Atsushi Oba1,2, Chiara Croce1, Patrick Hosokawa3, Cheryl Meguid1, Robert J Torphy1, Mohammed H Al-Musawi4, Steven Ahrendt1,5, Ana Gleisner1,5, Richard D Schulick1,5, Marco Del Chiaro1,5. 1. Division of Surgical Oncology, Department of Surgery, University of Colorado, Anschutz Medical Campus, Aurora, Colorado. 2. Department of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Ariake, Tokyo, Japan. 3. Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, Colorado. 4. Clinical Trials Office, Department of Surgery, University of Colorado, Anschutz Medical Campus, Denver, Colorado. 5. University of Colorado Cancer Center, Denver, Colorado.
Abstract
OBJECTIVE: To identify objective preoperative prognostic factors that are able to predict long-term survival of patients affected by PDAC. SUMMARY OF BACKGROUND DATA: In the modern era of improved systemic chemotherapy for PDAC, tumor biology, and response to chemotherapy are essential in defining prognosis and an improved approach is needed for classifying resectability beyond purely anatomic features. METHODS: We queried the National Cancer Database regarding patients diagnosed with PDAC from 2010 to 2016. Cox proportional hazard models were used to select preoperative baseline factors significantly associated with survival; final models for overall survival (OS) were internally validated and formed the basis of the nomogram. RESULTS: A total of 7849 patients with PDAC were included with a median follow-up of 19 months. On multivariable analysis, factors significantly associated with OS included carbohydrate antigen 19-9, neoadjuvant treatment, tumor size, age, facility type, Charlson/Deyo score, primary site, and sex; T4 stage was not independently associated with OS. The cumulative score was used to classify patients into 3 groups: good, intermediate, and poor prognosis, respectively. The strength of our model was validated by a highly significant randomization test, Log-rank test, and simple hazard ratio; the concordance index was 0.59. CONCLUSION: This new PDAC nomogram, based solely on preoperative variables, could be a useful tool to patients and counseling physicians in selecting therapy. This model suggests a new concept of resectability that is meant to reflect the biology of the tumor, thus partially overcoming existing definitions, that are mainly based on tumor anatomic features.
OBJECTIVE: To identify objective preoperative prognostic factors that are able to predict long-term survival of patients affected by PDAC. SUMMARY OF BACKGROUND DATA: In the modern era of improved systemic chemotherapy for PDAC, tumor biology, and response to chemotherapy are essential in defining prognosis and an improved approach is needed for classifying resectability beyond purely anatomic features. METHODS: We queried the National Cancer Database regarding patients diagnosed with PDAC from 2010 to 2016. Cox proportional hazard models were used to select preoperative baseline factors significantly associated with survival; final models for overall survival (OS) were internally validated and formed the basis of the nomogram. RESULTS: A total of 7849 patients with PDAC were included with a median follow-up of 19 months. On multivariable analysis, factors significantly associated with OS included carbohydrate antigen 19-9, neoadjuvant treatment, tumor size, age, facility type, Charlson/Deyo score, primary site, and sex; T4 stage was not independently associated with OS. The cumulative score was used to classify patients into 3 groups: good, intermediate, and poor prognosis, respectively. The strength of our model was validated by a highly significant randomization test, Log-rank test, and simple hazard ratio; the concordance index was 0.59. CONCLUSION: This new PDAC nomogram, based solely on preoperative variables, could be a useful tool to patients and counseling physicians in selecting therapy. This model suggests a new concept of resectability that is meant to reflect the biology of the tumor, thus partially overcoming existing definitions, that are mainly based on tumor anatomic features.
Authors: Atsushi Oba; Thomas F Stoop; Matthias Löhr; Thilo Hackert; Nicholas Zyromski; William H Nealon; Michiaki Unno; Richard D Schulick; Mohammed H Al-Musawi; Wenming Wu; Yupei Zhao; Sohei Satoi; Christopher L Wolfgang; Mohammad Abu Hilal; Marc G Besselink; Marco Del Chiaro Journal: Ann Surg Date: 2020-05-01 Impact factor: 12.969
Authors: Christopher W Mangieri; Cristian D Valenzuela; Richard A Erali; Perry Shen; Russell Howerton; Clancy J Clark Journal: Ann Surg Oncol Date: 2022-02-20 Impact factor: 5.344