
==== Front
eBioMedicine
EBioMedicine
eBioMedicine
2352-3964
Elsevier

S2352-3964(24)00347-5
10.1016/j.ebiom.2024.105311
105311
Articles
Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study
Li Ziyin abl
Gao Jing bl
Zhou Heng cl
Li Xianglin a
Zheng Tiantian ab
Lin Fan b
Wang Xiaodong ab
Chu Tongpeng de
Wang Qi de
Wang Simin f
Cao Kun g
Liang Yun h
Zhao Feng i
Xie Haizhu b
Xu Cong j
Zhang Haicheng de
Niu Qingliang Qingliangniu@126.com
k∗∗∗
Ma Heng mahengyhd@163.com
b∗∗
Mao Ning maoning@pku.edu.cn
b∗
a School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong, 264003, China
b Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
c School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, 264005, China
d Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
e Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
f Department of Radiology, Fudan University Cancer Center, Shanghai, 200433, China
g Department of Radiology, Beijing Cancer Hospital, Beijing, 100142, China
h Department of Radiology, Guilin Municipal Hospital of Traditional Chinese Medicine, Guilin, Yunnan, 541002, China
i School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, 264005, China
j Physical Examination Center, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
k Weifang NO.2 People's Hospital, Weifang, Shandong, 261041, China
∗ Corresponding author. Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, 264000, China. maoning@pku.edu.cn
∗∗ Corresponding author. Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, 264000, China. mahengyhd@163.com
∗∗∗ Corresponding author. Weifang NO.2 People's Hospital, NO. 7 College Street, Kuiwen District, Weifang, Shandong, 261041, China. Qingliangniu@126.com
l Co-first authors.

26 8 2024
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© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Background

The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer.

Methods

This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL.

Findings

1145 patients (mean age, 50 years ±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P < 0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways.

Interpretation

FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice.

Funding

This study was supported by the 10.13039/501100001809 National Natural Science Foundation of China (82371933 ), National 10.13039/501100007129 Natural Science Foundation of Shandong Province of China (ZR2021MH120 ), Mount Taishan Scholars and Young Experts Program (tsqn202211378 ), Key Projects of China Medicine Education Association (2022KTM030 ), 10.13039/501100002858 China Postdoctoral Science Foundation (314730 ), and Beijing Postdoctoral Research Foundation (2023-zz-012 ).

Keywords

Deep learning
Breast cancer
Axillary lymph node
Neoadjuvant chemotherapy
RNA sequencing analysis
==== Body
pmc Research in context

Evidence before this study

We searched PubMed for publications without date or language restrictions, with the terms (“deep learning” OR “radiomics” OR “artificial intelligence” OR “AI”) AND (“breast cancer” OR “breast tumour” OR “axillary lymph node” OR “ALN”) AND (“neoadjuvant chemotherapy”) AND (“axillary pathological complete response” OR “axillary pCR” OR “axillary response”). Only three artificial intelligence-based studies utilising MRI images have been published in the field of predicting axillary response after neoadjuvant chemotherapy. However, these studies exhibited insufficient predictive efficacy due to their small sample size or limited focus on a single region. Thus, the clinical application of these models remains limited. Furthermore, manual segmentation methods are time-consuming and labour-intensive, posing an additional challenge.

Added value of this study

Considering the association between the primary tumour and ALN, we developed a fully automated integrated system based on deep learning (FAIS-DL) using dynamic contrast-enhanced MRI (DCE-MRI) of tumour and ALN, as well as clinicopathologic characteristics. The FAIS-DL can automatically segment tumours and ALNs precisely, enabling comprehensive analysis and accurate prediction of axillary pCR. Higher AUCs of FAIS-DL were observed compared to models using single-regional DCE-MRI or clinicopathologic characteristics in the internal, pooled external, and prospective test sets. Furthermore, we investigated the biological basis of FAIS-DL prediction, demonstrating that a high FAIS-DL score was associated with the upregulation of immune-mediated genes and pathways.

Implications of all the available evidence

Our findings showed that the proposed FAIS-DL provides a non-invasive method to effectively predict axillary pCR before neoadjuvant chemotherapy for breast cancer with ALN metastasis. In particular, the early prediction of axillary pCR may assist clinicians in making treatment decisions. Additionally, the RNA sequencing analysis revealed an association between FAIS-DL predictions and the upregulation of immunity. Immunohistochemical staining demonstrated that tumours with high FAIS-DL scores showed increased infiltration of monocytes and macrophage M0 cells.

Introduction

Breast cancer is the most common malignancy in women worldwide,1 with its metastasis frequently occurring in axillary lymph node (ALN). Neoadjuvant chemotherapy (NAC) has been increasingly utilised to treat ALN-positive breast cancer, with approximately 35%–68% of patients achieving axillary pathological complete response (pCR) following NAC.2,3 Axillary lymph node dissection (ALND) currently serves as the reference standard for assessing axillary pCR; however, it may cause various degrees of adverse effects, including lymphedema, shoulder movement restrictions, and numbness of the upper extremity.4, 5, 6 Recent clinical trials have shown that for patients with ALN involvement, adjuvant radiotherapy offers significant clinical value by reducing surgical complications while maintaining low axillary recurrence rates and favourable overall survival and disease-free survival rates.7,8 Further research supports that adjuvant radiotherapy has the potential to serve as a viable alternative to ALND in patients achieving axillary pCR after NAC.9,10 Therefore, if a reliable preoperative method is available to predict ALN response, patients who achieve axillary pCR could receive adjuvant radiotherapy instead of ALND, thereby avoiding the inherent side effects associated with ALND. Therefore, accurately assessing axillary pCR is crucial for axillary management decisions.

Dynamic contrast-enhanced MRI (DCE-MRI) has been recommended as a more objective and less operator-dependent method for evaluating ALN status in breast cancer.11 However, due to the complexity and heterogeneity of response and the high variability in image interpretation by radiologists,12 the accuracy of DCE-MRI in predicting axillary pCR is limited. Radiomics can convert digital image information into high-dimensional spatial feature vectors and has gained increasing attention recently.13,14 However, radiomics relies on human-predefined features, which may only partially capture the relevant information encoded within the images.15 In addition, manual annotation is prone to subjective bias, hindering the clinical implementation of radiomics. Thus, reliable methods to accurately predict axillary pCR are still lacking.

Deep learning is a powerful technique that has achieved remarkable results in radiology.16,17 Many studies have applied deep learning in assessing therapeutic response,18, 19, 20 with promising outcomes illustrating its ability to predict axillary pCR.21 However, challenges remain in implementing this approach in clinical practice due to the limited sample size, manual annotation, and failure to utilise the rich feature information of ALN images. A recent study based on radiomics verified the importance of combining the dynamic contrast-enhanced MRI (DCE-MRI) of tumour and ALN in predicting axillary pCR,22 however, the utilisation of deep learning requires further clarification. In addition, the black-box nature obscures the decision-making logic of deep learning, and the underlying biological basis of the prediction mechanism requires further explanation.

This study presented a fully automated integrated system based on deep learning (FAIS-DL) that utilised clinicopathological characteristics and the DCE-MRI of tumour and ALN for early predicting axillary pCR after NAC in breast cancer. Moreover, we visualised the regions deemed important by FAIS-DL and further investigated the biological basis underlying FAIS-DL prediction.

Methods

Ethics

This study was approved by the Institutional Ethics Committee of the Yantai Yuhuangding Hospital (retrospective and prospective study approval number, 2023-312). Five other hospitals, including Fudan University Shanghai Cancer Center, Guilin Municipal Hospital of Traditional Chinese Medicine, Beijing Cancer Hospital, Affiliated Hospital of Qingdao University, and Binzhou Medical University Hospital have accepted the decision of the Institutional Ethics Committee of the Yantai Yuhuangding Hospital. Informed consent was waived for the retrospective study, and all the patients from the prospective study provided written informed consent (Chinese Clinical Trial Registration Centre, ChiCTR2300077197).

Patients

The inclusion criteria of retrospective study were as follows: (1) patients pathologically diagnosed with invasive breast cancer with ipsilateral ALN metastasis, (2) patients treated with standard NAC and surgery, (3) DCE-MRI scans were available and performed within 2 weeks before NAC, and (4) no other tumours occurred during NAC and the operation. The exclusion criteria were as follows: (1) previous history of radiotherapy, chemotherapy, or surgery before NAC, (2) patients did not complete the NAC regimen, (3) absence of surgery or incomplete results of postoperative pathology, (4) lack of clinicopathological data and DCE-MRI images for analysis, (5) poor quality of DCE-MRI images, and (6) patients with multifocal or bilateral breast tumours or positive with bilateral ALN.

For the prospective study, we enrolled patients who had biopsy-confirmed invasive breast cancer and ipsilateral axillary lymph node metastasis. Before enrolment, we obtained informed consent from patients and provided them with detailed information regarding the study's objectives, procedures, and potential risks. Patients in the study met specific criteria, including no history of other tumours and normal blood, liver, and kidney functions. All patients underwent DCE-MRI within two weeks prior to NAC to collect imaging data. Additionally, some patients agreed to retain biopsy tissue samples for subsequent RNA sequencing analysis. Enrolled patients were grouped into one of three categories: (1) scheduled to receive standard NAC followed by surgery, (2) currently undergoing standard NAC with plans for surgery after NAC, or (3) having completed NAC and awaiting surgery. The criteria of inclusion and exclusion for prospective study are detailed in Supplementary Material 1.

A total of 1145 patients from six hospitals located in different regions of China between May 2018 and December 2023 were included in this study. Fig. 1 shows the detailed patient enrolment process. For the development of FAIS-DL, 633 patients were retrospectively recruited from the Yantai Yuhuangding Hospital and Fudan University Shanghai Cancer Center and divided into training and validation sets (506 patients) and an internal test set (127 patients) at a ratio of 8:2. A 10-fold cross validation was used in the training and validation sets. In addition, 414 patients were recruited from the Guilin Municipal Hospital of Traditional Chinese Medicine, Beijing Cancer Hospital, Affiliated Hospital of Qingdao University, and Binzhou Medical University Hospital as the pooled external test set. A prospective test was performed on 98 participants from Yantai Yuhuangding Hospital between November 2023 and December 2023 to evaluate the clinical applicability of FAIS-DL.Fig. 1 Flowchart of patient enrolment. A total of 1145 patients were enrolled in this study. ALN, axillary lymph node; NAC, neoadjuvant chemotherapy; DCE-MRI, dynamic contrast-enhanced MRI.

Image acquisition and lesion annotation

All MRI scans were performed within 2 weeks before NAC using a 1.5 or 3.0 T scanner. Detailed information about the MRI acquisition can be found in Supplementary Table S1.

Development of multiregional DCE-MRI deep learning models

Fig. 2a illustrates the deep learning network architecture, which included two subtasks: automatic segmentation and axillary pCR prediction. Based on this architecture, we developed deep learning models utilising multiregional DCE-MRI collected before NAC. We have designated the deep learning model responsible for processing DCE-MRI data related to tumours as DL-T, and the model dedicated to analysing DCE-MRI data pertaining to ALN as DL-ALN.Fig. 2 Study workflow. (a) Development of FAIS-DL; (b) Evaluating the predictive performance of FAIS-DL; (c) Exploring the potential clinical benefit of FAIS-DL; (d) Underlying predictive mechanism of FAIS-DL was investigated using RNA-sequence data from 45 patients. ROC, receiver operating characteristic; ALN, axillary lymph node; pCR, pathological complete response; FAIS-DL, fully automated integrated system based on deep learning.

The automatic segmentation network, utilising the same design as our previously published research,23 was based on the U-Net architecture and integrated an edge feature extraction module and attention mechanisms. We adopted manual segmentation as the gold standard and retrained this network to segment tumours and ALNs automatically. The information about manual segmentation is detailed in Supplementary Material 3. The dice similarity coefficients (DSCs) for interobserver and intraobserver manual delineations were reported. Additionally, the DSCs between automatic segmentation and manually delineated tumours and ALNs were also calculated. During the training process, several steps were followed. Initially, data augmentation strategies were performed, including horizontal flipping, rotation, scaling, and horizontal and vertical shifting. Subsequently, DCE-MRI images with a pixel size of 256 × 256 were normalised before being fed into the segmentation network. Finally, Otsu was adopted to determine the segmentation threshold.

To ensure optimal predictive performance, we compared several network architectures, including ResNet18, ResNet34, and ResNet50. Among these architectures, ResNet50 demonstrated the best performance (Supplementary Tables S2 and S3). Additionally, ResNet50 was integrated with a convolutional block attention module to further enhance its performance in predicting axillary pCR. As the backbone network, ResNet50 comprised four residual blocks, each stacked with multiple convolution layers, zero padding layers, batch normalisation layers, and rectified linear units. Skip connections within residual blocks facilitate information flow between distant layers, strengthening gradient propagation and optimising learning. The convolutional block attention module integrates channel and spatial attention, emphasising effective features while suppressing irrelevant ones. Based on the automated segmentation masks, rectangle boxes with a size of 224 × 224 pixels were used to crop the tumours and ALNs from the original images. Normalisation and data augmentation strategies were also performed. Then, the processed images of tumours and ALNs were sent to the modified ResNet50 to yield axillary pCR prediction scores for DL-T and DL-ALN. The detailed information about MRI data standardisation is presented in Supplementary Material 4, and the detailed training process is presented in Supplementary Material 5.

Clinical model development

In the training and validation sets, we used univariate and multivariate logistic regressions to select independent predictors from age, tumour long-axis diameter, axillary lymph node short-axis diameter, ER status, PR status, HER2 status, Ki67 expression, and molecular subtypes. The logistic regression analysis incorporated a backward stepdown selection method, terminating the iterative process upon reaching the minimum Akaike information criterion. After the selection, characteristics with P < 0.05 were utilised to construct the clinical model through multivariate logistic regression for predicting the probability of axillary pCR after NAC.

FAIS-DL development

Several machine learning algorithms, namely, support vector machine, random forest, k-nearest neighbour, voting, and logistic regression, were employed to integrate the prediction scores from DL-T, DL-ALN, and the clinical model for predicting predict axillary pCR in the training and validation sets (Fig. 2a). The best performing algorithm was used to build the integrated system, namely, FAIS-DL. The output of FAIS-DL was the predicted probability of axillary pCR or non-pCR after NAC.

FAIS-DL was implemented using the Pytorch framework (version 1.6.0, USA) and executed in Python (version 3.6.6, Python Software Foundation, Wilmington, Del). FAIS-DL and source codes are fully available online (https://github.com/yyyhd/FAIS-DL).

Performance evaluation of FAIS-DL

The predictive performance of FAIS-DL was evaluated in the internal, pooled external, and prospective test sets and compared with that of DL-T, DL-ALN, and the clinical model. Moreover, gradient-weighted class activation mapping24 was applied to visualise the areas deemed important by FAIS-DL to understand its decision-making further.

Subgroup analysis and clinical benefit assessment

Different molecular subtypes result in different treatment plans and NAC responses. Thus, we conducted subgroup analysis on luminal A + B, HER2 overexpression and triple-negative types. Moreover, we explored subgroups with tumour long-axis diameter ≤2 and >2 cm. Additionally, for patients predicted by FAIS-DL as axillary pCR, we simulated a scenario where these patients would not undergo ALND. We used the actual pathological results as the gold standard to assess how many patients could benefit from this scenario. Conversely, for patients predicted by FAIS-DL as axillary non-pCR, we simulated a scenario where these patients would undergo ALND. We also used the actual pathological results as the gold standard to assess how many patients could benefit. Furthermore, the proportion of patients who benefited from avoiding and receiving ALND was calculated.

Biological basis exploration

RNA sequencing analysis was conducted on 45 patients with breast cancer from the prospective test set to investigate the underlying biological basis of FAIS-DL in predicting axillary pCR. According to the cutoff value of FAIS-DL in predicting axillary pCR, these patients were divided into high-score and low-score groups. Genes with a log fold change (FC) > 2 and FDR-adjusted P value < 0.05 were identified as differentially expressed between these two groups using the R package DESeq2. Gene Ontology (GO) database (“clusterProfiler” package in R) was employed to compare gene enriched pathways between patients with high and low FAIS-DL scores. Moreover, CibersortX (https://cibersortx.stanford.edu) was utilised to conduct immune microenvironment analyses for estimating the proportions of different cell types within a mixed cellular population. Wilcoxon rank-sum test was adopted to determine differences between the high-score and low-score groups. Additionally, we chose one patient from each of the high and low groups for immunohistochemical staining. Slides were prepared from paraffin-embedded surgical breast tumour specimens and baked at 60 °C for 1 h. They were then deparaffinised in xylene and rehydrated through graded ethanol. Antigen retrieval was performed by heating the slides under high pressure in EDTA antigen retrieval solution (ZSGB-BIO, Catalogue Number: ZLI-9068) for 3 min. Next, the slides were incubated at 37 °C for 1 h with antibodies against CD68 (RRID: AB_3076266) or IBA1 (RRID: AB_3081432). Detection was carried out using a biotin-conjugated secondary antibody and visualised with DAB-chromogen (Ventana Medical System, Catalogue Number: 760-500).

Statistics

Statistical analyses were implemented using R software (version 3.6.2, www.r-project.org) and Python (version 3.6.6). The Chi-square test or Fisher's exact test was used to compare ER, PR, HER2, Ki-67, and molecular subtypes, and the two-sample t-test or Mann–Whitney U test was employed to compare age, the long-axis diameter of tumour, and the short-axis diameter of axillary lymph node between the groups of patients achieving axillary pCR and those achieving axillary non-pCR. DSCs were used to evaluate the segmentation consistency. Receiver operating characteristic (ROC) curves were generated to evaluate the performances of different models, and the area under the ROC curve (AUC) values were calculated accordingly. Decision curve analysis was employed to evaluate the clinical utility, and calibration curves were utilised to assess the consistency between the predicted status of ALN and the actual status of ALN. Sensitivity, specificity, and accuracy were also calculated. The Youden index was used to determine the cutoff value, and the 95% confidence interval (CI) was computed using the bootstrap method (with 1000 bootstrap intervals). DeLong test was used to compare the AUCs between different models.25 For all analyses, two-sided P-values less than 0.05 were considered statistically significant.

Role of funders

The funders had no role in the study design, data collection and analysis, data interpretation, decision to publish, or manuscript preparation. The corresponding authors confirm that they have full access to the data and have the final responsibility for deciding to submit the manuscript for publication.

Results

Clinicopathological characteristics

In the retrospective study, 204 (40.3%) of 506 patients (mean age, 50 years ±10 [SD]) in the training and validation sets, 48 (37.8%) of 127 patients (mean age, 52 years ±9 [SD]) in the internal test set, and 202 (48.8%) of 414 patients (mean age, 49 years ±10 [SD]) in the pooled external test set achieved axillary pCR (Table 1). In the prospective study, 43 (43.9%) of 98 patients (mean age, 53 years ±10 [SD]) achieved axillary pCR. Significant differences were detected between patients with axillary and axillary non-pCR in ER, PR, and HER2 status (P < 0.05, Chi-square test) in all datasets. Molecular subtypes exhibited significant differences across all datasets except for the pooled external test set (P < 0.05, Chi-square test for the training and validation sets, internal test set, and external test set; Fisher's exact test for the prospective test set). Ki-67 index differed within the training and validation sets and prospective test set (P < 0.05, Chi-square test), and age differed between groups only within the training and validation sets (P < 0.05, two-sample t-test). Significant differences in axillary lymph node short-axis diameter were detected only in the pooled external test set (P < 0.05, Mann–Whitney U test), while no significant differences in tumour long-axis diameter were observed across all datasets (Mann–Whitney U test). The detailed clinicopathological characteristics of the patients are presented in Table 1.Table 1 Baseline characteristics in the training and validation sets, internal test set, pooled external test set, and prospective test set.

Characteristic	Training and validation sets (N = 506)	P	Internal test set (N = 127)	P	Pooled external test set (N = 414)	P	Prospective test set (N = 98)	P	
Axillary pCR (N = 204)	Axillary non-pCR (N = 302)	Axillary pCR (N = 48)	Axillary non-pCR (N = 79)	Axillary pCR (N = 202)	Axillary non-pCR (N = 212)	Axillary pCR (N = 43)	Axillary non-pCR (N = 55)	
Age, year (mean ± SD)	49.76 ± 10.06	50.98 ± 9.69	0.175	50.69 ± 8.60	52.66 ± 9.92	0.256	48.75 ± 10.04	49.68 ± 10.37	0.354	48.84 ± 10.19	55.65 ± 9.25	0.001	
Tumour long-axis diameter (mean), cm	3.55	3.28	0.090	3.35	3.53	0.560	3.94	4.00	0.765	3.56	3.55	0.970	
Axillary lymph node short-axis diameter (mean), cm	2.08	2.01	0.274	1.94	2.14	0.419	2.26	1.86	<0.001	2.15	2.08	0.701	
ER (rate%)			<0.001			0.037			<0.001			0.002	
 Positive	115 (56.3)	246 (81.4)		28 (58.3)	60 (75.9)		107 (53)	149 (70.3)		24 (55.8)	46 (83.6)		
 Negative	89 (43.7)	56 (18.6)		20 (41.7)	19 (24.1)		95 (47)	63 (29.7)		19 (44.2)	9 (16.4)		
PR (rate%)			<0.001			<0.001			0.003			<0.001	
 Positive	86 (42.1)	210 (69.5)		16 (33.3)	53 (67.1)		90 (44.6)	125 (59)		17 (40)	42 (76.4)		
 Negative	118 (57.9)	92 (30.5)		32 (66.7)	26 (32.9)		112 (55.4)	87 (41)		26 (60)	13 (23.6)		
HER-2 (rate%)			<0.001			<0.001			<0.001			<0.001	
 Positive	115 (56.3)	72 (23.8)		34 (70.8)	13 (16.5)		138 (68.3)	71 (33.5)		22 (51.2)	8 (14.5)		
 Negative	89 (43.7)	230 (76.2)		14 (29.2)	66 (83.5)		64 (31.7)	141 (66.5)		21 (48.8)	47 (85.5)		
Ki-67 (rate%)			<0.001			0.333			0.325			0.042	
 Positive	181 (88.7)	226 (74.8)		41 (85.4)	62 (78.5)		173 (85.6)	174 (82.1)		36 (83.7)	36 (65.5)		
 Negative	23 (11.3)	76 (25.2)		7 (14.6)	17 (21.5)		29 (14.4)	38 (17.9)		7 (16.3)	19 (34.5)		
Molecular subtypes (rate%)			<0.001			<0.001			0.313			0.005	
 Luminal A	6 (2.9)	40 (13.2)		4 (8.3)	11 (13.9)		13 (6.4)	15 (7.1)		2 (4.6)	14 (25.5)		
 Luminal B	113 (55.4)	207 (68.5)		26 (54.2)	48 (60.8)		122 (60.4)	113 (53.3)		24 (55.8)	33 (60)		
 HER2 overexpression	48 (23.5)	24 (8)		16 (33.3)	6 (7.6)		40 (19.8)	42 (19.8)		6 (14)	3 (5.5)		
 Triple-negative	37 (18.1)	31 (10.3)		2 (4.2)	14 (17.7)		27 (13.4)	42 (19.8)		11 (25.6)	5 (9)		
Note: P < 0.05 indicates that the variable distribution varied significantly between patients with axillary pCR and axillary non-pCR. pCR, pathological complete response; ER, oestrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor.

Performance of the FAIS-DL

The DSCs of tumours were from 0.92 to 0.95 for interobserver manual delineation, and from 0.91 to 0.93 for intraobserver manual delineation. The DSCs of ALNs were from 0.94 to 0.97 for interobserver manual delineation, and from 0.91 to 0.94 for intraobserver manual delineation. FAIS-DL demonstrated satisfactory segmentation performance, with average DSCs ranging 0.82–0.91 for tumours and 0.80–0.90 for ALNs across all datasets. An illustration of automatic segmentation results is shown in Supplementary Figure S1.

Logistic regression outperformed the other four machine learning algorithms and thus was used to construct FAIS-DL (Supplementary Table S4). The FAIS-DL score is calculated as follows: FAIS-DL score = −7.211 + 5.708 × DL-T score +4.128 × DL-ALN score +3.692 × clinical model score. Based on the FAIS-DL score, an optimal cutoff value of 0.469 was generated to classify the patients into high- and low-score groups in the training and validation sets. FAIS-DL showed robust performance in distinguishing axillary pCR with AUCs of 0.98 (95% CI: 0.91, 0.99), 0.95 (95% CI: 0.90, 0.98), 0.93 (95% CI: 0.86, 0.99), and 0.94 (95% CI: 0.88, 0.98) in the training and validation, internal, pooled external, and prospective test sets, respectively (Fig. 3a–d). Satisfactory results were also observed in sensitivity and specificity (Table 2). Moreover, patients with axillary pCR exhibited higher FAIS-DL scores than those with axillary non-pCR in all four datasets (all P < 0.001, Mann–Whitney U test) (Supplementary Figure S2). Additionally, the decision curve analysis suggested that if the threshold probability is in the range of 6.7%–98.5%, 0–91.5%, 4.0%–100%, and 3.1%–100%, the FAIS-DL can achieve higher net benefits than other models in the training and validation sets, internal test set, external test set, and prospective test set (Fig. 3e–h). Calibration curve demonstrated good consistency between the predicted status of ALN and the actual status of ALN (Fig. 3i–l).Fig. 3 Performances of FAIS-DL for predicting axillary pCR after NAC. ROC curves of four models in training and validation sets (a), internal test set (b), pooled external test set (c), and prospective test set (d); Decision curves of four models in training and validation sets (e), internal test set (f), pooled external test set (g), and prospective test set (h); Calibration curves of four models in training and validation sets (i), internal test set (j), pooled external test set (k), and prospective test set (l); Scatter graphs describing the cases corrected by FAIS-DL but falsely predicted by DL-T (m), DL-ALN (n), and the clinical model (o). DeLong test was used to compare the AUCs between different models. ROC, receiver operating characteristic curve; AUC, area under the ROC curve; NAC, neoadjuvant chemotherapy; pCR, pathological complete response; ALND, axillary lymph node dissection; DL-T, deep learning model designed for processing DCE-MRI of tumour; DL-ALN, deep learning model designed for processing DCE-MRI of axillary lymph node; FAIS-DL, fully automated integrated system based on deep learning.

Table 2 Prediction performance of FIS-DL compared with the DL-T, DL-ALN, and clinical model.

	DL-T	DL-ALN	Clinical model	FAIS-DL	
Training and validation sets	
 AUC (95% CI)	0.91 (0.85–0.95)	0.90 (0.84–0.94)	0.85 (0.80–0.91)	0.98 (0.91–0.99)	
 ACC (95% CI)	0.92 (0.86–0.97)	0.90 (0.84–0.95)	0.82 (0.77–0.88)	0.96 (0.90–0.98)	
 SENS (95% CI)	0.93 (0.87–0.97)	0.90 (0.85–0.93)	0.81 (0.75–0.86)	0.95 (0.91–0.97)	
 SPEC (95% CI)	0.89 (0.85–0.93)	0.90 (0.86–0.93)	0.83 (0.79–0.87)	0.97 (0.94–0.98)	
 P	<0.001	<0.001	<0.001		
Internal test set					
 AUC (95% CI)	0.88 (0.82–0.93)	0.80 (0.73–0.86)	0.75 (0.70–0.81)	0.95 (0.90–0.98)	
 ACC (95% CI)	0.84 (0.76–0.90)	0.80 (0.73–0.87)	0.79 (0.72–0.85)	0.90 (0.85–0.93)	
 SENS (95% CI)	0.88 (074–0.95)	0.83 (0.69–0.92)	0.71 (0.56–0.83)	0.92 (0.79–0.97)	
 SPEC (95% CI)	0.82 (0.72–0.89)	0.78 (0.68–0.87)	0.84 (0.73–0.91)	0.89 (0.79–0.94)	
 P	<0.001	<0.001	0.002		
Pooled external test set	
 AUC (95% CI)	0.88 (0.80–0.95)	0.83 (0.72–0.90)	0.72 (0.66–0.78)	0.93 (0.86–0.99)	
 ACC (95% CI)	0.83 (0.76–0.88)	079 (0.73–0.83)	0.72 (0.67–0.79)	0.86 (0.80–0.91)	
 SENS (95% CI)	0.83 (0.77–0.88)	0.79 (0.72–0.84)	0.74 (0.68–0.80)	0.83 (0.77–0.88)	
 SPEC (95% CI)	0.83 (0.77–0.87)	0.79 (0.73–0.84)	0.70 (0.63–0.76)	0.89 (0.84–0.93)	
 P	<0.001	0.011	<0.001		
Prospective test set		
 AUC (95% CI)	0.89 (0.84–0.95)	0.76 (0.71–0.82)	0.77 (0.71–0.83)	0.94 (0.88–0.98)	
 ACC (95% CI)	0.84 (0.78–0.89)	0.80 (0.75–0.85)	0.74 (0.65–0.80)	0.88 (0.82–0.92)	
 SENS (95% CI)	0.84 (0.69–0.93)	0.77 (0.61–0.88)	0.70 (0.54–0.82)	0.98 (0.86–0.99)	
 SPEC (95% CI)	0.84 (0.71–0.92)	0.82 (0.69–0.90)	0.76 (0.63–0.86)	0.80 (0.67–0.89)	
 P	<0.001	<0.001	<0.001		
Note: Data in parentheses are 95% CIs. The P represents the AUCs differences between the FAIS-DL and other models, including DL-T, DL-ALN, and clinical model. AUC, area under the curve; ACC, accuracy; SENS, sensitivity; SPEC, specificity; CI, confidence interval; DL-T, deep learning model designed for processing DCE-MRI of tumour; DL-ALN, deep learning model designed for processing DCE-MRI of axillary lymph node; FAIS-DL, fully automated integrated system based on deep learning.

Comparison with DL-T and DL-ALN

In the internal, pooled external, and prospective test sets, the AUCs for DL-T versus FAIS-DL were 0.88 versus 0.95, 0.88 versus 0.93, and 0.89 versus 0.94 (all P < 0.001, DeLong test), respectively, and the AUCs for DL-ALN versus FAIS-DL were 0.80 versus 0.95, 0.83 versus 0.93, and 0.76 versus 0.94 (all P < 0.001, DeLong test), respectively (Fig. 3b–d). FAIS-DL also has higher accuracy, sensitivity, and specificity than DL-T and DL-ALN (Table 2). In the pooled external and prospective test sets, FAIS-DL could correct 21 (51.2%) patients with axillary pCR and 31 (67.4%) patients with axillary non-pCR who were incorrectly diagnosed by DL-T (Fig. 3m) and 33 (62.3%) patients with axillary pCR and 37 (67.3%) patients with axillary non-pCR who were incorrectly diagnosed by DL-ALN (Fig. 3n).

Comparison with the clinical model

HER2 status (odds ratio: 3.742; 95% CI: 2.422–5.781; P < 0.001) and Ki67 index (odds ratio: 1.916; 95% CI: 1.065–3.449; P = 0.030) were identified as independent risk factors after univariate and multivariable analyses (Table 3). The performance of clinical model was unsatisfactory compared to FAIS-DL, with AUCs of 0.75 versus 0.95, 0.72 versus 0.93, and 0.77 versus 0.94 (all P < 0.05, DeLong test) in the internal, pooled external, and prospective test sets, respectively (Fig. 3b–d and Table 2). Moreover, FAIS-DL could correct 38 (58.5%) patients with axillary pCR and 45 (58.4%) patients with axillary non-pCR who were incorrectly diagnosed by the clinical model in the pooled external and prospective test sets (Fig. 3o).Table 3 Clinicopathological risk factors for predicting axillary pCR after NAC in the training and validation sets.

Variables	Univariate logistic regression	P	Multivariate logistic regression	P	
OR (95% CI)	OR (95% CI)	
Age, years	0.988 (0.970–1.006)	0.175	NA		
Tumour long-axis diameter	1.089 (0.987–1.201)	0.091	NA		
Axillary lymph node short-axis diameter	1.083 (0.892–1.313)	0.421	NA		
ER	0.294 (0.197–0.439)	<0.001	0.852 (0.310–2.343)	0.757	
PR	0.319 (0.220–0.462)	<0.001	0.678 (0.385–1.195)	0.179	
HER2	4.128 (2.815–6.053)	<0.001	3.742 (2.422–5.781)	<0.001	
Ki-67	2.646 (1.596–4.388)	<0.001	1.916 (1.065–3.449)	0.030	
Molecular subtypes	1.894 (1.504–2.384)	<0.001	1.402 (0.766–2.567)	0.273	
Note: ER, oestrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; pCR, pathological complete response; NAC, neoadjuvant chemotherapy; OR, odds ratio; CI, confidence interval.

Subgroup analysis of FAIS-DL

Within the luminal A + B, HER2 overexpression, and triple-negative subgroups, FAIS-DL achieved AUCs of 0.92, 0.96, and 0.93 in the pooled external test set, respectively, and AUCs of 0.93, 0.94, and 0.91 in the prospective test set, respectively (Supplementary Figure S3a, b). For the subgroups with tumour long-axis diameter ≤2 and >2 cm, FAIS-DL achieved AUCs of 0.88 and 0.94 in the pooled external test set, respectively, and 0.85 and 0.94 in the prospective test set, respectively (Supplementary Figure S3c, d).

Clinical benefit assessment

As illustrated in Fig. 2c, 512 patients from the pooled external and prospective test sets received ALND, of whom 245 (47.9%) were pathologically confirmed as axillary pCR and 267 (52.2%) as axillary non-pCR. For patients achieving axillary pCR, 210 (85.7%) were accurately identified, while 231 (87.3%) of those achieving axillary non-pCR were accurately identified. If ALND were performed based on the results of FAIS-DL among these patients, then the rate of unnecessary ALND would decrease from 47.9% to 6.8% (245–35), and the final benefit rate would increase from 52.2% to 86.5% (267–443) (Fig. 4a–c).Fig. 4 Clinical benefit assessment of FAIS-DL. (a) Recommendation for ALND according to FAIS-DL in patients with breast cancer accompanied by ALN metastasis; (b) Rate of unnecessary ALND assessed in two methods: patients all underwent ALND, and patients underwent ALND based on the findings of FAIS-DL; (c) Rate of benefit assessed in two methods: patients all underwent ALND, and patients underwent ALND based on the findings of FAIS-DL. NAC, neoadjuvant chemotherapy; pCR, pathological complete response; ALND, axillary lymph node dissection; FAIS-DL, fully automated integrated system based on deep learning.

Interpretability of FAIS-DL

Fig. 5 provides examples of correct and incorrect predictions made by FAIS-DL. In the correctly predicted examples, heatmaps revealed that the areas assigned great attention by FAIS-DL typically clustered within the tumours and ALNs (Fig. 5a and b). To a certain extent, this observation offers intuitive insights into what the model can learn by focusing on lesion areas. We further presented examples of incorrect prediction by FAIS-DL, emphasising non-tumour regions (Fig. 5c and d). This finding may be due to the insufficient learning of lesion features, ultimately leading to errors.Fig. 5 Visualisation of four examples. (a, b) Heatmaps of examples correctly predicted by FAIS-DL. (a) A 54-year-old woman with breast cancer accompanied by ALN metastasis was pathologically confirmed as axillary pCR after NAC; (b) A 49-year-old woman with breast cancer accompanied by ALN metastasis was pathologically confirmed as axillary non-pCR after NAC. (c, d) Heatmaps of examples incorrectly predicted by FAIS-DL. (c) A 52-year-old woman with axillary pCR after NAC was misclassified as axillary non-pCR; (d) A 49-year-old woman with axillary non-pCR after NAC was misclassified as axillary pCR. DCE-MRI, dynamic contrast-enhanced MRI; ALN, axillary lymph node; NAC, neoadjuvant chemotherapy; pCR, pathological complete response.

Biological function exploration

FAIS-DL divided 45 patients into 26 patients with high FAIS-DL scores and 19 patients with low FAIS-DL scores. Differentially expressed genes associated with immunity, including TMEM26, CCL5, and CXCL13, were discovered (Fig. 6a). GO analysis revealed that immune-related pathways, including modified amino acid metabolism, apical plasma membrane, and oxidoreduction-driven active transmembrane transporter activity, were significantly upregulated in patients with high FAIS-DL scores (Fig. 6b). Moreover, tumours with high FAIS-DL scores demonstrated more infiltrated monocytes (P = 0.036, Wilcoxon rank-sum test) and macrophage M0 cells (P = 0.022, Wilcoxon rank-sum test) compared with those with low FAIS-DL scores (Fig. 6c). Fig. 6d illustrates immunohistochemical staining results for monocytes (stained by CD68 antibody) and M0 macrophages (stained by IBA1 antibody) in two patients. Consistent with the immune microenvironment analyses, the top specimen with a high FAIS-DL score exhibited more monocytes (brown dots) and macrophage M0 cells (brown dots) than the bottom with a low FAIS-DL score in the internal tumour region.Fig. 6 RNA sequencing analysis for investigating the underlying biological basis of FAIS-DL prediction. (a) Volcano diagram of gene expression profiles in samples separated by low and high FAIS-DL scores; (b) Representative upregulated pathways in samples with high FAIS-DL score by gene set enrichment analysis based on GO database; (c) Box plot employed to represent the proportions of distinct cell types within a mixed cellular population; (d) Immunohistochemical staining images of two patients with breast cancer obtaining high and low FASI-DL scores. Wilcoxon rank-sum test was adopted to determine differences between the high-score and low-score groups. Red stars mean P < 0.05. The 53-year-old patient with breast cancer who had a high FAIS-DL score was pathologically confirmed as axillary pCR after NAC. The 59-year-old patient with breast cancer who had a low FAIS-DL score was pathologically confirmed as axillary non-pCR after NAC. FDR, false discovery rate; GO, Gene Ontology; BP, Biological Process; CC, Cell Component; MF, Molecular Function; NAC, neoadjuvant chemotherapy; pCR, pathological complete response; FAIS-DL, fully automated integrated system based on deep learning.

Discussion

In this study, we developed and validated FAIS-DL for early predicting axillary pCR in breast cancer. FAIS-DL demonstrated robust generalisation by integrating the DCE-MRI of tumour and ALN with clinicopathological characteristics. Moreover, our study validated the effectiveness of utilising FAIS-DL to guide ALND, emphasising its potential role in supporting axillary surgical decision-making. Further, high FAIS-DL scores were found to be associated with immune response up-regulation.

DCE-MRI is frequently used to assess the effectiveness of NAC in breast cancer. However, its ability to predict axillary pCR exhibits significant variation, with sensitivity between 55% and 88% and specificity between 50% and 82%.26, 27, 28 Although several studies attempted to incorporate DCE-MRI with clinicopathological characteristics for predicting axillary pCR,29,30 the performances of these models fall short of meeting clinical requirements. Recent studies demonstrated that radiomics can predict axillary pCR,21,22 however, the challenges posed by manual image segmentation and hand-crafted features hindered clinical implementation. Several studies reported that multitask deep learning networks may offer potential solutions for these deficiencies.18,31 However, as far as we know, this approach has not been implemented to predict axillary pCR. In this study, we developed a fully automated and multitask architecture that not only effectively generates reliable lesion contours but also automatically performs data analyses. Specifically, by inputting routinely available clinicopathological information, DCE-MRI of breast tumour, and DCE-MRI of ALN into FAIS-DL before NAC, tumour and ALN masks can be automatically segmented, and prediction results (axillary pCR or axillary non-pCR) can be obtained. Thus, our approach holds great potential for improving efficiency and facilitating clinical translation.

In a previous study, Gu J et al.21 used an ultrasonography-based deep learning model to predict axillary pCR; however, using tumour images alone may not comprehensively evaluate axillary pCR. The interaction between primary tumour and ALN microenvironment plays a crucial role in ALN metastasis. Our previous study also revealed that the deep learning model based on multiregional DCE-MRI outperformed that based solely on the DCE-MRI of tumour or ALN in predicting ALN metastasis.32 Therefore, we hypothesised that integrating DCE-MRI from tumour and ALN could enhance the predictive accuracy of deep learning models in predicting axillary pCR. In the present work, we developed FAIS-DL that utilises multiregional DCE-MRI to predict axillary pCR. As expected, it performed better than DL-T, DL-ALN, and the clinical model.

According to the guidelines of TRIPOD, SPIRIT-AI, and CONSORT-AI, applying artificial intelligence in clinical settings must undergo rigorous and prospective evaluation. However, to our best knowledge, no studies have prospectively tested the efficacy of deep learning models in predicting axillary pCR. Given these problems, the present work assessed FAIS-DL in multicentre and prospective test sets. Moreover, in contrast to previous studies using a single-centre training strategy,18,19 we adopted a multicentre approach for training, allowing the models to encompass global diversity and thus improving their generalisability.

Deep learning is frequently labelled as a “black box” due to its intricate structures and extensive number of parameters.33 Helping doctors understand these nameless features and the potential predictive mechanism is challenging and intriguing. To gain insights into the decision-making mechanism of FAIS-DL, we employed Grad-CAM for visualisation. RNA sequencing analysis revealed the link of FAIS-DL to immunity pathways, supporting that individuals with high FAIS-DL scores are likely to achieve axillary pCR. Several studies utilised immune microenvironment analysis to compare the proportions of infiltrated immune cells between high- and low-score patients.34, 35, 36 Although significant differences were observed, the actual infiltration levels remained unclear. We further used immunohistochemical staining for the evaluation and obtained consistent findings. In addition, the abundance of monocytes and macrophage M0 cells may be associated with enhanced immune response,37,38 which is aligned with our findings.

Despite the promising predictions, limitations still exist in this study. First, selection bias was inevitable in this retrospective study despite the inclusion of a prospective test set. Whether our findings can be generalised to other territories remains unknown; therefore, an international clinical trial is required to confirm our results. Second, while ALND was used as the gold standard in this study, it is associated with an increased risk of severe arm morbidity for patients. Sentinel lymph node biopsy (SLNB) with dual mapping holds great potential to assess axillary status after NAC and significantly reduces the risks associated with postoperative complications.39,40 Given that SLNB with dual mapping may provide more benefits, patients undergoing this procedure will be included in future studies to enhance clinical applicability. Third, this study included only patients diagnosed with breast cancer and ipsilateral ALN metastasis. Future studies will incorporate patients with multifocal or bilateral breast tumours or positive bilateral ALNs to enhance the generalization ability of FAIS-DL. Fourth, this study only utilised peak enhanced phase images. Thus, further investigations into the potential value of other MRI sequences are warranted. Fifth, we employed a standard breast MRI scanning protocol, which may have limited axillary coverage. Therefore, future studies will explore the inclusion of an axillary sequence to achieve a more comprehensive assessment of axillary status. Finally, bulk RNA sequencing may result in the loss of information regarding gene expression heterogeneity. In the future, studies must integrate bulk and single-cell RNA sequencing to achieve a comprehensive understanding of biological processes.

In this study, FAIS-DL showed favourable performance as a potential method to assess axillary pCR. The reduction in unnecessary ALND rate and the increase in benefit rate demonstrated its clinical applicability. Furthermore, the potential biological basis of FAIS-DL may be linked to pathways that modulate immune responses. In the future, studies incorporating extensive data from varying nations and ethnicities should be performed to enhance the predictive performance and generalisation ability for further clinical application.

Contributors

ZL, JG, HZ, QN, HM, and NM were responsible for concept and design. ZL, JG, and HZ provided statistical analysis. All authors were involved in drafting and technical support in deep learning methods. XL, TZ, FL, XW, TC, QW, SW, KC, YL, FZ, HX, CX, and HZ were responsible for acquisition, analysis, or interpretation of data. ZL, JG and HZ were involved in drafting the manuscript. QN, HM, and NM had full access to all the data and verified the underlying data. All authors were involved in reviewing the manuscript and approved the final manuscript for submission.

Data sharing statement

Readers can contact the corresponding author via email to request access to the DCE-MRI imaging data and clinicopathological information utilised in this study for academic research purposes. The network and source codes are provided at GitHub: https://github.com/yyyhd/FAIS-DL.

Declaration of interests

There are no conflicts of interest to declare.

Appendix A Supplementary data

Supplemental Data

Acknowledgements

We thank all the study participants. The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. This study was supported by the 10.13039/501100001809 National Natural Science Foundation of China (82371933 ), National 10.13039/501100007129 Natural Science Foundation of Shandong Province of China (ZR2021MH120 ), Mount Taishan Scholars and Young Experts Program (tsqn202211378 ), Key Projects of China Medicine Education Association (2022KTM030 ), 10.13039/501100002858 China Postdoctoral Science Foundation (314730 ), and Beijing Postdoctoral Research Foundation (2023-zz-012 ).

Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.ebiom.2024.105311.
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