Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma

doi:10.3748/wjg.124166

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Dec 14, 2026; 32(46): 124166
Published online Dec 14, 2026. doi: 10.3748/wjg.124166

Table 1 Baseline characteristics of the patients

Characteristics	Overall			Training cohort			Test cohort			Validation cohort			P value
Characteristics	Non-responder	Responder	P value	Non-responder	Responder	P value	Non-responder	Responder	P value	Non-responder	Responder	P value	P value
Number	94	94		58	49		22	25		14	20		0.367
Age	61 (57, 66)	63 (57.25, 69)	0.165	61 (57, 66)	64 (57, 69)	0.294	59 (55, 66.5)	61 (58.5, 68.5)	0.352	64 (59, 68)	66.5 (60.75, 69)	0.958	0.153
Gender			0.266			1.000			1.000			0.126	0.083
Male	89 (94.7)	85 (90.4)		56 (96.6)	47 (95.9)		19 (86.4)	22 (88)		14 (100)	16 (80)
Female	5 (5.3)	9 (9.6)		2 (3.4)	2 (4.1)		3 (13.6)	3 (12)		0 (0)	4 (20)
Smoking			< 0.001^b			0.003^b			0.058			0.011^a	0.623
Former or current	82 (87.2)	56 (59.6)		50 (86.2)	30 (61.2)		18 (81.8)	14 (56)		14 (100)	12 (60)
Never	12 (12.8)	38 (40.4)		8 (13.8)	19 (38.8)		4 (18.2)	11 (44)		0 (0)	8 (40)
Alcohol			< 0.001^b			0.012^a			0.004^b			0.422	0.961
Former or current	82 (87.2)	60 (63.8)		49 (84.5)	31 (63.3)		21 (95.5)	15 (60)		12 (85.7)	14 (70)
Never	12 (12.8)	34 (36.2)		9 (15.5)	18 (36.7)		1 (4.5)	10 (40)		2 (14.3)	6 (30)
cT stage			0.012^a			0.007^b			0.303			0.781	0.685
T2	1 (1.1)	2 (2.1)		0 (0)	1 (2)		0 (0)	1 (4)		1 (7.1)	0 (0)
T3	63 (67)	79 (84)		37 (63.8)	42 (85.7)		15 (68.2)	20 (80)		11 (78.6)	17 (85)
T4	30 (31.9)	13 (13.8)		21 (36.2)	6 (12.2)		7 (31.8)	4 (16)		2 (14.3)	3 (15)
cN stage			0.285			0.261			0.391			0.126	0.298
N0	31 (33)	27 (28.7)		16 (27.6)	15 (30.6)		9 (40.9)	10 (40)		6 (42.9)	2 (10)
N1	42 (44.7)	34 (36.2)		28 (48.3)	15 (30.6)		10 (45.5)	7 (28)		4 (28.6)	12 (60)
N2	17 (18.1)	27 (28.7)		12 (20.7)	16 (32.7)		3 (13.6)	7 (28)		2 (14.3)	4 (20)
N3	4 (4.3)	6 (6.4)		2 (3.4)	3 (6.1)		0 (0)	1 (4)		2 (14.3)	2 (10)
cTNM stage			0.100			0.057			0.202			0.427	0.865
II	21 (22.3)	27 (28.7)		12 (20.7)	14 (28.6)		3 (13.6)	9 (36)		6 (42.9)	4 (20)
III	41 (43.6)	48 (51.1)		23 (39.7)	26 (53.1)		12 (54.5)	11 (44)		6 (42.9)	11 (55)
IV	32 (34)	19 (20.2)		23 (39.7)	9 (18.4)		7 (31.8)	5 (20)		2 (14.3)	5 (25)
Tumor location			0.807			0.416			0.993			0.797	0.714
Upper	19 (20.2)	17 (18.1)		12 (20.7)	7 (14.3)		5 (22.7)	6 (24)		2 (14.3)	4 (20)
Middle	48 (51.1)	46 (48.9)		30 (51.7)	23 (46.9)		10 (45.5)	11 (44)		8 (57.1)	12 (60)
Lower	27 (28.7)	31 (33)		16 (27.6)	19 (38.8)		7 (31.8)	8 (32)		4 (28.6)	4 (20)
Tumor length (cm)	8.5 (6, 12)	9 (6.5, 13.5)	0.779	9.4(5.5, 12.375)	10 (6.5, 13.5)	0.341	8 (5.125, 12)	7 (5.5, 13)	0.898	8.5 (8, 9.625)	7.75 (7, 10)	0.370	0.287
Tumor diameter (mm)	36 (30, 41)	38 (34, 44)	0.069	36.5 (30, 41)	37 (34, 44)	0.231	36 (30.5, 44.25)	36 (31, 44)	0.522	36 (31, 39.5)	39 (34, 40)	0.211	0.796
Cycles	2.5 (2, 4)	2 (2, 3)	0.756	3 (2, 4)	3 (2, 3)	0.699	3 (2, 3.75)	2 (2, 3)	0.322	2 (2, 3)	2 (2, 3)	1.000	0.111
Ki-67	60 (40, 70)	35 (10, 60)	< 0.001^b	60 (50, 70)	40 (30, 70)	0.003^b	50 (40, 60)	30 (10, 32.5)	< 0.001^b	30 (25, 50)	60 (10, 70)	0.311	0.121

^aP < 0.05.

^bP < 0.01.

Data were showed as median (IQR) or n (%).

Full Size Table

Table 2 Predictive performances of the best performing models developing on different kind of features

Model	Cohort	AUC	95%CI	Sensitivity	Specificity	NPV	PPV	Accuracy
Clinical model	Train	0.998	0.995-1.000	0.927	1.000	0.957	1.000	0.972
	Internal validation	0.899	0.813-0.985	0.720	0.909	0.741	0.900	0.809
	External validation	0.636	0.441-0.830	0.700	0.571	0.700	0.571	0.647
Radiomics model	Train	0.996	0.989-1.000	0.951	0.985	0.970	0.975	0.972
	Internal validation	0.904	0.816-0.991	0.600	0.955	0.677	0.938	0.766
	External validation	0.439	0.228-0.650	0.450	0.429	0.529	0.353	0.441
Deep learning model	Train	0.985	0.968-1.000	0.951	0.924	0.968	0.886	0.935
	Internal validation	0.820	0.699-0.941	0.760	0.864	0.760	0.864	0.809
	External validation	0.664	0.470-0.859	0.900	0.429	0.750	0.692	0.706
Radiomics-deep learning model	Train	0.998	0.994-1.000	0.927	1.000	0.957	1.000	0.972
	Internal validation	0.855	0.742-0.967	0.600	0.864	0.655	0.833	0.723
	External validation	0.779	0.609-0.948	1.000	0.357	1.000	0.690	0.735
Clinical-radiomics-deep learning model	Train	1.000	1.000-1.000	1.000	1.000	1.000	1.000	1.000
	Internal validation	0.887	0.794-0.980	0.680	0.909	0.714	0.895	0.787
	External validation	0.643	0.450-0.836	0.700	0.286	0.400	0.583	0.529

AUC: Area under the curve; NPV: Negative predictive value; PPV: Positive predictive value.

Full Size Table

Table 3 Predictive performances of the radiomics-deep learning model With the Youden-derived threshold of 0.4173

Radiomics-deep learning model (Youden-derived threshold)	Sensitivity	Specificity	NPV	PPV	Accuracy
Train	1.000	0.970	1.000	0.953	0.981
Internal validation	0.760	0.682	0.714	0.731	0.723
External validation	0.900	0.500	0.778	0.720	0.735

NPV: Negative predictive value; PPV: Positive predictive value.

Full Size Table

Table 4 Brier score of the best performing models developing on different kind of features

	Train	Internal validation	External validation
Clinical model	0.026	0.131	0.279
Radiomics model	0.030	0.164	0.323
Deep learning model	0.058	0.179	0.294
Radiomics-deep learning models	0.052	0.182	0.189
Clinical-radiomics-deep learning models	< 0.001	0.168	0.296

Full Size Table

Citation: Zhao BX, Zhang M, Fu WQ, Li XY, Han ML, Zhang J, Gao W, Guo TH, Li HY, Ai SW, Wang HJ, Zhang BY, Wang Q. Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma. World J Gastroenterol 2026; 32(46): 124166
URL: https://www.wjgnet.com/1007-9327/full/v32/i46/124166.htm
DOI: https://dx.doi.org/10.3748/wjg.124166

Zhao BX, Zhang M, Fu WQ, Li XY, Han ML, Zhang J, Gao W, Guo TH, Li HY, Ai SW, Wang HJ, Zhang BY, Wang Q. Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma. World J Gastroenterol 2026; 32(46): 124166 [DOI: 10.3748/wjg.124166]

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