Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection DOI Open Access
Hongwei Liu, Wei Zhang, Yihao Zhang

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 29, 2023

ABSTRACT With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have developed to accurately predict patient’s outcome clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access yet be developed. Thus, we proposed a flexible computational framework construct learning-based integration model with elegant performance (Mime). Mime streamlined process developing predictive high accuracy, leveraging complex datasets identify critical genes associated prognosis. An in silico combined de novo PIEZO1-associated signatures constructed by demonstrated accuracy predicting outcomes patients compared other published models. In addition, could also precisely infer immunotherapy response applying different Mime. Finally, SDC1 selected from presented high-potential role glioma targeted prospect. Taken together, our provides solution constructing will greatly expanded provide valuable insights into current fields.

Язык: Английский

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection DOI Creative Commons
Hongwei Liu, Wei Zhang, Yihao Zhang

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2024, Номер 23, С. 2798 - 2810

Опубликована: Июнь 29, 2024

The widespread use of high-throughput sequencing technologies has revolutionized the understanding biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome clinical response. However, an open-source R package covering state-of-the-art algorithms for user-friendly access yet be developed. Thus, we proposed a flexible computational framework construct machine learning-based integration model with elegant performance (Mime). Mime streamlines process developing predictive high accuracy, leveraging complex datasets identify critical genes associated prognosis. An in silico combined de novo PIEZO1-associated signatures constructed by demonstrated accuracy predicting outcomes patients compared other published models. Furthermore, could also precisely infer immunotherapy response applying different Mime. Finally, SDC1 selected from potential as glioma target. Taken together, our provides solution constructing will greatly expanded provide valuable insights into current fields. is available GitHub (https://github.com/l-magnificence/Mime).

Язык: Английский

Процитировано

30
Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction DOI
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola

и другие.

La radiologia medica, Год журнала: 2024, Номер 129(7), С. 957 - 966

Опубликована: Май 18, 2024

Язык: Английский

Процитировано

5
Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging DOI
Vincenza Granata, Roberta Fusco,

Maria Chiara Brunese

и другие.

La radiologia medica, Год журнала: 2024, Номер 129(3), С. 420 - 428

Опубликована: Фев. 2, 2024

Язык: Английский

Процитировано

4
Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment DOI Creative Commons
Vincenza Granata, Roberta Fusco,

Maria Chiara Brunese

и другие.

Diagnostics, Год журнала: 2024, Номер 14(2), С. 152 - 152

Опубликована: Янв. 9, 2024

Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in pre-surgical setting, predict tumor budding liver metastases. Methods: Patients MRI setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, 851 features extracted as median values PyRadiomics Python package. Balancing performed inter- intraclass correlation coefficients calculated between observer within reproducibility all features. A Wilcoxon–Mann–Whitney nonparametric test receiver operating characteristics (ROC) carried out. feature selection procedures performed. Linear non-logistic regression models (LRM NLRM) different learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) support vector (SVM) considered. Results: The internal training set included 49 patients 119 validation cohort consisted total 28 single lesion patients. best predictor classify original_glcm_Idn obtained T1-W VIBE sequence arterial phase an accuracy 84%; wavelet_LLH_firstorder_10Percentile portal 92%; wavelet_HHL_glcm_MaximumProbability hepatobiliary excretion 88%; wavelet_LLH_glcm_Imc1 T2-W SPACE sequences 88%. Considering linear analysis, statistically significant increase 96% weighted combination 13 radiomic from phase. Moreover, classifier KNN trained sequence, obtaining 95% AUC 0.96. reached 94%, sensitivity 86% specificity 95%. Conclusions: Machine are promising tools predicting budding. there compared feature.

Язык: Английский

Процитировано

3
Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics DOI Open Access

Piero Trovato,

Igino Simonetti,

Alessio Morrone

и другие.

Journal of Clinical Medicine, Год журнала: 2024, Номер 13(2), С. 547 - 547

Опубликована: Янв. 18, 2024

Background: Small renal masses (SRMs) are defined as contrast-enhanced lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a cell carcinomas (RCCs). Currently, 50–61% of all tumors found incidentally. Methods: The characteristics the lesion influence choice type management, include several methods SRM including nephrectomy, partial ablation, observation, and also stereotactic body radiotherapy. Typical imaging available for differentiating benign from malignant ultrasound (US), (CEUS), computed tomography (CT), magnetic resonance (MRI). Results: Although is first technique used detect small lesions, it has limitations. CT main most widely characterization. advantages MRI compared better contrast resolution tissue characterization, use functional sequences, possibility performing examination patients allergic iodine-containing medium, absence exposure ionizing radiation. For a correct evaluation during follow-up, necessary reliable method assessment represented by Bosniak classification system. This was initially developed based on findings, 2019 revision proposed inclusion features; however, latest not yet received widespread validation. Conclusions: radiomics an emerging increasingly central field applications such characterizing masses, distinguishing RCC subtypes, monitoring response targeted therapeutic agents, prognosis metastatic context.

Язык: Английский

Процитировано

3
Artificial intelligence in fracture detection on radiographs: a literature review DOI
Antonio Lo Mastro,

Enrico Grassi,

Daniela Berritto

и другие.

Japanese Journal of Radiology, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 14, 2024

Язык: Английский

Процитировано

3
Diagnostic performance of contrast-enhanced CT combined with contrast-enhanced MRI for colorectal liver metastases: a case-control study DOI Creative Commons

L. J. Zhang,

Lianwei Bai

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

Опубликована: Март 20, 2025

Colorectal liver metastases (CRLM) are a major determinant of prognosis in colorectal cancer (CRC) patients. Their early and accurate detection is essential for appropriate therapeutic planning improving survival outcomes. To evaluate the diagnostic capabilities contrast-enhanced computed tomography (CT) magnetic resonance imaging (MRI) detecting metastases. We employed case-control design to compare patients with histologically confirmed against control group without condition. A total 85 each were selected retrospectively matched based on relevant factors. All subjects underwent both CT MRI. The performance these modalities was assessed by analysing sensitivity, specificity, positive negative predictive values, radiologists' confidence. Kappa statistics used inter-observer agreement. MRI scans performed using 3-Tesla (3-T) scanner ensure high-quality detailed lesion characterization. And all reviewed two radiologists. combination demonstrated statistically significant improvement sensitivity (90.6% alone vs. 96.5% combined modalities) specificity (95.3% 98.3% modalities). Positive values similarly enhanced. Radiologists' confidence higher imaging, achieving 'very high' level 78.8% cases compared 64.7% alone. agreement reached 'almost perfect' status approach. integration significantly enhanced accuracy metastases, representing valuable tool preoperative evaluation CRC. Not applicable.

Язык: Английский

Процитировано

0
What Ranges of Probe Pressure Are Applied During Ultrasound Examinations? A Systematic Review DOI Creative Commons
Sławomir Suchoń, Michał Burkacki, Miłosz Chrzan

и другие.

Sensors, Год журнала: 2025, Номер 25(11), С. 3415 - 3415

Опубликована: Май 29, 2025

The number of US exams has nearly doubled in the last ten years. Many researchers point out probe pressure force influence on image quality and other aspects examination. This review aims to identify range applied during examinations gather information compression values various (examination types, body regions, etc.). Methods: A systematic following PRISMA guidelines was conducted using IEEE Xplore, Web Science, Scopus, PubMed/MEDLINE. Studies with quantitative data by human operators or RUSs (robotic ultrasound systems) were included. Results: From 26 included studies, ranges varied up 34.5 N for abdominal exams. Robotic systems slightly higher maximum forces (34.5 N) than (30 N). Most studies reported positive impacts monitoring diagnostic precision, no adverse effects patient comfort. Conclusions: evidence collectively emphasizes critical role US. nonuniformity reviewed does not allow identifying a clearly defined protocols. Integrating RUS standardized protocols could improve consistency accuracy.

Язык: Английский

Процитировано

0
Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact DOI
Riccardo Ferrari, Margherita Trinci, Alice Casinelli

и другие.

La radiologia medica, Год журнала: 2024, Номер unknown

Опубликована: Окт. 30, 2024

Язык: Английский

Процитировано

3
Radiomics in Radiology: What the Radiologist Need to Know about Technical Aspects and Clinical Impact DOI Open Access
Riccardo Ferrari, Margherita Trinci, Alice Casinelli

и другие.

Опубликована: Янв. 11, 2024

Radiomics represents the science of extraction and analysis a multitude quantitative features from medical imaging, unrevealing potentiality Radiologic images. This scientific review aims to provide radiologists comprehensive understanding radiomics, emphasizing its principles, applications, challenges, limits, future prospects. Limits standardization actual production are analyzed with possible solutions proposed by some papers reported commented. As continuous evolution images is ongoing, must be aware new perspective perform central role in patients management.

Язык: Английский

Процитировано

1