Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 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.

Язык: Английский

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

Heliyon, Год журнала: 2025, Номер 11(3), С. e42398 - e42398

Опубликована: Фев. 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.

Язык: Английский

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The clinical value of radiomics models based on multi-parameter MRI features in evaluating the different expression status of HER2 in breast cancer

Acta Radiologica, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

Accurate preoperative non-invasive assessment of HER2 expression in breast cancer is crucial for personalized treatment and prognostic stratification. To evaluate the effectiveness radiomics models based on multi-parametric magnetic resonance imaging (MRI) distinguishing status invasive cancer. We conducted a retrospective analysis baseline MRI scans clinical data from 400 patients with between January 2018 December 2019. Two-dimensional regions interest were manually segmented maximum tumor images obtained turbo inversion recovery magnitude (TIRM), dynamic contrast-enhanced phase 2 (DCE2), 4 (DCE4), diffusion-weighted (DWI), apparent diffusion coefficient (ADC) sequences using ITK-SNAP software. Features extracted screened dimensionality reduction. Logistic regression developed to predict status. In HER2-overexpression non-HER2-overexpression, DCE2 model outperformed other single-parameter models, areas under curve (AUCs) 0.91 (training) 0.88 (test). Combination DCE features showed significantly improved performance (P ≤ 0.001). The multiparameter achieved highest AUCs 0.93 HER2-low HER2-zero, TIRM performed best among 0.80 0.72 further enhanced prediction, yielding an AUC 0.83 Radiomics demonstrated strong utility assessing cancer, particularly identifying subtypes.

Язык: Английский

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

Academic Radiology, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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Performance of 3-T Nonenhanced Whole-Heart bSSFP Coronary MR Angiography: A Comparison with 3-T Modified Dixon Water-Fat Separation Sequence

Radiology Cardiothoracic Imaging, Год журнала: 2025, Номер 7(2)

Опубликована: Апрель 1, 2025

Improved nonenhanced whole-heart balanced steady-state free precession coronary MR angiography performs better at 3-T imaging than the modified Dixon water-fat separation sequence.

Язык: Английский

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Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning

Frontiers in Medicine, Год журнала: 2024, Номер 11

Опубликована: Май 9, 2024

Background Fungal infections are associated with high morbidity and mortality in the intensive care unit (ICU), but their diagnosis is difficult. In this study, machine learning was applied to design define predictive model of ICU-acquired fungi (ICU-AF) early stage fungal using Random Forest. Objectives This study aimed provide evidence for warning management infections. Methods We analyzed data patients culture-positive during admission seven ICUs First Affiliated Hospital Chongqing Medical University from January 1, 2015, December 31, 2019. Patients whose first culture positive longer than 48 h after ICU were included ICU-AF cohort. A obtained Least Absolute Shrinkage Selection Operator learning, relationship between features within disease severity analyzed. Finally, relationships model, antifungal therapy empirical Results total 1,434 cases finally. used lasso dimensionality reduction all selected six importance ≥0.05 optimal namely, times arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary invasive mechanical ventilation. The area under curve predicting 0.981 test set, a sensitivity 0.960 specificity 0.990. catheter ( p = 0.011, OR 1.057, 95% CI 1.053–1.104) ventilation 0.007, 1.056, 95%CI 1.015–1.098) independent risk factors ICU-AF. 0.004, 1.098, 0.855–0.970) an factor therapy. Conclusion most important time-related clinical parameters (arterial ventilation), which occurrence infection. Furthermore, can help physicians assess whether empiric should be administered who susceptible

Язык: Английский

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Development and Validation of a Deep Learning System to Differentiate HER2‐Zero, HER2‐Low, and HER2‐Positive Breast Cancer Based on Dynamic Contrast‐Enhanced MRI

Journal of Magnetic Resonance Imaging, Год журнала: 2024, Номер unknown

Опубликована: Дек. 6, 2024

Background Previous studies explored MRI‐based radiomic features for differentiating between human epidermal growth factor receptor 2 (HER2)‐zero, HER2‐low, and HER2‐positive breast cancer, but deep learning's effectiveness is uncertain. Purpose This study aims to develop validate a learning system using dynamic contrast‐enhanced MRI (DCE‐MRI) automated tumor segmentation classification of HER2‐zero, statuses. Study Type Retrospective. Population One thousand two hundred ninety‐four cancer patients from three centers who underwent DCE‐MRI before surgery were included in the (52 ± 11 years, 811/204/279 training/internal testing/external testing). Field Strength/Sequence 3 T scanners, T1‐weighted 3D fast spoiled gradient‐echo sequence, enhanced sequence turbo field echo sequence. Assessment An model segmented tumors utilizing data, followed by models (ResNetGN) trained classify HER2 Three developed distinguish their respective non‐HER2 categories. Statistical Tests Dice similarity coefficient (DSC) was used evaluate performance model. Evaluation performances statuses involved receiver operating characteristic (ROC) curve analysis area under (AUC), accuracy, sensitivity, specificity. The P ‐values <0.05 considered statistically significant. Results automatic network achieved DSC values 0.85 0.90 compared manual across different sets. ResNetGN AUCs 0.782, 0.776, 0.768 HER2‐zero others training, internal test, external test sets, respectively. Similarly, 0.820, 0.813, 0.787 HER2‐low vs. others, 0.792, 0.745, 0.781 Data Conclusion proposed DCE‐MRI‐based may have potential preoperatively distinct expressions cancers with therapeutic implications. Evidence Level 4 Technical Efficacy Stage

Язык: Английский

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A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram

Frontiers in Oncology, Год журнала: 2023, Номер 13

Опубликована: Окт. 23, 2023

To analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) invasive ductal carcinoma (IDC), construct a predictive nomogram for SA.A total 865 patients were recruited at Second Hospital Shandong University from January 2016 to November 2022. All underwent routine ultrasound examinations before surgery, diagnosis was confirmed by histopathological examination following operation. Ultrasonic features recorded using Breast Imaging Data Reporting System (BI-RADS). Of patients, 203 (252 nodules) diagnosed as SA 662 (731 IDC. They randomly divided into training set validation ratio 6:4. Lastly, difference in comparatively analyzed.There statistically significant multiple between IDC (P<0.05). As age lesion size increased, probability significantly decreased, with cut-off value 36 years old 10 mm, respectively. In logistic regression analysis set, age, nodule size, menopausal status, symptoms, palpability lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, resistance index (RI) These indicators included static dynamic model, which showed high performance, calibration both sets.SA should be suspected asymptomatic young women, especially those younger than who present small-size lesions (especially less mm) distinct homogeneous lack blood supply. The model can provide more convenient tool clinicians.

Язык: Английский

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Personalized Optimization of Systematic Prostate Biopsy Core Number Based on mpMRI Radiomics Features

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Апрель 19, 2024

Abstract Background 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 Methods 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 by-layer. ROI reconstructions fused form VOIs, which exported applied subsequent extraction radiomics features. t-tests, Mann-Whitney U-tests chi-squared tests performed evaluated significance logistic regression was used for calculating PCa score(PCS). PCS model trained optimize core number, utilizing both mpMRI clinical Results 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 accuracies on validation set. Conclusion optimization described in this study could therefore effectively reduce obtained PCS. method enhance patient experiences without reducing detection rate.

Язык: Английский

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Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors

Translational Cancer Research, Год журнала: 2024, Номер 13(1), С. 317 - 329

Опубликована: Янв. 1, 2024

Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection challenging. Radiomics has potential improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can cancer prediction.

Язык: Английский

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