Dynamic contrast-enhanced MRI predicts PTEN protein expression which can function as a prognostic measure of progression-free survival in NPC patients

Wu, Gang; Huang, Weiyuan; Xu, Jian; Li, Wenzhu; Wu, Yi; Yang, Qianyu; Liu, Kun; Zhu, Mingyue; Balasubramanian, Priya; Li, Mengsen

doi:10.1007/s00432-021-03764-7

Cited by 3 publications

(6 citation statements)

References 40 publications

(32 reference statements)

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“…Lee S et al evaluated the prognostic value of magnetic resonance imaging (MRI)-based radiomics for newly diagnosed NPC in a systematic review and meta-analysis, which showed that MRI-based radiomics revealed an overall modest prognostic value in predicting PFS (mean C-index, 0.76; 95% CI, 0.69-0.84) (28). Wu G et al confirmed that dynamic contrast-enhanced MRI predicts PTEN protein expression, which can function as a prognostic measure of progression-free survival in NPC patients (38). Zhang Lu et al developed a distant metastasis MRI-based model (DMMM), which showed an AUC of 0•792(95% CI, 0•633-0•952) in validation cohorts (7).…”

Section: Discussionmentioning

confidence: 98%

Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging

et al. 2023

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BackgroundDistant metastases is the main failure mode of nasopharyngeal carcinoma. However, early prediction of distant metastases in NPC is extremely challenging. Deep learning has made great progress in recent years. Relying on the rich data features of radiomics and the advantages of deep learning in image representation and intelligent learning, this study intends to explore and construct the metachronous single-organ metastases (MSOM) based on multimodal magnetic resonance imaging.Patients and methodsThe magnetic resonance imaging data of 186 patients with nasopharyngeal carcinoma before treatment were collected, and the gross tumor volume (GTV) and metastatic lymph nodes (GTVln) prior to treatment were defined on T1WI, T2WI, and CE-T1WI. After image normalization, the deep learning platform Python (version 3.9.12) was used in Ubuntu 20.04.1 LTS to construct automatic tumor detection and the MSOM prediction model.ResultsThere were 85 of 186 patients who had MSOM (including 32 liver metastases, 25 lung metastases, and 28 bone metastases). The median time to MSOM was 13 months after treatment (7–36 months). The patients were randomly assigned to the training set (N = 140) and validation set (N = 46). By comparison, we found that the overall performance of the automatic tumor detection model based on CE-T1WI was the best (6). The performance of automatic detection for primary tumor (GTV) and lymph node gross tumor volume (GTVln) based on the CE-T1WI model was better than that of models based on T1WI and T2WI (AP@0.5 is 59.6 and 55.6). The prediction model based on CE-T1WI for MSOM prediction achieved the best overall performance, and it obtained the largest AUC value (AUC = 0.733) in the validation set. The precision, recall, precision, and AUC of the prediction model based on CE-T1WI are 0.727, 0.533, 0.730, and 0.733 (95% CI 0.557–0.909), respectively. When clinical data were added to the deep learning prediction model, a better performance of the model could be obtained; the AUC of the integrated model based on T2WI, T1WI, and CE-T1WI were 0.719, 0.738, and 0.775, respectively. By comparing the 3-year survival of high-risk and low-risk patients based on the fusion model, we found that the 3-year DMFS of low and high MSOM risk patients were 95% and 11.4%, respectively (p < 0.001).ConclusionThe intelligent prediction model based on magnetic resonance imaging alone or combined with clinical data achieves excellent performance in automatic tumor detection and MSOM prediction for NPC patients and is worthy of clinical application.

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Section: Discussionmentioning

confidence: 98%

Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging

et al. 2023

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“…V e refers to the amount of contrast agent every unit DCE-MRI allows. Previous studies reported that pre-treatment K trans and V e maps showed promise for predicting disease-specific survival and monitoring of treatment response in patients with NPC ( 19 , 25 , 26 ). In the present study, the multivariate Cox proportional hazards model suggested that the Radscore was the only independent prognostic biomarker.…”

Section: Discussionmentioning

confidence: 99%

“…With proper quantitative analysis, the data provides functional information on blood flow, vascular permeability, and angiogenesis, in addition to the morphological tumor characteristics used in clinical practice ( 18 ). Quantitative DCE-MRI parameters can be easily derived for each pixel within the tumor using commercially available software and can be visualized in a separate image for each parameter ( 19 ). This makes DCE-MRI a powerful prognostic tool.…”

Section: Introductionmentioning

confidence: 99%

Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma

et al. 2022

Front. Oncol.

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To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans) and extracellular extravascular volume (Ve) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan–Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans, Ve, and Ktrans+Ve images, respectively). The combined Radscorektrans+Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571–0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscorektrans (C-index, 0.693; 95% CI, 0.560–0.826) and RadscoreVe(C-index, 0.614; 95% CI, 0.481–0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscorektrans+Ve and risk factors (C-index, 0.732; 95% CI: 0.599–0.864) was better than Radscorektrans+Ve in predicting PFS in patients with advanced NPC. A lower Radscorektrans+Ve (HR 3.5584, 95% CI 2.1341–5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262–4.854), lower T stage (HR 1.4365, 95% CI 0.6745–3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899–1.267) were associated with longer PFS. Kaplan–Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.

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“…CeRNA networks at least partially promote NPC proliferation by inhibiting PTEN expression. PTEN is a tumor suppressor of NPC and a key negative regulatory gene of PI3K/Akt signaling pathway [ 60 , 61 ].…”

Section: Biological Functions Of Lncrna/circrna-mediated Cerna Networ...mentioning

confidence: 99%

“…Low expression of PTEN predicts poor prognosis and shorter progression-free survival in NPC patients [ 61 ]. Overexpression of circITCH significantly inhibits NPC cell proliferation [ 58 ].…”

Section: Biological Functions Of Lncrna/circrna-mediated Cerna Networ...mentioning

confidence: 99%

Insight into LncRNA- and CircRNA-Mediated CeRNAs: Regulatory Network and Implications in Nasopharyngeal Carcinoma—A Narrative Literature Review

Zhang

Xin

et al. 2022

Cancers

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Nasopharyngeal carcinoma (NPC) is a kind of head-and-neck malignant tumor, and distant metastasis treatment resistance is the leading cause of patient death. In-depth understanding of NPC progression and treatment failure remains to be explored. Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are noncoding RNAs that play key regulatory role in shaping tumor cell activities. Recent studies have revealed that lncRNA and circRNA function as competitive endogenous RNAs (ceRNAs) by regulating the posttranscriptional expression of genes as miRNA baits. The imbalanced ceRNA networks derived from lncRNA/circRNA-miRNA-mRNA interaction are widely found to contribute to NPC development. Herein, we summarize typical examples of lncRNA/circRNA-associated ceRNAs in recent years, which involved the potential molecular mechanisms in the regulation of proliferation, apoptosis, treatment resistance and metastasis of NPC, and discuss their potential clinical significance in the prognosis and treatment of NPC. Interpreting the involvement of ceRNAs networks will provide new insight into the pathogenesis and treatment strategies of NPC. However, ceRNA regulatory mechanism has some limitations currently. Screening the most effective ceRNA targets and the clinical application of ceRNA still has many challenges.

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Dynamic contrast-enhanced MRI predicts PTEN protein expression which can function as a prognostic measure of progression-free survival in NPC patients

Cited by 3 publications

References 40 publications

Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging

Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma

Insight into LncRNA- and CircRNA-Mediated CeRNAs: Regulatory Network and Implications in Nasopharyngeal Carcinoma—A Narrative Literature Review

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