A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Figures & data

Figure 1 The flow chart of patient selection and data process.

Table 1 The Predictive Performances of Different Models Associated with Pathological Remission

Model	95% CI				Discrimination
	OR	Lower	Upper	P-value	Brier	R^2	C-Index	AIC
Model1					0.04	0.06	0.882	325.70
Tumor_size*, cm	0.83	0.72	0.97	0.09
PI
<0.58:≥0.58	1.05	0.87	1.27	0.02
RI
≥0.47:<0.47	1.19	0.93	1.51	0.04
Pathology
IDC:SC	0.30	0.14	0.63	0.01
ILC:SC	0.48	0.35	0.66	0.02
MCB:SC	1.52	0.93	2.49	<0.01
Molecular_subtyping
Luminal:TNBC	0.95	0.74	1.21	<0.01
HER2(+):TNBC	0.58	0.45	0.75	0.03
LH:TNBC	0.74	0.55	0.99	0.03
Blood_flow
≥1.88:<1.88	1.34	1.02	1.78	0.04
NLR
≥3.14:<3.14	1.18	0.67	2.09	<0.01
BMI
<18:18–24	0.98	0.72	1.35	<0.01
≥25:18–24	1.53	1.18	1.98	0.02
Model2					0.05	0.06	0.857	331.50
Tumor_size*, cm	0.78	0.39	1.17	0.02
PI
<0.58:≥0.58	1.23	0.84	1.62	0.01
Ki67
≥0.50:<0.50	1.14	0.75	1.53	0.04
Pathology
IDC:SC	0.48	0.03	0.93	0.04
ILC:SC	0.52	0.11	0.93	<0.01
MCB:SC	1.39	0.94	1.84	<0.01
Molecular_subtyping
Luminal:TNBC	0.47	0.06	0.88	<0.01
HER2(+):TNBC	0.51	0.10	0.92	0.02
LH:TNBC	0.38	0.09	0.67	0.04
Blood_flow
≥1.88:<1.88	1.25	0.80	1.71	0.01
NLR
≥3.14:<3.14	1.13	0.67	1.53	<0.01
BMI
<18:18–24	0.89	0.46	1.32	0.06
≥25:18–24	1.27	0.86	1.68	0.03
Model3					0.04	0.06	0.764	334.60
Age*, year	0.67	0.16	1.18	0.05
PI
<0.58:≥0.58	0.85	0.42	1.28	0.02
RI
≥0.47:<0.47	1.22	0.81	1.63	0.04
Pathology
IDC:SC	0.38	0.32	0.79	0.01
ILC:SC	0.73	0.57	0.89	0.02
MCB:SC	1.25	0.90	1.60	0.05
Molecular_subtyping
Luminal:TNBC	0.79	0.46	1.12	0.02
HER2(+):TNBC	0.58	0.29	0.87	0.03
LH:TNBC	0.24	0.11	0.59	0.01
NLR
≥3.14:<3.14	1.09	0.60	1.58	<0.01
Model4					0.05	0.07	0.775	332.20
Tumor_size*, cm	0.88	0.47	1.29	0.02
Age*, year	1.07	0.66	1.48	0.05
Ki67
≥0.50:<0.50	1.22	0.75	1.69	0.03
Pathology
IDC:SC	0.46	0.30	0.62	<0.01
ILC:SC	0.45	0.31	0.59	<0.01
MCB:SC	1.21	0.79	1.62	<0.01
Molecular_subtyping
Luminal:TNBC	0.85	0.48	1.22	0.02
HER2(+):TNBC	0.57	0.02	1.12	<0.01
LH:TNBC	0.77	0.36	1.18	<0.01
Blood_flow
≥1.88:<1.88	1.25	0.84	1.66	0.01
NLR
≥3.14:<3.14	1.17	0.82	1.52	<0.01
BMI
<18:18–24	0.91	0.53	1.28	<0.01
≥25:18–24	1.03	0.64	1.42	<0.01
Model5					0.04	0.05	0.796	334.50
Tumor_size*, cm	1.12	0.77	1.47	0.05
Pathology
IDC:SC	0.67	0.32	1.02	0.04
ILC:SC	0.58	0.40	0.76	0.02
MCB:SC	1.42	1.05	1.77	<0.01
Molecular_subtyping
Luminal:TNBC	0.78	0.37	1.19	<0.01
HER2(+):TNBC	0.68	0.31	1.05	<0.01
LH:TNBC	0.67	0.32	1.02	<0.01
Ki67
≥0.50:<0.50	1.24	0.81	1.67	0.02
NLR
≥3.14:<3.14	1.13	0.80	1.46	0.04
BMI
<18:18–24	0.72	0.41	1.03	<0.01
≥25:18–24	0.98	0.49	1.47	<0.01

Note: *Continuous variable. Clinicopathological characteristics of 225 breast cancer patients treated with neoadjuvant chemotherapy.

Abbreviations: AIC, Akaike Information Criterion; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; SC, simplex carcinoma; MCB, medullary carcinoma of breast; LH, luminal-HER2(+); TNBC, triple negative breast cancer; BMI, body mass index; PI, pulsatility index; RI, resistance index; NLR, neutrophil lymphocyte ratio; OR, odds ratio; 95% CI, 95% confidence interval.

Table 2 The Predictive Ability and Parameter Inclusion of Prediction Models Reported in Previous Literature

Variables		95% CI		P-value
	OR	Lower	Upper
Tumor_size*, cm	0.83	0.72	0.97	<0.01
PI
<0.58:≥0.58	1.05	0.87	1.27	0.02
RI
≥0.47:<0.47	1.19	0.93	1.51	0.04
Pathology
IDC:SC	0.30	0.14	0.63	0.01
ILC:SC	0.48	0.35	0.66	0.02
MCB:SC	1.52	0.93	2.49	<0.01
Molecular_subtyping
Luminal:TNBC	0.95	0.74	1.21	<0.01
HER2(+):TNBC	0.58	0.45	0.75	0.03
LH:TNBC	0.74	0.55	0.99	0.03
Blood_flow
≥1.88:<1.88	1.34	1.02	1.78	0.04
NLR
≥3.14:<3.14	1.18	0.67	2.09	<0.01
BMI
<18:18–24	0.98	0.72	1.35	<0.01
≥25:18–24	1.53	1.18	1.98	0.02

Note: *Continuous variable. Multivariate logistic regression analysis for risk factors associated with rNACT in patients with breast cancer.

Abbreviations: IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; SC, simplex carcinoma; MCB, medullary carcinoma of breast; LH, luminal-HER2(+); TNBC, triple negative breast cancer; BMI, body mass index; PI, pulsatility index; RI, resistance index; NLR, neutrophil lymphocyte ratio.

Figure 2 Statistical analysis of features included in models by Akaike Information Criterion. (A) Venn diagrams showing candidate variables for predicting the degree of pathological remission in five models. (B) Scaled importance rank of all features included in nomogram for identifying the possibility of pathological remission.

Figure 3 Generalized linear model. (A) Nomogram conveying the results of the candidate factors for predicting the possibility of pathological remission. (B) Calibration curves for internal validation of the nomogram (blue line represented training set, the red line represented testing set). (C) Predicted risk histogram comparing predicted risk of the nomogram with the observed frequency.

Figure 4 Predictive performance of nomogram based on ultrasound imaging and clinical indices. (A) AUC for predicting the possibility of pathological remission (Nomogram1. Ultrasound imaging index; Nomogram2. Ultrasound imaging index, Clinicopathological parameters; Nomogram3. Clinicopathological parameters). (B) Clinical impact curve for the nomogram score (nomogram2).