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  1. Article: Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy.

    Huang, Dingpin / Lin, Chen / Jiang, Yangyang / Xin, Enhui / Xu, Fangyi / Gan, Yi / Xu, Rui / Wang, Fang / Zhang, Haiping / Lou, Kaihua / Shi, Lei / Hu, Hongjie

    Frontiers in oncology

    2024  Volume 14, Page(s) 1348678

    Abstract: Objective: To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant ... ...

    Abstract Objective: To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy.
    Methods: A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis.
    Results: Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91).
    Conclusion: We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.
    Language English
    Publishing date 2024-03-20
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2024.1348678
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy.

    Xie, Dong / Xu, Fangyi / Zhu, Wenchao / Pu, Cailing / Huang, Shaoyu / Lou, Kaihua / Wu, Yan / Huang, Dingpin / He, Cong / Hu, Hongjie

    Frontiers in oncology

    2022  Volume 12, Page(s) 990608

    Abstract: Objective: To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients ... ...

    Abstract Objective: To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy.
    Methods: Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2
    Results: The C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05), with a C-index of 0.83 and 0.70, respectively. Additionally, the delta radiomics model (C-index of 0.86) had a higher predictive accuracy compared to PD-L1 expression (C-index of 0.50) (P<0.0001).
    Conclusions: The combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.
    Language English
    Publishing date 2022-10-06
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2022.990608
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Arc concave sign on thin-section computed tomography：A novel predictor for invasive pulmonary adenocarcinoma in pure ground-glass nodules.

    Fu, Gangze / Yu, Huibo / Liu, Jinjin / Xia, Tianyi / Xiang, Lanting / Li, Peng / Huang, Dingpin / Lin, Liaoyi / Zhuang, Yuandi / Yang, Yunjun

    European journal of radiology

    2021  Volume 139, Page(s) 109683

    Abstract: Objective: We aimed to investigate the risk factors of invasive pulmonary adenocarcinoma, especially to report and validate the use of our newly identified arc concave sign in predicting invasiveness of pure ground-glass nodules (pGGNs).: Methods: ... ...

    Abstract Objective: We aimed to investigate the risk factors of invasive pulmonary adenocarcinoma, especially to report and validate the use of our newly identified arc concave sign in predicting invasiveness of pure ground-glass nodules (pGGNs).
    Methods: From January 2015 to August 2018, we retrospectively enrolled 302 patients with 306 pGGNs ≤ 20 mm pathologically confirmed (141 preinvasive lesions and 165 invasive lesions). Arc concave sign was defined as smooth and sunken part of the edge of the lesion on thin-section computed tomography (TSCT). The degree of arc concave sign was expressed by the arc chord distance to chord length ratio (AC-R); deep arc concave sign was defined as AC-R larger than the optimal cut-off value. Logistic regression analysis was used to identify the independent risk factors of invasiveness.
    Results: Arc concave sign was observed in 65 of 306 pGGNs (21.2 %), and deep arc concave sign (AC-R > 0.25) were more common in invasive lesions (P = 0.008). Under microscope, interlobular septal displacements were found at tumour surface. Multivariate analysis indicated that irregular shape (OR, 3.558; CI: 1.374-9.214), presence of deep arc concave sign (OR, 3.336; CI: 1.013-10.986), the largest diameter > 10.1 mm (OR, 4.607; CI: 2.584-8.212) and maximum density > -502 HU (OR, 6.301; CI: 3.562-11.148) were significant independent risk factors of invasive lesions.
    Conclusions: Arc concave sign on TSCT is caused by interlobular septal displacement. The degree of arc concave sign can reflect the invasiveness of pGGNs. Invasive lesions can be effectively distinguished from preinvasive lesions by the presence of deep arc concave sign, irregular shape, the largest diameter > 10.1 mm and maximum density > -502 HU in pGGNs ≤ 20 mm.
    MeSH term(s) Adenocarcinoma/diagnostic imaging ; Adenocarcinoma of Lung/diagnostic imaging ; Humans ; Lung Neoplasms/diagnostic imaging ; Neoplasm Invasiveness/diagnostic imaging ; Retrospective Studies
    Language English
    Publishing date 2021-03-27
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 138815-0
    ISSN 1872-7727 ; 0720-048X
    ISSN (online) 1872-7727
    ISSN 0720-048X
    DOI 10.1016/j.ejrad.2021.109683
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1630: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: Risk Factors for The Growth of Residual Nodule in Surgical Patients with Adenocarcinoma Presenting as Multifocal Ground-glass Nodules.

    Xia, Tianyi / Cai, Mengting / Zhuang, Yuandi / Ji, Xiaowei / Huang, Dingpin / Lin, Liaoyi / Liu, Jinjin / Yang, Yunjun / Fu, Gangze

    European journal of radiology

    2020  Volume 133, Page(s) 109332

    Abstract: Purpose: We aim to investigate the risk factors influencing the growth of residual nodule (RN) in surgical patients with adenocarcinoma presenting as multifocal ground-glass nodules (GGNs).: Method: From January 2014 to June 2018, we enrolled 238 ... ...

    Abstract Purpose: We aim to investigate the risk factors influencing the growth of residual nodule (RN) in surgical patients with adenocarcinoma presenting as multifocal ground-glass nodules (GGNs).
    Method: From January 2014 to June 2018, we enrolled 238 patients with multiple GGNs in a retrospective review. Patients were categorized into growth group 63 (26.5%), and non-growth group 175 (73.5%). The median follow-up time was 28.2 months (range, 6.3-73.0 months). To obtain the time of RN growth and find the risk factors for growth, data such as age, gender, history of smoking, history of malignancy, type of surgery, pathology and radiological characteristics were analyzed to use Kaplan-Meier method with the log-rank test and Cox regression analysis.
    Results: The median growth time of RN was 56.0 months (95% CI, 45.0-67.0 months) in all 238 patients. Roundness (HR 4.62, 95% CI 2.20-9.68), part-solid nodule (CTR ≥ 50%) (HR 4.39, 95% CI 2.29-8.45), vascular convergence sign (HR 2.32, 95% CI 1.36-3.96) of RN, and age (HR 1.04, 95% CI 1.01-1.07) were independent predictors of further nodule growth. However, radiological characteristics and pathology of domain tumour (DT) cannot be used as indicators to predict RN growth.
    Conclusions: RN showed an indolent growth pattern in surgical patients with multifocal GGNs. RN with a higher roundness, presence of vascular convergence sign, more solid component, and in the elder was likely to grow. However, the growth of RN showed no association with the radiological features and pathology of DT.
    MeSH term(s) Adenocarcinoma/diagnostic imaging ; Adenocarcinoma/surgery ; Aged ; Humans ; Lung Neoplasms ; Retrospective Studies ; Risk Factors ; Solitary Pulmonary Nodule/diagnostic imaging ; Solitary Pulmonary Nodule/surgery ; Tomography, X-Ray Computed
    Language English
    Publishing date 2020-10-21
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 138815-0
    ISSN 1872-7727 ; 0720-048X
    ISSN (online) 1872-7727
    ISSN 0720-048X
    DOI 10.1016/j.ejrad.2020.109332
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1630: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.

    Liu, Jinjin / Xu, Haoli / Chen, Qian / Zhang, Tingting / Sheng, Wenshuang / Huang, Qun / Song, Jiawen / Huang, Dingpin / Lan, Li / Li, Yanxuan / Chen, Weijian / Yang, Yunjun

    EBioMedicine

    2019  Volume 43, Page(s) 454–459

    Abstract: Background: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) method.!# ...

    Abstract Background: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) method.
    Methods: We retrospectively reviewed 1157 patients with spontaneous ICH who underwent initial computed tomography (CT) scan within 6 h and follow-up CT scan within 72 h from symptom onset in our hospital between September 2013 and August 2018. Hematoma region was manually segmented at each slice to guarantee the measurement accuracy of hematoma volume. Hematoma expansion was defined as a proportional increase of hematoma volume > 33% or an absolute growth of hematoma volume > 6 mL from initial CT scan to follow-up CT scan. Univariate and multivariate analyses were performed to assess the association between clinical variables and hematoma expansion. SVM machine learning model was developed to predict hematoma expansion.
    Findings: 246 of 1157 (21.3%) patients experienced hematoma expansion. Multivariate analyses revealed the following 6 independent factors associated with hematoma expansion: male patient (odds ratio [OR] = 1.82), time to initial CT scan (OR = 0.73), Glasgow Coma Scale (OR = 0.86), fibrinogen level (OR = 0.72), black hole sign (OR = 2.52), and blend sign (OR = 4.03). The SVM model achieved a mean sensitivity of 81.3%, specificity of 84.8%, overall accuracy of 83.3%, and area under receiver operating characteristic curve (AUC) of 0.89 in prediction of hematoma expansion.
    Interpretation: The designed SVM model presented good performance in predicting hematoma expansion from routinely available variables. FUND: This work was supported by Health Foundation for Creative Talents in Zhejiang Province, China, Natural Science Foundation of Zhejiang Province, China (LQ15H180002), the Science and Technology Planning Projects of Wenzhou, China (Y20180112), Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of Ministry of Education of China, and Project Foundation for the College Young and Middle-aged Academic Leader of Zhejiang Province, China. The funders had no role in study design, data collection, data analysis, interpretation, writing of the report.
    MeSH term(s) Adult ; Aged ; Aged, 80 and over ; Biomarkers ; Cerebral Hemorrhage/diagnosis ; Cerebral Hemorrhage/etiology ; Cerebral Hemorrhage/metabolism ; Female ; Glasgow Coma Scale ; Hematoma/complications ; Hematoma/metabolism ; Hematoma/pathology ; Humans ; Image Processing, Computer-Assisted ; Male ; Middle Aged ; Models, Biological ; Odds Ratio ; Prognosis ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity ; Support Vector Machine ; Tomography, X-Ray Computed ; Young Adult
    Chemical Substances Biomarkers
    Language English
    Publishing date 2019-05-03
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2851331-9
    ISSN 2352-3964
    ISSN (online) 2352-3964
    DOI 10.1016/j.ebiom.2019.04.040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 7090: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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