NIMG-60. PREDICTING OVERALL SURVIVAL IN GLIOBLASTOMA USING HISTOPATHOLOGY VIA AN END-TO-END DEEP LEARNING PIPELINE: A LARGE MULTI-COHORT STUDY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi143-vi143
Author(s):  
Ruchika Verma ◽  
Mark Cohen ◽  
Paula Toro ◽  
Mojgan Mokhtari ◽  
Pallavi Tiwari
Keyword(s):  
Overall Survival ◽  
Cohort Study ◽  
Deep Learning ◽  
Survival Data ◽  
Cox Model ◽  
Training Model ◽  
Cancer Center ◽  
Survival Prediction ◽  
Validation Data ◽  
End To End

Abstract PURPOSE Glioblastoma is an aggressive and universally fatal tumor. Morphological information as captured from cellular regions on surgically resected histopathology slides has the ability to reveal the inherent heterogeneity in Glioblastoma and thus has prognostic implications. In this work, we hypothesized that capturing morphological attributes from high cellularity regions on Hematoxylin and Eosin (H&E)-stained digitized tissue slides using an end-to-end deep-learning pipeline will enable risk-stratification of GBM tumors based on overall survival. METHODS A large multi-cohort study consisting of N=514 H&E-stained digitized tissue slides along with overall-survival data (OS) was obtained from the Ivy Glioblastoma atlas project (Ivy-GAP (N=41)), TCGA (N=379), and CPTAC (N=94). Our deep-learning pipeline consisted of two stages. First stage involved segmenting cellular tumor (CT) from necrotic-regions and background using Resnet-18 model, while the second stage involved predicting OS, using only the segmented CT regions identified in the first stage. For the segmentation stage, we leveraged the Ivy-GAP cohort, where CT annotations confirmed by expert neuropathologists were available, to serve as the training set. Using this training model, the CT regions on the remaining cohort (TCGA, CPTAC) (i.e. test set) were identified. For the survival-prediction stage, the last layer of ResNet18 model was replaced with a cox layer (ResNet-Cox), and further fine-tuned using OS and censor information. Independent validation of ResNet-Cox was performed on two hold-out sites from TCGA and one from CPTAC. RESULTS Our segmentation model achieved an accuracy of 0.89 in reliably identifying CT regions on the validation data. The segmented CT regions on the test cohort were further confirmed by two experts. Our ResNet-Cox model achieved a concordance-index of 0.73 on MD Anderson Cancer Center (N=60), 0.71 on Henry Ford Hospital (N=96), and 0.68 on CPTAC data (N=41). CONCLUSION Deep-learning features captured from cellular tumor of H&E-stained histopathology images may predict survival in Glioblastoma.

Novel functional proteomics approach to defining ovarian cancer molecular heterogeneity

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5502-5502
Author(s):  
M. S. Carey ◽  
B. T. Hennessy ◽  
A. M. Gonzalez-Angulo ◽  
W. Liu ◽  
K. R. Coombes ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Patient Outcomes ◽  
Cox Model ◽  
Survival Prediction ◽  
Functional Proteomics ◽  
Molecular Heterogeneity ◽  
Kinase Signaling ◽  
Survival Times ◽  
Modeling Approach

5502 Background: A number of clinicopathologic risk factors are used for survival prediction and clinical decision-making in epithelial ovarian cancer (EOC). Information from novel technologies such as gene arrays has not had an impact on patient management. We studied EOC protein signaling profiles to determine if their addition to accepted clinicopathologic factors improves their accuracy in predicting individual patient outcomes. Methods: We applied a novel functional proteomics technology, reverse phase protein array (RPPA), to quantify expression and activation of 42 steroid and kinase signaling pathway proteins in 106 high-grade EOCs from patients with stages 1–4 tumors managed with surgery and platinum-based chemotherapy. Cox regression analysis and a novel committee modeling approach were used to study the impact of functional proteomics on patient outcomes. Results: In a Cox model using only clinical variables, stage and residual disease were significantly related to overall survival. By adding the proteins to the clinical Cox model, two proteins that were significantly associated with overall survival on univariate analysis (phosphorylated-MAPK (p-MAPK; log rank p = 0.0047) and progesterone receptor (PR; log rank p = 0.027)) remained significant at the alpha=0.10 level (z-test p-values 0.074 and 0.034, respectively, when treated as binary variables according to martingale residual plots); as a result, these two proteins added to the predictive accuracy of the clinical survival model. However, using the novel committee modeling approach in test and validation EOC sets, a closest neighbor metric was applied to successfully define distinct proteins groups, each composed of nine proteins, that are predictive of specific survival times in patients with EOC. This granular approach to modeling is particularly suited to defining the molecular heterogeneity of EOC. Conclusions: EOC is a complex process with significant individual variability. Using novel approaches to functional proteomic study and statistical modeling, our striking finding is that distinct combinations of steroid and kinase signaling proteins are predictive markers of specific survival times in EOC. No significant financial relationships to disclose.

Survival analysis of maintenance therapy with capecitabine (Cape) in patients with resected pancreatic adenocarcinoma (PAC) after adjuvant therapy: A retrospective cohort study.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e14658-e14658
Author(s):  
Xuezhong Yang ◽  
Benjamin Weinberg ◽  
Jimmy J. Hwang ◽  
Christina Sing-Ying Wu ◽  
Madeeha Akram ◽  
...  
Keyword(s):  
Cohort Study ◽  
Adjuvant Chemotherapy ◽  
Adjuvant Therapy ◽  
Maintenance Therapy ◽  
Survival Data ◽  
Prolonged Exposure ◽  
Cancer Center ◽  
Control Group ◽  
Study Group

e14658 Background: The 5-year survival of PAC with surgery alone is below 10%, and with adjuvant chemotherapy increases to about 20%. The original GITSG adjuvant study demonstrating a survival benefit compared to surgery could be attributed to the use of 2-years of weekly IV bolus 5FU, and not only chemoradiation. In theory, the prolonged exposure to therapy could maintain pressure on dormant cancer cells that may remain in G0 arrest, by attacking them as they infrequently enter G1/S phase. To evaluate this hypothesis, we retrospectively evaluated our pts who were treated with or without maintenance Cape. Methods: Pts in the Georgetown/Lombardi Cancer Center EMR since Oct 2007 were sought for PAC that was resected with curative intent, received standard adjuvant chemotherapy with or without chemoradiation. The study group received maintenance cape for at least 2 months, and the control group was monitored until disease recurrence. Only pts with complete follow-up survival data were analyzed. Results: 20 pts met the criteria as study group, and 58 pts as the control group. In the study group, cape was usually given 1000mg orally twice a day, Monday through Friday following adjuvant therapy, for an indefinite period, up to 2 years. Pts received cape for median duration of 12.5 months (2 to 24 months), and the median follow-up duration was 33 months (16 to 78 months). The median overall survival (OS) for the study group was 48 months. The 2 year OS was 94%, and 5 year OS was 40%. The median recurrence free survival (RFS) was 39 months. The 2 year RFS was 67%, and the 5 year RFS was 25%. Common toxicities were mild hand-and-foot syndrome and fatigue. 4 pts discontinued cape due to toxicities: febrile neutropenia, severe fatigue, weight loss and diarrhea. The control group was of comparable staging, and the median OS was 22 months, 5 year OS rate was 16%, median RFS was 13 months, 2 year RFS was 19%. Conclusions: In this single institute retrospective controlled cohort study, Cape maintenance therapy following adjuvant therapy in resected PAC is associated with a significantly (p<0.05) higher OS and PFS compared to the control group. This approach should be studied in a RCT.

Prognosis of gastric cancer (GC) patients with positive peritoneal cytology.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 41-41
Author(s):  
Hironori Shiozaki ◽  
Elena Elimova ◽  
Rebecca Slack ◽  
Hsiang-Chun Chen ◽  
Gregg A Staerkel ◽  
...  
Keyword(s):  
Overall Survival ◽  
Diagnostic Laparoscopy ◽  
Cox Model ◽  
Peritoneal Metastases ◽  
Patient Treatment ◽  
Cancer Center ◽  
Peritoneal Cytology ◽  
Term Survival ◽  
Risk Of Death ◽  
One Year

41 Background: Laparoscopic staging of patients with GC can disclose peritoneal metastases. Although this finding is associated with a poor prognosis, some patients achieve a long-term survival. In an attempt to provide explanation we compared the overall survival (OS) of patients with GC peritoneal metastases from two settings: cytology positive only (Cy+) and grossly positive (Gross+). Methods: 146 GC patients with peritoneal metastases were identified between 2000 and 2014. Cox-model regression was used for overall survival (OS) analyses. Results: Patient/treatment characteristics were as follows: males (66%), good ECOG scores (0-1; 89%), metastases confirmed by a diagnostic laparoscopy (84%), poorly differentiated histology(92%), received chemotherapy (89%), received chemoradiation (22%), and received surgery (10%). The median follow-up time for all patients was 12.9 months and median OS was 15 months. Patients with Gross+ were at higher risk of death compared to Cy+ patients (50% vs. 83%1-year OS, respectively). Only diagnostic laparoscopy and metastasis type (Gross+ vs. Cy+) were significant in both univariate and multivariate OS models. With both factors in the same model, patients with Gross+ were more than twice as likely to die when compared to those with Cy+ (HR=2.23; p=0.001) while patients having a diagnostic laparoscopy were half as likely to die (HR=0.52; p=0.01). Conclusions: The one-year OS of patients with Cy+ peritoneal metastases is significantly longer than those with Gross+ findings. As such, novel strategies for Cy+ patients may further prolong their survival. From U. T. M. D. Anderson Cancer Center (UTMDACC), Houston, Texas, USA. (Supported in part by UTMDACC, and CA 138671 and CA172741 from the NCI).

scholarly journals Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Linmin Pei ◽  
Lasitha Vidyaratne ◽  
Md Monibor Rahman ◽  
Khan M. Iftekharuddin
Keyword(s):  
Overall Survival ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Classification ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Context Aware ◽  
Brain Tumor Segmentation ◽  
Subtype Classification ◽  
Overall Survival Prediction

AbstractA brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge.

Racial and socioeconomic disparities in overall survival in colorectal cancer (CRC) at West Cancer Center & Research Institute (WCCRI), Memphis, TN.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16122-e16122
Author(s):  
Vanessa Wookey ◽  
Gabriella Bufalino ◽  
Gregory A. Vidal ◽  
Bradley G. Somer ◽  
Lee S. Schwartzberg ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Survival Data ◽  
Private Insurance ◽  
Socioeconomic Disparities ◽  
Cancer Center ◽  
Survival Outcomes ◽  
Median Income ◽  
Entire Cohort ◽  
Stage 4

e16122 Background: WCCRI, a comprehensive regional community oncology center in Memphis, Tennessee and the Mid-South region, serves a racially, geographically and socioeconomically diverse patient cohort. We sought to evaluate disparity of outcomes in survival by race and socioeconomic status, in addition to patient and tumor characteristics. Methods: All consecutive patients referred to and treated at WCCRI with colorectal adenocarcinoma from 2007-2013 were included. Individual chart review was performed to verify diagnosis, stage, and date and cause of death. Kaplan-Meier Overall Survival curves were generated for the entire cohort and by race, sex, tumor location and income derived from zip code. WCCRI survival data were compared to SEER data. Results: From 2007-2013, 1,176 patients were included in the analysis: 405 blacks, 757 whites, 14 others. Median age at diagnosis: Blacks 58 yrs, whites 61 yrs. Stage distribution at diagnosis: stage 1: 100, stage 2: 275, stage 3: 425, stage 4: 376. All stages combined, blacks trended towards shorter OS vs whites (5-year OS: 52.8% vs 58.3%; median survival 71.0 mos vs 98.6 mos; p= 0.095). Blacks presented at later stages (71.4% at stage 3 or 4 vs 66.3% for whites) but no statistically significant OS differences were seen when compared by stage. Patients at or below the median income of $39,590 for WCC had worse 5-year OS (51.6% vs. 61.1%; p= 0.006), as did patients without private insurance (5-year OS: uninsured: 48.0%, Medicare/Medicaid: 50.0%, private: 62.0%; p< 0.001). Adjusted for stage, 5-year OS was statistically significant for stage 4 (private: 18.0%, Medicare/Medicaid: 9.4%, uninsured: 8.3%; p= 0.020). A higher proportion of blacks were below the median income (69% vs 39%) but no statistically significant OS differences were seen when adjusted by race. Overall, cancer survival outcomes were similar to SEER results. Conclusions: At WCCRI, black patients with CRC presented at a later stage than whites, however, adjusted for stage, no significant racial difference in OS was found. Income and insurance status influenced survival outcomes. Overall, our results reveal racial and socioeconomic disparities in colorectal cancer in a diverse US population and further detailed multivariate data analyses are underway.

Racial and socioeconomic disparities in overall survival in colorectal cancer (CRC) at West Cancer Center (WCC), Memphis, TN.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 80-80
Author(s):  
Vanessa Wookey ◽  
Gabriella Bufalino ◽  
Gregory A. Vidal ◽  
Bradley G. Somer ◽  
Lee S. Schwartzberg ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Survival Data ◽  
Private Insurance ◽  
Socioeconomic Disparities ◽  
Cancer Center ◽  
Survival Outcomes ◽  
Median Income ◽  
Entire Cohort ◽  
Stage 4

80 Background: WCC, a comprehensive regional community oncology center in Memphis, Tennessee and the Mid-South region, serves a racially, geographically and socioeconomically diverse patient cohort. We sought to evaluate disparity of outcomes in survival by race and socioeconomic status, in addition to patient and tumor characteristics. Methods: All consecutive patients referred to and treated at WCC with colorectal adenocarcinoma from 2007-2013 were included. Individual chart review was performed to verify diagnosis, stage, and date and cause of death. Kaplan-Meier Overall Survival curves were generated for the entire cohort and by race, sex, tumor location and income derived from zip code. WCC survival data were compared to SEER data. Results: From 2007-2013, 1,176 patients were included in the analysis: 405 blacks, 757 whites, 14 others. Median age at diagnosis: Blacks 58 yrs, whites 61 yrs. Stage distribution at diagnosis: stage 1: 100, stage 2: 275, stage 3: 425, stage 4: 376. All stages combined, blacks trended towards shorter OS vs whites (5-year OS: 52.8% vs 58.3%; median survival 71.0 mos vs 98.6 mos; p= 0.095). Blacks presented at later stages (71.4% at stage 3 or 4 vs 66.3% for whites) but no statistically significant OS differences were seen when compared by stage. Patients at or below the median income of $39,590 for WCC had worse 5-year OS (51.6% vs. 61.1%; p= 0.006), as did patients without private insurance (5-year OS: uninsured: 48.0%, Medicare/Medicaid: 50.0%, private: 62.0%; p< 0.001). Adjusted for stage, 5-year OS was statistically significant for stage 4 (private: 18.0%, Medicare/Medicaid: 9.4%, uninsured: 8.3%; p= 0.020). A higher proportion of blacks were below the median income (69% vs 39%) but no statistically significant OS differences were seen when adjusted by race. Overall, cancer survival outcomes were similar to SEER results. Conclusions: At WCC, black patients with CRC presented at a later stage than whites, however, adjusted for stage, no significant racial difference in OS was found. Income and insurance status affected survival outcomes. Overall, our results reveal racial and socioeconomic disparities in colorectal cancer in a diverse US population.

Large-scale transcriptome data analysis reveals prognostic signature genes for overall survival prediction in diffuse large B cell lymphoma

2020 ◽  
Author(s):  
Mengmeng Pan ◽  
Pingping Yang ◽  
Fangce Wang ◽  
Xiu Luo ◽  
Bing Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
Cox Model ◽  
B Cell Lymphoma ◽  
Risk Groups ◽  
Survival Prediction ◽  
Large B Cell Lymphoma ◽  
Overall Survival Prediction ◽  
Large B Cell

Abstract BACKGROUND With the improvement of clinical treatment outcomes in Diffuse large B cell lymphoma (DLBCL), the high rate of relapse in DLBCL patients is still an established barrier, due to the therapeutic strategy selection based on potential target remains unsatisfactory. Therefore, there is an urgent need in further exploration of prognostic biomarkers so as to improve the prognosis of DLBCL.METHODS The univariable and multivariable Cox regression models were employed to screen out gene signatures for DLBCL overall survival prediction. The differential expression analysis was used to identify representative genes in high-risk and low-risk groups, respectively, by student t test and fold change. The functional difference between the high-risk and low-risk groups were identified by the gene set enrichment analysis.RESULTS We conducted a systematic data analysis to screen the candidate genes significantly associated with overall survival of DLBCL in three NCBI Gene Expression Omnibus (GEO) datasets. To construct a prognostic model, five genes (CEBPA, CYP27A1, LST1, MREG, and TARP) were then screened and tested using the multivariable Cox model and the stepwise regression method. Kaplan-Meier curve confirmed the good predictive performance of the five-gene Cox model. Thereafter, the prognostic model and the expression levels of the five genes were validated by means of an independent dataset. All five genes were significantly favorable for the prognosis in DLBCL, both in training and validation datasets. Additionally, further analysis revealed the independence and superiority of the prognostic model in risk prediction. Functional enrichment analysis revealed some vital pathways resulting in unfavorable outcome and potential therapeutic targets in DLBCL.CONCLUSION We developed a five-gene Cox model for the clinical outcome prediction of DLBCL patients. Meanwhile, potential drug selection using this model can help clinicians to improve the clinical practice for the patients.

scholarly journals TMOD-35. PREDICTION OF OVERALL SURVIVAL, AND MOLECULAR MARKERS IN GLIOMAS VIA ANALYSIS OF DIGITAL PATHOLOGY IMAGES USING DEEP LEARNING

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi270-vi270 ◽  
Author(s):  
Saima Rathore ◽  
Muhammad Iftikhar ◽  
MacLean Nasrallah ◽  
Metin Gurcan ◽  
Nasir Rajpoot ◽  
...  
Keyword(s):  
Overall Survival ◽  
Deep Learning ◽  
Molecular Markers ◽  
Brain Tumors ◽  
Pearson Correlation ◽  
Digital Pathology ◽  
Optimization Method ◽  
Computational Method ◽  
Survival Prediction ◽  
Molecular Testing

Abstract BACKGROUND Microscopic features of brain tumors, such as tumor cell morphology, type/degree of microvascular hyperplasia, mitotic activity, and extent of zonal/geographic necrosis, among many others, are measurable and reflect underlying molecular markers that are predictive of patient prognosis, signifying that quantitative analysis may provide insight into disease mechanics. We developed a computational method to predict overall-survival and molecular markers for brain tumors using deep learning on whole-slide digital images (WSDI). METHODS The WSDI were acquired from TCGA for 663 patients [IDH:333 wildtype, 330 mutants, 1p/19q:201 non-codeleted, 129 codeleted]. A set of 100 region-of-interest each comprising 1024x1024 that contained viable tumor with descriptive histologic characteristics and that were free of artifacts were extracted. A modified version of ResNet with architecture of 50 convolutional layers was used. The network was optimized using stochastic gradient decent optimization method with binary cross-entropy loss. Sigmoid- and linear-activation were, respectively, used as final layer for mutation and survival prediction. Data was divided into training (50%), testing (25%), and validation (25%). RESULTS The model predicted IDH and 1p/19q with an accuracy of 88.92%[sensitivity(se)/ specificity(sp)=87.77/84.35] and 88.23%(se/sp=87.38/88.58), respectively. The accuracy was further improved, when classification was done within homogeneous grades, for IDH [II=90.50%(se/sp=91.52/78.57), III=91.21%(se/sp=91.89/89.47), IV=92.77%(se/sp=77.78/93.51)] and 1p/19q [II=91.51%(se/sp=91.30/91.66), III=92.56(se/sp =93.33,92.04)]. The Pearson correlation coefficient between the predicted scores and overall-survival was 0.79 (p< 0.0001). CONCLUSION Our findings suggest that deep learning techniques can be applied to WSDI for objective, and accurate prediction of mutations and survival. Our approach, when compared with expensive molecular based assays that invariably capture molecular markers from a small part of the tumor and also destroy the tissue, could (i) offer the same service at a reduced price, (ii) enable disease characterization across the entire landscape of the tissue, (iii) be beneficial for tissues inadequate for molecular testing, and (iv) does not need physical shipping of the tissue.

Factors associated with surgical resection for pancreatic and periampullary carcinomas in a cancer center in Mexico City.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 191-191
Author(s):  
Luis F. Onate-Ocana ◽  
Elyzabeth Bermudez-Benitez ◽  
Miguel Angel Ortiz-Toledo ◽  
Francisco J. Ochoa-Carrillo ◽  
Vincenzo Aiello-Crocifoglio
Keyword(s):  
Overall Survival ◽  
Surgical Resection ◽  
Cox Model ◽  
Ampullary Carcinoma ◽  
Cancer Center ◽  
Neuroendocrine Neoplasms ◽  
Malignant Neoplasms ◽  
Factors Associated ◽  
Periampullary Neoplasms ◽  
Periampullary Region

191 Background: Medical information regarding periampullary neoplasms is scarce in Mexico. Therefore, our aim is to report our experience with pancreatic and periampullary neoplasms, with attention to factors associated to surgical resection in a Cancer Center. Methods: A retrospective analysis of medical records of all patients with malignant neoplasms located at periampullary region demonstrated by biopsy from January 2005 to December 2010. Factors associated to resectability or survival were calculated employing logistic regression or Cox models. Results: A total of 464 patients with neoplasms of the periampullary region were identified, 249 women and 215 males (mean age 60.2 years). Pancreatic cancer was reported in 269 cases (58%), ampullary in 91 (19.6%), duodenal in 63 (13.6%), intrapancreatic bile duct in 15 (3.2%), neuroendocrine neoplasms in 13 (2.8%) and other types in 13 (2.8%). Sixty-two pancreatoduodenectomies were performed in this 6-year period (13.4% resectability). Sixty-one patients were stages I or II, and 403 stages III or IV. Age (odds ratio [OR] 0.97; 95% confidence interval [CI] 0.96-0.99) and ampullary carcinoma (OR 6.09; 95% CI 3.4-10.8) were the only factors associated to resectability (p<0.0001). Median overall survival of the cohort was 2.9 months (95% CI 2.4-3.4). Factors associated to overall survival with their estimators of the Cox model (p<0.00001) are shown in the Table. Conclusions: Resectability is low and advanced stages are frequent. Young age and location in the ampulla defines increased probability of resection. Overall survival is associated to younger age, being female, ampullary carcinoma, neuroendocrine carcinoma and surgical resection. [Table: see text]

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