Radiomics Analysis of 18F-FDG PET/CT for Prognosis Prediction in Patients with Stage III Non-Small Cell Lung Cancer Undergoing Neoadjuvant Chemoradiation Therapy Followed by Surgery

Cancers, Год журнала: 2023, Номер 15(7), С. 2012 - 2012

Опубликована: Март 28, 2023

We investigated the prognostic significance of radiomic features from 18F-FDG PET/CT to predict overall survival (OS) in patients with stage III NSCLC undergoing neoadjuvant chemoradiation therapy followed by surgery. enrolled 300 who underwent at initial work-up (PET1) and after concurrent chemoradiotherapy (PET2). Radiomic primary tumor were subjected LASSO regression select most useful OS. The score conventional PET parameters was assessed Cox proportional hazards analysis. In parameters, metabolic volume (MTV) total lesion glycolysis (TLG) each PET1 PET2 significantly associated addition, both PET1-LASSO PET2-LASSO multivariate analysis, only an independently significant factor for showed better predictive performance OS regarding time-dependent receiver operating characteristic curve decision analysis than parameters. independent estimation NSCLC. newly developed using results individualized

Язык: Английский

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Quantitative Proteomics of Maternal Blood Plasma in Isolated Intrauterine Growth Restriction

International Journal of Molecular Sciences, Год журнала: 2023, Номер 24(23), С. 16832 - 16832

Опубликована: Ноя. 27, 2023

Intrauterine growth restriction (IUGR) remains a significant concern in modern obstetrics, linked to high neonatal health problems and even death, as well childhood disability, affecting adult quality of life. The role maternal fetus adaptation during adverse pregnancy is still not completely understood. This study aimed investigate the disturbance biological processes associated with isolated IUGR via blood plasma proteomics. levels 125 proteins were quantified by liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) corresponding stable isotope-labeled peptide standards (SIS). Thirteen potential markers (Gelsolin, Alpha-2-macroglobulin, Apolipoprotein A-IV, B-100, Apolipoprotein(a), Adiponectin, Complement C5, D, Alpha-1B-glycoprotein, Serum albumin, Fibronectin, Glutathione peroxidase 3, Lipopolysaccharide-binding protein) found be inter-connected protein-protein network. These are involved lipoprotein assembly, remodeling, clearance; lipid metabolism, especially cholesterol phospholipids; hemostasis, including platelet degranulation; immune system regulation. Additionally, 18 specific particular type (early or late). Distinct patterns coagulation fibrinolysis systems observed between early- late-onset IUGR. Our findings highlight complex interplay factors differences other placenta-related conditions like PE. Understanding these mechanisms crucial for developing targeted interventions improving outcomes pregnancies affected

Язык: Английский

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Computed Tomography-based Radiomics Nomogram for the Preoperative Prediction of Tumor Deposits and Clinical Outcomes in Colon Cancer: a Multicenter Study

Academic Radiology, Год журнала: 2022, Номер 30(8), С. 1572 - 1583

Опубликована: Дек. 23, 2022

Язык: Английский

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Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013–2023)

Frontiers in Oncology, Год журнала: 2024, Номер 14

Опубликована: Окт. 30, 2024

Background The incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis precise treatment are essential for improving patient survival outcomes. Over the past decade, integration artificial intelligence (AI) medical imaging technologies has positioned radiomics as a critical area research in diagnosis, treatment, prognosis CRC. Methods We conducted comprehensive review CRC-related literature published between 1 January 2013 31 December 2023 using Web Science Core Collection database. Bibliometric tools such Bibliometrix, VOSviewer, CiteSpace were employed to perform an in-depth bibliometric analysis. Results Our search yielded 1,226 publications, revealing consistent annual growth CRC research, with significant rise after 2019. China led publication volume (406 papers), followed by United States (263 whereas dominated citation numbers. Notable institutions included General Electric, Harvard University, University London, Maastricht Chinese Academy Sciences. Prominent researchers this field Tian J from Sciences, highest count, Ganeshan B most citations. Journals leading counts Frontiers Oncology Radiology . Keyword analysis identified deep learning, texture analysis, rectal cancer, image management prevailing themes. Additionally, recent trends indicate growing importance AI multi-omics integration, focus on precision medicine applications Emerging keywords learning shown rapid bursts over 3 years, reflecting shift toward more advanced technological applications. Conclusions Radiomics plays crucial role clinical CRC, providing valuable insights medicine. It significantly contributes predicting molecular biomarkers, assessing tumor aggressiveness, monitoring efficacy. Future should prioritize advancing algorithms, enhancing data further expanding

Язык: Английский

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Computed tomography-based radiomics nomogram for the pre-operative prediction of BRAF mutation and clinical outcomes in patients with colorectal cancer: a double-center study

British Journal of Radiology, Год журнала: 2023, Номер 96(1148)

Опубликована: Май 17, 2023

Objective: To develop and validate a radiomics nomogram based on CT for the pre-operative prediction of BRAF mutation clinical outcomes in patients with colorectal cancer (CRC). Methods: A total 451 CRC (training cohort = 190; internal validation 125; external 136) from 2 centers were retrospectively included. Least absolute shrinkage selection operator regression was used to select features score (Radscore) calculated. Nomogram constructed by combining Radscore significant predictors. Receiver operating characteristic curve analysis, calibration decision analysis evaluate predictive performance nomogram. Kaplan‒Meier survival curves assess overall (OS) entire cohort. Results: The consisted nine which most relevant mutation. integrating independent predictors (age, tumor location cN stage) showed good discrimination AUCs 0.86 (95% CI: 0.80–0.91), 0.82 0.74–0.90) 0.75–0.90) training cohort, cohorts, respectively. Furthermore,the significantly better than that model (p < 0.05). nomogram-predicted high-risk group had worse OS low-risk 0.0001). Conclusion: predicting patients, could provide valuable information individualized treatment. Advances knowledge: effectively predict CRC. High-risk identified independently associated poor OS.

Язык: Английский

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The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study

Frontiers in Oncology, Год журнала: 2023, Номер 13

Опубликована: Июнь 14, 2023

To establish and validate a machine learning based radiomics model for detection of perineural invasion (PNI) in gastric cancer (GC). This retrospective study included total 955 patients with GC selected from two centers; they were separated into training (n=603), internal testing (n=259), external (n=93) sets. Radiomic features derived three phases contrast-enhanced computed tomography (CECT) scan images. Seven (ML) algorithms including least absolute shrinkage selection operator (LASSO), naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) support vector (SVM) trained development optimal signature. A combined was constructed by aggregating the radiomic signatures important clinicopathological characteristics. The predictive ability then assessed receiver operating characteristic (ROC) calibration curve analyses all PNI rates training, testing, sets 22.1, 22.8, 36.6%, respectively. LASSO algorithm signature establishment. signature, consisting 8 robust features, revealed good discrimination accuracy (training set: AUC = 0.86; 0.82; 0.78). risk significantly associated higher scores. that integrated T stage demonstrated enhanced excellent 0.89; 0.84; 0.82). suggested exhibited satisfactory prediction performance GC.

Язык: Английский

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A different perspective on 18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findings

Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), Год журнала: 2023, Номер 42(6), С. 359 - 366

Опубликована: Апрель 23, 2023

Язык: Английский

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Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)

Nuklearmedizin - NuclearMedicine, Год журнала: 2023, Номер 62(06), С. 361 - 369

Опубликована: Ноя. 23, 2023

Abstract Aim Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described publications for PET-based outcome prediction. Methods A systematic search original was run PubMed. All were rated according 17 criteria proposed by authors. Criteria with >2 rating categories binarized into “adequate” or “inadequate”. The association between per article date publication examined. Results One hundred identified (published 07/2017 09/2023). median proportion criterion that 65% (range: 23–98%). Nineteen (19%) mentioned neither test cohort nor cross-validation separate training from testing. an 12.5 out (range, 4–17), this did not increase later dates (Spearman’s rho, 0.094; p = 0.35). In 22 (22%), less than half items “adequate”. Only 8% published source code, 10% made dataset openly available. Conclusion Among investigated, weaknesses have been identified, degree compliance recommendations quality reporting shows potential improvement. Better adherence established guidelines could significance prediction finally lead widespread use routine practice.

Язык: Английский

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Usefulness of dual-layer detector spectral CT multi-parameter imaging in preoperative prediction of lymphovascular invasion and perineural invasion in patients with colorectal cancer

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Апрель 19, 2024

Background We aimed to investigate the usefulness of combining quantitative parameters obtained with dual-layer detector spectral CT (DLSCT) and clinical risk factors for preoperative prediction lymphovascular invasion (LVI) perineural (PNI) in patients colorectal cancer (CRC). Materials methods From June 2022 August 2023, 122 clinically suspected CRC were prospectively enrolled study scanning, 49 confirmed by histopathology. Using pathological results, divided into LVI-positive LVI-negative groups PNI-positive PNI-negative groups, their imaging characteristics determined. The arterial-phase (AP) venous-phase (VP) images analyzed this study. Intergroup comparisons performed using independent-sample t-test, Mann–Whitney U test, chi-square test. Independent predictors LVI group PNI determined univariate multifactorial logistic regression analysis, single parameter combined models constructed accordingly. Receiver operating characteristic curve (ROC) analysis was evaluate effect different models. Results Tumor maximum diameter (Tdia) normalized iodine density (NID) AP (NID_AP) independent (P < 0.05), while Tdia, carcinoembryonic antigen 19 − 9 (CA19-9) level, NID at VP (NID_VP) 0.05) patients. area under (AUC) values Tdia NID_AP predicting status 0.795 0.776, respectively. Similarly, AUC CA19-9 NID_VP among 0.804, 0.701, 0.735, Models these yielded 0.899 (95% confidence interval [CI]: 0.781–0.966) 0.871 CI: 0.771–0.971) status, Thus, model significantly better than any predictor alone. Conclusion models, which integrated DLSCT parameters, demonstrated good predictive capability determining patients, order provide references treatment decision-making.

Язык: Английский

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Radiomics in Precision Medicine for Colorectal Cancer: A Bibliometric Analysis (2013-2023)

Опубликована: Янв. 1, 2024

Background: The rising incidence and mortality of colorectal cancer (CRC) highlight the urgent need for enhanced early detection precision medicine. Powered by advancements in artificial intelligence, radiomics is rapidly evolving, significantly impacting diagnosis, treatment, prognosis CRC.Methods: Publications related to CRC, spanning from January 1, 2013, December 31, 2023, were collected Web Science Core Collection (WOSCC) database. Various analytical tools including Bibliometrix, VOSviewer, Scimago Graphica CiteSpace adopted visualize aspects such as co-authorship, co-occurrence, co-citation within CRC research provide a comprehensive view field's current status growth.Results: analysis encompassed 1226 publications, which exhibited yearly ascension publication volume. China emerged leading nation terms volume, with United States securing apex position citation frequency. Prominent institutions contributing this field include General Electric, Harvard University, University College London, Maastricht Chinese Academy Sciences. Among individual contributors, Jie Tian Sciences was identified most prolific author, whereas B. Ganeshan London achieved distinction being cited author. journal Frontiers Oncology featured highest number Radiology impact. Keyword pinpointed deep learning, texture analysis, cancer, image management prevailing focal points.Conclusion: Radiomics emerges pivotal innovation offering unprecedented insights into predicting molecular biomarkers, evaluating tumor malignancy, monitoring therapeutic outcomes. Future explorations should aim harness novel intelligence algorithms explore synergies between multi-omics data radiomics, thereby amplifying its utility realm medicine CRC.

Язык: Английский

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