Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

Chen, Mingzhen; Kong, Chunli; Lin, Guihan; Chen, Weiyue; Guo, Xinyu; Chen, Yaning; Cheng, Xue; Chen, Minjiang; Shi, Changsheng; Xu, Min; Sun, Junhui; Lu, Chenying; Ji, Jiansong

doi:10.1016/j.eclinm.2023.102176

Cited by 18 publications

(6 citation statements)

References 32 publications

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“…Additionally, unlike the neural network algorithm, the SVM model does not suffer from local minimum problems 51 . The excellent performance of SVM in various ML comparisons has also been verified 43 , 44 . A common challenge with many ML methods is that their predictions often lack interpretability, which may be due to their inherent black-box nature, such as with SVM 52 , 53 .…”

Section: Discussionmentioning

confidence: 69%

“…Together, these disciplines create a comprehensive understanding of disease from multiple levels of analysis. Previous studies have often only focused on single radiomics 41 , genomics 10 , or clinicopathological analysis 40 , but few studies have combined these three research methods to evaluate the ALN status and individualized medicine of BC patients In terms of the predictive ability of the model, the AUC of the multimodal model in this study is also excellent considering some other radiomics studies 42 – 44 and genomics studies 45 , although some of them combined clinicopathological analysis 46 . Although some researchers have reported associations between certain radiological features and genetic phenotypes 47 , medical imaging is still unlikely to fully reflect the microscopic characteristics of tumors, which was tested in the Durbin–Watson Test for the independence of rad-score, gene-score, and pathology ( P <0.95).…”

Section: Discussionmentioning

confidence: 96%

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A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study

Lai,

Chen,

Liu

et al. 2024

International Journal of Surgery

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Background: Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning (ML) and whole-transcriptome sequencing (WTS), to accurately preoperative evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. Methods: In this study, we conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort(N=103), TCIA validation cohort(N=51), Duke cohort(N=138), Foshan cohort(N=106), and TCGA cohort(N=680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using WTS technology. A support vector machine (SVM) model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM and stratify. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis (DCA). Risk stratification analysis of BC patients was performed by gene set enrichment analysis (GSEA), differential comparison of immune checkpoint gene expression, and drug sensitivity testing. Results: For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts (P<0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort (P<0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups (P<0.05). Additionally, different risk groups may exhibit varying treatment responses to chemotherapy (including Doxorubicin, Methotrexate and Lapatinib) (P<0.05). Conclusion: Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

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Section: Discussionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 96%

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study

Lai,

Chen,

Liu

et al. 2024

International Journal of Surgery

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“…Compared to conventional diagnostic methods, diagnostic models using AI algorithms have the advantages of reproducibility, objectivity, and immediacy. Currently, ML and DL models based on medical imaging are being actively evaluated for the determination of LN status and are showing great potential (45)(46)(47). The summary receiver-operating characteristic (SROC) curves of AI models for the prediction of LN metastasis.…”

Section: Discussionmentioning

confidence: 99%

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis

Deng,

Hu,

Tang

et al. 2024

Front. Oncol.

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BackgroundThe performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC.MethodsWe searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities.ResultsFourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89–0.94), 0.79 (95% CI 0.72–0.85), and 0.90 (95% CI 0.86–0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists.DiscussionIn conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).

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“…The best-performing CNN achieved 85% sensitivity and 73% specificity compared to the 73% sensitivity and 63% specificity of the radiologists [ 10 ]. Recently, Chen et al developed a CNN model to predict sentinel lymph node status based on dynamic contrast-enhanced MRI [ 11 ]. Their CNN model showed the best performance in tumor groups smaller than 0.2cm with an AUC of 0.081 at the internal validation set and 0.823 at the external test set 1 [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Chen et al developed a CNN model to predict sentinel lymph node status based on dynamic contrast-enhanced MRI [ 11 ]. Their CNN model showed the best performance in tumor groups smaller than 0.2cm with an AUC of 0.081 at the internal validation set and 0.823 at the external test set 1 [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Park,

Kim,

Cha

et al. 2023

Diagnostics

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Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

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Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

Cited by 18 publications

References 32 publications

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

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