Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible DOI Creative Commons
Guido Costa, Lara Cavinato, Francesco Fiz

et al.

Journal of Digital Imaging, Journal Year: 2023, Volume and Issue: 36(3), P. 1038 - 1048

Published: Feb. 27, 2023

Advanced imaging and analysis improve prediction of pathology data outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical lacking clinical significance. We aimed to generate a voxel-by-voxel visual map local tumor entropy, thus allowing (1) make explainable accessible clinicians; (2) disclose quantitively characterize any intra-tumoral heterogeneity; (3) evaluate associations between data. analyzed portal phase preoperative CT 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 voxels) was created used compute value each voxel tumor. The encoded color palette. performed two analyses: (a) qualitative assessment tumors' detectability pattern distribution; (b) quantitative values distribution. latter were compared standard Hounsfield predictors post-chemotherapy regression grade (TRG). Entropy maps successfully built all tumors. Metastases qualitatively hyper-entropic surrounding parenchyma. In four cases areas exceeded margin visible at CT. identified "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, mixed. At analysis, entropy-derived (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones = n.s.). present standardized technique visualize heterogeneity on assessment. association supports its role biomarker.

Language: Английский

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative DOI Creative Commons

Gaia Spadarella,

Arnaldo Stanzione, Tugba Akinci D’Antonoli

et al.

European Radiology, Journal Year: 2022, Volume and Issue: 33(3), P. 1884 - 1894

Published: Oct. 25, 2022

The main aim of the present systematic review was a comprehensive overview Radiomics Quality Score (RQS)-based reviews to highlight common issues and challenges radiomics research application evaluate relationship between RQS features.

Language: Английский

Citations

82
Imaging to Predict Prognosis in Hepatocellular Carcinoma: Current and Future Perspectives DOI
Maxime Ronot, Victoria Chernyak, Adam M. Burgoyne

et al.

Radiology, Journal Year: 2023, Volume and Issue: 307(3)

Published: April 4, 2023

The focus of hepatocellular carcinoma (HCC) research for many years has been on noninvasive diagnosis. Standardized systematic algorithms composed combinations precise features now serve as diagnostic imaging markers HCC and constitute a major innovation liver imaging. In clinical practice, the diagnosis is based primarily secondarily pathologic analysis if are not specific. Whereas accurate essential, next phase will likely encompass predictive prognostic markers. biologically heterogeneous malignancy because complex molecular, pathologic, patient-level factors that impact outcomes treatment. recent years, there have advances in systemic therapy to augment extend existing large cache local regional options. However, guideposts treatment decisions neither sophisticated nor individualized. This review provides an overview prognosis from patient feature level with future directions toward more individualized guidance. © RSNA, 2023 See also editorial by Fowler et al this issue.

Language: Английский

Citations

54
Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma DOI Creative Commons
Zhiyuan Bo,

Jiatao Song,

Qikuan He

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108337 - 108337

Published: March 24, 2024

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In past decade, artificial intelligence (AI) technology has undergone rapid development in field clinical medicine, bringing advantages efficient data processing accurate model construction. Promisingly, AI-based radiomics played increasingly important role decision-making HCC patients, providing new technical guarantees for prediction, diagnosis, prognostication. this review, we evaluated current landscape AI management HCC, including its individual treatment, survival Furthermore, discussed remaining challenges future perspectives regarding application HCC.

Language: Английский

Citations

16
HDLBP-stabilized lncFAL inhibits ferroptosis vulnerability by diminishing Trim69-dependent FSP1 degradation in hepatocellular carcinoma DOI
Jingsheng Yuan, Tao Lv, Jian Yang

et al.

Redox Biology, Journal Year: 2022, Volume and Issue: 58, P. 102546 - 102546

Published: Nov. 19, 2022

Language: Английский

Citations

64
Stereotactic body radiation therapy for hepatocellular carcinoma: From infancy to ongoing maturity DOI Creative Commons
Shirley Lewis, Laura A. Dawson, Aisling Barry

et al.

JHEP Reports, Journal Year: 2022, Volume and Issue: 4(8), P. 100498 - 100498

Published: May 14, 2022

Hepatocellular carcinoma (HCC) accounts for 90% of liver tumours and is one the leading causes mortality. Cirrhosis due to viral hepatitis, alcohol or steatohepatitis major risk factor, while dysfunction cirrhosis a deciding factor in its treatment. The treatment modalities HCC include transplant, hepatectomy, radiofrequency ablation, transarterial chemoembolisation, radioembolisation, targeted therapy, immunotherapy, radiation therapy. role therapy has been refined with increasing use stereotactic body (SBRT). Trials over past two decades have shown efficacy safety SBRT recurrent definitive HCC, acceptance adoption some more recent guidelines. However, high quality level I evidence supporting currently lacking. Smaller randomised trials external beam suggest compared other treatments patients unresectable phase III comparing are ongoing. In this review, we discuss rationale present on efficacy, associated toxicity, technological advances.

Language: Английский

Citations

58
International Liver Cancer Association (ILCA) white paper on hepatocellular carcinoma risk stratification and surveillance DOI Open Access
Amit G. Singal, Marco Sanduzzi‐Zamparelli, Pierre Nahon

et al.

Journal of Hepatology, Journal Year: 2023, Volume and Issue: 79(1), P. 226 - 239

Published: Feb. 26, 2023

Language: Английский

Citations

40
Prediction of Response to Lenvatinib Monotherapy for Unresectable Hepatocellular Carcinoma by Machine Learning Radiomics: A Multicenter Cohort Study DOI Open Access
Zhiyuan Bo, Bo Chen, Zhengxiao Zhao

et al.

Clinical Cancer Research, Journal Year: 2023, Volume and Issue: 29(9), P. 1730 - 1740

Published: Feb. 14, 2023

Abstract Purpose: We aimed to construct machine learning (ML) radiomics models predict response lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC). Experimental Design: Patients with HCC receiving at three institutions were retrospectively identified and assigned training external validation cohorts. Tumor after initiation of was evaluated. Radiomics features extracted from contrast-enhanced CT images. The K-means clustering algorithm used distinguish radiomics-based subtypes. Ten ML constructed internally validated by 10-fold cross-validation. These subsequently verified in an cohort. Results: A total 109 patients analysis, namely, 74 the cohort 35 Thirty-two showed partial response, 33 stable disease, 44 progressive disease. overall rate (ORR) 29.4%, disease control 59.6%. 224 extracted, 25 significant further analysis. Two distant subtypes clustering, subtype 1 associated a higher ORR longer progression-free survival (PFS). Among 10 algorithms, AutoGluon displayed highest predictive performance (AUC = 0.97), which relatively 0.93). Kaplan–Meier analysis that responders had better [HR 0.21; 95% confidence interval (CI): 0.12–0.36; P &lt; 0.001] PFS (HR 0.14; CI: 0.09–0.22; 0.001) than nonresponders. Conclusions: Valuable constructed, favorable predicting HCC.

Language: Английский

Citations

25
Emerging role of molecular diagnosis and personalized therapy for hepatocellular carcinoma DOI Creative Commons
Ming-Da Wang,

Yong‐Kang Diao,

Lan‐Qing Yao

et al.

iLiver, Journal Year: 2024, Volume and Issue: 3(1), P. 100083 - 100083

Published: Feb. 9, 2024

Hepatocellular carcinoma (HCC) is a prevalent malignancy worldwide, ranking as the sixth most common and third leading cause of cancer-related mortality. Late diagnosis, limited management options, its complex etiology contribute to poor prognosis high mortality rates. Recent advances in understanding molecular mechanisms HCC innovations high-throughput sequencing technologies have led development diagnostics personalized therapies for this challenging malignancy. This review provides comprehensive overview research on diagnosis individualized treatment HCC. We highlight key potential future directions discuss application next-generation identify characterize genetic epigenetic alterations patients. These may aid selection targeted therapies, prediction response, monitoring disease progression. Furthermore, we explore role liquid biopsy prediction, monitoring, focusing circulating tumor cells, DNA, extracellular vesicles. also evolving landscape therapy HCC, including against oncogenic signaling pathways, immune checkpoint inhibitors, tumor-agnostic innovative cell-based therapies. challenges opportunities that lie ahead quest improve patient outcomes through integration precision emphasize need multi-interdisciplinary collaboration, refinement predictive prognostic biomarkers, more effective combination strategies new area medicine.

Language: Английский

Citations

16
CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma DOI Creative Commons

Huai-wen Zhang,

Delong Huang, Yiren Wang

et al.

Cancer Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Jan. 26, 2024

Abstract Background & aims The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming establish an automatic classification model. Methods We recruited 104 pathologically confirmed patients for this study. GTV samples were manually segmented into regions of interest randomly divided five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models constructed via logistic regression, support vector (SVM), random forest, Xgboost, Adaboost algorithms. diagnostic efficacy, discrimination, calibration algorithms verified area under receiver operating characteristic curve (AUC) analyses plot comparison. Results Seven screened excelled at distinguishing area. Xgboost algorithm had best discrimination comprehensive performance with AUC 0.9975 [95% confidence interval (CI): 0.9973–0.9978] mean MCC 0.9369. SVM second 0.9846 (95% CI: 0.9835– 0.9857), Matthews correlation coefficient (MCC)of 0.9105, a better calibration. All other showed excellent ability distinguish between (mean 0.9825, 0.9861,0.9727,0.9644 Adaboost, naivem Bayes respectively). Conclusion CT based on can accurately tissue, while served as complementary

Language: Английский

Citations

11
Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment DOI Creative Commons
Caiqiang Xue, Qing Zhou,

Huaze Xi

et al.

Diagnostic and Interventional Imaging, Journal Year: 2022, Volume and Issue: 104(3), P. 113 - 122

Published: Oct. 22, 2022

Language: Английский

Citations

38