Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study

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

Published: Feb. 10, 2025

Language: Английский

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Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and -zero breast cancers: a multicenter study

Breast Cancer Research, Journal Year: 2024, Volume and Issue: 26(1)

Published: Nov. 22, 2024

Human epidermal growth factor receptor 2-targeted (HER2) therapy with antibody-drug conjugates has proven effective for patients HER2-low breast cancer. However, intratumoral heterogeneity (ITH) poses a great challenge in identifying tumors. ITH signatures were developed by quantifying to differentiate HER2-positive, -low and -zero cancers. This retrospective study included 614 from two institutions. The was structured into primary tasks: task 1 between HER2-positive -negative tumors, followed 2 Whole-tumor radiomics features habitat extracted MRI construct the signatures. Multivariable logistic regression analysis used determine significant independent predictors. A combined model integrating clinicopathologic variables, signature, signature (1) Subsequently, better-performing established using same approach (2) area under receiver operating characteristic curve (AUC) assess performance of each model. Task comprised (training, n = 348; validation, 149; test cohorts, 117). encompassed 501 283; 122; 96). For task1, showed outstanding performance, achieving AUCs 0.81, 0.81 training, validation respectively. achieved improved 0.83, 0.84 0.83 across three task2, maintained superior 0.94, 0.93 indicated that none characteristics retained as predictors associated odds Our quantified habitat-based radiomics, differentiating HER2-postive further

Language: Английский

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Multiparametric MRI Radiomics With Machine Learning for Differentiating HER2-Zero, -Low, and -Positive Breast Cancer: Model Development, Testing, and Interpretability Analysis

American Journal of Roentgenology, Journal Year: 2024, Volume and Issue: 224(1)

Published: Oct. 16, 2024

BACKGROUND. MRI radiomics has been explored for three-tiered classification of HER2 expression levels (i.e., HER2-zero, HER2-low, or HER2-positive) in patients with breast cancer, although an understanding how such models reach their predictions is lacking. OBJECTIVE. The purpose this study was to develop and test multiparametric machine learning differentiating as well explain the contributions model features through local global interpretations use Shapley additive explanation (SHAP) analysis. METHODS. This retrospective included 737 (mean age, 54.1 ± 10.6 [SD] years) cancer from two centers (center 1 [n = 578] center 2 159]), all whom underwent had determined after excisional biopsy. Analysis entailed tasks: HER2-negative HER2-zero HER2-low) tumors HER2-positive (task 1) HER2-low 2). For each task, were randomly assigned a 7:3 ratio training set 1: n 405; task 2: 284) internal 173; 122); formed external 159; 105). Radiomic extracted early phase dynamic contrast-enhanced (DCE) imaging, T2-weighted DWI. support vector (SVM) used feature selection, score (radscore) computed using weights SVM correlation coefficients, conventional combined constructed, performances evaluated. SHAP analysis provide outputs. RESULTS. In set, 1, AUCs model, radscore, 0.624, 0.757, 0.762, respectively; 2, AUC radscore 0.754, no could be constructed. identified DCE imaging having strongest influence both tasks; also prominent role 2. CONCLUSION. findings indicate suboptimal performance noninvasive characterization expression. CLINICAL IMPACT. provides example interpretation better understand imaging-based models.

Language: Английский

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A Machine Learning Model for Predicting the HER2 Positive Expression of Breast Cancer Based on Clinicopathological and Imaging Features

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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From Text to Insight: A Natural Language Processing-Based Analysis of Burst and Research Trends in HER2-low Breast Cancer Patients

Ageing Research Reviews, Journal Year: 2025, Volume and Issue: 106, P. 102692 - 102692

Published: Feb. 22, 2025

With the intensification of population aging, proportion elderly breast cancer patients is continuously increasing, among which those with low HER2 expression account for approximately 45 %-55 % all cases within traditional HER2-negative cancer. Concurrently, significant therapeutic effect T-DXd on HER2-low tumors has brought this group into public spotlight. Since clinical approval in 2019, there been a vertical surge volume publications domain. We analyzed 512 articles from Web Science Core Collection using bibliometrics, topic modeling, and knowledge graph techniques to summarize current state trends research Research efforts are particularly concentrated United States China. Our analysis revealed six main directions: detection, omics biomarkers, basic translational research, neoadjuvant therapy prognosis, progress ADC drugs trials. To enhance efficacy safety antibodydrug conjugates (ADCs), researchers actively exploring potential drug candidates other than T-DXd, numerous emerging practice By incorporating treatment strategies such as immunotherapy employing circulating tumor cell (CTC) detection techniques, made toward improving prognosis expression. believe that these hold promise compelling evidence may constitute distinct independent subtype.

Language: Английский

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Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study

Insights into Imaging, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 7, 2025

Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model identify three different HER2 states noninvasively. Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from hospitals. The status was divided into classes: positive, low, zero. Four peritumoral regions interest (ROI) auto-generated by dilating the manually segmented intratumoral ROI thicknesses 5 mm, 10 15 20 mm. After image preprocessing, 4720 features extracted each every patient. least absolute shrinkage selection operator LightBoost algorithm utilized construct single- multi-region signatures (RS). A clinical-radiomics combined developed integrating discriminative clinical-sonographic factors with optimal RS. data stitching strategy used build patient-level models. Shapley additive explanations (SHAP) approach explain contribution internal prediction. RS constructed 12 tumor 9 peritumoral-15mm features. Age, size, seven qualitative ultrasound retained In training, validation, test cohorts, showed best discrimination ability macro-AUCs 0.988 (95% CI: 0.983-0.992), 0.915 0.851-0.965), 0.862 0.820-0.899), respectively. built robust interpretable evaluate classes based on images. Ultrasound-based method can noninvasively HER2, which may guide treatment decisions implementation personalized HER2-targeted patients. Determination affect cancer. discriminate statuses. Our assist providing recommendations novel therapies.

Language: Английский

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Dual-Modality Virtual Biopsy System Integrating MRI and MG for Noninvasive Predicting HER2 Status in Breast Cancer

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Nomogram using bi-modal imaging for predicting low-level HER2 expression status in breast cancer

European Journal of Radiology, Journal Year: 2025, Volume and Issue: 187, P. 112118 - 112118

Published: April 14, 2025

Language: Английский

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Intratumoral and peritumoral radiomics based on automated breast volume scanner for predicting human epidermal growth factor receptor 2 status

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: April 16, 2025

To develop an intratumoral and peritumoral radiomics model using Automated Breast Volume Scanner (ABVS) for noninvasive preoperative prediction of Human Epidermal Growth Factor Receptor 2 (HER2) status. This retrospective study analyzed 384 lesions from 379 patients with pathologically confirmed breast cancer across four hospitals. Two tasks were defined: Task 1 to distinguish HER2-negative HER2-positive cases differentiate HER2-zero HER2-low For each classification task, three models built: Model included features the tumor region alone; both a 5mm region; 3 incorporated region, 5-10mm region. The performance was evaluated receiver operating characteristic (ROC) curves, key metrics including area under curve (AUC), sensitivity, specificity, accuracy. In tasks, demonstrated superior predictive multiple datasets. 1, it achieved highest AUC (0.844), exceptional sensitivity (0.955), satisfactory accuracy (0.787) in validation set, outperformed other test set 0.749 0.885, highlighting its robustness clinical applicability. 2, exhibited (0.801), (0.862), (0.808) consistent training (AUC 0.850) sets 0.801). 3, which combines features, did not demonstrate significant improvements over intratumoral-only two tasks. These results underscore value incorporating particularly within margin, enhance compared models. integrating appropriate significantly based on alone. integrated approach holds strong potential noninvasive, HER2

Language: Английский

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Identification of HER2-over-expression, HER2-low-expression, and HER2-zero-expression statuses in breast cancer based on 18F-FDG PET/CT radiomics

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

Published: May 12, 2025

Language: Английский

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