Artificial Intelligence in Lung Cancer Screening: The Future Is Now

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

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A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach

Pharmaceutics, Journal Year: 2023, Volume and Issue: 15(3), P. 868 - 868

Published: March 7, 2023

The new era of nanomedicine offers significant opportunities for cancer diagnostics and treatment. Magnetic nanoplatforms could be highly effective tools diagnosis treatment in the future. Due to their tunable morphologies superior properties, multifunctional magnetic nanomaterials hybrid nanostructures can designed as specific carriers drugs, imaging agents, theranostics. Multifunctional are promising theranostic agents due ability diagnose combine therapies. This review provides a comprehensive overview development advanced combining optical providing photoresponsive platforms medical applications. Moreover, this discusses various innovative developments using nanostructures, including drug delivery, treatment, tumor-specific ligands that deliver chemotherapeutics or hormonal resonance imaging, tissue engineering. Additionally, artificial intelligence (AI) used optimize material properties based on predicted interactions with cell membranes, vasculature, biological fluid, immune system enhance effectiveness therapeutic agents. Furthermore, an AI approaches assess practical utility Finally, presents current knowledge perspectives systems models.

Language: Английский

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18F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients

La radiologia medica, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

Abstract Purpose Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [ 18 F]FDG PET/CT. Material methods Primary tumor the most significant lymph node metastasis were manually segmented PET/CT of 52 newly diagnosed BC patients. Clinical parameters, NAC semiquantitative PET parameters collected. The standard reference considered was surgical pCR (ypT0;ypN0). Eight-hundred-fifty-four features (RFts) both CT datasets, according IBSI; robust RFTs selected. cohort split training (70%) validation (30%) sets. Four ML Models (Clinical Model, Model_T + N) each one with 3 learners (Random Forest (RF), Neural Network Stochastic Gradient Descendent) trained tested using RFts clinical signatures. built considering either primary alone (PET Model_T) or also including N). Results 72 uptakes (52 20 metastasis) at segmented. occurred 44.2% cases. Twelve, 46 141 selected N, respectively. showed better performance than Models. best performances obtained by RF algorithm N (AUC = 0.83;CA 0.74;TP 78%;TN 72%). Conclusion could concur prediction improve management.

Language: Английский

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A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers

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

Published: June 30, 2022

Language: Английский

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Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography

Cancers, Journal Year: 2022, Volume and Issue: 14(9), P. 2132 - 2132

Published: April 25, 2022

To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low moderate and high grading; identify positive or negative hormone receptors; discriminate human epidermal growth factor receptor 2 related to breast cancer.A total of 182 patients with known lesions that underwent Contrast-Enhanced Mammography were enrolled this retrospective study. The reference standard was pathology (118 64 lesions). A 837 textural metrics extracted by manually segmenting the region interest from both craniocaudally (CC) mediolateral oblique (MLO) views. Non-parametric Wilcoxon-Mann-Whitney test, receiver operating characteristic, logistic regression tree-based machine learning algorithms used. Adaptive Synthetic Sampling balancing approach used a feature selection process implemented.In univariate analysis, classification achieved best performance when considering original_gldm_DependenceNonUniformity on CC view (accuracy 88.98%). An accuracy 83.65% reached grading, whereas slightly lower value (81.65%) found presence receptor; original_glrlm_RunEntropy original_gldm_DependenceNonUniformity, respectively. results multivariate analysis performances using two more as predictors for classifying images (max test 95.83% non-regularized regression). Considering MLO images, (91.67%) obtained predicting grading classification-tree algorithm. Combinations only features, views, always showed values greater than equal 90.00%, exception being prediction 2, where (test 89.29%) random forest algorithm.The confirm identification differentiation histological outcomes some molecular subtypes tumors (mainly tumors) can be satisfactory through images.

Language: Английский

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Are deep models in radiomics performing better than generic models? A systematic review

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

Published: March 15, 2023

Application of radiomics proceeds by extracting and analysing imaging features based on generic morphological, textural, statistical defined formulas. Recently, deep learning methods were applied. It is unclear whether models (DMs) can outperform (GMs).

Language: Английский

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Radiomics in breast cancer: Current advances and future directions

Cell Reports Medicine, Journal Year: 2024, Volume and Issue: 5(9), P. 101719 - 101719

Published: Sept. 1, 2024

Language: Английский

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An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

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

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Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice

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

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Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

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

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