Treatment and overall survival of four types of non-metastatic periampullary cancer: nationwide population-based cohort study

Jong, Evelien J M de; Geest, Lydia G. van der; Besselink, Marc G.; Bouwense, Stefan; Buijsen, Jeroen; Dejong, Cornelis H. C.; Koerkamp, B. Groot; Heij, Lara; Hingh, Ignace H. J. T. de; Hoge, Chantal; Kazemier, Geert; Laarhoven, H.W.M. van; Meijer, Vincent E de; Stommel, Martijn W J; Tjan‐Heijnen, Vivianne C. G.; Iersel, Liselot Valkenburg-van; Wilmink, Johanna W.; Geurts, S.M.E.; Vos‐Geelen, Judith de

doi:10.1016/j.hpb.2022.01.009

Cited by 11 publications

(18 citation statements)

References 31 publications

(32 reference statements)

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“…After full-text revision, 57 studies were included, with a total of 35 176 patients. Among the 57 studies, 6 studies 27 , 28 , 29 , 30 , 31 , 32 were prospective cohort studies or phase 2 trials, and 51 studies 1 , 3 , 4 , 17 , 19 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 were retrospective in nature ( Table ; eTables 1 and 2 in…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Systemic Treatments of Small Intestinal Adenocarcinomas

et al. 2023

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ImportanceAlthough small intestinal adenocarcinomas (SIAs) are rare, they have a poor prognosis, and the optimal treatment strategies are largely unknown. Because of the lack of high-quality evidence, guidelines for colorectal cancer are often followed in the treatment of SIAs.ObjectiveTo review the current evidence regarding survival benefit of systemic therapies, including chemotherapy, targeted agents, and immunotherapy, for patients with SIAs.Data SourcesFollowing the Preferred Reporting Items for Systematic Reviews and Meta-analyses, MEDLINE and Embase were searched for articles published from January 1, 2005, until June 1, 2022.Study SelectionRetrospective cohort studies and prospective phase 2 or 3 trials describing survival after systemic therapies for patients with SIAs were eligible for inclusion. Assessment of study eligibility was blinded and performed by 3 reviewers.Data Extraction and SynthesisThe reviewers independently extracted data. Random effects, inverse variance, pairwise meta-analyses were performed.Main Outcomes and MeasuresPrimary outcomes were overall survival (OS) and progression-free survival (PFS) of patients with SIAs after systemic therapies. Measures of interest included hazard ratios for survival and median survival times.ResultsOverall, 57 retrospective cohort and phase 2 studies of 35 176 patients were included. Adjuvant chemotherapy, generally fluoropyrimidine-based, was associated with increased OS in stage I to III SIAs (hazard ratio [HR], 0.60; 95% CI, 0.53-0.68), especially in stage III tumors (HR, 0.55; 95% CI, 0.48-0.64), irrespective of tumor localization. Palliative chemotherapy was also associated with an OS benefit (HR, 0.48; 95% CI, 0.40-0.58). Fluoropyrimidine-oxaliplatin combinations were superior to other regimens (OS: HR, 0.54; 95% CI, 0.30-0.99; PFS: HR, 0.46; 95% CI, 0.30-0.71). Furthermore, bevacizumab added to chemotherapy compared with chemotherapy alone was associated with significantly prolonged PFS (HR, 0.62; 95% CI, 0.43-0.89). Immunotherapy showed a 50% overall response rate in previously treated defective mismatch repair tumors.Conclusions and RelevanceIn this systematic review and meta-analysis, adjuvant and palliative chemotherapy were both associated with improved survival of patients with SIAs, especially fluoropyrimidine-based regimens and fluoropyrimidine-oxaliplatin combinations. Adding bevacizumab to chemotherapy appears to prolong PFS and deserves further investigation. Immunotherapy seems beneficial and should be considered for patients with defective mismatch repair tumors. International collaborations should be undertaken to confirm and improve efficacy of systemic therapies for patients with SIAs.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Systemic Treatments of Small Intestinal Adenocarcinomas

et al. 2023

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“… 1 , 2 The incidence of AAC is estimated to be 0.5, 0.73, and 0.96 per 100,000 persons per year in the US, Japan, and the Netherlands, respectively. 3 , 4 , 5 Despite its rarity, the occurrence of AAC has increased worldwide over the past few decades. 6 , 7 Patients with AAC tend to present typical symptoms of biliary obstruction at relatively early stages, but these symptoms are mostly non-specific and are therefore often detected incidentally during examinations.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has used various types of prediction models to assess the short-term survival rates of AAC, including Kaplan–Meier survival analysis, logistic regression, and the Cox proportional hazards (CoxPH) model. 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 Kaplan–Meier survival analysis is the most commonly method used to investigate the prognostic factors for AAC, which does not allow adjustments for confounders and has a restrictive assumption of non-informative censoring. 24 , 25 Logistic regression models aim to estimate the relationship between potential factors and survival outcomes as well as adjust for confounders.…”

Section: Introductionmentioning

confidence: 99%

“…that originates in biliary, duodenal, or pancreatic ductal epithelial cells 1,2 . The incidence of AAC is estimated to be 0.5, 0.73, and 0.96 per 100,000 persons per year in the US, Japan, and the Netherlands, respectively [3][4][5] . Despite its rarity, the occurrence of AAC has increased worldwide over the past few decades 6,7 .…”

mentioning

confidence: 99%

“…Previous research has used various types of prediction models to assess the shortterm survival rates of AAC, including Kaplan-Meier survival analysis, logistic regression, and the Cox proportional-hazards (CoxPH) model [5][6][7][8][9][10][11][12][13][16][17][18][19][20][21][22][23] . Kaplan-Meier J o u r n a l P r e -p r o o f survival analysis is the most commonly method used to investigate the prognostic factors for AAC, which does not allow adjustments for confounders and has a restrictive assumption of noninformative censoring 24,25 .…”

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Machine learning models for predicting survival in patients with ampullary adenocarcinoma

Huang¹,

Yang²,

Li³

et al. 2022

Asia-Pacific Journal of Oncology Nursing

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Machine learning for differentiating between pancreatobiliary-type and intestinal-type periampullary carcinomas based on CT imaging and clinical findings

Chen,

Zhang,

Chen

et al. 2024

Abdom Radiol

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Purpose To develop a diagnostic model for distinguishing pancreatobiliary-type and intestinal-type periampullary adenocarcinomas using preoperative contrast-enhanced computed tomography (CT) findings combined with clinical characteristics. Methods This retrospective study included 140 patients with periampullary adenocarcinoma who underwent preoperative enhanced CT, including pancreaticobiliary (N = 100) and intestinal (N = 40) types. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Additionally, an independent external cohort of 28 patients was enrolled. Various CT features of the periampullary region were evaluated and data from clinical and laboratory tests were collected. Five machine learning classifiers were developed to identify the histologic type of periampullary adenocarcinoma, including logistic regression, random forest, multi-layer perceptron, light gradient boosting, and eXtreme gradient boosting (XGBoost). Results All machine learning classifiers except multi-layer perceptron used achieved good performance in distinguishing pancreatobiliary-type and intestinal-type adenocarcinomas, with the area under the curve (AUC) ranging from 0.75 to 0.98. The AUC values of the XGBoost classifier in the training set, internal validation set and external validation set are 0.98, 0.89 and 0.84 respectively. The enhancement degree of tumor, the growth pattern of tumor, and carbohydrate antigen 19–9 were the most important factors in the model. Conclusion Machine learning models combining CT with clinical features can serve as a noninvasive tool to differentiate the histological subtypes of periampullary adenocarcinoma, in particular using the XGBoost classifier.

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Treatment and overall survival of four types of non-metastatic periampullary cancer: nationwide population-based cohort study

Cited by 11 publications

References 31 publications

Evaluation of Systemic Treatments of Small Intestinal Adenocarcinomas

Evaluation of Systemic Treatments of Small Intestinal Adenocarcinomas

Machine learning models for predicting survival in patients with ampullary adenocarcinoma

Machine learning for differentiating between pancreatobiliary-type and intestinal-type periampullary carcinomas based on CT imaging and clinical findings

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