scholarly journals Endoscopic prediction model for differentiating upper submucosal invasion (< 200 μm) and beyond in superficial esophageal squamous cell carcinoma

Oncotarget ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 9156-9165 ◽  
Author(s):  
Joohwan Bae ◽  
In Seub Shin ◽  
Yang Won Min ◽  
Insuk Sohn ◽  
Joong Hyun Ahn ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Submucosal Invasion

scholarly journals A predictive model for treatment response in patients with locally advanced esophageal squamous cell carcinoma after concurrent chemoradiotherapy: based on SUVmean and NLR

2020 ◽  
Author(s):  
chunsheng wang ◽  
Kewei Zhao ◽  
Shanliang Hu ◽  
Yong Huang ◽  
Li Ma ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Treatment Outcomes ◽  
Predictive Model ◽  
Treatment Response ◽  
Squamous Cell ◽  
Concurrent Chemoradiotherapy ◽  
Training Set

Abstract Background: We conducted this study to combine the mean standardized uptake value (SUVmean) and neutrophil to lymphocyte ratio (NLR) to establish a strong predictive model for patients with esophageal squamous cell carcinoma (ESCC) after concurrent chemoradiotherapy (CCRT). Methods: We retrospectively analyzed 163 newly diagnosed ESCC patients treated with CCRT. Eighty patients (training set) were randomly selected to generate cut-off SUVmean and NLR values by receiver operating characteristic (ROC) curve analysis and to establish a predictive model by using the independent predictors of treatment outcomes. Then, we evaluated the performance of the prediction model regarding treatment outcomes in the testing set (n=83) and in all sets. Results: A high SUVmean (>5.81) and high NLR (> 2.42) at diagnosis were associated with unfavorable treatment outcomes in patients with ESCC. The prediction model had a better performance than the simple parameters (p<0.05). With a cut-off value of 0.77, the prediction model significantly improved the specificity and positive predictive value for treatment response (88.9% and 92.1% in the training set, 95.8% and 97.1% in the testing set, and 92.2% and 91.8% in all sets, respectively). Conclusions: The pretreatment SUVmean and NLR were independent predictors of treatment response in ESCC patients treated with CCRT. The predictive model was constructed based on these two parameters and provides a highly accurate tool for predicting patient outcomes.

scholarly journals Irregularly branched microvessels as visualized by magnifying endoscopy: a reliable marker for predicting deep submucosal invasion of superficial esophageal squamous cell carcinoma

2020 ◽  
Vol 08 (03) ◽  
pp. E234-E240
Author(s):  
Yoichiro Ono ◽  
Yasuhiro Takaki ◽  
Kenshi Yao ◽  
Satoshi Ishikawa ◽  
Masaki Miyaoka ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Magnifying Endoscopy ◽  
Submucosal Invasion ◽  
Histopathological Diagnosis ◽  
Invasion Depth

Abstract Background and study aims Magnifying endoscopy with narrow-band imaging (M-NBI) is reported to be useful in diagnosing invasion depth of superficial esophageal squamous cell carcinoma (SCC), but accurate diagnosis of deep submucosal invasion (SM2) has remained difficult. However, we discovered that irregularly branched microvessels observed with M-NBI are detected in SM2 cancers with high prevalence. Thus, this retrospective study aimed to investigate the diagnostic performance of irregularly branched microvessels as visualized by M-NBI for predicting SM2 cancers. Patients and methods Patients with superficial esophageal SCC lesions that were endoscopically or surgically resected at our hospital between September 2005 and December 2014 were included. Endoscopic findings by M-NBI of these lesions were presented to an experienced endoscopist who was unaware of the histopathological diagnosis and who then judged whether irregularly branched microvessels were present. Using the invasion depth according to postoperative histopathological diagnosis as the gold standard, we determined the diagnostic performance of the presence of irregularly branched microvessels as an indicator for SM2 cancers. Results A total of 302 superficial esophageal SCC lesions (228 patients) were included in the analysis. When irregularly branched microvessels were used as an indicator of SM2 cancers, the diagnostic accuracy was 94.0 % (95 % confidence interval [CI]: 91.1–96.1 %), sensitivity was 79.4 % (95 % CI: 66.6–88.4 %), specificity was 95.9 % (95 % CI: 94.3–97.0 %), positive predictive value was 71.1 % (95 % CI: 59.6–79.1 %), and negative predictive value was 97.3 % (95% CI: 95.7–98.5 %). Conclusions Irregularly branched microvessels may be a reliable M-NBI indicator for the diagnosis of cancers with deep submucosal invasion.

Transcription/Expression of KLRB1 Gene as A Prognostic Indicator in Human Esophageal Squamous Cell Carcinoma

2020 ◽  
Vol 23 (7) ◽  
pp. 667-674
Author(s):  
Guangwei Zhang ◽  
Ying Liu ◽  
Fajin Dong ◽  
Xianming Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Treatment Options ◽  
Risk Groups ◽  
Prognostic Indicator ◽  
Dismal Prognosis ◽  
N Stage

Aim and Objective: Esophageal squamous cell carcinoma (ESCC) is the most prevalent type of cancer with worldwide distribution and dismal prognosis despite ongoing efforts to improve treatment options. Therefore, it is essential to determine the prognostic factors for ESCC. Methods and Results: We determined KLRB1 to be a prognostic indicator of human ESCC. KLRB1 was expressed at low levels in ESCC patients. Based on the risk score, patients were divided into high and low-risk groups. High-risk patients showed a poor survival rate. The prediction model based on the N stage, sex, and KLRB1 was significantly better than that based on the N stage and sex. The modified prediction model showed a robust ROC curve with an AUC value of 0.973. The knockdown of KLRB1 inhibited the growth of human ESCC cells. KLRB1 regulated Akt, mTOR, p27, p38, NF-κB, Cyclin D1, and JNK signaling, which was consistent with the result of GSEA. Conclusion: KLRB1 is a potential prognostic marker for human ESCC patients.

scholarly journals A predictive model for treatment response in patients with locally advanced esophageal squamous cell carcinoma after concurrent chemoradiotherapy: based on SUVmean and NLR

2019 ◽  
Author(s):  
Chunsheng Wang ◽  
Kewei Zhao ◽  
Shanliang Hu ◽  
Yong Huang ◽  
Li Ma ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Treatment Outcomes ◽  
Predictive Model ◽  
Treatment Response ◽  
Squamous Cell ◽  
Concurrent Chemoradiotherapy ◽  
Training Set

Abstract Background: We conducted this study to combine the mean standardized uptake value (SUVmean) and neutrophil to lymphocyte ratio (NLR) to establish a strong predictive model for patients with esophageal squamous cell carcinoma (ESCC) after concurrent chemoradiotherapy (CCRT). Methods: We retrospectively analyzed 163 newly diagnosed ESCC patients treated with CCRT. Eighty patients (training set) were randomly selected to generate cut-off SUVmean and NLR values by receiver operating characteristic (ROC) curve analysis and to establish a predictive model by using the independent predictors of treatment outcomes. Then, we evaluated the performance of the prediction model regarding treatment outcomes in the testing set (n=83) and in all sets. Results: A high SUVmean (>5.81) and high NLR (> 2.42) at diagnosis were associated with unfavorable treatment outcomes in patients with ESCC. The prediction model had a better performance than the simple parameters (p<0.05). With a cut-off value of 0.77, the prediction model significantly improved the specificity and positive predictive value for treatment response (88.9% and 92.1% in the training set, 95.8% and 97.1% in the testing set, and 92.2% and 91.8% in all sets, respectively). Conclusions: The pretreatment SUVmean and NLR were independent predictors of treatment response in ESCC patients treated with CCRT. The predictive model was constructed based on these two parameters and provides a highly accurate tool for predicting patient outcomes.

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