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©The Author(s) 2025.
World J Gastroenterol. Sep 28, 2025; 31(36): 111293
Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.111293
Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.111293
Table 1 Demographic and clinical factors in pathological complete response and non-pathological complete response groups
All (n = 70) | pCR (n = 15) | Non-pCR (n = 55) | P value | |
Age, median (range) | 66 (45-78) | 64 (52-76) | 66 (45-78) | 0.97 |
Gender | ||||
Male/female | 60/10 | 12/3 | 48/7 | 0.48 |
Tumor site | ||||
Cervical/upper/middle/lower | 5/14/33/18 | 1/5/6/3 | 4/9/27/15 | 0.60 |
TNM staging | ||||
cT1/2/3/4 | 0/2/19/49 | 0/0/3/12 | 0/2/16/37 | 0.56 |
cN0/1/2/3 | 1/17/29/23 | 0/1/5/9 | 1/16/24/14 | 0.06 |
cStage I/II/III/IVA | 0/0/10/60 | 0/0/0/15 | 0/0/10/45 | 0.075 |
Total radiation dose | ||||
40 Gy/up to 60 Gy | 59/11 | 11/4 | 48/7 | 0.19 |
Table 2 Predictive performance and receiver operating characteristic curve analyses of the five radiomics features with the highest area under the curve values and the best performing artificial intelligence-based machine-learning model for pathological complete response
AUC | Sensitivity | Specificity | Accuracy | Cut off | 95%CI | P value | |
ADC skewness | 0.77 | 0.67 | 0.82 | 0.79 | 0.37 | 0.61-0.88 | 0.005 |
GLCM entropy (b = 1000 second/mm²) | 0.76 | 0.87 | 0.62 | 0.67 | 9.28 | 0.64-0.85 | 0.002 |
GLCM autocorrelation (b = 0 second/mm²) | 0.76 | 0.53 | 0.93 | 0.84 | 5276.1 | 0.60-0.87 | 0.009 |
Skewness (b = 0 second/mm²) | 0.73 | 0.87 | 0.42 | 0.64 | 0.72 | 0.61-0.84 | 0.006 |
Kurtosis (b = 0 second/mm²) | 0.72 | 0.73 | 0.64 | 0.66 | 5.05 | 0.55-0.82 | 0.005 |
Machine learning radiomics model | 0.85 | 0.80 | 0.85 | 0.81 | NA | 0.73-0.93 | < 0.001 |
Table 3 Univariate and multivariate Cox regression analyses for relapse-free survival
Univariate | Multivariate | |||||
HR | 95%CI | P value | HR | 95%CI | P value | |
Radiomics features | ||||||
ADC skewness | 0.42 | 0.16-1.09 | 0.076 | |||
GLCM entropy (b = 1000 second/mm²) | 0.25 | 0.11-0.56 | 0.001 | 0.32 | 0.14-0.75 | 0.009 |
GLCM autocorrelation (b = 0 second/mm²) | 2.00 | 0.61-6.57 | 0.25 | |||
Skewness (b = 0 second/mm²) | 0.60 | 0.29-1.22 | 0.16 | |||
Kurtosis (b = 0 second/mm²) | 0.76 | 0.37-1.54 | 0.45 | |||
Pathological features | ||||||
pT3 vs pT0-2 | 1.08 | 0.54-2.18 | 0.82 | |||
pN + vs pN - | 3.14 | 1.48-6.63 | 0.003 | 2.20 | 1.00-4.80 | 0.042 |
Grade 3 or 2 vs grade 1 | 0.50 | 0.25-1.01 | 0.054 |
- Citation: Hirata A, Hayano K, Tochigi T, Kurata Y, Shiraishi T, Sekino N, Nakano A, Matsumoto Y, Toyozumi T, Uesato M, Ohira G. Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma. World J Gastroenterol 2025; 31(36): 111293
- URL: https://www.wjgnet.com/1007-9327/full/v31/i36/111293.htm
- DOI: https://dx.doi.org/10.3748/wjg.v31.i36.111293