Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging

Dong, Mengshi; Hou, Gang; Shu, Li; Li, Nan; Zhang, Lina; Xu, Ke

doi:10.3389/fonc.2020.558428

Cited by 5 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it has been reported that PTX could promote lymphatic metastasis of breast cancer in some cases, besides inhibiting tumour growth 4 , 5 . Lymphatic metastasis is a significant basis for determining clinical treatment and predicting the prognosis of patients with tumours after treatment 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

Paclitaxel treatment enhances lymphatic metastasis of B16F10 melanoma cells via CCL21/CCR7 axis

Zhang¹,

Zhu²,

Yao³

et al. 2022

Int. J. Biol. Sci.

View full text Add to dashboard Cite

Chemotherapeutic drugs have been successfully used to treat several cancers, including melanoma. However, metastasis occasionally occurs after chemotherapy. Here, we reported that paclitaxel (PTX) treatment for B16F10 tumour in mice led to an enhanced lymphatic metastasis of the melanoma cells, although a significant inhibition of tumour growth at the injection site was observed. Further study demonstrated that PTX upregulated the expression of C-C chemokine receptor type 7 (CCR7) in B16F10 cells, enhancing their migration through the activation of JNK and p38 signalling pathways. Loss of CCR7 or blockade of C-C motif chemokine ligand 21 (CCL21)/CCR7 axis abolished the pro-migration effect of PTX on B16F10 melanoma cells. Importantly, combination of PTX and CCR7 mAb could simultaneously delay the tumour growth and reduce the lymphatic metastasis in B16F10 melanoma. The blockade of CCL21/CCR7 axis may collectively serve as a strategy for lymphatic metastasis in some melanoma after chemotherapy.

show abstract

Section: Introductionmentioning

confidence: 99%

Paclitaxel treatment enhances lymphatic metastasis of B16F10 melanoma cells via CCL21/CCR7 axis

Zhang¹,

Zhu²,

Yao³

et al. 2022

Int. J. Biol. Sci.

View full text Add to dashboard Cite

show abstract

“…The patients enrolled in the present study were divided into training and validation cohorts. Multivariate logistic regression analysis with the backwards method ( 25 ) was used to select independent predictors from clinical features, including consolidation, age, family history, sex and smoking history, in the training cohort, and receiver operating characteristic (ROC) curves were plotted. Area under the ROC curve (AUC) values represent the predictive ability of the clinical prediction model.…”

Section: Methodsmentioning

confidence: 99%

Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity

et al. 2022

View full text Add to dashboard Cite

Patients with preinvasive or invasive pulmonary ground-glass opacity (GGO) often face different clinical treatments and prognoses. The present study aimed to identify the invasiveness of pulmonary GGO by analysing clinical and radiomic features. Patients with pulmonary GGOs who were treated between January 2014 and February 2019 were included. Clinical features were collected, while radiomic features were extracted from computed tomography records using the three-dimensional Slicer software. Predictors of GGO invasiveness were selected by least absolute shrinkage and selection operator logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model. A total of 194 patients with pulmonary GGOs were included in the present study. The maximum diameter of the solid component, waveletHLL_ngtdm_ Coarseness (P=0.03), waveletLHH_firstorder_Maximum (P<0.01) and waveletLLH_glrlm_LongRunEmphasis (P<0.01) were significant predictors of invasive lung GGOs. The area under the ROC curve (AUC) for the prediction models of clinical features and radiomic features was 0.755 and 0.719, respectively, whereas the AUC for the combined prediction model was 0.864 (95% CI, 0.802-0.926). Finally, a nomogram was established for individualized prediction of invasiveness. The combination of radiomic and clinical features can enable the differentiation between preinvasive and invasive GGOs. The present results can provide some basis for the best choice of treatment in patients with lung GGOs. Radiomics refers to extrapolation of quantitative clinical features from radiology images (17). In oncology, tumour radiomic features measured by analysing imaging data, including nodal shape and volume, as well as intensity and a series of 'texture' features, can be used to investigate the correlation among the diagnosis, prediction and prognosis of patients with cancer (18-20). The purpose of the present study was to determine the invasiveness of GGOs on the basis of the clinical and radiomic features from chest CT. Patients and methodsPatient selection and grouping. The present study considered for inclusion a total of 268 patients who underwent

show abstract

“…For medical images with small datasets, radiomics is more suitable. Recently, some researchers have used radiomics based on CT or PET/CT images for the diagnosis of benign and malignant LNs ( 12 , 24 , 25 ). Most studies of LN benignity and malignancy are single-centre studies, and so reproducibility and generalisability are not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging using machine learning

Dong,

Fu,

Liu

2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background For patient management and prognosis, accurate assessment of mediastinal lymph node (LN) status is essential. This study aimed to use machine learning approaches to assess the status of confusing LNs in the mediastinum using positron emission tomography/computed tomography (PET/CT) images; the results were then compared with the diagnostic conclusions of nuclear medicine physicians. Methods A total of 509 confusing mediastinal LNs that had undergone pathological assessment or follow-up from 320 patients from three centres were retrospectively included in the study. LNs from centres I and II were randomised into a training cohort (N=324) and an internal validation cohort (N=81), while those from centre III patients formed an external validation cohort (N=104). Various parameters measured from PET and CT images and extracted radiomics and deep learning features were used to construct PET/CT-parameter, radiomics, and deep learning models, respectively. Model performance was compared with the diagnostic results of nuclear medicine physicians using the area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). Results The coupled model of gradient boosting decision tree-logistic regression (GBDT-LR) incorporating radiomic features showed AUCs of 92.2% [95% confidence interval (CI), 0.890–0.953], 84.6% (95% CI, 0.761–0.930) and 84.6% (95% CI, 0.770–0.922) across the three cohorts. It significantly outperformed the deep learning model, the parametric PET/CT model and the physician’s diagnosis. DCA demonstrated the clinical usefulness of the GBDT-LR model. Conclusions The presented GBDT-LR model performed well in evaluating confusing mediastinal LNs in both internal and external validation sets. It not only crossed radiometric features but also avoided overfitting.

show abstract

Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging

Cited by 5 publications

References 38 publications

Paclitaxel treatment enhances lymphatic metastasis of B16F10 melanoma cells via CCL21/CCR7 axis

Paclitaxel treatment enhances lymphatic metastasis of B16F10 melanoma cells via CCL21/CCR7 axis

Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity

Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging using machine learning

Contact Info

Product

Resources

About