Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2):International Improvement and Validation Study

Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America, Australia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%). Regions of interest were annotated and classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net model was trained to detect RPC. Validation consisted of by-scanner internal-external cross-validation. Overall, 528 unique hematoxylin and eosin (H &amp; E) slides from 528 patients were included. In the individual Philips, Hamamatsu, 3DHistech, and Leica scanner cross-validations, mean F1 scores of 0.81 (95% CI, 0.77-0.84), 0.80 (0.78-0.83), 0.76 (0.65-0.78), and 0.71 (0.65-0.78) were achieved, respectively. In the meta-analysis of the cross-validations, the mean F1 score was 0.78 (0.71-0.84). A final model was trained on the entire data set. This ISGPP model is the first segmentation model using artificial intelligence techniques to objectively quantify RPC following NAT. The internally-externally cross-validated model in this study demonstrated robust performance in detecting RPC in specimens. The ISGPP model, now made publically available, enables automated RPC segmentation and forms the basis for objective NAT response evaluation in pancreatic cancer.</p

Impact of Tumor Grade on Prognosis in Pancreatic Cancer: Should We Include Grade in AJCC Staging?

AJCC staging of pancreatic cancer (PAC) is used to determine prognosis, yet survival within each stage shows wide variation and remains unpredictable. We hypothesized that tumor grade might be responsible for some of this variation and that the addition of grade to current AJCC staging would provide improved prognostication. The Surveillance, Epidemiology, and End Results (SEER) database (1991–2005) was used to identify 8082 patients with resected PAC. The impact of grade on overall and stage-specific survival was assessed using Cox regression analysis. Variables in the model were age, sex, tumor size, lymph node status, and tumor grade. For each AJCC stage, survival was significantly worse for high-grade versus low-grade tumors. On multivariate analysis, high tumor grade was an independent predictor of survival for the entire cohort (hazard ratio [HR] 1.40, 95% confidence interval [95% CI] 1.31–1.48) as well as for stage I (HR 1.28, 95% CI 1.07–1.54), stage IIA (HR 1.43, 95% CI 1.26–1.61), stage IIB (HR 1.38, 95% CI 1.27–1.50), stage III (HR 1.28, 95% CI 1.02–1.59), and stage IV (HR 1.58, 95% CI 1.21–2.05) patients. The addition of grade to staging results in a statistically significant survival discrimination between all stages. Tumor grade is an important prognostic variable of survival in PAC. We propose a novel staging system incorporating grade into current AJCC staging for pancreas cancer. The improved prognostication is more reflective of tumor biology and may impact therapy decisions and stratification of future clinical trials

Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2): International Improvement and Validation Study.

Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America, Australia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%). Regions of interest were annotated and classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net model was trained to detect RPC. Validation consisted of by-scanner internal-external cross-validation. Overall, 528 unique hematoxylin and eosin (H & E) slides from 528 patients were included. In the individual Philips, Hamamatsu, 3DHistech, and Leica scanner cross-validations, mean F1 scores of 0.81 (95% CI, 0.77-0.84), 0.80 (0.78-0.83), 0.76 (0.65-0.78), and 0.71 (0.65-0.78) were achieved, respectively. In the meta-analysis of the cross-validations, the mean F1 score was 0.78 (0.71-0.84). A final model was trained on the entire data set. This ISGPP model is the first segmentation model using artificial intelligence techniques to objectively quantify RPC following NAT. The internally-externally cross-validated model in this study demonstrated robust performance in detecting RPC in specimens. The ISGPP model, now made publically available, enables automated RPC segmentation and forms the basis for objective NAT response evaluation in pancreatic cancer

Mucinous cystic neoplasms of the mesentery: a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Mucinous cystic neoplasms arise in the ovary and various extra-ovarian sites. While their pathogenesis remains conjectural, their similarities suggest a common pathway of development. There have been rare reports involving the mesentery as a primary tumour site.</p> <p>Case presentation</p> <p>A cystic mass of uncertain origin was demonstrated radiologically in a 22 year old female with chronic abdominal pain. At laparotomy, the mass was fixed within the colonic mesentery. Histology demonstrated a benign mucinous cystadenoma.</p> <p>Methods and results</p> <p>We review the literature on mucinous cystic neoplasms of the mesentery and report on the pathogenesis, biologic behavior, diagnosis and treatment of similar extra-ovarian tumors. We propose an updated classification of mesenteric cysts and cystic tumors.</p> <p>Conclusion</p> <p>Mucinous cystic neoplasms of the mesentery present almost exclusively in women and must be considered in the differential diagnosis of mesenteric tumors. Only full histological examination of a mucinous cystic neoplasm can exclude a borderline or malignant component. An updated classification of mesenteric cysts and cystic tumors is proposed.</p

Histopathologic Evidence of Tumor Regression in the Axillary Lymph Nodes of Patients Treated With Preoperative Chemotherapy Correlates With Breast Cancer Outcome

Background: The benefits of primary tumor downstaging and assessment of chemoresponsiveness have resulted in expanded applications for induction chemotherapy. However, the pathologic evaluation and prognostic significance of response in preoperatively treated lymph nodes have not been defined.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41400/1/10434_2003_Article_734.pd

A Phase II study of celecoxib, gemcitabine, and cisplatin in advanced pancreatic cancer

Background . Pancreatic cancer is amongst the most chemoresistant malignancies. Expression of the cyclooxygenase-2 (COX-2) enzyme plays a major role in tumor progression and resistance to therapy. A Phase II study was undertaken to determine the effect of gemcitabine by fixed-dose rate infusion (FDR), cisplatin and the COX-2 inhibitor, celecoxib, on the 6-month survival rate in patients with metastatic pancreatic cancer.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45281/1/10637_2005_Article_1028.pd