Share：
Share this content in WeChat
X
[Chinese][PDF]8866
• Special Focus •
Value of radiomics model based on spatiotemporal heterogeneity of MRI to predict pathological complete response in triple-negative breast cancer
ZHOU Jiayin  YOU Chao  WANG Zezhou  LIN Luyi  SHEN Yiyuan  GU Yajia 

Cite this article as: ZHOU J Y, YOU C, WANG Z Z, et al. Value of radiomics model based on spatiotemporal heterogeneity of MRI to predict pathological complete response in triple-negative breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(1): 28-34. DOI:10.12015/issn.1674-8034.2024.01.005.


[Abstract] Objective To develop a spatiotemporal heterogeneity based radiomics model for the early prediction of pathological complete response (pCR) in triple-negative breast cancer (TNBC).Materials and Methods The data of 173 TNBC patients who received neoadjuvant chemotherapy (NAC) in our hospital from September 2017 to March 2022 were retrospectively analyzed. MRI images of each patient were collected at pretreatment (Pre-) and after two cycle of NAC (During-). The 55 patients from the DUKE university constituted the external validation cohort. Radiomics features were extracted from the intratumoral subregions and peritumoral region to characterize spatial heterogeneity, and the changes of features before and during NAC (Delta-) were calculated to characterize temporal heterogeneity. The radiomics models were developed by least absolute shrinkage and selection operator (LASSO) regression using the Pre-, During-, and Delta- features. Multi-factor logistic regression was used to integrate single-mode models to develop the longitudinal fusion (Stacking) model. The diagnostic performance and clinical application value of models were evaluated by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).Results Finally, 8, 4 and 10 features were respectively selected from the Pre-, During- and Delta- feature sets to construct the models. The Pre- model based on spatial heterogeneity could predict pCR, with area under the curve (AUC) of 0.74, 0.71 and 0.71 in the training set, validation set and external validation set, respectively. In the training and validation sets, the Stacking model achieved the best performance to predict pCR, and the AUC was 0.86 in both sets. DCA indicated that the value of Stacking model was highest in clinical practice.Conclusions Features based on MRI spatial heterogeneity can effectively predict the pCR of TNBC. The longitudinal fusion model integrated spatiotemporal heterogeneity has the potential to further improve the prediction performance.
[Keywords] triple-negative breast cancer；radiomics；spatiotemporal heterogeneity；habitat imaging；neoadjuvant therapy；magnetic resonance imaging

ZHOU Jiayin1, 2   YOU Chao1, 2   WANG Zezhou3   LIN Luyi1, 2   SHEN Yiyuan1, 2   GU Yajia1, 2*  

1 Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China

2 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China

3 Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Shanghai 200032, China

Corresponding author: GU Y J, E-mail: guyajia@126.com

Conflicts of interest   None.

Received  2023-10-07
Accepted  2023-12-25
DOI: 10.12015/issn.1674-8034.2024.01.005
Cite this article as: ZHOU J Y, YOU C, WANG Z Z, et al. Value of radiomics model based on spatiotemporal heterogeneity of MRI to predict pathological complete response in triple-negative breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(1): 28-34. DOI:10.12015/issn.1674-8034.2024.01.005.

[1]
CAREY L, WINER E, VIALE G, et al. Triple-negative breast cancer: disease entity or title of convenience?[J]. Nat Rev Clin Oncol, 2010, 7(12): 683-692. DOI: 10.1038/nrclinonc.2010.154.
[2]
DENT R, TRUDEAU M, PRITCHARD K I, et al. Triple-negative breast cancer: clinical features and patterns of recurrence[J]. Clin Cancer Res, 2007, 13(15Pt 1): 4429-4434. DOI: 10.1158/1078-0432.CCR-06-3045.
[3]
YIN L, DUAN J J, BIAN X W, et al. Triple-negative breast cancer molecular subtyping and treatment progress[J/OL]. Breast Cancer Res, 2020, 22(1): 61 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/32517735/. DOI: 10.1186/s13058-020-01296-5.
[4]
TONG L, YU X L, WANG S, et al. Research progress on molecular subtyping and modern treatment of triple-negative breast cancer[J/OL]. Breast Cancer, 2023, 15: 647-658 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/37644916/. DOI: 10.2147/BCTT.S426121.
[5]
LOO C E, STRAVER M E, RODENHUIS S, et al. Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype[J]. J Clin Oncol, 2011, 29(6): 660-666. DOI: 10.1200/JCO.2010.31.1258.
[6]
HOUSSAMI N, MACASKILL P, VON MINCKWITZ G, et al. Meta-analysis of the association of breast cancer subtype and pathologic complete response to neoadjuvant chemotherapy[J]. Eur J Cancer, 2012, 48(18): 3342-3354. DOI: 10.1016/j.ejca.2012.05.023.
[7]
KUERER H M, SMITH B D, KRISHNAMURTHY S, et al. Eliminating breast surgery for invasive breast cancer in exceptional responders to neoadjuvant systemic therapy: a multicentre, single-arm, phase 2 trial[J]. Lancet Oncol, 2022, 23(12): 1517-1524. DOI: 10.1016/S1470-2045(22)00613-1.
[8]
GRADISHAR W J, MORAN M S, ABRAHAM J, et al. NCCN guidelines® insights: breast cancer, version 4.2021[J]. J Natl Compr Canc Netw, 2021, 19(5): 484-493. DOI: 10.6004/jnccn.2021.0023.
[9]
LOBBES M B, PREVOS R, SMIDT M, et al. The role of magnetic resonance imaging in assessing residual disease and pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy: a systematic review[J]. Insights Imaging, 2013, 4(2): 163-175. DOI: 10.1007/s13244-013-0219-y.
[10]
GU Y L, PAN S M, REN J, et al. Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: a meta-analysis[J]. Clin Breast Cancer, 2017, 17(4): 245-255. DOI: 10.1016/j.clbc.2016.12.010.
[11]
WOLF D M, YAU C, WULFKUHLE J, et al. Redefining breast cancer subtypes to guide treatment prioritization and maximize response: predictive biomarkers across 10 cancer therapies[J/OL]. Cancer Cell, 2022, 40(6): 609-623.e6 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/35623341/. DOI: 10.1016/j.ccell.2022.05.005.
[12]
NONNEVILLE A D, HOUVENAEGHEL G, COHEN M, et al. Pathological complete response rate and disease-free survival after neoadjuvant chemotherapy in patients with HER2-low and HER2-0 breast cancers[J/OL]. Eur J Cancer, 2022, 176: 181-188 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/36257173/. DOI: 10.1016/j.ejca.2022.09.017.
[13]
ZHANG L, LI X Y, YANG L, et al. Multi-sequence and multi-regional MRI-based radiomics nomogram for the preoperative assessment of muscle invasion in bladder cancer[J]. J Magn Reson Imaging, 2023, 58(1): 258-269. DOI: 10.1002/jmri.28498.
[14]
MA X W, GONG J, HU F X, et al. Pretreatment multiparametric MRI-based radiomics analysis for the diagnosis of breast Phyllodes tumors[J]. J Magn Reson Imaging, 2023, 57(2): 633-645. DOI: 10.1002/jmri.28286.
[15]
CHEN Y, TANG W, LIU W, et al. Multiparametric MR imaging radiomics signatures for assessing the recurrence risk of ER+/HER2- breast cancer quantified with 21-gene recurrence score[J]. J Magn Reson Imaging, 2023, 58(2): 444-453. DOI: 10.1002/jmri.28547.
[16]
LIU S S, DU S Y, GAO S, et al. A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients[J/OL]. BMC Cancer, 2023, 23(1): 15 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/36604679/. DOI: 10.1186/s12885-022-10496-5.
[17]
HUANG Y H, ZHU T, ZHANG X L, et al. Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study[J/OL]. EClinicalMedicine, 2023, 58: 101899 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/37007742/. DOI: 10.1016/j.eclinm.2023.101899.
[18]
GILLIES R J, BALAGURUNATHAN Y. Perfusion MR imaging of breast cancer: insights using "habitat imaging"[J]. Radiology, 2018, 288(1): 36-37. DOI: 10.1148/radiol.2018180271.
[19]
WU J, CAO G H, SUN X L, et al. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy[J]. Radiology, 2018, 288(1): 26-35. DOI: 10.1148/radiol.2018172462.
[20]
SHI Z W, HUANG X M, CHENG Z L, et al. MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer[J/OL]. Radiology, 2023, 308(1): e222830 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/37432083/. DOI: 10.1148/radiol.222830.
[21]
SU G H, XIAO Y, JIANG L, et al. Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer[J/OL]. J Transl Med, 2022, 20(1): 471 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/36243806/. DOI: 10.1186/s12967-022-03688-x.
[22]
SU G H, JIANG L, XIAO Y, et al. A multiomics signature highlights alterations underlying homologous recombination deficiency in triple-negative breast cancer[J]. Ann Surg Oncol, 2022, 29(11): 7165-7175. DOI: 10.1245/s10434-022-11958-7.
[23]
BRAMAN N M, ETESAMI M, PRASANNA P, et al. Erratum to: Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J/OL]. Breast Cancer Res, 2017, 19(1): 80 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/28693537/. DOI: 10.1186/s13058-017-0862-1.
[24]
ZHANG Y, YOU C, PEI Y C, et al. Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets[J/OL]. J Transl Med, 2022, 20(1): 256 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/35672824/. DOI: 10.1186/s12967-022-03452-1.
[25]
MARCOULIDES K M, RAYKOV T. Evaluation of variance inflation factors in regression models using latent variable modeling methods[J]. Educ Psychol Meas, 2019, 79(5): 874-882. DOI: 10.1177/0013164418817803.
[26]
DELONG E R, DELONG D M, CLARKE-PEARSON D L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach[J]. Biometrics, 1988, 44(3): 837-845.
[27]
PENG Y S, CHENG Z L, GONG C, et al. Pretreatment DCE-MRI-based deep learning outperforms radiomics analysis in predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer[J/OL]. Front Oncol, 2022, 12: 846775 [2023-10-06]. https://pubmed.ncbi.nlm.nih.gov/35359387/. DOI: 10.3389/fonc.2022.846775.
[28]
LIU Z Y, LI Z L, QU J R, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study[J]. Clin Cancer Res, 2019, 25(12): 3538-3547. DOI: 10.1158/1078-0432.CCR-18-3190.
[29]
EUN N L, KANG D, SON E J, et al. Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer[J]. Radiology, 2020, 294(1): 31-41. DOI: 10.1148/radiol.2019182718.
[30]
KEENAN T E, TOLANEY S M. Role of immunotherapy in triple-negative breast cancer[J]. J Natl Compr Canc Netw, 2020, 18(4): 479-489. DOI: 10.6004/jnccn.2020.7554.
[31]
SCHMID P, CORTES J, PUSZTAI L, et al. Pembrolizumab for early triple-negative breast cancer[J]. N Engl J Med, 2020, 382(9): 810-821. DOI: 10.1056/NEJMoa1910549.

PREV Different MRI features between BRCA mutant and non-mutant breast cancer patients
NEXT Constructing a MR-clinicopathological based nomogram to predict the shrinkage patterns of neoadjuvant therapy in breast cancer
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn