Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study DOI Open Access
Vincenza Granata, Roberta Fusco, Federica De Muzio

et al.

Cancers, Journal Year: 2022, Volume and Issue: 14(5), P. 1110 - 1110

Published: Feb. 22, 2022

To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous tumor budding) surgical resection margin. This retrospective analysis was approved local Ethical Committee board, radiological databases were used to select patients with proof study a pre-surgical setting after neoadjuvant chemotherapy. The cohort included training set (51 61 years median age 121 metastases) an external validation (30 single lesion 60 age). For each segmented volume interest on two expert radiologists, 851 extracted as values using PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating (ROC) analysis, linear regression modelling pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), decision tree (DT)) considered. best predictor discriminate expansive versus infiltrative growth front wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis accuracy 82%, sensitivity 84%, specificity 77%. budding wavelet_LLH_firstorder_10Percentile 92%, 96%, 81%. differentiate mucinous type wavelet_LLL_glcm_ClusterTendency 88%, 38%, 100%. identify recurrence wavelet_HLH_ngtdm_Complexity 90%, 71%, 95%. model identification considering 13 textural significant metrics (accuracy 94%, 77% 99%). results eleven KNN 95%, Our confirmed capacity biomarkers several prognostic that could affect treatment choice order obtain more personalized approach.

Language: Английский

Survey of Explainable AI Techniques in Healthcare DOI Creative Commons
Ahmad Chaddad,

Jihao Peng,

Jian Xu

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(2), P. 634 - 634

Published: Jan. 5, 2023

Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the field, any judgment or decision is fraught risk. A doctor will carefully judge whether a patient sick before forming reasonable explanation based on patient's symptoms and/or an examination. Therefore, to be viable accepted tool, AI needs mimic human interpretation skills. Specifically, explainable (XAI) aims explain information behind black-box model of that reveals how decisions are made. This paper provides survey most recent XAI techniques used related applications. We summarize categorize types, highlight algorithms increase interpretability topics. addition, we focus challenging problems applications provide guidelines develop better interpretations using concepts image text analysis. Furthermore, this future directions guide developers researchers for prospective investigations clinical topics, particularly imaging.

Language: Английский

Citations

275
Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling DOI Creative Commons
Yuanpeng Zhang, Xinyun Zhang, Yu‐Ting Cheng

et al.

Military Medical Research, Journal Year: 2023, Volume and Issue: 10(1)

Published: May 16, 2023

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of images can be highly subjective and dependent expertise clinicians. Moreover, some potentially useful quantitative information in images, especially that which not visible to naked eye, often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from enables analysis prediction endpoints. Studies have reported exhibits promising performance diagnosis predicting treatment responses prognosis, demonstrating its potential a non-invasive auxiliary tool personalized medicine. remains developmental phase as numerous technical challenges yet solved, engineering statistical modeling. this review, we introduce current utility by summarizing research application diagnosis, patients with cancer. We focus machine learning approaches, selection imbalanced datasets multi-modality fusion Furthermore, stability, reproducibility, interpretability features, generalizability models. Finally, offer possible solutions research.

Language: Английский

Citations

91
Artificial Intelligence in Lung Cancer Screening: The Future Is Now DOI Open Access
Michaela Cellina, Laura Maria Cacioppa, Maurizio Cè

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(17), P. 4344 - 4344

Published: Aug. 30, 2023

Lung cancer has one of the worst morbidity and fatality rates any malignant tumour. Most lung cancers are discovered in middle late stages disease, when treatment choices limited, patients’ survival rate is low. The aim screening identification malignancies early stage more options for effective treatments available, to improve outcomes. desire efficacy efficiency clinical care continues drive multiple innovations into practice better patient management, this context, artificial intelligence (AI) plays a key role. AI may have role each process workflow. First, acquisition low-dose computed tomography programs, AI-based reconstruction allows further dose reduction, while still maintaining an optimal image quality. can help personalization programs through risk stratification based on collection analysis huge amount imaging data. A computer-aided detection (CAD) system provides automatic potential nodules with high sensitivity, working as concurrent or second reader reducing time needed interpretation. Once nodule been detected, it should be characterized benign malignant. Two approaches available perform task: first represented by segmentation consequent assessment lesion size, volume, densitometric features; consists first, followed radiomic features extraction characterize whole abnormalities providing so-called “virtual biopsy”. This narrative review aims provide overview all possible applications screening.

Language: Английский

Citations

56
Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine DOI Creative Commons
Maurizio Cè, Giovanni Irmici,

Chiara Foschini

et al.

Current Oncology, Journal Year: 2023, Volume and Issue: 30(3), P. 2673 - 2701

Published: Feb. 22, 2023

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages diagnostic and therapeutic process. Although histological investigation will remain difficult to replace, in near future radiomic approach allow a complementary, repeatable non-invasive characterization lesion, assisting oncologists neurosurgeons selecting best option correct molecular target chemotherapy. AI-driven tools are already playing an important role surgical planning, delimiting extent lesion (segmentation) its relationships with structures, thus allowing precision surgery as radical reasonably acceptable preserve quality life. Finally, AI-assisted prediction complications, recurrences response, suggesting most appropriate follow-up. Looking future, AI-powered promise integrate biochemical clinical data stratify risk direct patients personalized screening protocols.

Language: Английский

Citations

50
Radiomics signature of osteoarthritis: Current status and perspective DOI Creative Commons
Tianshu Jiang, S. H. Lau, Jiang Zhang

et al.

Journal of Orthopaedic Translation, Journal Year: 2024, Volume and Issue: 45, P. 100 - 106

Published: March 1, 2024

Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected quality patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There currently no cure for OA once bone damage established. Unfortunately, existing radiological examination limited to grading disease's severity insufficient precisely diagnose OA, detect early or predict progression. Therefore, there a pressing need develop novel approaches image analysis subtle changes identifying development rapid progressors. Recently, radiomics emerged as unique approach extracting high-dimensional imaging features that quantitatively characterise visible hidden information from routine images. Radiomics data mining via machine learning empowered precise diagnoses prognoses disease, mainly oncology. Mounting evidence shown its great potential aiding diagnosis contributing study musculoskeletal diseases. This paper will summarise current at crossroads between engineering medicine discuss application perspectives prognosis. used oncology, it may also play an essential role prognosis OA. By transforming images qualitative interpretation quantitative data, could be solution detection, progression tracking, treatment efficacy prediction. Since still stages primarily focuses on fundamental studies, this review inspire more explorations bring promising diagnoses, prognoses, management results

Language: Английский

Citations

17
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
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 textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases DOI
Vincenza Granata, Roberta Fusco, Federica De Muzio

et al.

La radiologia medica, Journal Year: 2022, Volume and Issue: 127(5), P. 461 - 470

Published: March 26, 2022

Language: Английский

Citations

60
Radiomics in medical imaging: pitfalls and challenges in clinical management DOI
Roberta Fusco, Vincenza Granata, Giulia Grazzini

et al.

Japanese Journal of Radiology, Journal Year: 2022, Volume and Issue: 40(9), P. 919 - 929

Published: March 28, 2022

Language: Английский

Citations

57
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