The stomach cancer pooling (STOP) project: a global consortium of epidemiological studies of gastric cancer, updated to 2021
Notice bibliographique
Résumé
The assessment of risk factors in cancer etiology is necessary for defining optimal preventive strategies, as well as for identifying high risk individuals, and it is therefore relevant for medical practice and cancer prevention. The Stomach cancer Pooling (StoP) Project is a consortium of epidemiological studies of gastric cancer (GC), established in year 2012. The StoP Project aims to examine the role of lifestyle, environmental and genetic determinants of GC through pooled analyses of subject-level data. The consortium is the major GC dataset globally, including original data from 35 studies – with case–control study design, including 5 nested case–control within cohort studies – conducted in the Americas, Asia and Europe (Table 1), for a total of about 13,500 cases and 32,000 controls, and it is continuously expanding. To date, the StoP Project contributed a detailed quantification of the risk of GC associated to several factors, including cigarette smoking (relative risk, RR, of 1.32 for heavy vs. never smokers), alcohol drinking (RR=1.48 for heavy vs. never drinkers), socio-economic status (RR=0.60 for high vs. low education), selected dietary factors (RR=1.30 for high vs. low meat intake; RR=0.65 for high vs. low vegetables consumption; RR=0.80 for high vs. low citrus fruit; RR=0.67 for high vs. low polyphenols intake) and occupational exposures (RR=1.70 for miners; RR=1.30 for construction workers; RR=1.33 for agricultural and animal husbandry workers; RR=1.41 for blacksmiths and machine-tool operators). Planned future developments are to analyze the role of rare exposures on GC risk and to examine risk factors in understudied patient subgroups (e.g., young onset GC, gastric cardia cancer, etc.); to integrate additional studies from East Asia; to develop a genome-wide modeling of polygenic risk score in GC; to include survival analyses and to apply machine learning methods in GC risk prediction and prognostication. Table 1 - Studies included in the StoP Project (at 13-05-2021) # Study area(s) Period Study type Reference 1 Milan, Italy 1985-1997 Case-control, hospital-based (La Vecchia et al., 1995) 2 Harbin, China 1987-1989 Case-control, hospital-based Hu (Deandrea et al., 2010) 3 Milan, Italy 1997-2007 Case-control, hospital-based (Pelucchi et al., 2009) 4 Rome, Italy 2006-ongoing Case-control, hospital-based (De Feo et al., 2012) 5 4 areas, Italy 1985-1987 Case-control, population-based (Buiatti et al., 1989) 6 Athens, Greece 1981-1984 Case-control, hospital-based (Lagiou et al., 2004) 7 8 provinces, Canada 1994-1997 Case-control, population-based (Mao et al., 2002) 8 Taixing, Jiangsu, China 2000 Case-control, population-based (Mu et al., 2005) 9 Moscow, Russia 1996-1997 Case-control, hospital-based (Zaridze et al., 1999) 10 Ardabil, Iran 2004-2005 Case-control, population-based (Pourfarzi et al., 2009) 11 Ardabil, Iran 2005-2007 Case-control, population-based (Pakseresht et al., 2011) 12 Shanghai, Qingdao, China 1991-1993 Case-control, population-based (Setiawan et al., 2005) 13 Yangzhong, China 1995 Case-control, population-based (Setiawan et al., 2001) 14 New York, USA 1992-1994 Case-control, hospital-based (Zhang et al., 1999) 15 New York, USA 1980-1990 Case-control, hospital-based Unpublished data 16 Porto, Portugal 1999-2006 Case-control, population-based (Lunet et al., 2007) 17 2 counties, Sweden 1998-2010 Cohort, nested case-control (SMC study) (Harris et al., 2013) 18 Ardabil, Iran 2001-2004 Case-control, hospital-based (Derakhshan et al., 2008) 19 2 counties, Sweden 1998-2010 Cohort, nested case-control (COSM study) (Harris et al., 2013) 20 10 provinces, Spain 2008-2012 Case-control, population-based (Castaño-Vinyals et al., 2015) 21 5 counties, Sweden 1989-1995 Case-control, population-based (Ye et al., 1999) 22 Valencia, Spain 1995-1999 Case-control, hospital-based (Santibanez et al., 2012) 23 Mexico City, Mexico 2004-2005 Case-control, population-based (Hernández-Ramírez et al., 2009) 24 Mexico City, Mexico 1989-1990 Case-control, population-based (López-Carrillo et al., 1994) 25 3 areas, Mexico 1994-1996 Case-control, hospital-based (López-Carrillo et al., 2003) 26 Sao Paulo, Brazil 1991-1994 Case-control, hospital-based (Brazilian residents) (Nishimoto et al., 2002) 27 Sao Paulo, Brazil 1991-1994 Case-control, hospital-based (Japanese residents) (Hamada et al., 2002) 28 Nagano, Japan 1998-2002 Case-control, hospital-based (Machida-Montani et al., 2004) 29 Riga, Latvia 2007-ongoing Case-control, hospital-based (Leja et al., 2017) 30 Nebraska, USA 1988-1993 Case-control, population-based (Ward et al., 1997) 31 Greece 1994-1999 Cohort, nested case-control (EPIC study) (Psaltopoulou et al., 2008) 32 Finland 1985-1988 Cohort, nested case-control (ATBC study) (Ann Epidemiol 1994; 4:1-10) 33 6 states, USA 1995-1996 Cohort, nested case-control (AARP study) (Schatzkin et al., 2001) 34 Multicentric, Brazil 2016-2020 Case-control, hospital-based None available yet 35 Lithuania 2005-2017 Case-control, genetic data only (Dargiene et al, 2018)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».