Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules DOI
Shanshan Shi, Xin An, Yuhong Li

et al.

Journal of Ultrasound in Medicine, Journal Year: 2022, Volume and Issue: 42(4), P. 869 - 879

Published: Aug. 16, 2022

To explore the potential value of ultrasound radiomics in differentiating between benign and malignant breast nodules by extracting radiomic features two-dimensional (2D) grayscale images establishing a logistic regression model.The clinical data 1000 female patients (500 pathologically patients, 500 patients) who underwent examinations at our hospital were retrospectively analyzed. The cases randomly divided into training validation sets ratio 7:3. Once region interest (ROI) lesion was manually contoured, Spearman's rank correlation, least absolute shrinkage selection operator (LASSO) regression, Boruta algorithm adopted to determine optimal establish classification model. performance model assessed using area under receiver operating characteristic curve (AUC), calibration decision curves (DCA).Eight selected AUC values 0.979 0.977 sets, respectively (P = .0029), indicating good discriminative ability both datasets. Additionally, DCA suggested that model's efficiency application superior.The proposed based on 2D could facilitate differential diagnosis nodules. model, which constructed identified this study, demonstrated diagnostic be useful helping clinicians formulate individualized treatment plans for patients.

Language: Английский

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging DOI Creative Commons
Reabal Najjar

Diagnostics, Journal Year: 2023, Volume and Issue: 13(17), P. 2760 - 2760

Published: Aug. 25, 2023

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, move that is catalysing transformational shifts in the healthcare landscape. It traces evolution from initial discovery X-rays to application machine learning and deep modern medical image analysis. The primary focus this shed light on AI applications elucidating their seminal roles segmentation, computer-aided diagnosis, predictive analytics, workflow optimisation. A spotlight cast profound impact diagnostic processes, personalised medicine, clinical workflows, with empirical evidence derived series case studies across multiple disciplines. However, integration radiology not devoid challenges. ventures labyrinth obstacles are inherent AI-driven radiology—data quality, ’black box’ enigma, infrastructural technical complexities, as well ethical implications. Peering future, contends road ahead for paved promising opportunities. advocates continuous research, embracing avant-garde imaging technologies, fostering robust collaborations between radiologists developers. conclusion underlines role catalyst change stance firmly rooted sustained innovation, dynamic partnerships, steadfast commitment responsibility.

Language: Английский

Citations

311
Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging DOI Open Access
Reabal Najjar

Published: June 16, 2023

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, move that is catalysing transformational shifts in the healthcare landscape. It sheds light on journey from pioneering discovery X-rays to today’s intricate imaging technologies, infused with machine learning and deep medical image analysis. At crux this lies an in-depth study AI applications elucidating seminal roles segmentation, computer-aided diagnosis, predictive analytics, workflow optimisation. A spotlight cast profound impact diagnostic processes, personalised medicine, clinical workflows, empirical evidence derived series case studies across multiple disciplines. However, integration radiology not devoid challenges. The ventures labyrinth obstacles are inherent AI-driven — data quality, ’black box’ enigma, infrastructural technical complexities, as well ethical implications. Peering future, contends road ahead for paved promising opportunities. advocates continuous research, embracing avant-garde fostering robust collaborations between radiologists developers. concludes by firmly cementing role catalyst change stance rooted sustained innovation, dynamic partnerships, steadfast commitment responsibility.

Language: Английский

Citations

52
Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques DOI

Nishita Kalra,

Prachi Verma,

Surajpal Verma

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108917 - 108917

Published: July 25, 2024

Language: Английский

Citations

20
The application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer: A systematic review DOI
Roberta Fusco, Vincenza Granata, Sergio Venanzio Setola

et al.

Physica Medica, Journal Year: 2025, Volume and Issue: 130, P. 104891 - 104891

Published: Jan. 8, 2025

Language: Английский

Citations

2
Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence DOI
Hiroko Satake, Satoko Ishigaki, Rintaro Ito

et al.

La radiologia medica, Journal Year: 2021, Volume and Issue: 127(1), P. 39 - 56

Published: Oct. 26, 2021

Language: Английский

Citations

67
How Radiomics Can Improve Breast Cancer Diagnosis and Treatment DOI Open Access
Filippo Pesapane, P. De Marco,

Anna Rapino

et al.

Journal of Clinical Medicine, Journal Year: 2023, Volume and Issue: 12(4), P. 1372 - 1372

Published: Feb. 9, 2023

Recent technological advances in the field of artificial intelligence hold promise addressing medical challenges breast cancer care, such as early diagnosis, subtype determination and molecular profiling, prediction lymph node metastases, prognostication treatment response probability recurrence. Radiomics is a quantitative approach to imaging, which aims enhance existing data available clinicians by means advanced mathematical analysis using intelligence. Various published studies from different fields imaging have highlighted potential radiomics clinical decision making. In this review, we describe evolution AI its frontiers, focusing on handcrafted deep learning radiomics. We present typical workflow practical "how-to" guide. Finally, summarize methodology implementation cancer, based most recent scientific literature help researchers gain fundamental knowledge emerging technology. Alongside this, discuss current limitations integration into practice with conceptual consistency, curation, technical reproducibility, adequate accuracy, translation. The incorporation clinical, histopathological, genomic information will enable physicians move forward higher level personalized management patients cancer.

Language: Английский

Citations

42
Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer DOI

Wenyan Jiang,

Ruiqing Meng,

Yuan Cheng

et al.

Journal of Magnetic Resonance Imaging, Journal Year: 2023, Volume and Issue: 59(2), P. 613 - 625

Published: May 18, 2023

Background Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. Purpose To investigate value of intra‐ radiomics LVI, to develop a nomogram assist making treatment decisions. Study Type Retrospective. Population Three hundred sixteen enrolled two centers divided into training ( N = 165), internal validation 83), external 68) cohorts. Field Strength/Sequence 1.5 T 3.0 T/dynamic contrast‐enhanced (DCE) diffusion‐weighted imaging (DWI). Assessment extracted selected based on magnetic resonance (MRI) sequences create multiparametric MRI combined signature (RS‐DCE plus DWI). The clinical model was built MRI‐axillary lymph nodes (MRI ALN), MRI‐reported edema (MPE), apparent diffusion coefficient (ADC). constructed RS‐DCE DWI, ALN, MPE, ADC. Statistical Tests Intra‐ interclass correlation analysis, Mann–Whitney U test, least absolute shrinkage selection operator regression used feature selection. Receiver operating characteristic decision curve analyses compare performance model, nomogram. Results A total 10 found be associated 3 7 areas. showed good (AUCs, vs. 0.884 0.695 0.870), 0.813 0.794), 0.862 0.601 0.849) Data Conclusion preoperative might effectively assess LVI. Level Evidence Technical Efficacy Stage 2

Language: Английский

Citations

26
An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies DOI Creative Commons
Roberta Fusco, Vincenza Granata,

Igino Simonetti

et al.

Current Oncology, Journal Year: 2024, Volume and Issue: 31(1), P. 403 - 424

Published: Jan. 10, 2024

The aim of this informative review was to investigate the application radiomics in cancer imaging and summarize results recent studies support oncological with particular attention breast cancer, rectal primitive secondary liver cancer. This also aims provide main findings, challenges limitations current methodologies. Clinical published last four years (2019–2022) were included review. Among 19 analyzed, none assessed differences between scanners vendor-dependent characteristics, collected images individuals at additional points time, performed calibration statistics, represented a prospective study registered database, conducted cost-effectiveness analysis, reported on clinical application, or multivariable analysis non-radiomics features. Seven reached high radiomic quality score (RQS), seventeen earned by using validation steps considering two datasets from distinct institutes open science data domains (radiomics features calculated set representative ROIs are source). potential is increasingly establishing itself, even if there still several aspects be evaluated before passage into routine practice. There challenges, including need for standardization across all stages workflow cross-site real-world heterogeneous datasets. Moreover, multiple centers more samples that add inter-scanner characteristics will needed future, as well collecting time points, reporting statistics performing database.

Language: Английский

Citations

11
Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients With MRI-Radiomics: A Systematic Review and Meta-analysis DOI
Filippo Pesapane, Giorgio Maria Agazzi, Anna Rotili

et al.

Current Problems in Cancer, Journal Year: 2022, Volume and Issue: 46(5), P. 100883 - 100883

Published: July 21, 2022

Language: Английский

Citations

29
Deep learning performance for detection and classification of microcalcifications on mammography DOI Creative Commons
Filippo Pesapane,

Chiara Trentin,

Federica Ferrari

et al.

European Radiology Experimental, Journal Year: 2023, Volume and Issue: 7(1)

Published: Nov. 7, 2023

Breast cancer screening through mammography is crucial for early detection, yet the demand services surpasses capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model localizing characterizing microcalcifications.Three expert radiologists annotated a dataset mammograms using histology-based ground truth. The was partitioned training, validation, testing. Three neural networks (AlexNet, ResNet18, ResNet34) were trained evaluated specific metrics including receiver operating characteristics area under curve (AUC), sensitivity, specificity. reported computed test set (10% whole dataset).The included 1,000 patients aged 21-73 years 1,986 (180 density A, 220 B, 380 C, D), with 389 malignant 611 benign groups microcalcifications. AlexNet achieved best performance 0.98 0.89 specificity of, AUC detection 0.85 specificity, 0.94 classification. For ResNet18 ResNet34 0.96 0.97 0.91 0.90 AUC, retrospectively. classification, exhibited 0.75 0.84 0.88 0.92 respectively.The models accurately detect characterize mammography.AI-based systems have potential to interpreting mammograms. study highlights importance developing reliable deep learning possibly applied breast screening.• A novel tool aid interpretation by detecting • trained, validated, demonstrated high accuracy detecting/localizing mammography, highlighting AI-based

Language: Английский

Citations

19