
==== Front
Breast
Breast
The Breast : Official Journal of the European Society of Mastology
0960-9776
1532-3080
Elsevier

S0960-9776(24)00117-6
10.1016/j.breast.2024.103786
103786
Original Article
Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data
Yu Yushuai ab1
Chen Ruiliang a1
Yi Jialu a
Huang Kaiyan c
Yu Xin b
Zhang Jie zjie1979@fjmu.edu.cn
a⁎⁎
Song Chuangui songcg1971@outlook.com
ab⁎
a Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, China
b Department of Breast Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, 350014, China
c Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
⁎ Corresponding author. Fujian Medical University Union Hospital No.29, Xin Quan Road, Gulou District, Fuzhou, Fujian Province, 350001, China. songcg1971@outlook.com
⁎⁎ Corresponding author. zjie1979@fjmu.edu.cn
1 These authors have contributed equally to this work and share first authorship.

09 8 2024
10 2024
09 8 2024
77 10378623 4 2024
15 7 2024
8 8 2024
© 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/).
Purpose

In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise method to assess the axillary lymph node (ALN) status in these patients.

Materials and methods

A retrospective analysis was conducted on 160 BC patients undergoing NAT at Fujian Medical University Union Hospital. We analyzed baseline and two-cycle reassessment dynamic contrast-enhanced MRI (DCE-MRI) images, extracting 3668 radiomic and 4096 deep learning features, and computing 1834 delta-radiomic and 2048 delta-deep learning features. Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), RandomForest, and Multilayer Perceptron (MLP) algorithms were employed to develop risk models and were evaluated using 10-fold cross-validation.

Results

Of the patients, 61 (38.13 %) achieved ypN0 status post-NAT. Univariate and multivariable logistic regression analyses revealed molecular subtypes and Ki67 as pivotal predictors of achieving ypN0 post-NAT. The SVM-based “Data Amalgamation” model that integrates radiomic, deep learning features, and clinical data, exhibited an outstanding AUC of 0.986 (95 % CI: 0.954–1.000), surpassing other models.

Conclusion

Our study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based “Data Amalgamation” model, we propose a way towards accurate, dynamic ALN assessments, offering potential for personalized therapeutic strategies in BC.

Highlights

• Feasible exemption of axillary lymph node dissection post-NAT.

• Developed a precise method for assessing axillary lymph node status post-NAT.

• Model using longitudinal DCE-MRI data improves surgical risk stratification.

Keywords

Axillary lymph node dissection exemption
Neoadjuvant therapy
Breast cancer
Radiomics
Deep learning
Longitudinal data
==== Body
pmc1 Introduction

Neoadjuvant therapy (NAT) is an increasingly pivotal treatment approach for breast cancer (BC), especially for patients presenting with clinical axillary lymph node metastasis (cN+) [[1], [2], [3]]. Historically, NAT's primary aim was tumor downstaging prior to surgery, enhancing surgical options for otherwise inoperable cases [4]. However, the variability in pathological complete nodal response (ypN0) rates post-NAT, ranging from 41 % to 74 %, points to the potential of sparing a significant number of patients from axillary lymph node dissection (ALND) [[5], [6], [7]].

Despite the importance of ALND for tumor burden reduction and axillary staging, it may lead to complications such as lymphedema, neuropathic pain, and upper limb functional impairment [8,9]. However, the clinical application of sentinel lymph node biopsy (SLNB) for cN + patients post-NAT remains controversial due to the high false-negative rate (FNR) and limited long-term follow-up data [[10], [11], [12]]. Previous clinical research findings have suggested that in this patient population, the adoption of SLNB should be combined with dual tracer (including radioisotope), targeted axillary lymph node dissection (TAD), and ensuring the removal of more than three lymph nodes (LNs) [[10], [11], [12], [13], [14]]. However, these criteria pose several challenges in clinical practice. Difficulties arise in procuring and managing the required isotopes, accurately targeting marker insertion, and ensuring that the number of removed LNs deviate from the anatomical principles guiding SLNB [15,16]. Consequently, there is a critical demand for a precise method to assess the axillary lymph node (ALN) status in these patients, which is of paramount clinical importance for enhancing surgical risk stratification and optimizing patient management.

Precisely assessing ALN status post-NAT remains a clinical conundrum. Current imaging modalities, including dynamic contrast-enhanced MRI (DCE-MRI), are unable to consistently predict post-NAT ALN metastasis [17]. With the rise of radiomics and deep learning, several models have showcased promising prediction abilities [[18], [19], [20], [21], [22], [23]]. Given DCE-MRI's pivotal role in assessing NAT efficacy, we hypothesize that leveraging radiomics and deep learning can extract more nuanced, quantitative features from DCE-MRI scans at multiple treatment stages. Notably, a gap exists in models that harness DCE-MRI data both pre-treatment and early evaluation of therapeutic response for ypN0 prediction.

This study seeks to evaluate the feasibility of ALND exemption post-NAT and to advance the field with a comprehensive machine learning approach based on longitudinal DCE-MRI data. The results have the potential to impact surgical risk stratification and optimize patient management in BC treatment.

2 Material and methods

2.1 Data source and study population

Ethical approval for this study was obtained from the Ethics Committee of Fujian Medical University Union Hospital (2021WSJK043). We retrospectively recruited patients with BC from Fujian Medical University Union Hospital. We included patients based on the following criteria: (1) histologically proven primary invasive BC; (2) clinical stage T1-3N1-2M0; (3) receiving NAT treatment following by mastectomy or breast-conserving surgery; (4) performing ALND (with or without SLNB). The exclusion criteria were as follows: (1) clinical stage N3; (2) clinical stage M1; (3) poor DCE-MRI images quality and lack of available DCE-MRI data. Regarding the definitions of cN3 and cM1, cN3 encompasses ipsilateral infraclavicular, ipsilateral internal mammary and axillary, or ipsilateral supraclavicular lymph node metastases. Whereas cM1 refers to clinically or radiologically detectable metastatic lesions, or histologically confirmed lesions larger than 0.2 mm. Given the necessity for comprehensive systemic treatment in such cases and the limited impact of local axillary surgical intervention on long-term prognosis, we have excluded these patients from our study. More details about DCE-MRI images quality were provided in Supplement 1. All patients received the standard NAT regimen, which was based on anthracycline and/or taxane drugs. Patients with human epidermal growth factor receptor 2 positive (HER2+) status received targeted therapy with either trastuzumab or a combination of trastuzumab and pertuzumab. For hormone receptor-positive (HR+) and HER2 negative (HER2-) breast cancer, according to current guidelines, hormone therapy is not added to neoadjuvant treatment for this patient population. All patients underwent ALND following the completion of NAT. For patients who were intended to undergo SLNB, only the single-tracer method using blue-dye was performed due to the unavailability of radioisotope in our hospital.

Clinical and pathological staging were defined according to the 8th edition of the American Joint Committee on Cancer (AJCC) Cancer Staging Manual [24]. Tumor and ALN specimens were evaluated by pathologists. LNs with no residual tumor detected in the final pathology were considered as achieving ypN0, while those with residual tumor were categorized as non-pCR. The design and workflow of the study are illustrated in Fig. 1.Fig. 1 The flowchart of machine learning model development process.

Fig. 1

2.2 Image Acquisition and Processing

All patients underwent their first DCE-MRI examination within 2 weeks before the start of NAT, and the second DCE-MRI examination was conducted within 2 weeks after completing the second cycle of NAT. All DCE-MRI scans were performed using 1.5- or 3.0-T scanners (Siemens, Bavaria, Germany). The details of DCE-MRI Scan protocol and parameters were provided in Supplement 2.

Two radiologists used the IKT-SNAP software (version 4.0.1, http://www.itksnap.org) to delineate the regions of interest (ROI) for LNs with suspected metastasis. We selected the DCE-MRI phase that most effectively highlights tumor contrast enhancement for importing into ITK-SNAP to delineate the Region of Interest (ROI). This strategic choice was made to ensure the capture of diagnostically vital information, focusing on the most relevant phase for accurate and meaningful analysis. Prior to commencing NAT, we conducted ultrasound-guided fine-needle aspiration cytology or ultrasound-guided core needle biopsy on the LNs identified as most suspicious by radiologists based on color Doppler ultrasound examination. The target LN was identified on the initial DCE-MRI scan based on its morphological features and spatial orientation observed during ultrasound examination. Moreover, it had been previously confirmed to be metastatic through a biopsy. Subsequently, the same LN was located on the second DCE-MRI scan, utilizing the initial position and adjacent anatomical structures from the initial DCE-MRI as a reference (Fig. 2). To ensure feature reliability, we randomly selected 50 patients and had two different radiologists perform ROI segmentation twice for each patient. We then calculated interclass correlation coefficients for the extracted features.Fig. 2 The red color covered region denoting the target LN. (a) DCE-MRI examination for the target LN before NAT; (b) Ultrasound examination for the target LN before NAT; (c) DCE-MRI examination for the target LN after completing the second cycle of NAT; (d) Ultrasound examination for the target LN after completing the second cycle of NAT.

Note: The target LN, previously confirmed as metastatic via biopsy, was identified on the initial DCE-MRI scan using its morphological features and ultrasound observations. The same LN was later located on the second DCE-MRI using the initial scan and nearby anatomical structures as references. LN: lymph node; DCE-MRI: dynamic contrast-enhanced MRI; NAT: Neoadjuvant therapy.

Fig. 2

3 Data analysis and model development

3.1 Radiomic features analysis

Radiomic features were extracted using the Pyradiomics Module, a widely used open-source tool designed for radiomic analysis (https://github.com/Radiomics/pyradiomics). These features can be categorized into eight distinct groups, capturing various aspects of the tumor's characteristics: (1) geometric features, which encompass shape and size measurements; (2) intensity features, representing pixel intensity values; (3) texture features, revealing spatial patterns within the image; (4) morphological features, providing insights into the structural characteristics of the tumor; (5) wavelet features, describing the frequency content of the image; (6) histogram features, depicting the distribution of pixel intensities; (7) run-length features, measuring the length of consecutive pixels with similar intensity; and (8) Gray Level Co-occurrence Matrix (GLCM) features, exploring the spatial relationship of pixel pairs. In total, 1834 radiomic features were carefully extracted from the DCE-MRI sequences of each ROI. This comprehensive collection of features allowed for a detailed representation of the tumor's underlying properties, facilitating the subsequent analysis. Furthermore, in order to assess the changes in the metastatic LN during NAT, we calculated delta radiomic features (Delta data). These features represented the relative net change from the Baseline radiomic feature values to the Two-cycle-reassessment radiomic feature values, providing valuable information on the tumor's response to the therapeutic intervention. In total, each patient's data incorporated 5502 radiomic features, combining baseline, two-cycle reassessment, and delta radiomic features. This ensured a thorough evaluation and enhanced the reliability of our study results. The use of the Pyradiomics Module ensured standardized and efficient radiomic feature extraction, adding to the rigor and reproducibility of our study.

3.2 Deep learning features analysis

We pretrained the ResNet-50 using a supervised learning approach using the ImageNet dataset. ImageNet dataset (www.image-net.org), encompassing about more than 14 million images distributed over 1000 distinct classes, and the results demonstrated robust performance in the classification task. ResNet-50 was specifically selected to develop our deep learning model for its ability to discern subtle distinctions within images and its depth allows for the extraction of intricate, nuanced features. Our choice of loss function was the cross-entropy function. Specifically, we selected the largest slice containing the metastatic LN as the input image for ROI including intratumoral regions and peritumoral regions, encompassing both Baseline and Two-cycle-reassessment DCE-MRI images. Our focus was on the DCE-MRI phase that best highlighted tumor contrast enhancement, ensuring that the most diagnostically relevant information was captured for analysis. To focus on the ROI and eliminate unnecessary background noise, the input images were cropped accordingly. Additionally, we normalized the pixel values of the images to a standardized scale (0–1000) to enhance model convergence and stability during training. To mitigate the risk of overfitting, we incorporated L2 regularization and early stopping techniques into our deep learning model. This helped prevent excessive adaptation to the training data and ensured better generalization to new, unseen data.

Each preprocessed DCE-MRI image, resampled to 448 × 448 pixels, was utilized as an individual input for the development of a deep learning model constructed on the ResNet-50 framework. After the training phase of the deep learning model, we connected its output to a global average pooling (GAP) layer. This process facilitated the extraction of a vector of 2048 features from the model, empowering us to conduct image classification and various other tasks with heightened accuracy and efficiency [25]. A total of 4096 deep learning features were obtained from the Baseline and Two-cycle-reassessment DCE-MRI images, providing valuable insights into the underlying patterns and characteristics of the ROI. Moreover, we calculated the net change of deep learning features (Delta data). In conclusion, each patient's data was enriched with a comprehensive set of 6144 deep learning features, combining baseline, two-cycle-reassessment, and delta deep learning features. This comprehensive feature set enabled a more thorough and detailed analysis, contributing to the accuracy and reliability of our research findings. The detailed information on deep learning model construction and feature extraction is provided in Supplement 3.

3.3 Model development

We integrate ‘Baseline Images’, ‘Two-cycle-reassessment Images’, ‘Delta Data’, and clinical-pathological factors into what we term as ‘Data Amalgamation’ category. This ‘Data Amalgamation’ refers to the merging of data from multiple sources and formats into a cohesive and organized data category, ensuring comprehensive analysis. Firstly, we utilized rigorous feature screening methods to select the most informative radiomic and deep learning features. The Mann-Whitney U test was employed to retain features with a significant p-value (<0.05). Additionally, to ensure robustness, we considered features with high repeatability, keeping those with a Spearman's rank correlation coefficient greater than 0.9 between any two features. Applying the LASSO regression model on the discovery dataset with 10-fold cross-validation, we constructed a radiomics signature by effectively shrinking regression coefficients based on the regularization weight λ. This process eliminated irrelevant features with coefficients set to zero, enabling us to retain only the most relevant features for model fitting. Secondly, we utilized various machine learning algorithms, including Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), RandomForest, and Multilayer Perceptron (MLP), to construct risk models based on four data categories: ‘Baseline Images’, ‘Two-cycle-reassessment Images’, ‘Delta Data’, and ‘Data Amalgamation’. These models incorporated the selected clinical-pathological factors, radiomic and deep learning features and were trained to differentiate between ypN0 and non-ypN0 labels in a supervised learning context. Thirdly, to mitigate overfitting and provide a more reliable performance estimate, the resampling method known as k-fold cross-validation was employed [26]. To ensure a more robust evaluation of the model given the small sample size, we adopted a 10-fold cross-validation approach. Specifically, we divided the dataset into 10 subsets, using 9 subsets for training and the remaining 1 subset for testing in each iteration. This process was repeated 10 times, and the performance metrics were averaged to provide a more stable estimate of model performance. This method maximizes data utilization and mitigates variability in performance estimates due to random data splits. The details about machine learning algorithms were provided in Supplement 3.

3.4 Statistical analysis

The statistical analyses were performed using Python (version 3.8; Python Software Foundation) and SPSS (version 26.0; IBM Corp). Continuous variables were presented as mean ± standard deviation, while categorical variables were described as frequencies and percentages. Group comparisons for continuous variables were conducted using t-test. Chi-square test was used to compare demographic and clinical characteristics among different groups. Logistic regression models were employed to identify predictive factors associated with achieving ypN0. The performance of all machine learning algorithms was evaluated for average over classes, using various metrics including accuracy, sensitivity, specificity, NPV, PPV, F1 score and the area under the receiver operating characteristic curve (AUC). We conducted decision curve analysis (DCA) on the established model to evaluate its clinical utility. This analysis assessed the net benefits across different threshold probabilities. Statistical significance was considered at p < 0.05 for all tests, indicating the presence of significant differences between groups or associations between variables.

4 Results

4.1 Baseline characteristics of patients

We enrolled 160 eligible patients from the Department of Breast Surgery, Fujian Medical University Union Hospital. Summarized clinical characteristics of the cohort are presented in Table 1. Out of these patients, 61 (38.13 %) achieved ypN0 status, while the remaining 99 (61.88 %) did not achieve this status. HER2+ BC exhibited the highest ypN0 rate at 66.13 % (41/62), whereas HR + HER- subtype had the lowest rate at 12.35 % (10/81). Significant differences were observed between the ypN0 and non-ypN0 groups in terms of Family history, Grade, Molecular subtypes, Ki67, and NAT regimen.Table 1 Baseline characteristics of patients.

Table 1Characteristics	non-ypN0 (n = 99)	ypN0 (n = 61)	Pa	
Age, mean (SD), year	48.04 (10.30)	49.85 (9.36)	0.27	
Menopausal state	Postmenopause	45	45.5 %	30	49.2 %	0.65	
Premenopause	54	54.5 %	31	50.8 %		
Family history	No	98	99.0 %	56	91.8 %	0.02	
Yes	1	1.0 %	5	8.2 %		
Laterality	Left	49	49.5 %	37	60.7 %	0.17	
Right	50	50.5 %	24	39.3 %		
Grade	I	11	11.1 %	1	1.6 %	<0.001	
II	72	72.7 %	30	49.2 %		
III	9	9.1 %	9	14.8 %		
NA	7	7.1 %	21	34.4 %		
Molecular subtypes	HR+, HER2-	71	71.7 %	10	16.4 %	<0.001	
HR+, HER2+	15	15.2 %	20	32.8 %		
HR-, HER2+	6	6.1 %	21	34.4 %		
TNBC	7	7.1 %	10	16.4 %		
Ki67	<15	12	12.1 %	1	1.6 %	<0.001	
15–35	32	32.3 %	10	16.4 %		
35–55	35	35.4 %	15	24.6 %		
≥55	20	20.2 %	35	57.4 %		
Clinical tumor status	T1	9	9.2 %	10	16.4 %	0.28	
T2	80	80.8 %	43	70.5 %		
T3	10	10.1 %	8	13.1 %		
Clinical nodal status	N1	94	94.9 %	56	91.8 %	0.43	
N2	5	5.1 %	5	8.2 %		
NAT regimen	HER2-targeted based regimens	21	21.2 %	42	68.9 %	<0.001	
Combination chemotherapy (anthracycline followed by taxane)	60	60.6 %	11	18.0 %		
Taxane based regimens	9	9.1 %	0	0.0 %		
Combination chemotherapy (with carboplatin)	9	9.1 %	8	13.1 %		
Surgery approach	Mastectomy	92	92.9 %	55	90.2 %	0.534	
BCS	7	7.1 %	6	9.8 %		
BMI, mean (SD)	23.93 (3.20)	23.95 (3.17)	0.98	
PLT, mean (SD), 10^9/L	267.19 (74.87)	274.03 (56.09)	0.54	
ANC, mean (SD), 10^9/L	3.95 (1.43)	3.97 (1.51)	0.92	
ALC, mean (SD), 10^9/L	2.03 (0.60)	2.08 (0.55)	0.57	
CEA, mean (SD), U/Ml	2.96 (3.49)	3.85 (8.44)	0.36	
CA153, mean (SD), U/Ml	17.59 (11.74)	14.93 (11.49)	0.16	
CA125, mean (SD), U/Ml	17.25 (13.76)	14.79 (7.72)	0.21	
Abbreviation: ypN0: pathological complete nodal response; SD: Standard Deviation; HR: Hormone receptor; HER2+: Human epidermal growth factor receptor-2 positive; TNBC: Triple negative breast cancer; BCS, Breast-conserving surgery; BMI: Body mass index; PLT: Platelet; ANC: Absolute neutrophil count; ALC: Absolute lymphocyte count; NA: Not available.

a The P value of the Chi-square test or t-test was calculated between the ypN0 and non-ypN0 groups, and bold type indicates significance.

Univariate and multivariable logistic regression analyses revealed molecular subtypes and Ki67 as pivotal predictors of achieving ypN0 post-NAT (Table 2). Notably, compared to triple-negative breast cancer (TNBC), HR+, HER2- BC had a significantly lower ypN0 rate (OR, 0.186; 95 % CI, 0.049–0.714; p = 0.014). High Ki67 levels also emerged as significant factors for axillary pCR (Ki: 15–35 vs. Ki: ≥55, OR = 0.210, 95 % CI: 0.066–0.662, p = 0.008; Ki: 35–55 vs. Ki: ≥55, OR = 0.251, 95%CI: 0.089–0.708, p = 0.009). Among patients undergoing the combined SLNB + ALND procedure, the FNRs was 0.183 (95 % CI: 0.0992–0.267). Consequently, for hospitals unable to employ dual tracer and TAD techniques, SLNB is not recommended.Table 2 Predictors of axillary lymph node pathological complete response to neoadjuvant therapy using univariate and multivariable logistic regression analysis.

Table 2Characteristics	Total (N)	Univariate analysis	Multivariate analysis	
Odds Ratio (95 % CI)	P value	Odds Ratio (95 % CI)	P value	
Age	160	1.019 (0.986–1.052)	0.264			
Menopausal state	160					
Premenopause	85	Reference				
Postmenopause	75	1.161 (0.613–2.200)	0.647			
BMI	160	1.002 (0.906–1.108)	0.975			
Family history	160					
No	154	Reference		Reference		
Yes	6	8.750 (0.997–76.788)	0.050	9.834 (0.618–156.503)	0.105	
Laterality	160					
Right	74	Reference				
Left	86	1.573 (0.823–3.006)	0.170			
PLT	160	1.001 (0.997–1.006)	0.538			
ANC	160	1.012 (0.813–1.261)	0.915			
ALC	160	1.176 (0.678–2.042)	0.564			
CEA	160	1.026 (0.968–1.088)	0.386			
CA153	160	0.978 (0.948–1.009)	0.168			
CA125	160	0.979 (0.948–1.012)	0.214			
Clinical tumor status	160					
T2	123	Reference				
T3	18	1.488 (0.547–4.049)	0.436			
T1	19	2.067 (0.781–5.474)	0.144			
Clinical nodal status	160					
N1	150	Reference				
N2	10	1.679 (0.465–6.055)	0.429			
Surgery	160					
Mastectomy	147	Reference				
BCS	13	1.434 (0.458–4.485)	0.536			
Grade	160					
III	18	Reference		Reference		
II	102	0.417 (0.151–1.152)	0.092	0.644 (0.185–2.238)	0.489	
I	12	0.091 (0.010–0.858)	0.036	0.711 (0.059–8.493)	0.787	
NA	28	3.000 (0.852–10.567)	0.087	2.592 (0.556–12.085)	0.225	
Molecular subtypes	160					
TNBC	17	Reference		Reference		
HR+, HER2-	81	0.099 (0.031–0.318)	< 0.001	0.186 (0.049–0.714)	0.014	
HR+, HER2+	35	0.933 (0.288–3.023)	0.908	1.360 (0.353–5.249)	0.655	
HR-, HER2+	27	2.450 (0.651–9.219)	0.185	3.500 (0.781–15.673)	0.102	
Ki67	160					
≥55	55	Reference		Reference		
15–35	42	0.179 (0.073–0.438)	< 0.001	0.210 (0.066–0.662)	0.008	
35–55	50	0.245 (0.108–0.554)	< 0.001	0.251 (0.089–0.708)	0.009	
<15	13	0.048 (0.006–0.394)	0.005	0.204 (0.021–2.014)	0.174	
Abbreviation: HR: Hormone receptor; HER2+: Human epidermal growth factor receptor-2 positive; TNBC: Triple negative breast cancer; BCS: Breast-conserving surgery; BMI: Body Mass Index; PLT: Platelet; ANC: Absolute neutrophil count; ALC: Absolute lymphocyte count; NA: Not available.

4.2 Features analysis

A total of 3668 radiomic features and 4096 deep learning features were extracted from the baseline and two-cycle reassessment DCE-MRI images. Additionally, 1834 delta-radiomic features and 2048 delta-deep learning features were obtained. The radiomic and Deep Learning features were finalized after applying the appropriate selection methods. We employed the LASSO logistic regression model to construct the Rad score, selecting non-zero coefficients. The coefficients and Mean Squared Error (MSE) from the 10-fold cross-validation are illustrated in Supplement 4 (a-j). The coefficient values of the ultimately selected non-zero features are displayed in Supplement 4 (k-o). The clinical-pathological models incorporated features including family history of breast cancer, specific neoadjuvant chemotherapy regimens, molecular subtypes, and Ki67 expression levels. For the ‘Baseline Images’ category, we selected 11 radiomic and 20 deep learning features. Similarly, the ‘Two-cycle-reassessment Images’ category incorporated 6 radiomic and 14 deep learning features. The ‘Delta data’ category included 11 radiomic and 19 deep learning features. Lastly, the ‘Data Amalgamation’ category combined 11 radiomic with 19 deep learning features. All the selected features showed no high correlation to the other features (Supplement 5).

4.3 Models performance

We employed machine learning algorithms, specifically ‘LightGBM’, ‘SVM’, ‘RandomForest’, and ‘MLP’, to develop a comprehensive suite of 20 prediction models, with each tailored to distinct data categories. With the 10-fold cross-validation approach, the performance metrics (accuracy and AUC) of these models are summarized in Table 3. Additional metrics, including sensitivity, specificity, NPV, PPV, and F1 score of the various machine learning models, are provided in Supplement 6. The average AUC values show SVM-based “Data Amalgamation” model consistently demonstrated remarkable efficacy, outperforming other classifiers (Fig. 3). Moreover, models integrating baseline DCE-MRI images, two-cycle-reassessment DCE-MRI images, changes between the two time points, and clinical-pathological factors (“Data Amalgamation” category) exhibited significantly enhanced predictive capabilities compared to models reliant solely on individual datasets. The models built on different data categories using various algorithms show nearly similar average AUC values, ranging from 0.710 to 0.986 (Supplement 7). However, the SVM algorithm yielded distinct AUC values for diverse data categories: “Clinical Characteristics” category - AUC of 0.871 (95 % CI: 0.767–0.976), “Baseline Images” category - AUC of 0.900 (95 % CI: 0.729–1.000), “Two-cycle-reassessment Images” category - AUC of 0.802 (95 % CI: 0.643–0.961), “Delta Data” category - AUC of 0.870 (95 % CI: 0.744–0.995), and “Data Amalgamation” category - AUC of 0.986 (95 % CI: 0.954–1.000) (Fig. 3a). The DeLong test indicated that all these values significantly lagged behind the “Data Amalgamation” Model (p < 0.05). Remarkably, in the ‘Data Amalgamation’ category, the SVM algorithm stood out, exhibiting superior performance (Fig. 3b). Within this dataset, the SVM algorithm attained the highest AUC value of 0.986 (95 % CI: 0.954–1.000) among all models. In comparison, the AUC values for the LightGBM, RandomForest, and MLP algorithms were 0.928 (95 % CI: 0.832–1.000), 0.790 (95 % CI: 0.633–0.946), and 0.971 (95 % CI: 0.922–1.000), respectively. Furthermore, DCA has gained acceptance for evaluating the clinical utility of models. As illustrated in Fig. 4, when trained using the SVM machine learning algorithm on the “Data Amalgamation” category, it significantly outperforms the other models, underscoring its substantial clinical practicality in predicting ypN0 post-NAT. Therefore, it becomes evident that the “Data Amalgamation” category, constructed through the SVM algorithm, stands as the prime choice for this study.Table 3 Performances of different machine learning models for predicting axillary lymph node pathological complete response to neoadjuvant therapy.

Table 3Algorithm	Data category	Accuracy	AUC 95 % CI	
LightGBM	Clinical characteristics	0.854	0.925 (0.843–1.000)	
Baseline images	0.625	0.750 (0.498–1.000)	
Two cycle reassessment images	0.719	0.797 (0.619–0.976)	
Delta dataa	0.688	0.710 (0.507–0.914)	
Data Amalgamationb	0.875	0.928 (0.832–1.000)	
SVM	Clinical characteristics	0.771	0.871 (0.767–0.976)	
Baseline images	0.812	0.900 (0.729–1.000)	
Two cycle reassessment images	0.719	0.802 (0.643–0.961)	
Delta dataa	0.750	0.870 (0.744–0.995)	
Data Amalgamationb	0.938	0.986 (0.954–1.000)	
RandomForest	Clinical characteristics	0.833	0.833 (0.800–0.957)	
Baseline images	0.688	0.842 (0.642–1.000)	
Two cycle reassessment images	0.781	0.756 (0.586–0.926)	
Delta dataa	0.719	0.734 (0.549–0.919)	
Data Amalgamationb	0.750	0.790 (0.633–0.946)	
MLP	Clinical characteristics	0.812	0.855 (0.750–0.961)	
Baseline images	0.688	0.867 (0.674–1.000)	
Two cycle reassessment images	0.812	0.850 (0.717–0.984)	
Delta dataa	0.750	0.845 (0.704–0.987)	
Data Amalgamationb	0.906	0.971 (0.922–1.000)	
Note.

a “Delta data” represented the relative net change from the Baseline radiomic and deep learning feature values to the Two cycle reassessment radiomic and deep learning feature values.

b “Data Amalgamation” refers to the model constructed using radiomic and deep learning features encompassing baseline-features, two-cycle-reassessment-features, delta-features and clinical characteristics.

Fig. 3 Receiver operating characteristic curves for all models. (a) Different data categories based on the SVM algorithm; (b) Different machine learning algorithms using the ‘Data Amalgamation’ category.

Fig. 3

Fig. 4 Analysis of decision curves for each model in the testing dataset. (a) SVM-based ‘Clinical Characteristics’ model; (b) SVM-based ‘Baseline Images’ model; (c) SVM-based ‘Two-cycle-reassessment Images’ model; (d) SVM-based ‘Delta Data’ model; (e) SVM-based ‘Data Amalgamation’ model; (f) LightGBM-based ‘Data Amalgamation’ model; (g) RandomForest-based ‘Data Amalgamation’ model; (h) MLP-based ‘Data Amalgamation’ model.

Fig. 4

5 Discussion

The primary goal of this study was to evaluate the potential for ALND exemption post-NAT and to assess the effectiveness of our predictive model in preoperative ALN evaluation. The findings from our study facilitate a deeper discussion in these vital areas, highlighting their broader impact on BC management in the neoadjuvant context.

Concerns surrounding SLNB arise from the potential of NAT to modify lymphatic drainage patterns, which may result in false-negative results. Our results align with prior studies, indicating a FNR that surpasses 10 % [[10], [11], [12]]. The heightened FNR underscores the challenges and risks of relying exclusively on post-NAT SLNB for BC staging. In our patient cohort, we observed a distinct trend in ypN0 status, with 38.13 % of them achieving this classification. This suggests a substantial portion of breast cancer patients potentially qualify for ALND exemption. Additionally, the higher ypN0 rate among HER2+ or high Ki67 level BC patients offers insights into the groups that might benefit most from the exemption. This observation underscores the imperative need to identify accurate preoperative ALN assessment tools, paving the way for personalized therapeutic strategies.

Recent investigations have utilized radiomics from breast DCE-MRI to predict ALN metastasis in early-stage BC patients. Yet, these studies mainly predict ALN involvement by examining the features of the primary breast tumor, rather than characterizing the metastatic LNs directly [27,28]. Li et al. utilized radiomics from CT scans to predict the status of ALN by analyzing the condition of the metastatic LN itself [22]. Their study incorporated both baseline images and those taken after the completion of NAT. However, concerning the completion of NAT images, some LNs diminished in size or even disappeared after treatment, potentially leading to inaccurate ROI delineation. Our study is specifically focused on metastatic ALN. We employed both baseline and two-cycle-reassessment DCE-MRI images captured at an early stage, which facilitates a more straightforward delineation of the ROI. Utilizing longitudinal DCE-MRI data, especially baseline and post two cycles of NAT, introduces a dynamic component to our evaluation. The heterogeneity of BC primarily stems from the variability in tumor cells and the microenvironment around tumors [[29], [30], [31]]. This heterogeneity may undergo changes due to NAT, and such alterations can potentially manifest in radiographic presentations [[32], [33], [34], [35]]. The ‘Data Amalgamation’ category comprises seven delta radiomic features and twelve delta deep learning features, representing the early response to treatment. Our approach provides a multifaceted evaluation, incorporating information from pre-treatment, post-treatment, and the changes after treatment. This could more accurately mirror the tumor's biological behavior and its associated nodal involvement.

Given the current challenges in assessing LNs, our SVM-based ‘Data Amalgamation’ model signifies a potential breakthrough in this field. Leveraging the combined strength of radiomic attributes, deep learning techniques, and clinical data, our model demonstrates a notable AUC, especially when compared to models relying on single data types. The model stands out due to its comprehensive methodology, merging varied data sources into a unified predictive system. Furthermore, the use of SVM, known for its robustness in dealing with high-dimensional data, is a strategic choice that has borne fruit in our study [36,37]. The efficacy of the SVM algorithm, especially when applied to the intricate, multidimensional landscape of the “Data Amalgamation” category, underscores its potential role as the effective tool in future surgical risk stratification modeling for BC.

Our research highlights the need for a multidimensional assessment, integrating clinical, molecular, and longitudinal DCE-MRI data, to improve patient stratification post-NAT. The superior performance of the SVM-based “Data Amalgamation” model can pave the way for its incorporation into clinical settings, facilitating personalized therapeutic strategies. While our results are noteworthy, there are inherent limitations in our study. Firstly, the sample size of 160 patients, derived from a single-center dataset, may introduce a high risk of bias and limit the generalizability of our findings. Single-center studies can be influenced by institutional practices and patient demographics, which may not be representative of broader populations. To address these limitations, future studies should aim to validate our model using larger, multi-center datasets that encompass a more diverse patient population. Additionally, while SVM showed superiority in our dataset, its performance might vary in different populations or when applied to other BC subtypes. In conclusion, although our study provides valuable insights, the inherent limitations highlight the need for further research to enhance the model's applicability and robustness in varied clinical environments.

6 Conclusion

In summary, this study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based “Data Amalgamation” model, we propose a way forward that marries accuracy with clinical pragmatism, setting the stage for improved patient outcomes in the neoadjuvant setting.

Role of the funding source

This study was funded by Fujian provincial health technology project (2021GGA022).

Ethics approval

Ethical approval for this study was obtained from the Ethics Committee of Fujian Medical University Union Hospital (2021WSJK043).

Conflict of interest

All authors declare no financial or non-financial competing interests.

Consent to publish

The authors affirm that human research participants provided informed consent for publication of the images in Fig. 2 and Supplement 1.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to concerns related to patient privacy but are available from the corresponding author on reasonable request.

Code availability

The underlying code for this study [and training/validation datasets] is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.

CRediT authorship contribution statement

Yushuai Yu: Writing – original draft, Methodology, Data curation, Conceptualization. Ruiliang Chen: Formal analysis, Data curation. Jialu Yi: Formal analysis, Data curation. Kaiyan Huang: Writing – review & editing. Xin Yu: Writing – review & editing. Jie Zhang: Writing – original draft, Supervision, Resources. Chuangui Song: Supervision, Resources, Conceptualization.

Appendix A Supplementary data

The following are the Supplementary data to this article.Multimedia component 1

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Acknowledgements

This study was funded by Fujian provincial health technology project (2021GGA022 ). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. We also thank OnekeyAI platform and its developers.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.breast.2024.103786.
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