Artificial intelligence-integrated multimodal data-assisted magnetic resonance imaging for neoadjuvant chemoradiotherapy decision-making in cT1-2N0 rectal cancer

Table 1 Baseline characteristic of patients, n (%)/median (interquartile range)

Variable	Non-SNC (n = 756)	SNC (n = 253)	P value
T stage			< 0.0011
1	89 (12)	80 (32)
2	667 (88)	173 (68)
Sex			< 0.0011
Female	303 (40)	133 (53)
Male	453 (60)	120 (47)
Fecal occult blood			0.241
Negative	173 (23)	49 (19)
Positive	583 (77)	204 (81)
Smoking and alcohol history			0.131
Negative	585 (77)	200 (79)
Smoking history	84 (11)	35 (14)
Alcohol consumption history	15 (2.0)	5 (2.0)
Combined smoking-alcohol history	72 (9.5)	13 (5.1)
Surgery history			0.0561
Negative	82 (11)	17 (6.7)
Positive	674 (89)	236 (93)
Cardiovascular disease			0.831
Negative	737 (97)	246 (97)
Positive	19 (2.5)	7 (2.8)
Hypertension			0.881
Negative	267 (35)	88 (35)
Positive	489 (65)	165 (65)
Diabetes			0.901
Negative	403 (53)	136 (54)
Positive	353 (47)	117 (46)
ABO blood group			0.471
A	326 (43)	109 (43)
B	186 (25)	55 (22)
AB	82 (11)	36 (14)
O	162 (21)	53 (21)
RH blood group			0.992
Negative	5 (1)	2 (1)
Positive	751 (99)	251 (99)
Histological type			< 0.0011
Ulcerative	517 (68)	133 (53)
Infiltrating	9 (1.2)	17 (6.7)
Protruding	230 (30)	103 (41)
Differentiation			< 0.0012
Poorly differentiated	114 (15.1)	15 (6)
Moderately differentiated	570 (75.4)	222 (88)
Well differentiated	72 (9.5)	15 (6)
Margin			> 0.992
Negative	6 (0.8)	2 (0.8)
Positive	750 (99)	251 (99)
S100			< 0.0011
Negative	106 (14)	74 (29)
Positive	650 (86)	179 (71)
CD34			> 0.992
Negative	1 (0.1)	0 (0)
Positive	755 (100)	253 (100)
D2-40			< 0.0011
Negative	192 (25)	157 (62)
Positive	564 (75)	96 (38)
NRS2002			< 0.0012
0	151 (20)	61 (24)
1	333 (44)	95 (38)
2	158 (21)	59 (23)
3	53 (7.0)	36 (14)
4	61 (8.1)	0 (0)
5	0 (0)	2 (0.8)
Caprini			< 0.0012
0	8 (1.1)	2 (0.8)
1	13 (1.7)	31 (12)
2	243 (32)	84 (33)
3	304 (40)	78 (31)
4	145 (19)	43 (17)
5	35 (4.6)	10 (4.0)
6	6 (0.8)	2 (0.8)
7	2 (0.3)	3 (1.2)
Eastern Cooperative Oncology Group			< 0.0012
0	509 (67)	225 (89)
1	223 (29)	26 (10)
2	23 (3.0)	2 (0.8)
3	1 (0.1)	0 (0)
Age	61.000 (53.500-67.500)	62.000 (57.000-69.000)	0.0123
Body mass index	22.676 (20.950-25.000)	24.030 (23.000-26.600)	< 0.0013
Alfa-fetoprotein	2.795 (2.235-3.960)	3.130 (2.530-3.870)	0.0243
Carcinoembryonic antigen	2.260 (1.440-3.720)	2.280 (1.370-3.380)	0.293
CA19-9	10.650 (7.175-15.700)	11.000 (6.460-15.530)	0.703
CA724	3.210 (1.110-10.070)	2.510 (0.970-10.070)	0.0343
CA125	10.700 (8.250-14.600)	11.300 (8.520-15.900)	0.163
Ki67	0.750 (0.600-0.800)	0.800 (0.600-0.800)	0.0163

¹Pearson's χ² test.

²Fisher's exact test.

³Wilcoxon rank sum test.

CA: Carbohydrate antigen; SNC: Stage missed neoadjuvant candidates.

Table 2 Performance comparison of ten machine-learning models trained on multimodal radiomics and clinical data vs radiologist diagnosis

Model	Area under the curve	Accuracy	Sensitivity	Specificity	Positive predictive value	Negative predictive value	F1
Logistic	0.798 (0.739-0.857)	0.817	0.547	0.907	0.661	0.858	0.699
Support vector machine	0.817 (0.764-0.870)	0.744	0.827	0.717	0.492	0.926	0.717
Gradient boosting machine	0.917 (0.881-0.953)	0.904	0.760	0.951	0.838	0.923	0.897
NeuralNetwork	0.798 (0.739-0.857)	0.731	0.733	0.730	0.474	0.892	0.676
RandomForest	0.922 (0.889-0.954)	0.857	0.827	0.867	0.674	0.938	0.843
XGBoost	0.938 (0.911-0.965)	0.870	0.867	0.872	0.692	0.952	0.869
K-nearest neighbors	0.910 (0.874-0.946)	0.787	0.920	0.743	0.543	0.966	0.783
Adaboost	0.747 (0.684-0.810)	0.748	0.680	0.770	0.496	0.879	0.673
LightGBM	0.922 (0.889-0.956)	0.834	0.893	0.814	0.615	0.958	0.728
CatBoost	0.886 (0.845-0.928)	0.807	0.813	0.805	0.581	0.929	0.678
Reader 1	1	0.660	0.760	0.626	0.404	0.923	0.528
Reader 2	1	0.750	0.840	0.720	0.500	0.931	0.627
Reader 3	1	0.790	0.920	0.746	0.548	0.966	0.687

¹The physicians’ diagnostic decisions were recorded as binary categorical judgments (e.g., presence vs absence of disease) rather than as continuous probability scores. Therefore, receiver operating characteristic curve analysis and the corresponding area under the curve were not applicable. Instead, sensitivity and specificity were calculated.