A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

Wang, Dawei; Hu, Yongbin; Zhan, Chenao; Zhang, Qi; Wu, Yiping; Ai, Tao

doi:10.3389/fonc.2022.940655

Cited by 16 publications

(15 citation statements)

References 34 publications

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“…In addition, we also developed RNWCF, an integrated model combining clinical characteristics (N stage, long axis, short axis, cortical thickness of the ALN, ER, Ki-67) and radiomics signatures based on the multivariable logistic regression and had an AUC of 0.858 in the external test cohort. Currently, most studies focused on radiomic models or integrated models involved radiomics features for the prediction of preoperative ALN status in initially diagnosed breast cancer [17,[23][24][25]. Only a few studies have attempted to explore the effective approach involved radiomic signatures to predict ALN status after NAC for breast cancer patients.…”

Section: Discussionmentioning

confidence: 99%

“…The status of estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), and Ki-67 was assessed based on the immunohistochemical staining of breast tumors. The definition of ER-positive (≥ 10% immunostained cells) and HER2 positive (≥ 3+ in hematoxylin–eosin staining, or 2+ with confirmation of HER2 gene amplification by fluorescence in situ hybridization) has been widely reported in previous studies [ 17 ]. In the present study, Ki-67 with a proliferation index higher than 20% was considered positive.…”

Section: Methodsmentioning

confidence: 99%

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Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound

Zhang,

Cao,

Liu

et al. 2023

J Transl Med

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Objectives To explore an optimal model to predict the response of patients with axillary lymph node (ALN) positive breast cancer to neoadjuvant chemotherapy (NAC) with machine learning using clinical and ultrasound-based radiomic features. Methods In this study, 1014 patients with ALN-positive breast cancer confirmed by histological examination and received preoperative NAC in the Affiliated Hospital of Qingdao University (QUH) and Qingdao Municipal Hospital (QMH) were included. Finally, 444 participants from QUH were divided into the training cohort (n = 310) and validation cohort (n = 134) based on the date of ultrasound examination. 81 participants from QMH were used to evaluate the external generalizability of our prediction models. A total of 1032 radiomic features of each ALN ultrasound image were extracted and used to establish the prediction models. The clinical model, radiomics model, and radiomics nomogram with clinical factors (RNWCF) were built. The performance of the models was assessed with respect to discrimination and clinical usefulness. Results Although the radiomics model did not show better predictive efficacy than the clinical model, the RNWCF showed favorable predictive efficacy in the training cohort (AUC, 0.855; 95% CI 0.817–0.893), the validation cohort (AUC, 0.882; 95% CI 0.834–0.928), and the external test cohort (AUC, 0.858; 95% CI 0.782–0.921) compared with the clinical factor model and radiomics model. Conclusions The RNWCF, a noninvasive, preoperative prediction tool that incorporates a combination of clinical and radiomics features, showed favorable predictive efficacy for the response of node-positive breast cancer to NAC. Therefore, the RNWCF could serve as a potential noninvasive approach to assist personalized treatment strategies, guide ALN management, avoiding unnecessary ALND.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound

Zhang,

Cao,

Liu

et al. 2023

J Transl Med

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“…In this study, ER/PR ≥ 1% and HER2 ≥ +++ were defined as positive (+); otherwise, they were negative ( − ). 15 According to the recommendations of the International Breast Cancer Ki67 Working Group, Ki67 ≤ 5% was classified as low expression, and Ki67 ≥ 30% was classified as high expression. Ki67 = 6%–29% is defined as the medium expression and indicates that the consistency is poor and that the prognosis needs to be evaluated comprehensively in combination with other factors.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram

Zhang

Zhao

Zhang

et al. 2023

Technol Cancer Res Treat

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Background: Axillary lymph node (ALN) metastatic load is very important in the diagnosis and treatment of breast cancer (BC). We aimed to construct a model for predicting ALN metastatic load using deep learning radiomics (DLR) techniques based on the preoperative ultrasound and clinicopathologic information of patients with stage T1-2 BC. Methods: Retrospective analysis was performed on 176 patients with pathologically confirmed BC in our hospital from February 2018 to April 2020. ALN metastases were divided into a low-load group (< 3 lymph node metastases) and a high-load group (≥ 3 lymph node metastases) according to pathological results. Pyradiomics and pre-trained ResNet50 were used to extract radiomics and deep learning features, respectively. Independent sample T-test, random forest recursive elimination, and Lasso were used to screen the features to construct the deep learning radiomics signature (DLRS). Based on single/multivariate logistic regression analysis results, a DLR nomogram (DLRN) model was constructed by combining valuable clinical features and DLRS. Results: The DLRS was composed of 3 radiomics features and 14 deep learning features and combined with the maximum diameter of lesions to construct the DLRN. The AUCs of the training and test sets were 0.900 (95% CI: 0.853-0.931) and 0.821 (95% CI: 0.769-0.868), respectively. The calibration curve and Hosmer–Lemeshow test confirmed that the DLRN model has a good consistency. The decision curve also confirmed its good clinical practicality. Conclusion: Ultrasound-based DLRN has an excellent performance in predicting ALN load in patients with BC.

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“…124 The current review extracted and organized the data in tabular form and summarized the application of MRI in breast cancer diagnosis (Table 3). 109,119,120,123,125–166…”

Section: Introductionmentioning

confidence: 99%

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

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Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

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A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

Cited by 16 publications

References 34 publications

Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound

Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound

Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram

Machine learning and new insights for breast cancer diagnosis

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