Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research

Computers in Biology and Medicine, Год журнала: 2024, Номер 179, С. 108827 - 108827

Опубликована: Июль 3, 2024

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) offers unique insights into tumor biology treatment response, it is imperative elucidate challenges constraints inherent this domain facilitate their translation clinical practice. This review examines limitations applying PET imaging, synthesizing findings last five years (2019-2023) highlights significance addressing these realize full molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, Web Science, using keywords relevant issues Only studies published peer-reviewed journals were eligible inclusion review. Although many have highlighted predicting assessing heterogeneity, enabling risk stratification, personalized therapy selection, various regarding practical implementation proposed models still need be addressed. illustrates cancer types, encompassing both phantom investigations. The analyzed highlight importance reproducible segmentation methods, standardized pre-processing post-processing methodologies, create large multicenter registered a centralized database promote continuous validation integration

Язык: Английский

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Unsupervised Learning Techniques for Breast Lesion Segmentation on MRI Images: Are We Ready for Automation?

Applied Sciences, Год журнала: 2025, Номер 15(5), С. 2401 - 2401

Опубликована: Фев. 24, 2025

In the era of precision medicine, increasing importance is given to machine learning (ML) applications. breast cancer, advanced analyses, such as radiomic process, characterise tumours and predict therapy responses. Breast magnetic resonance imaging (MRI) plays a key role in screening, staging, treatment monitoring. Lesion segmentation on MRI essential both assess tumour growth baseline for feature extraction. Manual time-consuming prone inter-operator variability, limiting access large labelled datasets robust analyses. The use ML lesion has been investigated through systematic review PubMed, exploring studies published over last 10 years. Results are compared terms performance, primarily using Dice score. Early unsupervised methods achieved mean score ∼0.75, surpassing traditional supervised (∼0.70). contrast, deep (DL) approaches based U-Net higher average scores 0.79. Further customised DL reached ∼0.83. However, there still gap research techniques, which could help reduce bias human variability. Future work may also explore multiparametric multitechnique data, integrating more representative samples, including non-mass lesions.

Язык: Английский

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Impact of different reconstruction algorithms and setting parameters on radiomics features of PSMA PET images: A preliminary study

European Journal of Radiology, Год журнала: 2024, Номер 172, С. 111349 - 111349

Опубликована: Фев. 1, 2024

Язык: Английский

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Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography

Diagnostics, Год журнала: 2025, Номер 15(8), С. 953 - 953

Опубликована: Апрель 9, 2025

Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% cases. Early diagnosis, based on identification radiological features, such as masses and microcalcifications in mammograms, crucial reducing rates. However, manual interpretation by radiologists complex subject to variability, emphasizing need automated diagnostic tools enhance accuracy efficiency. This study compares a radiomics workflow machine learning (ML) with deep (DL) approach classifying breast lesions benign or malignant. Methods: matRadiomics was used extract features from mammographic images 1219 patients CBIS-DDSM public database, including 581 cases 638 masses. Among ML models, linear discriminant analysis (LDA) demonstrated best performance both lesion types. External validation conducted private dataset 222 evaluate generalizability an independent cohort. Additionally, EfficientNetB6 model employed comparison. Results: The LDA achieved mean AUC 68.28% 61.53% In external validation, values 66.9% 61.5% were obtained, respectively. contrast, superior performance, achieving 81.52% 76.24% masses, highlighting potential DL improved accuracy. Conclusions: underscores limitations ML-based diagnosis. Deep proves be more effective approach, offering enhanced supporting clinicians improving patient management.

Язык: Английский

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Decision-Level Fusion Classification of Ovarian CT Benign and Malignant Tumors Based on Radiomics and Deep Learning of Dual Views

IEEE Access, Год журнала: 2024, Номер 12, С. 102381 - 102395

Опубликована: Янв. 1, 2024

Ovarian cancer is one of the most prevalent malignant tumors in female reproductive system, and its early diagnosis has always posed a challenge. Computed tomography (CT) widely utilized clinical management tool that can extract much detail through computer algorithms, playing vital role ovarian cancer. This research aims to develop an benign-malignant classification model based on radiomics deep learning dual views. A retrospective analysis CT images from 135 tumor patients was conducted using StratifiedKFold method (K =5) for cross-validation. Radiomics features were extracted data inputted into automated machine (A-ML) framework. Meanwhile, (DL) called Dual-View Global Representation Local Cross Transformer (D_GR_LCT) proposed global-local parallel approach end-to-end training. results indicate superiority 3D input over 2D, with average AUC-ROC 88.35% AUC-PR 88.73%. Comparative experiments demonstrate enhanced performance parameter settings. The DL achieves 88.15% 85.17%, respectively, validated by ablative comparative experiments. At decision-making level, fusion models demonstrates 91.35% 90.20%, utilizing stacking method. outperformed individual models. Thus, dual-view are recommended identification screening practice.

Язык: Английский

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Prognostic nomogram combining 18F-FDG PET/CT radiomics and clinical data for stage III NSCLC survival prediction

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 4, 2024

Язык: Английский

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A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Сен. 30, 2024

Abstract The aim of this study is to investigate the role [ 18 F]-PSMA-1007 PET in differentiating high- and low-risk prostate cancer (PCa) through a robust radiomics ensemble model. This retrospective included 143 PCa patients who underwent PET/CT imaging. areas were manually contoured on images 1781 image biomarker standardization initiative (IBSI)-compliant features extracted. A 30 times iterated preliminary analysis pipeline, comprising least absolute shrinkage selection operator (LASSO) for feature fivefold cross-validation model optimization, was adopted identify most dataset variations, select candidate models modelling, optimize hyperparameters. Thirteen subsets selected features, 11 generated from plus two additional subsets, first based combination fine-tuning second only used train ensemble. Accuracy, area under curve (AUC), sensitivity, specificity, precision, f -score values calculated provide models’ performance. Friedman test, followed by post hoc tests corrected with Dunn-Sidak correction multiple comparisons, verify if statistically significant differences found different over iterations. trained obtained highest average accuracy (79.52%), AUC (85.75%), specificity (84.29%), precision (82.85%), (78.26%). Statistically ( p < 0.05) some performance metrics. These findings support improving risk stratification PCa, reducing dependence biopsies.

Язык: Английский

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Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model

Journal of Imaging, Год журнала: 2024, Номер 10(11), С. 290 - 290

Опубликована: Ноя. 14, 2024

Radiomics provides a structured approach to support clinical decision-making through key steps; however, users often face difficulties when switching between various software platforms complete the workflow. To streamline this process, matRadiomics integrates entire radiomics workflow within single platform. This study extends

Язык: Английский

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Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma

Cancers, Год журнала: 2024, Номер 16(19), С. 3369 - 3369

Опубликована: Окт. 1, 2024

Positron emission tomography (PET) using radiolabeled prostate-specific membrane antigen targeting PET-imaging agents has been increasingly used over the past decade for imaging and directing prostate carcinoma treatment. Here, we summarize available literature data on radiomics machine learning these in carcinoma. Gleason scores derived from biopsy after resection are discordant a large number of patients. Available studies suggest that applied to PSMA-radioligand avid primary might be better performing than biopsy-based Gleason-scoring could serve as an alternative non-invasive GS characterization. Furthermore, it may allow prediction biochemical recurrence with net benefit clinical utilization. Machine based PET/CT features was also shown able differentiate benign malignant increased tracer uptake PSMA-targeting radioligand examinations, thus paving way fully automated image reading nuclear medicine. As treatment outcome following 177Lu-PSMA therapy overall survival, limited have reported promising results images this purpose. Its added value parameters warrants further exploration larger datasets

Язык: Английский

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Role of [18F]-PSMA-1007 PET radiomics for seminal vesicle invasion prediction in primary prostate cancer

Computers in Biology and Medicine, Год журнала: 2024, Номер 183, С. 109249 - 109249

Опубликована: Окт. 9, 2024

Язык: Английский

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