Predicting gastric cancer survival using machine learning: A systematic review

Table 1 Extracted characteristics of the included articles

Ref.	Country	Data source	Samples	Pre-processing	Feature selection	Survival	Data types	Machine learning algorithms	Validation	Evaluation	Hyperparameter tuning
Rahman et al[19] (2021)	United Kingdom	NOGCA (2012-2018)	2931	Yes	Yes	OS	Clinical	RF	Internal	AUC, C-index, Brier-score, Calibration	No
Chen et al[20] (2019)	China	TANRICTCGA	134	Yes	Yes	OS, DFS	Clinical, molecular	SVM	Internal	C-index, AUC	Yes
Tian et al[21] (2024)	China	Zhongshan Hospital	1120	Yes	No	OS, DFS	Clinical, image	DL	Internal, external	C-index, AUC	Yes
Islam et al[29] (2024)	United States	Fujian Medical University Union Hospital	135	Yes	Yes	OS	Image	RF, SVM, KNN, NB	Internal	AUC, accuracy,sensitivity, specificity, F1-score	Yes
Chen et al[22] (2024)	China	TCGA	Not reported	Yes	Yes	OS	Clinical, molecular	Multiple machine earning	External	C-Index, AUC, calibration	Yes
Kuwayama et al[30] (2023)	Japan	Chiba Cancer Center (2007-2016)	1687	Yes	Yes	OS	Clinical	LR, GB, DL, RF	Internal	AUC, accuracy	No
Zeng et al[27] (2024)	China	SEER (2000-2019)	11076	Yes	Yes	OS	Clinical	RF, DL	Internal, external	C-Index, AUC, Brier-score, Calibration	Yes
Wu et al[25] (2024)	China	SEER	11414	Yes	Yes	OS	Clinical	DL, RF LR	Internal, external	C-Index, AUC, calibration, decision curve analysis	Yes
Li et al[26] (2022)	China	Nanfang Hospital (2004-2016)	695	Yes	Yes	OS, DFS	Clinical	SVM	Internal, external	AUC	No
Aznar-Gimeno et al[32] (2024)	Spain	16 general hospitals (2023-2012)	1246	Yes	Yes	OS	Clinical, molecular	RF, XGboost, DL, SVM	External	Index, Brier-score	Yes
Jiang et al[34] (2022)	China	Nanfang Hospital (2005-2012)	510	No	No	DFS	Clinical CT	DL	Internal, external	C-index, AUC, Brier-score, Calibration	Yes
Li et al[28] (2024)	China	TCGA	325	Yes	Yes	OS	Clinical, molecular	Multiple machine learning	External	C-index, AUC, Calibration	Yes
Liao et al[33] (2024)	China	SEER (2000–2019)	775	Yes	Yes	CSS	Clinical	Multiple machine learning	Internal	AUC, Calibration	Yes
Wei et al[23] (2022)	China	TCGA	357	Yes	Yes	OS	Clinical, molecular, image	MultiDeepCox-SC	External	C-index, AUC	Yes
Afrash et al[31] (2023)	Iran	Ayatollah Talleghani Hospital (2010-2017)	974	Yes	Yes	OS	Clinical	XGBoost, HGB, SVM	Internal	Accuracy, specificity, sensitivity, AUC, F1-score	Yes
Zeng et al[27] (2023)	China	SEER (2000-2019)	14177	Yes	Yes	OS	Clinical	RF, DL	Internal	C-index, AUC, Brier-score, Calibration, IBS	Yes

TCGA: The Cancer Genome Atlas; SEER: Surveillance, epidemiology, and end results; NOGCA: National oeosophago-gastric cancer audit; OS: Overall survival; CSS: Cancer-specific survival; DFS: Disease-free survival; RF: Random forest; SVM: Support vector machine; DL: Deep learning; KNN: K-nearest neighbors; NB: Naive bayes; GB: Gradient boosting; XGboost: Extreme gradient boosting; HGB: Histogram-based gradient boosting; AUC: Area under the curve; IBS: Integrated brier score; LR: Logistic regression.

Table 2 Classification of the features of the included articles

Characteristics	Categories	Number (n)
		OS	CSS	DFS
Dataset sources	Hospitals	6	-	3
	SEER	3	1	-
	TCGA	4	-	1
	NOGCA	1	-	-
	TANRIC	1	-	1
Dataset privacy	Public	8	1	1
	Private	6	-	3
Data source	Single	6	1	1
	Multiple	8	-	3
Preprocessing	Yes	14	1	3
	No	-	-	1
Feature selection	Yes	13	1	2
	No	1	-	2
Models	One	5	-	4
	Two or more	9	1	-
Models type	GB	1	-	-
	HGB	1	-	-
	KNN	1	-	-
	LR	2	-	-
	NB	1	-	-
	RF	6	-	-
	SVM	5	-	2
	XGboost	2	-	-
	DL	6	-	2
	MultiDeepCox-SC	1	-	-
	Ensemble learning	2	1	-
Validation	Internal	14	1	3
	External	8	-	2
Evaluation	C-index	10	-	3
	AUC	13	1	4
	Calibration	6	1	1
	Brier-score	4	-	1
	Accuracy	3	-	-
	Specificity	2	-	-
	Sensitivity	2	-	-
	F1-score	2	-	-
	IBS	1	-	-
Data types	Clinical	7	1	1
	Image	1	-	-
	Clinical + Image	1	-	2
	Clinical + Molecular	4	-	1
	Clinical + Molecular + Image	1	-	-

TCGA: The Cancer Genome Atlas; SEER: Surveillance, epidemiology, and end results; NOGCA: National oeosophago-gastric cancer audit; TANRIC: The atlas of noncoding RNAs in cancer; OS: Overall survival; CSS: Cancer-specific survival; DFS: Disease-free survival; RF: Random forest; SVM: Support vector machine; DL: Deep learning; KNN: K-nearest neighbors; NB: Naive bayes; GB: Gradient boosting; XGboost: Extreme gradient boosting; HGB: Histogram-based gradient boosting; LR: Logistic regression; AUC: Area under the curve; IBS: Integrated brier score.

Table 3 Risk of bias and applicability assessment of included articles based on the Prediction Model Risk of Bias Assessment Tool criteria

Ref.	Risk of bias				Concern regarding applicability			Overall
	Participant	Predictors	Outcomes	Analysis	Participant	Predictors	Outcomes	Risk of bias	Concern regarding applicability
Rahman et al[19] (2021)	Low	Low	Low	Low	Low	Moderate	Low	Low	Moderate
Chen et al[20] (2019)	Low	Low	Moderate	Low	Low	Low	Moderate	Moderate	Moderate
Tian et al[21] (2024)	Low	Low	Low	High	Moderate	Low	Low	High	Moderate
Islam et al[29] (2024)	Low	Low	Low	Low	Low	Low	Low	Low	Low
Chen et al[22] (2024)	High	Low	Low	High	Moderate	Low	Low	High	Moderate
Kuwayama et al[30] (2023)	Low	Low	Low	Moderate	Moderate	Low	Low	Moderate	Moderate
Zeng et al[27] (2024)	Low	Low	Low	Low	Low	Low	Low	Low	Low
Wu et al[25] (2024)	Moderate	Low	Low	Low	Moderate	Moderate	Low	Moderate	Moderate
Li et al[26] (2022)	Low	Low	Low	Low	Moderate	Low	Low	Low	Low
Aznar-Gimeno et al[32] (2024)	Low	Low	Low	Low	Moderate	Moderate	Low	Low	Moderate
Jiang et al[34] (2022)	Low	Low	Low	High	Low	Low	Low	High	Low
Li et al[28] (2024)	Low	Low	Low	Low	Low	Low	Low	Low	Low
Liao et al[33] (2024)	Low	Low	Low	Moderate	Low	Moderate	Low	Moderate	Moderate
Wei et al[23] (2022)	Low	Low	Low	Low	Low	Low	Low	Low	Low
Afrash et al[31] (2023)	Moderate	Low	Low	Moderate	Low	Moderate	Low	Moderate	Moderate
Zeng et al[27] (2023)	Low	Low	Low	Low	Moderate	Low	Low	Low	Moderate

Table 4 Classification of the used evaluation indicators into types of survival from the lowest to the highest

Evaluation method	OS		CSS		DFS
	Min (%)	Max (%)	Min (%)	Max (%)	Min (%)	Max (%)
AUC	66.90	98.00	92.00	96.00	71.00	85.60
C-index	63.00	0.84.00	-	-	65.40	71.00
Brier-score	13.70	0.25.00	-	-	-	-
Accuracy	89.10	0.92.00	-	-	-	-
Specificity	87.15	0.90.00	-	-	-	-
Sensitivity	89.42	0.94.00	-	-	-	-
F1-score	90.80	92.00	-	-	-	-
IBS	14.20	15.10	-	-	-	-

OS: Overall survival; CSS: Cancer-specific survival; DFS: Disease-free survival; AUC: Area under the curve; IBS: Integrated brier score.

Table 5 Predictive variables for survival types extracted from the articles

Selected features	Number (n)	Percentage (%)
Age	7	87.5
Stage	7	87.5
Grade	6	75.0
Treatment modality	6	75.0
Primary tumor site	5	62.5
Sex	4	50.0
Tumor size	4	50.0
Race	3	37.5
Histopathology type	3	37.5
Marital status	3	37.5
Positive lymph node numbers	2	25.0
Lymph node metastasis	2	25.0
Metastasis status	2	25.0
Regional nodes examined	1	12.5
Lymph node dissection	1	12.5
ASA grade	1	12.5
History of other cancers	1	12.5
Blood markers	1	12.5
Lauren type	1	12.5
Lymphovascular invasion	1	12.5
Months from diagnosis to treatment	1	12.5
Body weight	1	12.5

ASA: American Society of Anesthesiologists physical status classification system.