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

Lei Wu

Lei Wu

Qinghai University Affiliated HospitalXining, China
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Wu, Lei
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Fan Liu

Fan Liu

Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua UniversityBeijing, China
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Liu, Fan
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Sisi Li

Sisi Li

MR Research Collaboration Team, Siemens Healthineers Ltd.Beijing, China
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Li, Sisi
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Xinyi Luo

Xinyi Luo

Tsinghua Shenzhen International Graduate School, Tsinghua UniversityShenzhen, China
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Luo, Xinyi
, 
Yishi Wang

Yishi Wang

MR Research Collaboration Team, Siemens Healthineers Ltd.Beijing, China
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Wang, Yishi
, 
Wen Zhong

Wen Zhong

Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua UniversityBeijing, China
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Zhong, Wen
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Thorsten Feiweier

Thorsten Feiweier

MR Research Collaboration Team, Siemens Healthineers AGErlangen, Germany
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Feiweier, Thorsten
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Junzhong Xu

Junzhong Xu

  • Institute of Imaging Science, Vanderbilt University Medical CenterNashville, United States
  • Department of Biomedical Engineering, Vanderbilt UniversityNashville, United States
  • Department of Radiology and Radiological Sciences, Vanderbilt University Medical CenterNashville, United States
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Xu, Junzhong
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Haihua Bao

Haihua Bao

Qinghai University Affiliated HospitalXining, China
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Bao, Haihua
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Diwei Shi

Diwei Shi

Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua UniversityBeijing, China
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Shi, Diwei
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Hua Guo

Hua Guo

Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua UniversityBeijing, China
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Guo, Hua
  
06 ago 2025
Comparison of MR cytometry methods in predicting immunohistochemical factor status and molecular subtypes of breast cancer's Cover Image
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Categoria dell'articolo: Research Article
Pubblicato online: 06 ago 2025
Pagine: 337 - 348
Ricevuto: 03 mar 2025
Accettato: 19 mag 2025
DOI: https://doi.org/10.2478/raon-2025-0044
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© 2025 Lei Wu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

FIGURE 1.

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

FIGURE 2.

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

FIGURE 3.

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

Figure 4.

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)
td-dMRI ADCPGSE 0.631 (0.508, 0.755) 0.693 (0.584, 0.803) 0.594 (0.470, 0.718) 0.553 (0.427, 0.678)
ADC25Hz 0.630 (0.508, 0.752) 0.682 (0.571, 0.793) 0.639 (0.055, 0.767) 0.580 (0.458, 0.702)
ADC50Hz 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)
Vin 0.664 (0.550, 0.779) 0.686 (0.576, 0.796) 0.554 (0.433, 0.675) 0.545 (0.419, 0.670)
Dex 0.529 (0.389, 0.669) 0.587 (0.461, 0.714) 0.518 (0.337, 0.659) 0.558 (0.438, 0.679)
Din 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)
vin 0.643 (0.523, 0.764) 0.673 (0.559, 0.787) 0.453 (0.330, 0.577) 0.517 (0.394, 0.641)
kin 0.623 (0.507, 0.740) 0.535 (0.415, 0.655) 0.459 (0.335, 0.583) 0.520 (0.392, 0.649)
Dex 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)
vin 0.596 (0.466, 0.725) 0.671 (0.555, 0.788) 0.511 (0.380, 0.642) 0.466 (0.343, 0.590)
kin 0.666 (0.552, 0.781) 0.643 (0.526, 0.760) 0.528 (0.407, 0.650) 0.547 (0.420, 0.675)
Dex 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 ADCPGSE 0.617 (0.470, 0.763) 0.681 (0.519, 0.844) 0.570 (0.438, 0.703) 0.577 (0.458, 0.697)
ADC25Hz 0.518 (0.435, 0.727) 0.745 (0.614, 0.877) 0.600 (0.470, 0.729) 0.551 (0.429, 0.672)
ADC50Hz 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)
Vin 0.657 (0.522, 0.793) 0.651 (0.489, 0.813) 0.572 (0.433, 0.711) 0.593 (0.475, 0.710)
Dex 0.537 (0.367, 0.707) 0.582 (0.375, 0.790) 0.565 (0.445, 0.684) 0.558 (0.436, 0.680)
Din 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)
vin 0.644 (0.496, 0.791) 0.611 (0.438, 0.785) 0.545 (0.412, 0.678) 0.593 (0.475, 0.712)
kin 0.630 (0.489, 0.772) 0.486 (0.349, 0.624) 0.558 (0.414, 0.701) 0.541 (0.420, 0.663)
Dex 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)
vin 0.627 (0.477, 0.778) 0.532 (0.309, 0.755) 0.492 (0.364, 0.621) 0.601 (0.481, 0.721)
kin 0.696 (0.561, 0.831) 0.459 (0.299, 0.618) 0.543 (0.402, 0.684) 0.606 (0.489, 0.723)
Dex 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
td-dMRI ADCPGSE 0.85 (0.49) 0.90 (0.25) 0.74 (0.25) 0.81 (0.25) 0.106
ADC25Hz 1.10 (0.51) 1.21 (0.31) 1.03 (0.19) 1.08 (0.22) 0.055
ADC50Hz 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
Vin 0.38 (0.13) 0.37 (0.07) 0.42 (0.13) 0.41 (0.09) 0.063
Dex 1.91 (0.54) 2.08 (0.28) 2.02 (0.24) 1.95 (0.32) 0.712
Din 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
vin 0.51 (0.15) 0.51 (0.09) 0.54 (0.09) 0.55 (0.07) 0.144
kin 18.12 (6.66) 16.38 (2.89) 15.68 (6.75) 16.74 (5.09) 0.374
Dex 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
vin 0.58 (0.10) 0.58 (0.07) 0.58 (0.05) 0.59 (0.05) 0.280
kin 8.12 (5.50) 7.00 (3.44) 6.70 (3.9) 6.67 (2.2) 0.061
Dex 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
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