Imaging-Based Prediction of Molecular Therapy Targets in NSCLC by Radiogenomics and AI Approaches: A Systematic Review DOI Creative Commons
Gaia Ninatti, Margarita Kirienko, Emanuele Neri

и другие.

Diagnostics, Год журнала: 2020, Номер 10(6), С. 359 - 359

Опубликована: Май 30, 2020

The objective of this systematic review was to analyze the current state art imaging-derived biomarkers predictive genetic alterations and immunotherapy targets in lung cancer. We included original research studies reporting development validation imaging feature-based models. overall quality, standard advancements towards clinical practice were assessed. Eighteen out 24 selected articles classified as "high-quality" according Quality Assessment Diagnostic Accuracy Studies 2 (QUADAS-2). 18 "high-quality papers" adhered Transparent Reporting a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) with mean 62.9%. majority (16/18) phase II. most commonly used predictors radiomic features, followed by visual qualitative computed tomography (CT) convolutional neural network-based approaches positron emission (PET) parameters, all alone combined clinicopathologic features. (14/18) focused on epidermal growth factor receptor (EGFR) mutation. Thirty-five imaging-based models built predict EGFR status. model's performances ranged from weak (n = 5) acceptable 11), excellent 18) outstanding 1) set. Positive outcomes also reported ALK rearrangement, ALK/ROS1/RET fusions programmed cell death ligand 1 (PD-L1) expression. Despite promising results terms performance, image-based models, suffering methodological bias, require further before replacing traditional molecular pathology testing.

Язык: Английский

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping DOI
Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah

и другие.

Radiology, Год журнала: 2020, Номер 295(2), С. 328 - 338

Опубликована: Март 10, 2020

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of biomarkers from acquired imaging for purpose high-throughput quantitative analysis (radiomics). Lack reproducibility and validation studies considered to be a major challenge field. Part this lies in scantiness consensus-based guidelines definitions process translating into biomarkers. IBSI therefore seeks provide nomenclature definitions, benchmark data sets, values verify processing calculations, as well reporting guidelines, analysis.

Язык: Английский

Процитировано

2825
Predicting cancer outcomes with radiomics and artificial intelligence in radiology DOI
Kaustav Bera, Nathaniel Braman, Amit Gupta

и другие.

Nature Reviews Clinical Oncology, Год журнала: 2021, Номер 19(2), С. 132 - 146

Опубликована: Окт. 18, 2021

Язык: Английский

Процитировано

485
AI-based computer-aided diagnosis (AI-CAD): the latest review to read first DOI
Hiroshi Fujita

Radiological Physics and Technology, Год журнала: 2020, Номер 13(1), С. 6 - 19

Опубликована: Янв. 2, 2020

Язык: Английский

Процитировано

251
Radiomics and deep learning in lung cancer DOI
Michele Avanzo,

Joseph Stancanello,

G. Pirrone

и другие.

Strahlentherapie und Onkologie, Год журнала: 2020, Номер 196(10), С. 879 - 887

Опубликована: Май 4, 2020

Язык: Английский

Процитировано

206
Machine Learning in oncology: A clinical appraisal DOI
Renato Cuocolo, Martina Caruso, Teresa Perillo

и другие.

Cancer Letters, Год журнала: 2020, Номер 481, С. 55 - 62

Опубликована: Апрель 4, 2020

Язык: Английский

Процитировано

164
Understanding Sources of Variation to Improve the Reproducibility of Radiomics DOI Creative Commons

Binsheng Zhao

Frontiers in Oncology, Год журнала: 2021, Номер 11

Опубликована: Март 29, 2021

Radiomics is the method of choice for investigating association between cancer imaging phenotype, genotype and clinical outcome prediction in era precision medicine. The fast dispersal this new methodology has benefited from existing advances core technologies involved radiomics workflow: image acquisition, tumor segmentation, feature extraction machine learning. However, despite rapidly increasing body publications, there no real use a developed signature so far. Reasons are multifaceted. One major challenges lack reproducibility generalizability reported signatures (features models). Sources variation exist each step workflow; some controllable or can be controlled to certain degrees, while others uncontrollable even unknown. Insufficient transparency reporting studies further prevents translation bench bedside. This review article first addresses sources variation, which illustrated using demonstrative examples. Then, it reviews number published progresses made date investigation improvement model performance. Lastly, discusses potential strategies practical considerations reduce variability improve quality study. focuses on CT quantitative extraction, disease lung cancer.

Язык: Английский

Процитировано

120
Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential DOI Creative Commons
Xingping Zhang, Yanchun Zhang, Guijuan Zhang

и другие.

Frontiers in Oncology, Год журнала: 2022, Номер 12

Опубликована: Фев. 17, 2022

The high-throughput extraction of quantitative imaging features from medical images for the purpose radiomic analysis, i.e., radiomics in a broad sense, is rapidly developing and emerging research field that has been attracting increasing interest, particularly multimodality multi-omics studies. In this context, analysis multidimensional data plays an essential role assessing spatio-temporal characteristics different tissues organs their microenvironment. Herein, recent developments method, including manually defined features, acquisition preprocessing, lesion segmentation, feature extraction, selection dimension reduction, statistical model construction, are reviewed. addition, deep learning-based techniques automatic segmentation being analyzed to address limitations such as rigorous workflow, manual/semi-automatic annotation, inadequate criteria, multicenter validation. Furthermore, summary current state-of-the-art applications technology disease diagnosis, treatment response, prognosis prediction perspective radiology images, histopathology three-dimensional dose distribution data, oncology, presented. potential value diagnostic therapeutic strategies also further analyzed, first time, advances challenges associated with dosiomics radiotherapy summarized, highlighting latest progress radiomics. Finally, robust framework presented recommendations future development discussed, but not limited factors affect stability (medical big multitype expert knowledge medical), data-driven processes (reproducibility interpretability studies, alternatives various institutions, prospective researches clinical trials), thoughts on directions (the capability achieve open platform analysis).

Язык: Английский

Процитировано

117
Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine DOI Open Access
Sanjay Saxena, Biswajit Jena, Neha Gupta

и другие.

Cancers, Год журнала: 2022, Номер 14(12), С. 2860 - 2860

Опубликована: Июнь 9, 2022

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates prediction model through various AI methods to stratify risk patients, monitor therapeutic approaches, assess clinical outcomes. shown tremendous achievements prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, progression-free for human cancer study. Although immense performance care aspects, it several challenges limitations. The proposed review provides an overview radiogenomics viewpoints on role terms its promises computational well oncological aspects offers opportunities era medicine. also presents recommendations diminish these obstacles.

Язык: Английский

Процитировано

88
Artificial intelligence in pancreatic cancer DOI Creative Commons
Bowen Huang, Haoran Huang, Shuting Zhang

и другие.

Theranostics, Год журнала: 2022, Номер 12(16), С. 6931 - 6954

Опубликована: Янв. 1, 2022

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%.The pancreatic patients diagnosed early screening have median nearly ten years, compared 1.5 years for those not screening.Therefore, diagnosis and treatment are particularly critical.However, as rare general cost high, accuracy existing tumor markers enough, efficacy methods exact.In terms diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, other aspects, then lesions early.At same time, algorithm also be used to predict recurrence risk, metastasis, therapy response which could affect prognosis.In addition, widely in health records, estimating imaging parameters, developing computer-aided systems, etc. Advances AI applications will require concerted effort among clinicians, basic scientists, statisticians, engineers.Although it has some limitations, play an essential role overcoming foreseeable future due its mighty computing power.

Язык: Английский

Процитировано

74
Radiogenomics in Renal Cancer Management—Current Evidence and Future Prospects DOI Open Access
Matteo Ferro, Gennaro Musi, Michele Marchioni

и другие.

International Journal of Molecular Sciences, Год журнала: 2023, Номер 24(5), С. 4615 - 4615

Опубликована: Фев. 27, 2023

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses cystic lesions the differential benign or malignant tissues has potential pitfalls when imaging even biopsy applied. The recent artificial intelligence, techniques, genomics advancements have ability help clinicians set stratification risk, selection, follow-up strategy, prognosis disease. combination radiomics features data achieved good results but currently limited by retrospective design number patients included in clinical trials. road ahead for radiogenomics open new, well-designed prospective studies, with large cohorts required validate previously obtained enter practice.

Язык: Английский

Процитировано

58