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

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(2), P. 152 - 152

Published: Jan. 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.

Language: Английский

A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers DOI
Simone Vicini, Chandra Bortolotto, Marco Rengo

et al.

La radiologia medica, Journal Year: 2022, Volume and Issue: 127(8), P. 819 - 836

Published: June 30, 2022

Language: Английский

Citations

67
Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases DOI
Vincenza Granata, Roberta Fusco, Federica De Muzio

et al.

La radiologia medica, Journal Year: 2022, Volume and Issue: 127(7), P. 763 - 772

Published: June 2, 2022

Language: Английский

Citations

49
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
Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes DOI Creative Commons
Xingping Zhang, Guijuan Zhang,

Xingting Qiu

et al.

Biomarker Research, Journal Year: 2024, Volume and Issue: 12(1)

Published: Jan. 25, 2024

Abstract Background Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and dynamic changes. We aim develop validate a deep learning radiomics framework obtain imaging features that reflect various changes, aiding first-line treatment decisions patients. Methods conducted retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images clinicopathologic data. Two radiomic scores network feature were constructed on data sources in the 3D region. Using these features, we developed validated ‘Deep-RadScore,’ model predict prognostic factors, gene mutations, immune molecule expression levels. Findings The Deep-RadScore exhibits strong discrimination features. In independent test cohort, it achieved impressive AUCs: 0.889 lymphovascular invasion, 0.903 pleural 0.894 T staging; 0.884 EGFR ALK, 0.896 KRAS PIK3CA, TP53, 0.895 ROS1; 0.893 PD-1/PD-L1. Fusing yielded optimal predictive power, surpassing any single feature. Correlation interpretability analyses confirmed effectiveness customized capturing additional phenotypes beyond known Interpretation This proof-of-concept demonstrates new biomarkers across can be provided by fusing multiple sources. holds potential offer valuable insights radiological phenotyping characterizing diverse alterations, thereby advancing pursuit non-invasive personalized

Language: Английский

Citations

10
Dual-Time-Point Radiomics for Prognosis Prediction in Colorectal Liver Metastasis Treated with Neoadjuvant Therapy Before Radical Resection: A Two-Center Study DOI

Z. Li,

Jianing Zhang, Song Tian

et al.

Annals of Surgical Oncology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Language: Английский

Citations

1
Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern DOI Creative Commons
Vincenza Granata, Roberta Fusco, Federica De Muzio

et al.

Diagnostics, Journal Year: 2022, Volume and Issue: 12(5), P. 1115 - 1115

Published: April 29, 2022

To assess Radiomics and Machine Learning Analysis in Liver Colon Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases an external validation 30 single lesion. All were subjected to MRI studies pre-surgical setting. For each segmented volume interest (VOI), 851 radiomics features extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis patter recognition approaches performed. The best results discriminate expansive versus infiltrative front tumor growth the highest accuracy AUC at univariate obtained by wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase contrast study. With regard model, this increased performance respect for sequence except that EOB-phase sequence. model 15 significant T2-W SPACE Furthermore, pattern approaches, diagnostic again classifier was weighted KNN trained 9 metrics study, 92% on 91% set. In present have demonstrated as Analysis, based EOB-MRI allow identify several biomarkers permit recognise different Patterns CRLM.

Language: Английский

Citations

30
Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice DOI Creative Commons
Orlando Catalano, Roberta Fusco, Federica De Muzio

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(5), P. 980 - 980

Published: March 4, 2023

Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including new microvasculature imaging modalities, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced US, MicroPure, 3D automated S-Detect, nomograms, images fusion, and virtual navigation. subsequent section, discuss broadened current application US in breast clinical scenarios, distinguishing among primary complementary second-look US. Finally, mention still ongoing limitations challenging aspects

Language: Английский

Citations

21
Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma DOI Creative Commons

Maria Chiara Brunese,

Maria Rita Fantozzi,

Roberta Fusco

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(8), P. 1488 - 1488

Published: April 20, 2023

Background: This paper offers an assessment of radiomics tools in the evaluation intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, prediction staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic developed through machine learning, deep neural network recurrence biological characteristics. majority were retrospective. Conclusions: It is possible conclude that many performing models been make differential diagnosis easier radiologists predict genomic However, all retrospective, lacking further external validation prospective multicentric cohorts. Furthermore, expression results should be standardized automatized applicable clinical practice.

Language: Английский

Citations

19
Liver metastases: The role of magnetic resonance imaging DOI Creative Commons
Cesare Maino, Federica Vernuccio, Roberto Cannella

et al.

World Journal of Gastroenterology, Journal Year: 2023, Volume and Issue: 29(36), P. 5180 - 5197

Published: Sept. 20, 2023

The liver is one of the organs most commonly involved in metastatic disease, especially due to its unique vascularization. It’s well known that metastases represent common hepatic malignant tumors. From a practical point view, it’s utmost importance evaluate presence when staging oncologic patients, select best treatment possible, and finally predict overall prognosis. In past few years, imaging techniques have gained central role identifying metastases, thanks ultrasonography, contrast-enhanced computed tomography (CT), magnetic resonance (MRI). All these techniques, CT MRI, can be considered non-invasive reference standard for assessment involvement by metastases. On other hand, affected different focal lesions, sometimes benign, malignant. bases, radiologists should face differential diagnosis between benign secondary lesions correctly allocate patients management. Considering above-mentioned principles, extremely important underline refresh broad spectrum features occur everyday clinical practice. This review aims summarize with special focus on typical atypical appearance, using MRI.

Language: Английский

Citations

19
Percutanous Electrochemotherapy (ECT) in Primary and Secondary Liver Malignancies: A Systematic Review DOI Creative Commons
Vincenza Granata, Roberta Fusco,

Valeria D’Alessio

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(2), P. 209 - 209

Published: Jan. 5, 2023

The aim of the study was to analyse papers describing use Electrochemotherapy (ECT) in local treatment primary and secondary liver tumours located at different sites with histologies. Other Local Ablative Therapies (LAT) are also discussed. Analyses these demonstrate that ECT is safe effective lesions large size, independently histology treated lesions. performed better than other thermal ablation techniques > 6 cm size can be safely used treat distant, close, or adjacent vital structures. spares vessel bile ducts, repeatable, between chemotherapeutic cycles. fill gap ablative therapies due being too localized highly challenging anatomical sites.

Language: Английский

Citations

17