Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Cancers, Год журнала: 2023, Номер 15(17), С. 4344 - 4344

Опубликована: Авг. 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.

Язык: Английский

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Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine

Current Oncology, Год журнала: 2023, Номер 30(3), С. 2673 - 2701

Опубликована: Фев. 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.

Язык: Английский

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Fusion of shallow and deep features from 18F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer

Quantitative Imaging in Medicine and Surgery, Год журнала: 2024, Номер 14(8), С. 5460 - 5472

Опубликована: Фев. 23, 2024

Non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor-sensitizing (EGFR-sensitizing) mutations exhibit a positive response to tyrosine kinase inhibitors (TKIs). Given the limitations of current clinical predictive methods, it is critical explore radiomics-based approaches. In this study, we leveraged deep-learning technology multimodal radiomics data more accurately predict EGFR-sensitizing mutations.

Язык: Английский

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Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Diagnostics, Год журнала: 2022, Номер 12(11), С. 2644 - 2644

Опубликована: Окт. 31, 2022

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase lung management, from detection to assessment response treatment. The development imaging-based artificial intelligence (AI) models has potential play a key early customized treatment planning. Computer-aided nodules screening programs revolutionized disease. Moreover, possibility use AI approaches identify patients at risk developing during their life can help more targeted program. combination imaging features clinical laboratory data through giving promising results prediction patients’ outcomes, specific therapies, for toxic reaction development. In this review, we provide overview main AI-based tools imaging, including automated lesion detection, characterization, segmentation, outcome, radiologists clinicians foundation these applications scenario.

Язык: Английский

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Artificial Intelligence in Emergency Radiology: Where Are We Going?

Diagnostics, Год журнала: 2022, Номер 12(12), С. 3223 - 3223

Опубликована: Дек. 19, 2022

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management different pathologies essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications emergency radiology: firstly, image acquisition can be facilitated by reducing times through automatic positioning minimizing artifacts with AI-based reconstruction systems optimize quality, even critical patients; secondly, it enables an efficient workflow (AI algorithms integrated RIS-PACS workflow), analyzing characteristics images patients, detecting high-priority examinations patients emergent findings. Different machine deep learning have been trained for automated detection types disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), help radiologists detect relevant smart reporting, summarizing clinical data, grading imaging abnormalities, provide objective indicator disease's severity, resulting quick optimized treatment planning. In this review, we overview AI tools available radiology, keep up date on current technological evolution field.

Язык: 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, Год журнала: 2024, Номер 31(1), С. 403 - 424

Опубликована: Янв. 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.

Язык: Английский

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Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes

Biomarker Research, Год журнала: 2024, Номер 12(1)

Опубликована: Янв. 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

Язык: Английский

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Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research

Diagnostics, Год журнала: 2023, Номер 13(21), С. 3314 - 3314

Опубликована: Окт. 26, 2023

Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis reveal important genomic biomarkers in patients.A total of 98 primary BC samples was analyzed, comprising 34 from patients who developed distant metastases within 5-year follow-up period 44 remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected biostatistical analysis, followed by the application elastic net feature selection method. technique identified restricted number associated with metastasis. A light gradient boosting (LightGBM), categorical (CatBoost), Extreme Gradient Boosting (XGBoost), Trees (GBT), Ada (AdaBoost) algorithms utilized prediction. To assess models' predictive abilities, accuracy, F1 score, precision, recall, area under ROC curve (AUC), Brier score calculated as performance evaluation metrics. promote interpretability overcome "black box" problem ML models, SHapley Additive exPlanations (SHAP) method employed.The LightGBM outperformed other yielding remarkable accuracy 96% an AUC 99.3%. In addition evaluation, XAI-based SHAP results, increased expression levels TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, UBE2T (p ≤ 0.05) found be incidence Finally, decreased CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, CROCC genes also determined increase risk BC.The findings this study may prevent disease progression potentially improve clinical outcomes recommending customized treatment approaches patients.

Язык: Английский

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

Diagnostics, Год журнала: 2023, Номер 13(5), С. 980 - 980

Опубликована: Март 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

Язык: Английский

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

Diagnostics, Год журнала: 2023, Номер 13(8), С. 1488 - 1488

Опубликована: Апрель 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.

Язык: Английский

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