Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: A systematic review and Meta-analysis

International Journal of Surgery, Journal Year: 2024, Volume and Issue: unknown

Published: March 11, 2024

Background: Colorectal cancer (CRC) stands as the third most prevalent globally, projecting 3.2 million new cases and 1.6 deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, addresses this shortfall converting medical images reproducible, quantitative data. Methods: Following PRISMA, Supplemental Digital Content 1, http://links.lww.com/JS9/C77, 2, http://links.lww.com/JS9/C78 AMSTAR-2 guidelines, 3, http://links.lww.com/JS9/C79, we systematically searched PubMed, Web of Science, Embase, Cochrane Library, Google Scholar databases until January 11, 2024, to evaluate radiomics models’ diagnostic precision in predicting LNM patients. The quality bias risk included studies were assessed using Radiomics Quality Score (RQS) modified Assessment Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses conducted. Results: Thirty-six encompassing 8,039 patients included, with a significant concentration 2022-2023 (20/36). models demonstrated pooled area under curve (AUC) 0.814 (95% CI: 0.78-0.85), featuring sensitivity specificity 0.77 0.69, 0.84) 0.73 0.67, 0.78), respectively. Subgroup revealed similar AUCs CT MRI-based models, rectal outperformed colon colorectal cancers. Additionally, utilizing cross-validation, 2D segmentation, internal validation, manual prospective design, single-center populations tended have higher AUCs. these differences not statistically significant. Radiologists collectively achieved AUC 0.659 0.627, 0.691), differing from performance ( P < 0.001). Conclusion: Artificial intelligence-based shows promise staging CRC, exhibiting predictive performance. These findings support integration clinical practice enhance strategies management.

Language: Английский

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Revolutionizing Prostate Cancer Therapy: Artificial intelligence – based Nanocarriers for Precision Diagnosis and Treatment

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104653 - 104653

Published: Feb. 1, 2025

Language: Английский

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Role of Artificial Intelligence in Medical Image Analysis: A Review of Current Trends and Future Directions

Journal of Medical and Biological Engineering, Journal Year: 2024, Volume and Issue: 44(2), P. 231 - 243

Published: April 1, 2024

Language: Английский

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Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach

Journal of Cancer, Journal Year: 2024, Volume and Issue: 15(12), P. 3943 - 3957

Published: Jan. 1, 2024

Triple-negative breast cancer (TNBC) poses significant diagnostic challenges due to its aggressive nature. This research develops an innovative deep learning (DL) model based on the latest multi-omics data enhance accuracy of TNBC subtype and prognosis prediction. The study focuses addressing constraints prior studies by showcasing a with substantial advancements in integration, statistical performance, algorithmic optimization.

Language: Английский

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An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 27, 2025

Objective To establish a combined radiomics-clinical model for the early prediction of prostate-specific antigen(PSA) response in patients with metastatic castration-resistant prostate cancer(mCRPC) after treatment abiraterone acetate(AA). Methods The data total 60 mCRPC from two hospitals were retrospectively analyzed and randomized into training group(n=48) or validation group(n=12). By extracting features biparametric MRI, including T2-weighted imaging(T2WI), diffusion-weighted imaging(DWI), apparent diffusion coefficient(ADC) maps, radiomics dataset selected using least absolute shrinkage selection operator(LASSO) regression. Four predictive models developed to assess efficacy treating mCRPC. primary outcome variable was PSA following AA treatment. performance each evaluated area under receiver operating characteristic curve(AUC). Univariate multivariate analyses performed Cox regression identify significant predictors Results integrated constructed seven extracted T2WI, DWI, ADC sequence images data. This demonstrated highest AUC both cohorts, values 0.889 (95% CI, 0.764-0.961) 0.875 0.564-0.991). Rad-score served as an independent predictor (HR: 2.21, 95% CI: 1.01-4.44). Conclusion MRI-based has potential predict Clinical relevance statement could be used noninvasively patients, which is helpful clinical decision-making.

Language: Английский

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Bone tumors: a systematic review of prevalence, risk determinants, and survival patterns

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 21, 2025

Language: Английский

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Future Research Directions

Published: March 7, 2025

Language: Английский

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Is it possible to detect cribriform adverse pathology in prostate cancer with magnetic resonance imaging machine learning-based radiomics?

Computing and artificial intelligence., Journal Year: 2024, Volume and Issue: 2(1), P. 1257 - 1257

Published: June 24, 2024

Rationale and objectives: Cribriform patterns are accepted as aggressive variants of prostate cancer. These adverse pathologies closely associated with early biochemical recurrence, metastasis, castration resistance, poor disease-related survival. A few publications exist to diagnose these two multiparametric magnetic resonance imaging (mpMRI). Most retrospective not studies that have made a difference in diagnosing pathology. It is also known fusion biopsies taken from lesions detected mpMRI insufficient detect pathologies. Our study aims this pathology using machine learning-based radiomics data MR images. Materials methods: total 88 patients who had results indicating the presence cribriform pattern adenocarcinoma underwent preoperative MRI examinations radical prostatectomy. Manual slice-by-slice 3D volumetric segmentation was performed on all axial Data processing learning analysis were conducted Python 3.9.12 (Jupyter Notebook, Pycaret Library). Results: Two radiologists, SE MAG, 7 8 years post-graduate experience, respectively, evaluated images 3D-Slicer software without knowledge histopathological findings. One hundred seventeen radiomic tissue features extracted T1 weighted (T1W) apparent diffusion coefficient (ADC) sequences for each patient. The interobserver agreement analyzed intraclass correlation (ICC). Features excellent (ICC > 0.90) further collinearity between predictors Pearson’s correlation. Variables showing very high (r ≥ ±0.80) disregarded. selected T1W ADC First-order maximum, skewness, 10th percentile ADC, Gray level size zone matrix, Large area low gray emphasis T1W.As result classification PyCaret, three best models found. single model obtained by blending models. AUC, accuracy, recall, precision, F1 scores 0.79, 0.77, 0.85, 0.82, 0.83, respectively. Conclusion: ML-based cancer can predict pattern. This prognostic factor cannot be determined through qualitative radiological evaluation may overlooked specimens.

Language: Английский

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Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making

Seminars in Radiation Oncology, Journal Year: 2024, Volume and Issue: 34(4), P. 379 - 394

Published: Sept. 11, 2024

Language: Английский

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End-to-end [18F]PSMA-1007 PET/CT radiomics-based pipeline for predicting ISUP grade group in prostate cancer

Abdominal Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 30, 2024

Language: Английский

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