Radiogenomics: a key component of precision cancer medicine

British Journal of Cancer, Год журнала: 2023, Номер 129(5), С. 741 - 753

Опубликована: Июль 6, 2023

Язык: Английский

---

Novel Liquid Biomarkers and Innovative Imaging for Kidney Cancer Diagnosis: What Can Be Implemented in Our Practice Today? A Systematic Review of the Literature

European Urology Oncology, Год журнала: 2021, Номер 4(1), С. 22 - 41

Опубликована: Янв. 3, 2021

Язык: Английский

---

The role of imaging in the management of renal masses

European Journal of Radiology, Год журнала: 2021, Номер 141, С. 109777 - 109777

Опубликована: Май 15, 2021

The wide availability of cross-sectional imaging is responsible for the increased detection small, usually asymptomatic renal masses. More than 50 % cell carcinomas (RCCs) represent incidental findings on noninvasive imaging. Multimodality imaging, including conventional US, contrast-enhanced US (CEUS), CT and multiparametric MRI (mpMRI) pivotal in diagnosing characterizing a mass, but also provides information regarding its prognosis, therapeutic management, follow-up. In this review, data masses that urologists need accurate treatment planning will be discussed. role CEUS, mpMRI characterization masses, RCC staging follow-up surgically treated or untreated localized presented. percutaneous image-guided ablation management reviewed.

Язык: Английский

---

Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature

Cancers, Год журнала: 2023, Номер 15(2), С. 354 - 354

Опубликована: Янв. 5, 2023

Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which crucial for subsequent treatment. Currently, CT limited its ability differentiate benign from malignant disease. Therefore, various modalities have been investigated identify imaging-based parameters improve noninvasive diagnosis of masses and cell carcinoma (RCC) subtypes. MRI was reported predict grading RCC subtypes, has shown a small cohort response targeted therapy. Dynamic promising staging RCC. PET/CT radiotracers, such as 18F-fluorodeoxyglucose (FDG), 124I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), 11C-acetate, identification histology, grading, detection metastasis, assessment systemic therapy, oncological outcomes. Moreover, 99Tc-sestamibi SPECT scans results distinguishing low-grade lesions. Radiomics used further characterize based on semantic textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved be more accurate compared radiologists’ interpretations. radiogenomics are complement risk classification models Imaging-based biomarkers hold strong potential RCC, but require standardization external validation before integration into clinical routines.

Язык: Английский

---

Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic

Clinical Cancer Research, Год журнала: 2023, Номер 30(4), С. 663 - 672

Опубликована: Окт. 24, 2023

Abstract The incidence of renal cell carcinoma (RCC) is increasing worldwide, yet research within this field lagging behind other cancers. Despite increased detection early disease as a consequence the widespread use diagnostic CT scans, 25% patients have disseminated at diagnosis. Similarly, around progress to metastatic following curatively intended surgery. Surgery cornerstone in treatment RCC; however, when disseminated, immunotherapy or combination with tyrosine kinase inhibitor patient's best option. Immunotherapy potent treatment, durable responses and potential cure patient, but only half benefit from administered there are currently no methods that can identify which will respond immunotherapy. Moreover, need greatest risk relapsing after surgery for localized direct adjuvant there. Even though several molecular biomarkers been published date, we still lacking routinely used guide optimal clinical management. purpose review highlight some most promising biomarkers, discuss efforts made describe barriers needed be overcome reliable robust predictive prognostic clinic cancer.

Язык: Английский

---

Accurate detection and delineation boundary of renal cell carcinoma based on dual-targeted magnetic-fluorescent carbon dots

Chemical Engineering Journal, Год журнала: 2022, Номер 440, С. 135801 - 135801

Опубликована: Март 15, 2022

Язык: Английский

---

Artificial Intelligence in Kidney Cancer

American Society of Clinical Oncology Educational Book, Год журнала: 2022, Номер 42, С. 300 - 310

Опубликована: Май 17, 2022

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field medicine. The diagnosis, characterization, management, and treatment kidney cancer ripe with areas for improvement that may be met promises artificial intelligence. Here, we explore impact current research work in clinicians caring patients renal cancer, a focus on perspectives radiologists, pathologists, urologists. Promising preliminary results indicate assist diagnosis risk stratification newly discovered masses help guide clinical cancer. However, much this still its early stages, limited broader applicability, hampered by small datasets, varied appearance presentation cancers, intrinsic limitations rigidly structured tasks algorithms are trained to complete. Nonetheless, continued exploration holds promise toward improving care

Язык: Английский

---

MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma

European Radiology, Год журнала: 2020, Номер 31(2), С. 1029 - 1042

Опубликована: Авг. 27, 2020

Язык: Английский

---

Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease

European Radiology, Год журнала: 2022, Номер 33(3), С. 1862 - 1872

Опубликована: Окт. 18, 2022

Язык: Английский

---

Feature Robustness and Diagnostic Capabilities of Convolutional Neural Networks Against Radiomics Features in Computed Tomography Imaging

Investigative Radiology, Год журнала: 2021, Номер 57(3), С. 171 - 177

Опубликована: Сен. 15, 2021

Imaging phantoms were scanned twice on 3 computed tomography scanners from 2 different manufactures with varying tube voltages and currents. Phantoms segmented, features extracted using PyRadiomics a pretrained CNN. After standardization the concordance correlation coefficient (CCC), mean feature variance, range, of variant calculated to assess robustness. In addition, cosine similarity was for vectorized activation maps an exemplary phantom. For in vivo comparison, radiomics CNN 30 patients hepatocellular carcinoma (HCC) hepatic colon metastasis compared.In total, 851 256 each all phantoms, global CCC above 98%, whereas highest 36%. The variance range significantly lower features. Using ≤0.2 as threshold define robust averaging across 346 (41%) 196 (77%) found be robust. greater than 0.98 scanner parameter variations. retrospective analysis, 122 (49%) showed significant differences between HCC metastasis.Convolutional neural network more stable compared against technical Moreover, possibility tumor entity differentiation based shown. Combined visualization methods, are expected increase reproducibility quantitative image representations. Further studies warranted investigate impact stability radiological image-based prediction clinical outcomes.

Язык: Английский

---