Nonenhanced MRI-based radiomics model for preoperative prediction of nonperfused volume ratio for high-intensity focused ultrasound ablation of uterine leiomyomas

Figures & data

Figure 1. The cases for delineating ROI. The preoperative MR images for uterine leiomyomas received NPV ratios ≤50%, 50–80% and ≤80% after HIFU ablation on (a–c) T2W images and (d–f) ADC maps.

Figure 2. The flowchart of machine learning model construction.

Table 1. The comparison of demographic information and radiological characteristics between the training and testing cohorts.

Characteristics	Training cohort	Testing cohort	p Value
N	164	41
NPV ratio
≥80%	84	21	0.999^b
50–80%	48	12
≤50%	32	8
Age	38.32 ± 6.27	39.14 ± 6.59	0.216^a
Volume (cm3)	91.54 (52.08-159.07)	89.36 (49.22-152.87)	0.327^c
Size (mm)	56.85 (45.32-67.87)	51.26 (44.37-65.83)	0.548^c
Fibroid type
Submucous	8	3	0.621^b
Myometrial	105	28
Subserous	51	10
Fibroid location
Anterior wall	116	23	0.108^b
Posterior wall	48	18
T2 signal intensity
Hyperintensity	63	13	0.538^b
Hypointensity	101	28
T2 signal homogeneity
Homogeneous	36	7	0.637^b
Inhomogeneous	128	34
Uterine position
Anteversion	115	27	0.733^b
Retroversion	49	14
Energy efficiency factor (J/mm3)	3.4 (1.7-5.8)	3.6 (1.9-5.2)	0.476^c
Sonication time (s)	746 (412-1461)	759 (433-1432)	0.323^c

^aP values obtained using independent-sample t-test.

^bP values obtained using chi-squared test or Fisher's exact test.

^cP values obtained using Wilcoxon rank-sum test.

Table 2. The clinical and radiological characteristics among NPV ratio classification groups.

characteristics	Training cohort NPV ratio			p Value	Testing cohort NPV ratio			p Value
	≥80%	50–80%	≤50%		≥80%	50%-80%	≤50%
N	84	48	32		21	12	8
Age	38.76 ± 5.96	37.23 ± 7.33	36.81 ± 7.82	0.457^a	39.07 ± 5.86	38.11 ± 6.36	37.38 ± 7.69	0.324^a
Volume (cm^3)	84.65(50.6-152.9)	95.05(54.39-154.67)	99.05(66.17-174.32)	0.271^c	81.23(52.17-149.75)	92.25(58.78-146.82)	97.66(68.65-169.47)	0.183^c
Size (mm)	53.15(41.47-63.62)	55.72(43.59-67.25)	59.65(45.43-69.45)	0.233^c	49.80(42.34-52.75)	54.14(45.71-68.07)	57.75(47.64-70.05)	0.196^c
Fibroid type
Submucous	4	2	2	0.868^b	2	0	1	0.837^b
Myometrial	55	32	18		14	9	5
Subserous	25	14	12		5	3	2
Fibroid location
Anterior wall	59	32	21	0.856^b	12	6	5	0.851^b
Posterior wall	25	16	11		9	6	3
T2 signal intensity
Hyperintensity	34	12	22	0.001^b	9	3	1	0.244^b
Hypointensity	50	36	10		12	9	7
T2 signal homogeneity
Homogeneous	22	10	4	0.274^b	5	2	0	0.313^b
Inhomogeneous	62	38	28		16	10	8
Uterine position
Anteversion	59	32	24	0.727^b	12	9	6	0.484^b
Retroversion	25	16	8		9	3	2

^aP values obtained using analysis of variance.

^bP values obtained using chi-squared test or Fisher's exact test.

^cP values obtained using Kruskal–Wallis test.

Figure 3. The performance of radiomics models constructed by different combinations of feature selection algorithms and machine learning classifiers in the training cohort.

Figure 4. The average AUC shown by ROC curves for clinico-radiological model, radiomics model, and radiomics-clinical model in predicting NPV ratio classification for (a) the training and (b) testing cohorts.

Table 3. The performance of clinico-radiological model, radiomics model, and radiomics-clinical model in predicting NPV ratio classification.

Model	NPV ratio classification	Training cohort				Testing cohort
		Sensitivity	Specificity	Accuracy [95% CI]	Average AUC [95% CI]	Sensitivity	Specificity	Accuracy [95% CI]	Average AUC [95% CI]
Clinico-radiological model	≤50%	0.804	0.691	0.709 (0.625-0.787)	0.763 (0.561-0.882)	0.777	0.588	0.637 (0.524-0.738)	0.715 (0.603-0.794)
	50-80%	0.758	0.592			0.732	0.564
	≥80%	0.833	0.654			0.802	0.623
Radiomics model	≤50%	0.836	0.729	0.769 (0.742-0.811)	0.804 (0.757-0.849)	0.797	0.643	0.698 (0.597-0.786)	0.769 (0.701-0.842)
	50-80%	0.773	0.648			0.751	0.627
	≥80%	0.854	0.722			0.829	0.669
Radiomics-clinical model	≤50%	0.886	0.773	0.804 [0.742-0.866]	0.857 (0.814-0.903)	0.844	0.737	0.762 (0.698-0.815)	0.802 (0.796-0.850)
	50-80%	0.842	0.725			0.809	0.687
	≥80%	0.870	0.793			0.836	0.760

Figure 5. Further model performance evaluation by (a) calibration curve analysis in terms of the agreement between the predicted and actual probability and (b) decision curve analysis in terms of net benefit for the clinico-radiological model, radiomics model, and radiomics-clinical model in the testing cohort.

Data availability statement

The data used to support the findings of this study are available from the corresponding author upon request