to pancreatoduodenectomy were identified between November 2009 and January 2018. The College of American Pathologists Tumor Regression Grades 0-2 were defined as pathologic response (PR) and grade 3 as no response (NR). Axial images from preoperative computed tomography scans were used to create a 5-layer convolutional neural network and LeNet deep learning model to predict PRs. The hybrid model incorporated decrease in carbohydrate antigen 19-9 (CA19-9) of 10%. Accuracy was determined by area under the curve. Results A total of 81 patients were included in the study. Patients were divided between PR (333 images) and NR (443 images). The pure model had an area under the curve (AUC) of .738 ( P < .001), whereas the hybrid model had an AUC of .785 ( P < .001). CA19-9 decrease alone was a poor predictor of response with an AUC of .564 ( P = .096). Conclusions A deep learning model can predict pathologic tumor response to neoadjuvant therapy for patients with pancreatic adenocarcinoma and the model is improved with the incorporation of decreases in serum CA19-9. Further model development is needed before clinical application.


HPB ◽  
2020 ◽  
Vol 22 ◽  
pp. S38
Author(s):  
M. Watson ◽  
M. Baimas-George ◽  
K. Murphy ◽  
R. Pickens ◽  
D. Iannitti ◽  
...  

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