Japanese Journal of Radiology, Год журнала: 2023, Номер 41(10), С. 1051 - 1061
Опубликована: Май 12, 2023
Язык: Английский
Diagnostics, Год журнала: 2023, Номер 13(2), С. 209 - 209
Опубликована: Янв. 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.
Язык: Английский
Процитировано
17
Deleted Journal, Год журнала: 2024, Номер 37(3), С. 1038 - 1053
Опубликована: Фев. 13, 2024
Abstract Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing is highly complicated error-prone process due their diverse sizes, shapes, subtle variations. In this study, we propose radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, malignant microcalcifications. Radiomic features were extracted from proprietary dataset, composed 380 136 benign, 242 ROIs. Subsequently, two distinct signatures selected differentiate tissue (detection task) (classification task). Machine learning models, namely Support Vector Machine, Random Forest, XGBoost, employed as classifiers. The shared for both tasks was then used train multi-class model capable simultaneously classifying healthy, A significant overlap discovered the detection classification signatures. performance models promising, with XGBoost exhibiting an AUC-ROC 0.830, 0.856, 0.876 classification, respectively. intrinsic interpretability features, use Mean Score Decrease method introspection, enabled models’ clinical validation. fact, most important GLCM Contrast, FO Minimum Entropy, compared found other studies on breast cancer.
Язык: Английский
Процитировано
7
La radiologia medica, Год журнала: 2024, Номер 129(8), С. 1197 - 1214
Опубликована: Июль 17, 2024
Abstract Background Radiomics can provide quantitative features from medical imaging that be correlated with various biological and diverse clinical endpoints. Delta radiomics, on the other hand, consists in analysis of feature variation at different acquisition time points, usually before after therapy. The aim this study was to a systematic review delta radiomics approaches. Methods Eligible articles were searched Embase, Pubmed, ScienceDirect using search string included free text and/or Medical Subject Headings (MeSH) 3 key terms: 'radiomics,' 'texture,' 'delta.' Studies analyzed QUADAS-2 RQS tool. Results Forty-eight studies finally included. divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 12.5%); lung (12 25%); sarcoma prostate (3 6.3%), head neck gastrointestinal malignancies excluding rectum (7 14.6%) disease sites (4 8.3%). median all 25% (mean 21% ± 12%), 13 (30.2%) achieving quality score < 10% 22 (51.2%) 25%. Conclusions shows potential benefit for several endpoints oncology, such asdifferential diagnosis, prognosis prediction treatment response, evaluation side effects. Nevertheless, suffer bias overall low methodological rigor, so conclusions are currently heterogeneous, not robust hardly replicable. Further research prospective multicenter is needed validation
Язык: Английский
Процитировано
7
Exploration of Targeted Anti-tumor Therapy, Год журнала: 2022, Номер unknown, С. 795 - 816
Опубликована: Дек. 27, 2022
The advent of artificial intelligence (AI) represents a real game changer in today's landscape breast cancer imaging. Several innovative AI-based tools have been developed and validated recent years that promise to accelerate the goal patient-tailored management. Numerous studies confirm proper integration AI into existing clinical workflows could bring significant benefits women, radiologists, healthcare systems. approach has proved particularly useful for developing new risk prediction models integrate multi-data streams planning individualized screening protocols. Furthermore, help radiologists pre-screening lesion detection phase, increasing diagnostic accuracy, while reducing workload complications related overdiagnosis. Radiomics radiogenomics approaches extrapolate so-called imaging signature tumor plan targeted treatment. main challenges development are huge amounts high-quality data required train validate these need multidisciplinary team with solid machine-learning skills. purpose this article is present summary most important applications imaging, analyzing possible perspectives widespread adoption tools.
Язык: Английский
Процитировано
25
Current Oncology, Год журнала: 2023, Номер 30(1), С. 839 - 853
Опубликована: Янв. 7, 2023
breast cancer (BC) is the world's most prevalent in female population, with 2.3 million new cases diagnosed worldwide 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led significant improvement patients' survival. Full-Field Digital Mammograph (FFDM) considered gold standard method for diagnosis of BC. From several previous studies, it has emerged that density (BD) a risk factor development BC, affecting periodicity plans present today at an international level.in this study, focus mammographic image processing techniques allow extraction indicators derived from textural patterns mammary parenchyma indicative BD factors.a total 168 patients were enrolled internal training test while 51 compose external validation cohort. Different Machine Learning (ML) have been employed classify breasts based on values tissue density. Textural features extracted only which train classifiers, thanks aid ML algorithms.the accuracy different tested classifiers varied between 74.15% 93.55%. best results reached by Support Vector (accuracy 93.55% percentage true positives negatives equal TPP = 94.44% TNP 92.31%). was not influenced choice selection approach. Considering cohort, SVM, as classifier 7 selected wrapper method, showed 0.95, sensitivity 0.96, specificity 0.90.our preliminary Radiomics analysis approach us objectively identify BD.
Язык: Английский
Процитировано
16
Tomography, Год журнала: 2023, Номер 9(3), С. 909 - 930
Опубликована: Апрель 30, 2023
Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available contrast administration image acquisition timing, with different strengths limits, mainly related to kidney enhancement, ureters distension opacification, radiation exposure. The availability of new reconstruction algorithms, such as iterative deep-learning-based has dramatically improved the quality reducing exposure at same time. Dual-Energy also an important role in this type examination, possibility renal stone characterization, synthetic unenhanced phases reduce dose, iodine maps better interpretation masses. We describe artificial intelligence applications CTU, focusing on radiomics predict tumor grading patients’ outcome personalized therapeutic approach. In narrative review, we provide comprehensive overview CTU from traditional newest techniques advanced up-to-date guide radiologists who want comprehend technique.
Язык: Английский
Процитировано
16
Journal of Cancer Research and Clinical Oncology, Год журнала: 2023, Номер 149(17), С. 16179 - 16190
Опубликована: Сен. 1, 2023
Язык: Английский
Процитировано
14
European Journal of Radiology, Год журнала: 2024, Номер 176, С. 111510 - 111510
Опубликована: Май 18, 2024
Язык: Английский
Процитировано
5
Cancers, Год журнала: 2023, Номер 15(2), С. 351 - 351
Опубликована: Янв. 5, 2023
Pancreatic cancer (PC) is one of the deadliest cancers, and it responsible for a number deaths almost equal to its incidence. The high mortality rate correlated with several explanations; main late disease stage at which majority patients are diagnosed. Since surgical resection has been recognised as only curative treatment, PC diagnosis initial believed tool improve survival. Therefore, patient stratification according familial genetic risk creation screening protocol by using minimally invasive diagnostic tools would be appropriate. cystic neoplasms (PCNs) subsets lesions deserve special management avoid overtreatment. current programs based on annual employment magnetic resonance imaging cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For unfit MRI, computed tomography (CT) could proposed, although CT results in lower detection rates, compared small lesions. actual major limit incapacity detect characterize pancreatic intraepithelial neoplasia (PanIN) EUS MR/MRCP. possibility utilizing artificial intelligence models evaluate higher-risk favour these entities, more data needed support real utility applications field screening. motives, appropriate realize research settings.
Язык: Английский
Процитировано
13
La radiologia medica, Год журнала: 2023, Номер 128(11), С. 1310 - 1332
Опубликована: Сен. 11, 2023
Язык: Английский
Процитировано
13