Integrating clinical data and ultrasonographic imaging for non-invasive prediction of HER2 status in breast cancer DOI Creative Commons

Anli Zhao,

Jiang‐Feng Wu,

YanHong Du

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 14, 2024

Abstract Background The most common cancer in the world, breast (BC), poses serious problems to healthcare. Making an accurate diagnosis of these patients' HER2 status is essential for therapy planning.Methods A prospective cohort patients with BC was enrolled between June 2020 and october 2023. patient's clinical data features from their ultrasonography were gathered. Postoperative tumor pathology specimens subjected immunohistochemistry fluorescence situ hybridization examinations ascertain status. Lasso regression used choose characteristic variables. Univariate multivariate logistic analysis find status-independent factors. performance nomogram model then assessed using calibration curves decision curve (DCA).Result 97 (22.25%) 436 research had positive results. Progesterone receptor expression, Ki-67 levels, estrogen expression differed statistically amongst different statuses. identified six ultrasonographic variables closely associated a pool 786 features, leading generation radiomic score each patient. Multivariate revealed that PR (OR = 0.15, 95%CI 0.06–0.36, p < 0.001), 1.02, 1.00-1.03, 0.012), Radiomic 5.89, 2.58–13.45, 0.001) independent predictors demonstrated areas under (AUC) 0.823 (95% CI 0.772–0.874) 0.812 0.717–0.906) training validation cohort, respectively.Conclusions methodology integrates data, cutting-edge imaging, machine learning provide individualized treatment plans presented non-invasive prediction cancer.

Language: Английский

Multiparametric MRI and Radiomics for the Prediction of HER2-Zero, -Low, and -Positive Breast Cancers DOI
Toulsie Ramtohul, Lounes Djerroudi,

Émilie Lissavalid

et al.

Radiology, Journal Year: 2023, Volume and Issue: 308(2)

Published: Aug. 1, 2023

Background Half of breast cancers exhibit low expression levels human epidermal growth factor receptor 2 (HER2) and can be targeted by new antibody-drug conjugates. The imaging differences between HER2-zero (immunohistochemistry [IHC] score 0), HER2-low (IHC 1+ or 2+ with negative findings at fluorescence in situ hybridization [FISH]), HER2-positive positive FISH IHC 3+) were unknown. Purpose To assess whether multiparametric dynamic contrast-enhanced MRI-based radiomic features help distinguish HER2 expressions cancer. Materials Methods This study included women cancer who underwent MRI two different centers December 2020 2022. Tumor segmentation feature extraction performed on T2-weighted T1-weighted images. Unsupervised correlation analysis reproducible least absolute shrinkage selector operation used for the selection to build a radiomics signature. area under receiver operating characteristic curve (AUC) was performance Multivariable logistic regression identify independent predictors distinguishing both training prospectively acquired external data set. Results set 208 patients from center 1 (mean age, 53 years ± 14 [SD]), test 131 54 13). In set, signature achieved an AUC 0.80 (95% CI: 0.71, 0.89) -positive tumors versus significant predictive these groups (odds ratio = 7.6; 95% 2.9, 19.8; P < .001). Among cancers, histology type, associated nonmass enhancement, multiple lesions had 0.77 0.68, 0.86) prediction cancers. Conclusion tumor descriptors may predict distinct therapeutic implications. © RSNA, 2023 Supplemental material is available this article. See also editorial Kataoka Honda issue.

Language: Английский

Citations

57
Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI‐Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2‐Low Status of Breast Cancer DOI

Xiaoqian Bian,

Siyao Du,

Zhibin Yue

et al.

Journal of Magnetic Resonance Imaging, Journal Year: 2023, Volume and Issue: 58(5), P. 1603 - 1614

Published: Feb. 10, 2023

Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low cancers has not been investigated.To investigate whether multiparametric MRI-based can HER2-positive (task 1) and 2).Retrospective.Task 1: 310 operable cancer patients center 1 (97 213 HER2-negative); task 2: (108 105 HER2-zero); 59 (16 HER2-positive, 27 16 HER2-zero) external validation.A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted (DWI)-derived apparent diffusion coefficient (ADC).Patients in were assigned to a training internal validation cohort at 2:1 ratio. Intratumoral peritumoral features extracted T1CE ADC. After dimensionality reduction, the signatures (RS) of two tasks developed using (RS-T1CE), ADC (RS-ADC) alone + combination (RS-Com).Mann-Whitney U tests, least absolute shrinkage selection operator, receiver operating characteristic (ROC) curve, calibration decision curve analysis (DCA).For 1, RS-ADC yielded higher area under ROC (AUC) training, internal, 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For 2, AUC 0.765/0.755/0.678 0.706/0.608/0.630). both RS-Com achieved best performance with 0.793/0.778/0.760 0.820/0.776/0.711, respectively, obtained clinical benefit DCA compared RS-ADC. The curves all RS demonstrated good fitness.Multiparametric noninvasively robustly cancers.3.Stage 2.

Language: Английский

Citations

30
Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics DOI

Shaoyan Zheng,

Zehong Yang,

Guangzhou Du

et al.

European Radiology, Journal Year: 2024, Volume and Issue: 34(9), P. 6132 - 6144

Published: Feb. 16, 2024

Language: Английский

Citations

17
Development and Validation of MRI Radiomics Models to Differentiate HER2-Zero, -Low, and -Positive Breast Cancer DOI

Yuqin Peng,

Xiang Zhang,

Ya Qiu

et al.

American Journal of Roentgenology, Journal Year: 2024, Volume and Issue: 222(4)

Published: Jan. 24, 2024

Breast cancer HER2 expression has been redefined using a three-tiered system, with HER2-zero cancers considered ineligible for HER2-targeted therapy, HER2-low candidates novel drugs, and HER2-positive treated traditional medications.

Language: Английский

Citations

11
Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study DOI Creative Commons
Jing Zhou, Xuan Yu, Qingxia Wu

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(7), P. e28722 - e28722

Published: April 1, 2024

To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer.

Language: Английский

Citations

7
The Emergence of the Potential Therapeutic Targets: Ultrasound-Based Radiomics in the Prediction of Human Epidermal Growth Factor Receptor 2-Low Breast Cancer DOI
Yu Du, Fang Li, Manqi Zhang

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: 31(7), P. 2674 - 2683

Published: Feb. 2, 2024

Language: Английский

Citations

6
MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses DOI
Kexin Xie, Can Cui, Xiaoqing Li

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: 31(10), P. 4231 - 4241

Published: April 21, 2024

Language: Английский

Citations

4
A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study DOI Creative Commons

Zongjie Wei,

Xuesong Bai, Yingjie Xv

et al.

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 28, 2024

Abstract Objective To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation. Methods In this retrospective study, 207 patients pathologically confirmed BCa were enrolled and divided into the training set ( n = 154) test 53). Least absolute shrinkage selection operator (LASSO) regression was used identify most discriminative features set. Five ML models, namely logistic (LR), support vector (SVM), k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost) random forest (RF), developed. The predictive performance of established models evaluated by area under receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) analyze interpretability models. Results A total 1218 radiomics extracted from nephrographic phase CT images, 11 filtered for constructing set, AUCs LR, SVM, KNN, XGBoost, RF 0.803, 0.709, 0.679, 0.794, 0.815, corresponding accuracies 71.7%, 69.8%, 60.4%, 75.5%, respectively. identified as optimal classifier. SHAP analysis showed that texture (gray level size zone matrix gray co-occurrence matrix) significant predictors HER2 status. Conclusions provides noninvasive tool satisfactory discriminatory performance. Critical relevance statement An can cancer, potentially aiding clinical decision-making process. Key Points could cancer. more robust accurate demonstrated favorable through method. Graphical

Language: Английский

Citations

4
Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis DOI Creative Commons
Zhenlin Chen, Zhihao Li,

Ruiling Dou

et al.

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 22, 2025

Prostate cancer (PCa) is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can increase the risk of urinary retention, infection and bleeding due to excessive number We retrospectively analyzed 622 patients who underwent SBx multiparametric MRI (mpMRI) from two centers between January 2014 June 2022. The data were collected manually segment Regions Interest (ROI) tumor layer layer. ROI reconstructions fused form outline volume interest (VOI), which exported applied subsequent extraction radiomics features. t-tests, Mann-Whitney U-tests chi-squared tests performed evaluate significance logistic regression was used for calculating PCa score (PCS). PCS model trained optimize core number, utilizing both mpMRI clinical predicted cores determined model. Optimal numbers subgroups 1–5 calculated as 13, 10, 8, 6, respectively. Accuracies high: 100%, 95.8%, 91.7%, 90.6%, 92.7% 1–5. Optimized reduced rate 41.9%. Leakage rates clinically significant 8.2% 3.4%, optimized also demonstrated high accuracy on validation set. optimization described in this study could therefore effectively reduce obtained PCS, especially far away suspicious lesions. method enhance patient experience without reducing detection rate.

Language: Английский

Citations

0
Multi-sequence MRI-based Nomogram for Prediction of Human Epidermal Growth Factor Receptor 2 Expression in Breast Cancer DOI Creative Commons

Mengyi Shen,

Li Zhang, Dingyi Zhang

et al.

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e42398 - e42398

Published: Feb. 1, 2025

To develop a nomogram based on multi-sequence MRI (msMRI) radiomics features and imaging characteristics for predicting human epidermal growth factor receptor 2 (HER2) expression in breast cancer (BC). 206 women diagnosed with invasive BC were retrospectively enrolled randomly divided into training set (n = 144) validation 62) at the ratio of 7 : 3. Tumor segmentation feature extraction performed dynamic contrast-enhanced (DCE) MRI, T2-weighted (T2WI), apparent diffusion coefficient (ADC) map. Radiomics models constructed using score (Rad-score) was calculated. Rad-score significant included multivariate analysis to establish nomogram. The performance mainly evaluated via area under receiver operating characteristic curve (AUC). Edema types T2WI (OR 4.480, P 0.008), enhancement type 7.550, 0.002), 5.906, < 0.001) independent predictors HER2 expression. model msMRI (including DCE-MRI, T2WI, ADC map) had AUCs 0.936 0.880 sets, respectively, exceeding one sequence or dual sequences. With combination edema types, achieved highest (AUC: 0.940) 0.893). developed MRI-based presents promising tool expression, is expected improve diagnosis treatment BC.

Language: Английский

Citations

0