Editorial: Reshaping the diagnostic process in oncology: science versus technology DOI Creative Commons
Fabio Grizzi, Carmen Bax, Laura Capelli

et al.

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 13

Published: Oct. 24, 2023

EDITORIAL article Front. Oncol., 24 October 2023Sec. Molecular and Cellular Oncology Volume 13 - 2023 | https://doi.org/10.3389/fonc.2023.1321688

Language: Английский

Can We Rely on Machine Learning Algorithms as a Trustworthy Predictor for Recurrence in High-Grade Glioma? A Systematic Review and Meta-Analysis DOI Creative Commons

Ibrahim Mohammadzadeh,

Behnaz Niroomand, Bardia Hajikarimloo

et al.

Clinical Neurology and Neurosurgery, Journal Year: 2025, Volume and Issue: unknown, P. 108762 - 108762

Published: Jan. 1, 2025

Language: Английский

Citations

3
MRI-based Radiomics for Predicting Prostate Cancer Grade Groups: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies DOI
Nima Broomand Lomer, Mohammad Amin Ashoobi, Amir Mahmoud Ahmadzadeh

et al.

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

Published: Dec. 1, 2024

Language: Английский

Citations

4
Artificial Intelligence for Drug Discovery: An Update and Future Prospects DOI
Harrison Howell, Jeremy McGale,

Aurélie Choucair

et al.

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
MRI-derived radiomics models for prediction of Ki-67 index status in meningioma: a systematic review and meta-analysis DOI
Nima Broomand Lomer, Fattaneh Khalaj, Hamed Ghorani

et al.

Clinical Imaging, Journal Year: 2025, Volume and Issue: 120, P. 110436 - 110436

Published: Feb. 18, 2025

Language: Английский

Citations

0
Tumour surface regularity predicts survival and benefit from gross total resection in IDH-wildtype glioblastoma patients DOI Creative Commons
Peng Lin, Jin-shu Pang, Yulan Lin

et al.

Insights into Imaging, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 17, 2025

Language: Английский

Citations

0
Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models? DOI Creative Commons
Reza Reiazi, Surendra K. Prajapati, Leonard Che Fru

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 786 - 786

Published: March 20, 2025

Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly medical diagnostics radiation oncology. Predictive designed to assess tumor recurrence rely on comprehensive high-quality datasets, encompassing treatment planning parameters, imaging protocols, patient-specific data. However, dependency, arising from variations dose calculation algorithms, computed tomography (CT) density conversion curves, modalities, institutional can significantly undermine model reliability clinical utility. Methods: This study evaluated differences the head neck cancer plans of 19 patients using two systems, Pinnacle 9.10 RayStation 11, with similar algorithms. Variations grid size CT curves were assessed their impact dependency. Results: Results showed that had a more significant influence within than Pinnacle, while curve introduced potential discrepancies. The findings underscore role precise standardized enhancing modeling assessment. Conclusions: Incorporating such as distribution target volumes, explicit features training mitigate enhance prediction accuracy. Solutions multi-institutional data harmonization adaptation techniques essential improve generalizability robustness. These strategies support better integration into workflows, ultimately optimizing patient outcomes personalized strategies.

Language: Английский

Citations

0
Advances in liquid biopsy and virtual biopsy for care of patients with glioma: a narrative review DOI
Muhammad Awais, Abdul Rehman,

Syed Sarmad Bukhari

et al.

Expert Review of Anticancer Therapy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: April 5, 2025

The World Health Organization's 2021 classification of central nervous system neoplasms incorporated molecular and genetic features for classifying gliomas. Classification gliomas located in deep-seated structures became a clinical conundrum given the absence crucial pathological data. Advances noninvasive imaging modalities offered virtual biopsy as novel solution to this problem by identifying surrogate radiomic signatures. Liquid biopsies blood or cerebrospinal fluid provided another enormous opportunity genomic, metabolomic proteomic We summarize appraise current state evidence with regards liquid care patients PubMed, Embase Google Scholar were searched on 7/30/2024 relevant articles published after year 2013 English language. A large body preclinical preliminary suggests that is possible combined use multiple conjunction machine learning radiomics. Likewise, focused ultrasound may be valuable tool obtain genomic data regarding glioma minimally invasive manner. These will likely become an integral part future.

Language: Английский

Citations

0
Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis DOI
Nima Broomand Lomer, A.M.E. Nouri, Roshan Kumar Singh

et al.

Abdominal Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Language: Английский

Citations

0
A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof-of-Concept Study DOI Creative Commons
José Pablo Martínez Barbero,

Francisco Javier Pérez García,

David López Cornejo

et al.

Life, Journal Year: 2025, Volume and Issue: 15(4), P. 606 - 606

Published: April 5, 2025

Differentiating tumor progression from radionecrosis in patients with treated brain glioma represents a significant clinical challenge due to overlapping imaging features. This study aimed develop and evaluate machine learning model that integrates radiomics features T2*-weighted Dynamic Susceptibility Contrast MRI perfusion (DSC MRI) parameters improve diagnostic accuracy distinguishing these entities. A retrospective cohort of 46 (25 confirmed radionecrosis, 21 progression) was analyzed. From lesion segmentation on DSC MRI, 851 were extracted using PyRadiomics, alongside seven (e.g., relative cerebral blood volume, time peak) obtained time–intensity curves (TICs). These combined into single dataset 14 classification algorithms evaluated GroupKFold cross-validation (k = 4). The top-performing selected based predictive area under the curve (AUC) yield. Logistic Regression classifier achieved highest performance, an AUC 0.88, followed by multilayer perceptron AdaBoost values 0.85 0.79, respectively. precision 72%, 74%, 78% for three models, respectively, while 63%, 70%, 71%. Key variables included like wavelet-HHH_firstorder_Mean mean normalized TIC values. Our approach integrating shows strong potential progression. However, further validation larger cohorts is essential confirm generalizability this approach.

Language: Английский

Citations

0
Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma DOI

Young-ju Yun,

Johann M. E. Jende,

Freya Garhöfer

et al.

Journal of Neuro-Oncology, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Language: Английский

Citations

0