Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

Huang, Jiaxin; Ding, Saisai; Zhang, Huili; Wang, Xueyan; Lin, Shi-Yang; Xu, Yan-Fen; Wei, Ming-Jie; Liu, Longzhong; Pei, Xiao-Qing

doi:10.1016/j.acra.2023.03.036

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…In addition, several antecedent studies underscore the superiority of pretreatment SWE over ultrasound alone in predicting neoadjuvant chemotherapy response. Furthermore, an artificial intelligence model or nomogram developed by incorporating radiologic parameters from medical images including SWE and clinicopathologic data has exhibited excellent diagnostic predictive performance for the neoadjuvant chemotherapy response [32,33]. These results not only highlight the synergistic potential of various data sources but also emphasize the role of elasticity values as a pivotal factor in accurately predicting the response to neoadjuvant chemotherapy.…”

Section: Discussionmentioning

confidence: 82%

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Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer

Kim,

Eun,

Ahn

et al. 2024

Cancers

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Shear-wave elastography (SWE) is an effective tool in discriminating malignant lesions of breast and axillary lymph node metastasis in patients with breast cancer. However, the association between the baseline elasticity value of breast cancer and the treatment response of neoadjuvant chemotherapy is yet to be elucidated. Baseline SWE measured mean stiffness (E-mean) and maximum stiffness (E-max) in 830 patients who underwent neoadjuvant chemotherapy and surgery from January 2012 to December 2022. Association of elasticity values with breast pCR (defined as ypTis/T0), pCR (defined as ypTis/T0, N0), and tumor-infiltrating lymphocytes (TILs) was analyzed. Of 830 patients, 356 (42.9%) achieved breast pCR, and 324 (39.0%) achieved pCR. The patients with low elasticity values had higher breast pCR and pCR rates than those with high elasticity values. A low E-mean (adjusted odds ratio (OR): 0.620; 95% confidence interval (CI): 0.437 to 0.878; p = 0.007) and low E-max (adjusted OR: 0.701; 95% CI: 0.494 to 0.996; p = 0.047) were independent predictive factors for breast pCR. Low elasticity values were significantly correlated with high TILs. Pretreatment elasticity values measured using SWE were significantly associated with treatment response and inversely correlated with TILs, particularly in HR+HER2- breast cancer and TNBC.

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Section: Discussionmentioning

confidence: 82%

“…The association between low elasticity values and a favorable response to neoadjuvant chemotherapy has been consistently represented [30][31][32][33][34][35]. In addition, several antecedent studies underscore the superiority of pretreatment SWE over ultrasound alone in predicting neoadjuvant chemotherapy response.…”

Section: Discussionmentioning

confidence: 99%

Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer

Kim,

Eun,

Ahn

et al. 2024

Cancers

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“…Previous research on tumor ferroptosis has primarily focused on scanning relevant databases using bioinformatics analyses to identify any abnormally expressed ferroptosis-related genes that are closely associated with malignancies ( 16 - 18 ). Machine learning is the primary way to implement human intelligence and aims to develop algorithms that can automatically learn from data ( 19 ). It can use complex arithmetic to capture large datasets with multidimensional variables to obtain high-dimensional, non-linear relationships between clinical characteristics and make outcome predictions.…”

Section: Introductionmentioning

confidence: 99%

Screening of key genes related to ferroptosis and a molecular interaction network analysis in colorectal cancer using machine learning and bioinformatics

Feng¹,

Jiang²,

Kou³

2023

J Gastrointest Oncol

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Background This study sought to identify the key genes and molecular interactions related to ferroptosis in colorectal cancer (CRC) using machine-learning and bioinformatics analyses. Methods The Gene Expression Omnibus (National Institutes of Health, US) datasets for CRC were downloaded from the National Center for Biotechnology Information (NCBI) ( https://www.ncbi.nlm.nih.gov/ ). The 291 ferroptosis genes were downloaded and screened from the FerrDb ( http://www.zhounan.org/ferrdb ) and GeneCards ( https://www.genecards.org/ ) databases. The least absolute shrinkage and selection operator regression model and support vector machine model were constructed to identify the different ferroptosis-related hub genes. The immune infiltrates were identified and a survival curve analysis was conducted. Results We identified 11 ferroptosis-related differentially expressed genes (DEGs) from the COADREAD (Colon and Rectal Cancer) dataset. We found that angiopoietin-related protein 7 ( ANGPTL7 ) gene expression was positively correlated to both the neuroglobin ( NGB ) (r=0.678) and ceruloplasmin (CP) (r=0.454) genes but was negatively correlated with transferrin receptor 2 ( TFR2 ) expression (r=–0.426). In addition, TFR2 gene expression was positively correlated with the arachidonate lipoxygenase 3 ( ALOXE3 ) (r=0.452) and carbonic anhydrase 9 ( CA9 ) (r=0.411) genes. A total of 4 hub genes were identified by the machine-learning analysis [i.e., NADPH oxidase 4 ( NOX4 ), TFR2, ALOXE3 , and CA9 ]. The expression of the NOX4 gene was significantly positively correlated with neutrophil (r=0.543) and M0 macrophage (r=0.422) infiltration. In addition, a positive correlation between ALOXE3 and activated natural-killer cells (r=0.356) was found. Conversely, the NOX4, TFR2 , and CA9 genes were negatively correlated with the resting mast cells. A strong negative correlation was observed between NOX4 and CD160 antigen ( CD160 ) expression; however, a significant positive correlation was observed between NOX4 and transforming growth factor beta receptor 1 ( TGFBR1 ) expression (r=0.397). The patients had a more favorable prognosis when the NOX4 expression levels were relatively low. Conclusions Our study identified 4 ferroptosis-related DEGs in CRC (i.e., NOX4, TFR2 , ALOXE3 , and CA9 ), and fur...

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Delta Radiomics Based on Longitudinal Dual-modal Ultrasound Can Early Predict Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Huang,

Wu,

Wang

et al. 2024

Academic Radiology

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Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

Cited by 6 publications

References 44 publications

Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer

Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer

Screening of key genes related to ferroptosis and a molecular interaction network analysis in colorectal cancer using machine learning and bioinformatics

Delta Radiomics Based on Longitudinal Dual-modal Ultrasound Can Early Predict Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

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