Comparison of MR cytometry methods in predicting immunohistochemical factor status and molecular subtypes of breast cancer

Wu, Lei; Liu, Fan; Li, Sisi; Luo, Xinyi; Wang, Yishi; Zhong, Wen; Feiweier, Thorsten; Xu, Junzhong; Bao, Haihua; Shi, Diwei; Guo, Hua

doi:10.2478/raon-2025-0044

Figures & Tables

The ADC and microstructural maps overlaid on b = 1000 s/mm2 diffusion-weighted images of five representative breast cancer patients.
ADC = apparent diffusion coefficient; ER = estrogen receptor; d = diameter; Dex = apparent extracellular diffusivity; Din = intracellular intrinsic diffusivity; HER2 = human epidermal growth factor receptor 2; Ki67 = nuclear associated antigen; Kin = water exchange rate; TNBC = triple-negative breast cancer; vin = intracellular volume fraction; ΔADC = (ADC50Hz – ADCPGSE) / ADCPGSE — The ADC and microstructural maps overlaid on b = 1000 s/mm2 diffusion-weighted images of five representative breast cancer patients. ADC = apparent diffusion coefficient; ER = estrogen receptor; d = diameter; Dex = apparent extracellular diffusivity; Din = intracellular intrinsic diffusivity; HER2 = human epidermal growth factor receptor 2; Ki67 = nuclear associated antigen; Kin = water exchange rate; TNBC = triple-negative breast cancer; vin = intracellular volume fraction; ΔADC = (ADC50Hz – ADCPGSE) / ADCPGSE

Intergroup comparison of td-MRI metrics and microstructural parameters respectively fitted from IMPULSED, JOINT and EXCHANGE between positive and negative immunohistochemical factor status.
* = p < 0.05, ** = p < 0.01. + represents outliers — Intergroup comparison of td-MRI metrics and microstructural parameters respectively fitted from IMPULSED, JOINT and EXCHANGE between positive and negative immunohistochemical factor status. * = p < 0.05, ** = p < 0.01. + represents outliers

The performance of derived parameters in predicting immunohistochemistry (IHC) factor status. In each sub-plot, the four curves respectively correspond to: the classifier with the highest AUC based on a single td-dMRI metric (ADCPSGE, ADC25Hz, ADC50Hzor ΔADC), the classifier based on the combination of all td-dMRI metrics, the classifier with the highest AUC based on a single model-fitted microstructural parameter (vin, d, kin, Dex or Din obtained from IMPULSED, JOINT, or EXCHANGE), the classifier based on the combination of all parameters obtained from a specific MR cytometry method (IMPULSED, JOINT, or EXCHANGE) that provided the highest combined AUC. (A) ER; (B) PR; (C) HER2; (D) Ki67. The numbers within the parentheses in the legend represent the AUC of the corresponding parameters.
ADC = apparent diffusion coefficient; ER = estrogen receptor; d = diameter; Dex = apparent extracellular diffusivity; Din = intracellular intrinsic diffusivity; HER2 = human epidermal growth factor receptor 2; Ki67 = nuclear associated antigen; kin = water exchange rate; PR = progesterone receptor; vin = intracellular volume fraction — The performance of derived parameters in predicting immunohistochemistry (IHC) factor status. In each sub-plot, the four curves respectively correspond to: the classifier with the highest AUC based on a single td-dMRI metric (ADCPSGE, ADC25Hz, ADC50Hzor ΔADC), the classifier based on the combination of all td-dMRI metrics, the classifier with the highest AUC based on a single model-fitted microstructural parameter (vin, d, kin, Dex or Din obtained from IMPULSED, JOINT, or EXCHANGE), the classifier based on the combination of all parameters obtained from a specific MR cytometry method (IMPULSED, JOINT, or EXCHANGE) that provided the highest combined AUC. (A) ER; (B) PR; (C) HER2; (D) Ki67. The numbers within the parentheses in the legend represent the AUC of the corresponding parameters. ADC = apparent diffusion coefficient; ER = estrogen receptor; d = diameter; Dex = apparent extracellular diffusivity; Din = intracellular intrinsic diffusivity; HER2 = human epidermal growth factor receptor 2; Ki67 = nuclear associated antigen; kin = water exchange rate; PR = progesterone receptor; vin = intracellular volume fraction

The performance of derived parameters in predicting breast cancer molecular subtypes. In each sub-plot, the four curves respectively correspond to: the classifier with the highest AUC based on a single td-dMRI metric (ADCPSGE, ADC25Hz, ADC50Hz or ΔADC), the classifier based on the combination of all td-dMRI metrics, the classifier with the highest AUC based on a single model-fitted microstructural parameter (vin, d, kin, Dex or Din obtained from IMPULSED, JOINT, or EXCHANGE), the classifier based on the combination of all parameters obtained from a specific MR cytometry method (IMPULSED, JOINT, or EXCHANGE) that provided the highest combined AUC. (A) TNBC; (B) HER2-enriched; (C) Luminal A; (D) Luminal B. The numbers within the parentheses in the legend represent the AUC of the corresponding parameters.
ADC = apparent diffusion coefficient; AUC = area under the receiver operating characteristic curve; TNBC = triple-negative breast cancer; d = diameter; Dex = apparent extracellular diffusivity; Din = intracellular intrinsic diffusivity; Kin = water exchange rate; PR = progesterone receptor; Vin = intracellular volume fraction — The performance of derived parameters in predicting breast cancer molecular subtypes. In each sub-plot, the four curves respectively correspond to: the classifier with the highest AUC based on a single td-dMRI metric (ADCPSGE, ADC25Hz, ADC50Hz or ΔADC), the classifier based on the combination of all td-dMRI metrics, the classifier with the highest AUC based on a single model-fitted microstructural parameter (vin, d, kin, Dex or Din obtained from IMPULSED, JOINT, or EXCHANGE), the classifier based on the combination of all parameters obtained from a specific MR cytometry method (IMPULSED, JOINT, or EXCHANGE) that provided the highest combined AUC. (A) TNBC; (B) HER2-enriched; (C) Luminal A; (D) Luminal B. The numbers within the parentheses in the legend represent the AUC of the corresponding parameters. ADC = apparent diffusion coefficient; AUC = area under the receiver operating characteristic curve; TNBC = triple-negative breast cancer; d = diameter; Dex = apparent extracellular diffusivity; Din = intracellular intrinsic diffusivity; Kin = water exchange rate; PR = progesterone receptor; Vin = intracellular volume fraction

The diagnostic performance of imaging metrics for the prediction of immunohistochemistry (IHC) factor status

Model	Parameter	AUC (ER)	AUC (PR)	AUC (HER2)	AUC (Ki67)
t_d-dMRI	ADC_PGSE	0.631 (0.508, 0.755)	0.693 (0.584, 0.803)	0.594 (0.470, 0.718)	0.553 (0.427, 0.678)
	ADC_25Hz	0.630 (0.508, 0.752)	0.682 (0.571, 0.793)	0.639 (0.055, 0.767)	0.580 (0.458, 0.702)
	ADC_50Hz	0.624 (0.493, 0.755)	0.674 (0.560, 0.788)	0.627 (0.500, 0.755)	0.571 (0.449, 0.693)
	ΔADC	0.660 (0.540, 0.779)	0.694 (0.583, 0.806)	0.468 (0.328, 0.608)	0.496 (0.369, 0.623)
	Combined	0.645 (0.522, 0.768)	0.688 (0.576, 0.800)	0.623 (0.476, 0.770)	0.633 (0.516, 0.750)
IMPULSED	d	0.590 (0.454, 0.726)	0.621 (0.501, 0.742)	0.652 (0.512, 0.793)	0.612 (0.494, 0.730)
	V_in	0.664 (0.550, 0.779)	0.686 (0.576, 0.796)	0.554 (0.433, 0.675)	0.545 (0.419, 0.670)
	D_ex	0.529 (0.389, 0.669)	0.587 (0.461, 0.714)	0.518 (0.337, 0.659)	0.558 (0.438, 0.679)
	D_in	0.540 (0.407, 0.673)	0.595 (0.473, 0.716)	0.567 (0.433, 0.700)	0.524 (0.399, 0.649)
	Cellularity	0.646 (0.521, 0.771)	0.638 (0.519, 0.758)	0.567 (0.426, 0.708)	0.638 (0.521, 0.754)
	Combined	0.744 (0.641, 0.846)	0.705 (0.597, 0.813)	0.689 (0.552, 0.826)	0.646 (0.532, 0.760)
JOIN	d	0.575 (0.443, 0.707)	0.601 (0.481, 0.721)	0.697 (0.567, 0.827)	0.595 (0.476, 0.714)
	v_in	0.643 (0.523, 0.764)	0.673 (0.559, 0.787)	0.453 (0.330, 0.577)	0.517 (0.394, 0.641)
	k_in	0.623 (0.507, 0.740)	0.535 (0.415, 0.655)	0.459 (0.335, 0.583)	0.520 (0.392, 0.649)
	D_ex	0.487 (0.351, 0.623)	0.601 (0.478, 0.724)	0.536 (0.399, 0.673)	0.524 (0.403, 0.646)
	Cellularity	0.619 (0.490, 0.747)	0.613 (0.491, 0.736)	0.577 (0.438, 0.716)	0.632 (0.513, 0.750)
	Combined	0.731 (0.625, 0.837)	0.718 (0.609, 0.827)	0.734 (0.601, 0.867)	0.666 (0.552, 0.781)
EXCHANGE	d	0.584 (0.450, 0.718)	0.624 (0.504, 0.744)	0.650 (0.510, 0.790)	0.640 (0.525, 0.755)
	v_in	0.596 (0.466, 0.725)	0.671 (0.555, 0.788)	0.511 (0.380, 0.642)	0.466 (0.343, 0.590)
	k_in	0.666 (0.552, 0.781)	0.643 (0.526, 0.760)	0.528 (0.407, 0.650)	0.547 (0.420, 0.675)
	D_ex	0.521 (0.382, 0.661)	0.608 (0.483, 0.732)	0.562 (0.424, 0.699)	0.522 (0.401, 0.643)
	Cellularity	0.618 (0.490, 0.745)	0.617 (0.496, 0.739)	0.594 (0.445, 0.732)	0.632 (0.515, 0.748)
	Combined	0.725 (0.610, 0.839)	0.727 (0.620, 0.835)	0.668 (0.542, 0.794)	0.679 (0.565, 0.793)

Patient information and lesion characteristics

Characteristics	Luminal A (n = 26)	Luminal B (n = 38)	TNBC (n = 18)	HER2-enriched (n = 8)
Age(years)	55.11 ± 8.52	51.16 ± 11.01	52.89 ± 8.45	51.00 ± 9.04
Tumor size(mm)	27.65 ± 6.93	27.37 ± 8.25	27.83 ± 9.93	24.50 ± 6.35
Menstruation state
Premenopausal women	11	17	5	3
Postmenopausal women	15	21	13	5
Tumor border
Well-defined	9	10	8	4
ill-defined	17	28	10	4
Tumor sharp
Oval or round	21	32	11	3
Irregular	5	6	7	5
ER status
Positive	26	38	0	0
Negative	0	0	18	8
PR status
Positive	24	29	0	0
Negative	2	9	18	8
HER2 status
Positive	0	13	0	8
Negative	26	25	18	0
Ki67 status
Positive	3	27	16	6
Negative	23	11	2	2

The diagnostic performance of imaging metrics for the prediction of molecular subtypes

Model	Parameter	AUC (TNBC)	AUC (HER2- enriched)	AUC (Luminal A)	AUC (Luminal B)
ADC	ADC_PGSE	0.617 (0.470, 0.763)	0.681 (0.519, 0.844)	0.570 (0.438, 0.703)	0.577 (0.458, 0.697)
	ADC_25Hz	0.518 (0.435, 0.727)	0.745 (0.614, 0.877)	0.600 (0.470, 0.729)	0.551 (0.429, 0.672)
	ADC^50Hz	0.575 (0.411, 0.739)	0.744 (0.624, 0.863)	0.576 (0.449, 0.703)	0.566 (0.446, 0.686)
	ΔADC	0.648 (0.511, 0.785)	0.360 (0.141, 0.579)	0.474 (0.340, 0.609)	0.622 (0.506, 0.738)
	Combined	0.644 (0.501, 0.786)	0.765 (0.623, 0.907)	0.659 (0.538, 0.781)	0.633 (0.517, 0.748)
IMPULSED	d	0.519 (0.316, 0.676)	0.784 (0.609, 0.958)	0.614 (0.487, 0.741)	0.490 (0.370, 0.610)
	V_in	0.657 (0.522, 0.793)	0.651 (0.489, 0.813)	0.572 (0.433, 0.711)	0.593 (0.475, 0.710)
	D_ex	0.537 (0.367, 0.707)	0.582 (0.375, 0.790)	0.565 (0.445, 0.684)	0.558 (0.436, 0.680)
	D_in	0.507 (0.348, 0.666)	0.622 (0.468, 0.776)	0.514 (0.376, 0.653)	0.533 (0.412, 0.654)
	Cellularity	0.593 (0.447, 0.738)	0.720 (0.503, 0.936)	0.606 (0.474, 0.737)	0.455 (0.336, 0.574)
	Combined	0.748 (0.629, 0.868)	0.739 (0.531, 0.947)	0.666 (0.544, 0.789)	0.630 (0.513, 0.747)
JOIN	d	0.519 (0.367, 0.671)	0.809 (0.675, 0.944)	0.590 (0.460, 0.719)	0.515 (0.394, 0.635)
	v_in	0.644 (0.496, 0.791)	0.611 (0.438, 0.785)	0.545 (0.412, 0.678)	0.593 (0.475, 0.712)
	k_in	0.630 (0.489, 0.772)	0.486 (0.349, 0.624)	0.558 (0.414, 0.701)	0.541 (0.420, 0.663)
	D_ex	0.521 (0.363, 0.679)	0.642 (0.438, 0.845)	0.507 (0.383, 0.631)	0.539 (0.417, 0.662)
	Cellularity	0.549 (0.396, 0.703)	0.733 (0.537, 0.929)	0.584 (0.450, 0.718)	0.461 (0.342, 0.580)
	Combined	0.742 (0.616, 0.869)	0.819 (0.657, 0.980)	0.648 (0.525, 0.770)	0.609 (0.492, 0.727)
EXCHANGE	d	0.509 (0.357, 0.661)	0.784 (0.602, 0.965)	0.638 (0.513, 0.764)	0.516 (0.396, 0.636)
	v_in	0.627 (0.477, 0.778)	0.532 (0.309, 0.755)	0.492 (0.364, 0.621)	0.601 (0.481, 0.721)
	k_in	0.696 (0.561, 0.831)	0.459 (0.299, 0.618)	0.543 (0.402, 0.684)	0.606 (0.489, 0.723)
	D_ex	0.478 (0.313,0.644)	0.666 (0.468, 0.865)	0.514 (0.390, 0.637)	0.553 (0.431, 0.674)
	Cellularity	0.542 (0.393, 0.692)	0.756 (0.559, 0.953)	0.620 (0.490, 0.750)	0.488 (0.368, 0.608)
	Combined	0.751 (0.633, 0.869)	0.784 (0.598, 0.969)	0.730 (0.616, 0.843)	0.633 (0.518, 0.748)

The intergroup comparison for the imaging metrics across four breast cancer molecular subtypes

Model	Parameter	TNBC Median (IQR)	HER2-enriched Median (IQR)	Luminal A Median (IQR)	Luminal B Median (IQR)	^p
t_d-dMRI	ADC_PGSE	0.85 (0.49)	0.90 (0.25)	0.74 (0.25)	0.81 (0.25)	0.106
	ADC_25Hz	1.10 (0.51)	1.21 (0.31)	1.03 (0.19)	1.08 (0.22)	0.055
	ADC_50Hz	1.44 (0.52)	1.53 (0.26)	1.38 (0.18)	1.38 (0.18)	0.071
	ΔADC	0.73 (0.38)	0.66 (0.29)	0.81 (0.41)	0.83 (0.42)	0.075
IMPULSE	d	15.00 (1.73)	16.16 (1.85)	14.79 (1.33)	14.96 (1.07)	0.038
	V_in	0.38 (0.13)	0.37 (0.07)	0.42 (0.13)	0.41 (0.09)	0.063
	D_ex	1.91 (0.54)	2.08 (0.28)	2.02 (0.24)	1.95 (0.32)	0.712
	D_in	2.09 (0.20)	2.15 (0.08)	2.07 (0.31)	2.05 (0.21)	0.598
	Cellularity	0.074 (0.03)	0.058 (0.03)	0.078 (0.05)	0.075 (0.03)	0.071
JOIN	d	15.73 (1.57)	17.17 (2.09)	15.65 (2.02)	16.05 (1.42)	0.031
	v_in	0.51 (0.15)	0.51 (0.09)	0.54 (0.09)	0.55 (0.07)	0.144
	k_in	18.12 (6.66)	16.38 (2.89)	15.68 (6.75)	16.74 (5.09)	0.374
	D_ex	2.35 (0.41)	2.54 (0.22)	2.40 (0.26)	2.37 (0.32)	0.596
	Cellularity	0.081 (0.04)	0.063 (0.03)	0.083 (0.06)	0.082 (0.03)	0.114
EXCHANGE	d	13.91 (1.31)	15.07 (1.79)	13.67 (1.20)	13.94 (1.07)	0.025
	v_in	0.58 (0.10)	0.58 (0.07)	0.58 (0.05)	0.59 (0.05)	0.280
	k_in	8.12 (5.50)	7.00 (3.44)	6.70 (3.9)	6.67 (2.2)	0.061
	D_ex	2.30 (0.45)	2.52 (0.20)	2.36 (0.31)	2.30 (0.34)	0.442
	Cellularity	0.12 (0.03)	0.09 (0.03)	0.12 (0.08)	0.12 (0.03)	0.053

Comparison of MR cytometry methods in predicting immunohistochemical factor status and molecular subtypes of breast cancer

Figures & Tables

FIGURE 1.

FIGURE 2.

FIGURE 3.

Figure 4.

The diagnostic performance of imaging metrics for the prediction of immunohistochemistry (IHC) factor status

Patient information and lesion characteristics

The diagnostic performance of imaging metrics for the prediction of molecular subtypes

The intergroup comparison for the imaging metrics across four breast cancer molecular subtypes

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