Intratumoral and peritumoral PET/CT-based radiomics for non-invasively and dynamically predicting immunotherapy response in NSCLC

British Journal of Cancer, Год журнала: 2025, Номер unknown

Опубликована: Фев. 10, 2025

Язык: Английский

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[18F]PSMA-1007 PET/CT-based radiomics may help enhance the interpretation of bone focal uptakes in hormone-sensitive prostate cancer patients

European Journal of Nuclear Medicine and Molecular Imaging, Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Abstract Purpose We hypothesised that applying radiomics to [ 18 F]PSMA-1007 PET/CT images could help distinguish Unspecific Bone Uptakes (UBUs) from bone metastases in prostate cancer (PCa) patients. compared the performance of radiomic features human visual interpretation. Materials and methods retrospectively analysed 102 hormone-sensitive PCa patients who underwent exhibited at least one focal uptake with known clinical follow-up (reference standard). Using matRadiomics, we extracted PET CT each identified best predictor model for using a machine-learning approach generate score. Blinded readers low ( n = 2) high experience rated as either UBU or metastasis. The same performed second read three months later, access Results Of 178 uptakes, 74 (41.5%) were classified by reference standard. A combining achieved an accuracy 84.69%, though it did not surpass expert round. Less-experienced had significantly lower diagnostic baseline p < 0.05) but improved addition scores 0.05 first round). Conclusion Radiomics might differentiate UBUs. While exceed assessments, has potential enhance less-experienced evaluating uptakes.

Язык: Английский

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A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities

Computers in Biology and Medicine, Год журнала: 2025, Номер 187, С. 109768 - 109768

Опубликована: Фев. 1, 2025

Язык: Английский

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The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Апрель 17, 2025

Extracting image features can predict the prognosis and treatment effect of non-small cell lung cancer, which has been increasingly confirmed. However, specific operation using 3D-Slicer still lacks standardization. For example, segmentation is manually performed based on window or automatically through mediastinal window. The images used for feature extraction are either enhanced plain scanned. It questionable whether these influencing factors will affect results be affected. This article intends to preliminarily explore above issues. downloaded 22 patients with cancer from Cancer Imaging Archive (TCIA), including 11 cases adenocarcinoma squamous carcinoma. Perform tumor scan image, image. Manual drawing window, automatic make manual modifications. radiomics Python radiomics. Firstly, analyze original sequence perform Shapiro test. If it follows a normal distribution, an analysis variance. does not follow Friedman Compare significantly different pairwise. Then, preliminary was conducted differences between carcinoma in each group. A total 88 sets imaging were extracted, 107 Among them, 33 showed significant differences. Continuing pairwise repeated testing, found that there 2 windows. There 12 windows one difference scanning enhancement 14 groups. scan. 13 According pathological grouping 54 adenocarcinoma. CT relatively small impact extracting features, while selecting features. Therefore, choosing should carefully considered, as size range also significant, indicating high possibility distinguishing (Liu C, He Y, Luo J, Influence Image Selection Segmentation Extraction Lung Radiomics Features Using Software, 2024).

Язык: Английский

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Insights into radiomics: impact of feature selection and classification

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 15, 2024

Radiomics is an innovative discipline in medical imaging that uses advanced quantitative feature extraction from radiological images to provide a non-invasive method of interpreting the intricate biological panorama diseases. This takes advantage unique characteristics imaging, where radiation or ultrasound combines with tissues, reveal disease features and important biomarkers are invisible human eye. plays crucial role healthcare, spanning diagnosis, prognosis, recurrences, treatment response assessment, personalized medicine. systematic approach includes image preprocessing, segmentation, extraction, selection, classification, evaluation. survey attempts shed light on roles selection classification play discovering forecasting directions despite challenges posed by high dimensionality (i.e., when data contains huge number features). By analyzing 47 relevant research articles, this study has provided several insights into key techniques used across different stages radiology workflow. The findings indicate 27 articles utilized SVM classifier, while 23 surveyed studies LASSO approach. demonstrates how these particular methodologies have been widely research. assessment did, however, also point out areas require more research, such as evaluating stability algorithms adopting novel approaches like ensemble hybrid methods. Additionally, we examine some emerging subfields within field radiomics.

Язык: Английский

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Intuitive Human–Artificial Intelligence Theranostic Complementarity

Cancer Biotherapy and Radiopharmaceuticals, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Deep learning artificial intelligence (AI) algorithms are poised to subsume diagnostic imaging specialists in radiology and nuclear medicine, where radiomics can consistently outperform human analysis reporting capability, do it faster, with greater accuracy reliability. However, claims made for generative AI respect of decision-making the clinical practice theranostic medicine highly contentious. Statistical computer cannot emulate emotion, reason, instinct, intuition, or empathy. simulates without possessing it. has no understanding meaning its outputs. The unique statistical probability attributes large language models must be complemented by innate intuitive capabilities physicians who accept responsibility accountability direct care each individual patient referred management specified cancers. Complementarity envisions synergistic engagement radiomics, genomics, radiobiology, dosimetry, data collation from multidimensional sources, including electronic medical record, enable physician spend informed face time their patient. Together discernment, application technical insights will facilitate optimal formulation a personalized precision strategy empathic, efficacious, targeted treatment cancer accordance wishes.

Язык: Английский

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Radiomics in Dermatological Optical Coherence Tomography (OCT): Feature Repeatability, Reproducibility, and Integration into Diagnostic Models in a Prospective Study

Cancers, Год журнала: 2025, Номер 17(5), С. 768 - 768

Опубликована: Фев. 24, 2025

Radiomics has seen substantial growth in medical imaging; however, its potential optical coherence tomography (OCT) not been widely explored. We systematically evaluate the repeatability and reproducibility of handcrafted radiomics features (HRFs) from OCT scans benign nevi examine impact bin width (BW) selection on HRF stability. The effect using stable a classification model was also assessed. In this prospective study, 20 volunteers underwent test-retest imaging 40 nevi, resulting 80 scans. HRFs extracted manually delineated regions interest (ROIs) were assessed concordance correlation coefficients (CCCs) across BWs ranging 5 to 50. A unique set identified at each BW after removing highly correlated eliminate redundancy. These robust incorporated into multiclass classifier trained distinguish basal cell carcinoma (BCC), Bowen's disease. Six all BWs, with 25 emerging as optimal choice, balancing ability capture meaningful textural details. Additionally, intermediate (20-25) yielded 53 reproducible features. six achieved 90% accuracy AUCs 0.96 0.94 for BCC disease, respectively, compared 76% 0.86 0.80 conventional feature approach. This study highlights critical role enhancing stability provides methodological framework optimizing preprocessing radiomics. By demonstrating integration diagnostic models, we establish promising tool aid non-invasive diagnosis dermatology.

Язык: Английский

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Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation

European Journal of Nuclear Medicine and Molecular Imaging, Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

PET/CT imaging data contains a wealth of quantitative information that can provide valuable contributions to characterising tumours. A growing body work focuses on the use deep-learning (DL) techniques for denoising PET data. These models are clinically evaluated prior use, however, image assessment provides potential further evaluation. This uses radiomic features compare two manufacturer enhancement algorithms, one which has been commercialised, against 'gold-standard' reconstruction in phantom and sarcoma patient set (N=20). All studies retrospective clinical [ 18 F]FDG dataset were acquired either GE Discovery 690 or 710 scanner with volumes segmented by an experienced nuclear medicine radiologist. The modular heterogeneous used this was filled F]FDG, five repeat acquisitions scanner. DL-enhanced images compared algorithms trained emulate input images. difference between sets tested significance 93 international biomarker standardisation initiative (IBSI) standardised features. Comparing 'gold-standard', 4.0% 9.7% measured significantly different (pcritical < 0.0005) respectively (averaged over DL algorithms). Larger differences observed comparing algorithm 29.8% 43.0% measuring found be similar generated using target method more than 80% not all comparisons across unseen result offers insight into performance demonstrate applications harmonisation radiomics evaluation algorithms.

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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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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